Human activities alter response of alpine grasslands on Tibetan Plateau to climate change

Human activities alter response of alpine grasslands on Tibetan Plateau to climate change

Journal of Environmental Management 262 (2020) 110335 Contents lists available at ScienceDirect Journal of Environmental Management journal homepage...

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Journal of Environmental Management 262 (2020) 110335

Contents lists available at ScienceDirect

Journal of Environmental Management journal homepage: http://www.elsevier.com/locate/jenvman

Research article

Human activities alter response of alpine grasslands on Tibetan Plateau to climate change Da Wei a, 1, Hui Zhao a, 1, Jianxin Zhang a, b, Yahui Qi a, b, Xiaodan Wang a, * a

Key Laboratory of Mountain Surface Processes and Ecological Regulation, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, 610041, China b University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, 100049, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Livestock management Grazing Climate warming Remote sensing Model simulation

The world’s largest alpine pastures are found on the Tibetan Plateau, where considerable climate changes and human impacts have been experienced. Identifying their contributions to terrestrial productivity is essential if we are to adapt to, or mitigate the effects of, climate change. In this work, we begin by showing how the current warming and wetting of the climate over the last three decades has favored plant growth, as consistently captured by satellite observations and 15 models. However, the interactions between climate factors explain less of the variation in greenness observed by satellites after the 2000s, implying non-climatic influences. Next, we show that there is a significant negative impact of livestock grazing on pasture greenness, especially in peak summer. Official statistics across 72 counties verify these negative impacts, especially in poorer pastures with a higher density of grazing livestock. The variation in grazing density has a stronger negative effect on vegetation growth during the early part of the growing season after the 2000s, as compared with that before the 2000s. We found a compensatory effect of grazing and climate on alpine grassland growth, and the grazing regulates the response of vegetation greenness to climate change by modulating the dependency of vegetation growth on temperature. Thus, we suggest there is a weakening influence of climate on the greenness of alpine pastures, largely due to a strengthening influence of management, which should be considered by both the scientific community and policymakers.

1. Introduction The Tibetan Plateau (TP) is home to the world’s largest alpine pas­ tures, covering about 200 million ha at over 4000 m (Fig. 1a; Fig. S1). The recent rapid warming and wetting of the climate on the TP, amounting to 0.26 K decade 1, has significantly altered the alpine pastures (Kuang and Jiao, 2016; Piao et al., 2012; Zhuang et al., 2010). Compared with the numerous studies on the impacts of climate on alpine grasslands (Cong et al., 2017a, 2017b; Li et al., 2016; Shen et al., 2015a; Zhang et al., 2013), relatively fewer studies have considered the impact of human activity in this region, although sediment and ice core proxies have dated human activity to several millennia before present (Chen et al., 2015; Meyer et al., 2017; Miehe et al., 2009, 2019). In fact, overstocking of grazing animals on these fragile pastures has caused degradation of the ecosystem and damaged soils (Chen et al., 2013;

Klein et al., 2004). To tackle grassland degradation, China initiated the Retire Livestock and Restore Pastures campaign in the 2000s, and an example outcome of that campaign is that 9% of the alpine grasslands have been fenced for restoration in the Tibetan Autonomous Region (TAR) (Fig. 1b). Identifying the contributions of climate and human activity to terrestrial productivity is essential if we are to adapt to, or mitigate the effects of, global climate change on the TP (Zhang et al., 2015). Three approaches have been used to tackle the above issue: manipulative ex­ periments, statistical analyses, and model intercomparisons (Chen et al., 2014; Hopping et al., 2018a; Li et al., 2018). Manipulative experiments consider the variation in temperature, precipitation, snow, nitrogen and grazing to explore the mechanisms through which alpine grasslands response to the changing climate (Hopping et al., 2018a; Klein et al., 2004; Wang et al., 2012). Statistical analyses—particularly correlation

* Corresponding author. Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, No. 9, Section 4, Renminnanlu Road, Chengdu, Sichuan, China. E-mail address: [email protected] (X. Wang). 1 Equal contributions of these authors. https://doi.org/10.1016/j.jenvman.2020.110335 Received 2 October 2019; Received in revised form 22 February 2020; Accepted 22 February 2020 Available online 28 February 2020 0301-4797/© 2020 Elsevier Ltd. All rights reserved.

