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Journal of Geographical Sciences >

2018 , Vol. 28 >Issue 12: 1953 - 1964

DOI: https://doi.org/10.1007/s11442-019-1573-y

Research Articles

Responses of aboveground biomass of alpine grasslands to climate changes on the Qinghai-Tibet Plateau

  • WANG Li , 1, 2, 4 ,
  • YU Haiying 3 ,
  • ZHANG Qiang , 1, 4, * ,
  • XU Yunjia 5, 6 ,
  • TAO Zexing 5, 6 ,
  • ALATALO Juha 7 ,
  • DAI Junhu , 5, 6, *
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  • 1. College of Atmospheric Science, Lanzhou University, Lanzhou 730000, China
  • 2. Qinghai Institute of Meteorological Science, Xining 810001, China
  • 3. College of Agroforestry Engineering and Planning, Tongren University, Tongren 554300, Guizhou, China
  • 4. Institute of Arid Meteorology, China Meteorological Administration, Lanzhou 730020, China
  • 5. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 6. University of Chinese Academy of Sciences, Beijing 100049, China
  • 7. Department of Biological and Environmental Sciences, College of Arts and Sciences, Qatar University, Doha P.O. Box 2713, Qatar
Corresponding author:Zhang Qiang (1965-), PhD, E-mail: ; Dai Junhu (1968-), PhD, E-mail:

Author: Wang Li (1981-), PhD Candidate, specialized in climate change and biological response. E-mail:

Received date: 2018-05-30

  Accepted date: 2018-07-26

  Online published: 2018-12-20

Supported by

National Key R&D Program of China, No.2018YFA0606102; National Natural Science Foundation of China, No.41771056; National Key Technology Support Program, No.2012BAH31B02

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Journal of Geographical Sciences, All Rights Reserved
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Abstract

Aboveground biomass in grasslands of the Qinghai-Tibet Plateau has displayed an overall increasing trend during 2003-2016, which is profoundly influenced by climate change. However, the responses of different biomes show large discrepancies, in both size and magnitude. By applying partial least squares regression, we calculated the correlation between peak aboveground biomass and mean monthly temperature and monthly total precipitation in the preceding 12 months for three different grassland types (alpine steppe, alpine meadow, and temperate steppe) on the central and eastern Qinghai-Tibet Plateau. The results showed that mean temperature in most preceding months was positively correlated with peak aboveground biomass of alpine meadow and alpine steppe, while mean temperature in the preceding October and February to June was significantly negatively correlated with peak aboveground biomass of temperate steppe. Precipitation in all months had a promoting effect on biomass of alpine meadow, but its correlations with biomass of alpine steppe and temperate steppe were inconsistent. It is worth noting that, in a warmer, wetter climate, peak aboveground biomass of alpine meadow would increase more than that of alpine steppe, while that of temperate steppe would decrease significantly, providing support for the hypothesis of conservative growth strategies by vegetation in stressed ecosystems.

Key words： grasslands; aboveground biomass; partial least squares; Qinghai-Tibet Plateau; climate change

Cite this article

WANG Li , YU Haiying , ZHANG Qiang , XU Yunjia , TAO Zexing , ALATALO Juha , DAI Junhu . Responses of aboveground biomass of alpine grasslands to climate changes on the Qinghai-Tibet Plateau[J]. Journal of Geographical Sciences, 2018 , 28(12) : 1953 -1964 . DOI: 10.1007/s11442-019-1573-y

1 Introduction

Aboveground net primary productivity (ANPP) by vegetation is of vital importance for the global cycling of carbon (Mokany et al., 2006; Wang et al., 2011; Dai et al., 2016). Climate change has already significantly altered productivity in terrestrial ecosystems (Fang et al., 200l; Jiao et al., 2017). Global temperature increased by 0.85°C over the period 1880-2012 and precipitation has displayed an overall increase at mid-latitude areas in the Northern Hemisphere since 1901 (IPCC, 2013). Alpine areas in Sweden have experienced a 2°C increase in mean annual temperature in the past two decades (Alatalo et al., 2017). However, the Qinghai-Tibet Plateau has been experiencing temperature increases that are three-fold the global average, because of its location and high elevation (Xu et al., 2009). Consequently, ANPP on the plateau has increased markedly, particularly in alpine meadow (Wu et al., 2011; Xu et al., 2016).
The impacts of climate change on vegetation activity vary between grassland ecosystems (Dukes et al., 2005; Guo et al., 2012; Yu et al., 2012; Sun et al., 2013). Precipitation is commonly a primary factor influencing vegetation growth in arid and semi-arid regions, since vegetation growth depends on the timing and amount of precipitation events in environments with scarce groundwater and low soil moisture (Liu et al., 2012; Gamon et al., 2013; Fan et al., 2016). Many studies have reported a positive linear correlation between mean annual precipitation (MAP) and biomass in a specific grassland type (Briggs and Knapp, 1995; Paruelo et al., 1999; Guo et al., 2012). However, some researchers have found an exponential response of biomass to MAP variations when aggregating three grassland types in Inner Mongolia (Ma et al., 2008; Hu et al., 2010; Guo et al., 2012). Moreover, previous studies indicated that the rising temperatures over recent decades have had significant effects on the biomass of different grassland ecosystems. In alpine meadow, temperature is a dominant factor controlling the dynamics of gross primary production (GPP) (Kato et al., 2006). In alpine tundra and wet meadow ecosystems, which are often water-saturated, vegetation activity is enhanced under warming conditions (Hollister et al., 2005). In drier grasslands, warmer temperatures have an additional indirect effect on biomass, by exacerbating dry season water limitation (Kato et al., 2006). In addition, warming can cause significant increases in biomass by extending the growing season (Wan et al., 2005; Angert et al., 2005). Aboveground biomass is mainly correlated with mean temperature during the growing season, but the maximum temperature during the growing season is reported to be the major driver of productivity change in some ecosystems (Huxman et al., 2004).
The sensitivity of aboveground biomass to climate change in different grassland ecosystems also shows large discrepancies. Grime (1977) suggests that natural selection of