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analysis and partial correlation—have been employed to explore the relationships among satellite-based vegetation greenness and environ­ mental and socioeconomic factors (Cai et al., 2015). Model in­ tercomparisons are widely used across the TP by calculating the difference between the potential and actual productivity (Fig. 1c). An earlier study using the above approach found that the impact of human activity doubled from 20% in the 1980s to 40% in the 1990s by regu­ lating the interannual variability in vegetation growth (Chen et al., 2014). However, another study found that the contribution of human activities decreased from 58.45% in 2000–2004 to 16.25% in 2004–2012 (Xu et al., 2016). Furthermore, there is still large discrep­ ancies regarding their relative contributions; for example, an indepen­ dent study found that climate contributed 2/3 to vegetation dynamics during the 1980s–2010s (Pan et al., 2017), which is much higher compared with the above studies. Therefore, it remains challenging to identify the contributions of human activities and climate changes (Cao et al., 2019; Wu et al., 2019), because calculating the potential pro­ ductivity involves lots of fieldwork for model parameterization and validation, considering the spatial heterogeneities in climate, altitude, soil and human activity across the TP. These difficulties have led us to use alternative approaches to tackle these issues. The pastoral practice on the TP, in which most grasslands are used as summer pastures (Tibet Exploration Team - Chinese Acad­ emy of Sciences, 1988), suggesting that the response of grasslands to livestock grazing may be different in different periods of the growing season. Tibetan herders gradually move their livestock from winter pastures at relatively lower altitudes around settlements to summer pastures at higher altitudes far away home (usually >5000 m) from April to May. The livestock are raised and fended for themselves in these vast high-altitude summer pastures for several months before returning

to their winter pastures in late August to October (Fig. 1d). The winter pastures are at lower altitude (i.e., warmer) and have more available water, guaranteeing a supply of hay forages for the livestock during the long cold winters on the TP. It is clear that vegetation growth both in spring and summer are influenced by climate change and grazing. Though this nomadic practice of between seasonal pastures implies that spring greenness may be more climate-driven and less impact from grazing, whereas the summer greenness, especially in the peak summer months, is driven by both climate and grazing. Thus, we hypothesized that the interannual variation in vegetation growth in the early growing season (the difference between the peak greenness and spring greenness, referred to as the apparent vegetation growth), may be largely affected by variations in the grazing density. This study investigated how climate change and human activities, i. e., grazing, regulate vegetation growth on the alpine pastures of the TP. We used satellite-based vegetation greenness datasets, a FluxNet-based empirical upscaling dataset, process-based model simulations, and official statistics. Three satellite-based greenness observations, proxies for ecosystem productivity (e.g., gross primary productivity, GPP), document the impacts of both climate and human activity (Beck et al., 2011). The empirical upscaling FluxNet dataset and an ensemble of 15 models consider only variations in climate but exclude human contri­ butions (Huntzinger et al., 2018; Jung et al., 2011). The official statistics include the livestock population of cattle, goats and sheep across the TP at the county level, a proxy for human activities on the TP. The inter­ annual variability in satellite-based greenness should be well repro­ duced by the integration of climate (model simulations) and human activities (grazing density). By collecting these datasets, our aim was to explore the drivers of alpine pasture growth and, more importantly, how their relative roles have evolved.

Fig. 1. (a) Location map showing the TP. (b) Grasslands of China (green pixels) and restored grasslands (red pixels) in the TAR. (c) Schematic diagram showing the change in greenness of vegetation from spring to autumn. The red line shows the method used in this study; the blue line shows the traditional approach used by previous studies. Previous studies assessed the impacts of livestock consumption (light green) by calculating the difference between the potential and actual GPP. We emphasize that the summer grazing practices on the TP can be reflected by the difference between the peak and spring vegetation greenness—that is, the seasonality of the alpine pasture. (d) Typical winter and summer grazing practice on the TP. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) 2

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2. Methods and materials

cover (e.g., barren land and forests), we did not define a threshold as the alpine pastures. Based on our field experience, removing pixels <0.1 NDVI causes the loss of vast amounts of summer pastures, especially in the western TP. Unlike other studies concerned with the average growing-season greenness, we used the terms growing-season peak greenness (NDVImax) and the vegetation greenness in May (NDVIspr). Given the importance of livestock grazing on pasture vegetation, we used the term seasonality (NDVIseason) to represent the apparent growth (Rickbeil et al., 2015). NDVIseason is the difference between NDVImax and NDVIspr because it is difficult to obtain the intrinsic growth during this period, i.e. NDVIseason ¼ NDVImax ‒ NDVIspr.

2.1. Satellite-based vegetation datasets A long-term satellite product of Normalized Difference Vegetation Index (NDVI) was used to explore the variation in vegetation greenness of alpine pastures on the TP. The Global Inventory Modeling and Map­ ping Studies NDVI3g dataset (GIMMS3g) was used (Pinzon and Tucker, 2014), providing a single record of 30-year vegetation greenness (1982–2016), which has been used in numerous studies after evaluation against other satellites and ground-based observations (Brown et al., 2006; Fensholt and Proud, 2012; Fensholt et al., 2009). We extracted the monthly maximum values from the original GIMMS3g dataset and resampled them to 0.1� . Evaluation revealed high consistency with respect to their seasonal magnitudes, consistent with other studies in this region (Ding et al., 2018; Shen et al., 2015b; Shi et al., 2018).

2.5. Statistical analysis We used the anomalies to evaluate the temporal variations in alpine pasture greenness and other environmental factors. Partial correlations were made at interannual scales between the variation in vegetation greenness and a number of environmental factors. To calculate the variation in the correlation between vegetation NVDI (or GPP) on temperature and precipitation, we used a 15-year moving time window, which has been widely used in previous studies (Ding et al., 2018; Piao et al., 2014).

2.2. Climate-driven model ensemble A FluxNet-based upscaling GPP dataset for 1980–2011 was used, which provides global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirical upscaling eddy covari­ ance measurements (Jung et al., 2011), which are only driven by climate change. Process-based models are useful in reproducing observational results and separating the contribution of climate change; thus, we used an ensemble of 15 process-based models (Multi-scale Synthesis and Terrestrial Model Inter-comparison Project; MsTMIP) (Huntzinger et al., 2018). The BG1 group was simulated by the time-varying climate, land use, and the deposition of CO2 and reactive N (eight models); the SG1 group by the time-varying climate (14 models); the SG2 group by the time-varying climate and land use (14 models); and the SG3 group by the time-varying climate, land use and CO2 (14 models). All the models were driven by Climate Research Unit–National Centers for Environ­ mental Prediction climate data and a soil map, and were run at a 0.5� resolution with a monthly step (Table S1 lists the participant models of MsTMIP).

3. Results 3.1. Robust vegetation growth under a warming and wetting climate There has been a general greening in alpine pastures on the TP in terms of the mean growing-season NDVI (Fig. 2a; Fig. S3). The FluxNetbased dataset and the ensemble of 15 process-based models (Huntzinger et al., 2018) generally reproduced the increase in vegetation growth in these alpine pastures. Among the model ensemble, the SG1 group per­ formed better in capturing the interannual variability in vegetation growth in the alpine pastures (Fig. 2a; Figs. S4–S5). The BG1 model of MsTMIP simulated a straightforward increase in vegetation growth, and disagreed with that revealed by satellite data.

2.3. Livestock data

3.2. Weakening climate dominance on vegetation growth

We obtained statistical data on livestock from the yearbooks of the TAR and Qinghai Province (Stastistics Bureau of Qinghai Province, 1984–2016; Stastistics Bureau of Tibetan Automonous Region, 1984–2016). We also collected livestock statistics for each county and extracted their greenness to verify the results at the county level. We obtained data for 72 counties in the TAR, covering the period from 1994 to 2015. Qinghai Province only provided livestock statistics at the county level for three years (1992, 1995 and 1996), which were not included in the county-level analyses. To convert the number of cattle to sheep equivalent units (SEUs), we used the equation “1 head of cows (or bulls, or horses) ¼ 5 sheep”. Although some other studies have used “1 head of cows (or bulls, or horses) ¼ 4 sheep” (Hopping et al., 2018b), we found that this did not affect our conclusions (Fig. S2). To obtain the grazing pressure of each county, we divided the SEU by the number of grassland pixels and the average greenness of the growing season: Grazing density ¼ SEU/(AREAgrass � NDVImax). The AREA was calcu­ lated using the grassland pixels within each county and the area of one grassland pixel was roughly 10 km � 10 km. The units of the grazing density were 10,000 SEU/100 km2.

The greenness of vegetation showed a nonlinear variation (R ¼ 0.20, P ¼ 0.13; Fig. 2a), especially since the 2000s (R ¼ 0.08, P ¼ 0.77), for both the average and peak growing-season greenness, despite an almost straightforward increase in both temperature and precipitation on the TP (Fig. S6). Another two satellites verified a similar nonlinear variation in the greenness of alpine pastures on the TP (Fig. S2), largely since the 2000s, and were highly correlated with each other. By contrast, the FluxNet-based dataset and 13 of the 14 models within the SG1 group did not capture the nonlinear variation in greenness during the growing season after the year 2000(Fig. 2b; Fig. S7). 3.3. More negative impact of summer grazing on vegetation growth The official statistics for the livestock population showed opposite trends to the growth of vegetation during the early growing season (Fig. 3a; Fig. S8). We observed significant negative correlations between the livestock population and NDVIseason (Fig. 3b). These negative cor­ relations were more evident in July and August than in May and June (Fig. 3c). The official statistics for the 72 counties also documented a negative correlation between grazing density and NDVIseason (Fig. 4a). The negative correlation between the livestock population and NDVImax and NDVIseason was mostly evident in the western TP (Fig. 4b), where the NDVImax was <0.3 (Fig. 4c), indicating a stronger impact of grazing on poor pastures. Furthermore, Fig. 5 further shows that the negative cor­ relation between NDVIseason and livestock grazing has even been getting stronger, especially since the 2000s, either for the whole TP, the TAR or Qinghai. Multiple regression modeling also validated our hypothesis

2.4. Grassland extraction To focus on the grasslands, we extracted grassland pixels from the survey-based China Vegetation Atlas (1:1,000,000). We defined the months of May to September as the growing season, although the exact dates differ slightly across different regions. Because we used the China Vegetation Atlas to differentiate grasslands from other types of land 3

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Province. Further analysis across 72 counties can hardly reach a sig­ nificant correlation between the changes in grazing density and their dependency on precipitation either (RNDVIseason-PRE; Fig. S9b). 4. Discussion In this study, we found that the increase in vegetation greenness over the last three decades was dominated by, and benefited from, the warmer and wetter climate (Cong et al., 2017a, 2017b; Ding et al., 2018). Unlike most regions, which may have generally experienced warming with a deficit of water (Yuan et al., 2019), the TP has experi­ enced both warming and increasing precipitation. The warming of the TP by 0.26 K decade 1 is double the rate of the global terrestrial average of 0.12 K decade 1 (Kuang and Jiao, 2016). Both the satellite observa­ tions of greenness and the climate-driven models captured the greening of alpine pastures by vegetation, consistent with previous simulation studies (Jin et al., 2015; Piao et al., 2012; Zhuang et al., 2010). Among the MsTMIP ensemble, the SG1 group better captured the interannual variability of greenness, whereas the inclusion of time-varying fertil­ ization by CO2 and N either did not improve the performance of the model or led to a poorer performance. This indicates that the changing climate (temperature, precipitation and radiation), especially tempera­ ture, is still dominant and constrains greening of vegetation in these alpine ecosystems. This is different from previous studies, which found a strong effect from fertilization with CO2 and reactive N in the model simulations (Piao et al., 2012), although this has not yet been verified in experimental studies on the TP. Regarding the role of grazing impacts, these observations verified our hypothesis that there is a negative effect of an increase in grazing density on summer greenness, supporting strong human regulation on the alpine pastures of the TP, rather than the human adaptation theory in which the biomass controls the livestock population (Fauchald et al., 2017). Relatively fewer studies have considered the effect of livestock grazing on large-scale vegetation activities on the TP, especially in the peak growing season (Klein et al., 2014; Shen et al., 2016). Our statis­ tical analysis of 72 counties supports the negative impacts of grazing, especially in poor pastures in the western TP. By contrast, in those areas in the eastern TP with a higher supply of biomass, but a lower grazing intensity (Wang et al., 2019), we see a positive relationship between the greenness of vegetation and the grazing density of livestock, indicating human adaptation to the supply of biomass in alpine pastures—that is, herders carefully consider the supply of biomass and increase or decrease the population of livestock accordingly (Yeh et al., 2017). It is a reasonable conclusion that livestock grazing generally has a negative role on the TP because livestock consume up to 45% of the living biomass in alpine pastures (Lu et al., 2017), which is large enough to be detected by satellites (Chen et al., 2019), compared with a global average of only 20% (Zhou et al., 2018). We also verified our hypothesis that the effects of climate and grazing on the growth of vegetation have evolved during the past three decades. We found that variations in the intensity of grazing have an even stronger role in regulating the long-term growth of vegetation during the early growing season (NDVIseason). The more negative impact of grazing density on NDVIseason may reflect a more policy-driven live­ stock population since the 2000s, before which time the livestock were more regulated by climate—that is, the herders carefully evaluated the climate and the quality of the pasture and adjusted their livestock population accordingly (Yeh et al., 2017). The harsh climate on the TP strongly affects the livestock population—for example, there was a snowstorm-induced decrease in the livestock population between 1998 and 1999 (Klein et al., 2014). However, livestock populations are becoming more dependent on human management, especially after the Retire Livestock and Restore Pastures campaign in the 2000s, with a remarkable reduction of 22% in the SEUs from 2003 (the maximum numbers during the research period) to 2015, highlighting the ongoing management of livestock and its effect on alpine vegetation.

Fig. 2. (a) Temporal variations in the growing-season average NDVI from the GIMMS3g dataset and the GPP (kg C m 2 s 1) in the alpine pastures of the TP during the past three decades. The correlations between the growing-season average NDVI and the GPP from the GIMMS3g dataset and the MsTMIP model groups are 0.51***, 0.50***, 0.47** and 0.45** for SG1, SG2, SG3 and BG1, respectively. (b) Differences between simulation groups from the MsTMIP ensemble in reproducing the NDVI from the GIMMS3g dataset in the alpine pastures of the TP before and after the year 2000. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

that grazing density strongly regulated the early growing season growth of the alpine pastures, especially after 2000 (Table S2), much stronger than the climate change. 3.4. Grazing regulates the apparent response of vegetation to climate change Indeed, we found a significant compensatory effect of grazing and climate change on alpine grassland greenness (Fig. 6a and b), i.e., a more negative grazing impact on vegetation greenness may cause a weaker relationship between the greenness variation and interactions of climate factors. Then, we analyzed the livestock population in terms of the de­ pendency of vegetation seasonality on temperature and precipitation (RNDVIseason-TMP and RNDVIseason-PRE, where TMP and PRE are tempera­ ture and precipitation, respectively). We found significant positive correlations between livestock population and RNDVIseason-TMP, either on the TP, the TAR or Qinghai (R reached 0.93***, 0.90*** and 0.92***, respectively; Fig. 7a). At the county level, the 72 counties verified the negative impact of grazing density on regulating vegetation growth in the early part of the growing season in response to climate warming (Fig. 7b). Similar analyses revealed that there is none of a significant correlation between the variation in livestock population size and RNDVIseason-PRE at the reginal level (Fig. S10a), either for TAR or Qinghai 4

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Fig. 3. (a) Variation in the domestic livestock population (in 10,000 heads of SEU) on the TP and in TAR and Qinghai. Note that we present the SEU data inversely for better comparison with NDVIseason. (b) Correlation between SEU and NDVIseason on the TP (red) and in TAR (blue) and Qinghai (gray). (c) Correlations between SEU and NDVImonth in different months. Because the livestock population reported in the yearbooks was surveyed at the end of each year, which is not comparable with the NDVImonth in summer, we used three approaches to explore the relationship RNDVImonth-SEU: (1) “original” indicates using the number of livestock at the end of each year; (2) “adjusted” means using the average of the previous year and the present year; and (3) “LastYear” means using the livestock population at the end of the previous year. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4. (a and b) Correlation between SEU and NDVIseason across 72 counties in the TAR. (c) Correlation between RNDVIseason-SEU and the two-decade average NDVImax across 72 counties. The two-decade average NDVImax of each county is an indicator of the quality of pasture. The numbers are the codes used for each county in the statistical yearbooks.

Our finding of a weakening climate but strengthening impact of grazing on alpine pasture on the TP is meaningful for policymakers. The Retire Livestock and Restore Pastures campaign has lasted nearly two decades, and has been widely reported as useful in combating pasture degradation across the TP. However, debate regarding the campaign remains intense (Cao et al., 2018; Hopping et al., 2018a). The man­ agement of the livestock population has previously been proven to be an effective approach to prevent further degradation (Zhang et al., 2015), as also verified in the present study, before a better approach was pro­ posed and attested (Cao et al., 2018), i.e., crop–livestock integration (Duan et al., 2019). Our results are also highly relevant to the global change community. Climate change has driven changes in the biosphere,

and numerous studies have explored the dependency of vegetation growth on temperature and hydrological factors, e.g., global vegetation growth tends to acclimate to the warming climate across the Northern Hemisphere due to water shortages and shrubland expansion (Piao et al., 2014). On the TP, though the vegetation growth shows nonlinear vari­ ation in its dependency on temperature variability (Ding et al., 2018), which can barely be explained by climate only. Our findings in this study, i.e., a stronger grazing impact may cause a weaker relationship between the greenness variation and climate, may well explain the nonlinear variation in the temperature dependency of alpine pasture, which should also be considered by the global change community. Given the importance of water supply across the semi-arid and arid ecosystems 5

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Fig. 5. Impacts of livestock population on NDVIseason on the TP and in the TAR and Qinghai using a moving correlation approach (RNDVIseason-SEU). The years on the horizontal axis indicate the middle year of the 15-year moving window, e.g., 1992 indicates the period 1985–1999.

Fig. 7. (a) Correlation between livestock population and RNDVIseason-TMP. (b) Validation of the effect of grazing density variation on the regulation of RNDVIseason-TMP using statistics from 72 counties. The trend of SEU is 10,000 heads per year. The numbers are the codes of each county in the statisti­ cal yearbooks.

5. Conclusion During the past three decades, the interactions between climate factors explain less of the variation in greenness of alpine pasture observed by satellites after the 2000s than before. By contrast, there is a shift towards a greater effect of policies on the greenness of alpine pasture. The grazing regulates the response of vegetation greenness to climate by modulating the dependency on temperature. Therefore, ecologists and policymakers should consider our results when analyzing the drivers of changing vegetation growth and developing a sustainable pastoral society on the TP. There are also several shortcomings in the present study. First, our analyses did not observe a significant grazing impact on the hydrological dependency of alpine pasture greenness, and further work should be done to explore how grazing affects the hydro­ logical cycle, e.g. change in water use efficiency. Second, our study represents a macro-scale study by employing 18 independent datasets, though ground-based studies are necessary, e.g. several years’ obser­ vations in a village of Gouli Township in Qinghai Province (Hopping et al., 2018a,b). Finally, finer-resolution data of township level would better support our conclusion, and a closer collaboration should be achieved between the scientific community and the decision makers.

Fig. 6. Compensatory effect of RNDVIseason-SEU and RNDVIseason-GPP: (a) GPP data from the JUNG dataset; (b) GPP data from the MsMTIP-SG1 ensemble.

on the TP, it has been found that the alpine grasslands show increasing hydrological dependency (Ding et al., 2018), even in alpine wetlands (Wei et al., 2017). This may be due to increasing water requirements under warming climate and increasing evapotranspiration, despite slight stimulus of rising atmospheric CO2 to water use efficiency (Piao et al., 2012). However, analyses at the regional or county level in the present study did not observe a significant grazing impact on hydro­ logical dependency of vegetation greenness on the alpine grasslands. This indicates more work to be done regarding the grazing impact on hydrological dependency, considering the strong spatial heterogeneity of the changes in precipitation across the TP and increasing water use efficiency.

Acknowledgments This study was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) (XDA20020401), the Second Tibetan Plateau Scientific Exploration (2019QZKK0402), the Youth Innovation Promotion Association (2020369) and the Science & Tech­ nology Service program of CAS (KFJ-STS-QYZD-075). The GIMMS3g dataset can be accessed from www.nasa.gov, the Jung-GPP dataset from 6

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www.bgc-jena.mpg.de, and the China Meteorological Forcing Data dataset from the Institute of Tibetan Plateau Research, CAS. NASA’s Terrestrial Ecology Program (NNX11AO08A) funded MsTMIP Phase 1. During Phase 1, data management for MsMTIP was conducted by MASTDC, with funding from NASA’s Terrestrial Ecology Program (NASA Grant NNH10AN68I) We thank Dr. Kelly Hopping for her help with this manuscript. The authors declare no conflicts of interest.

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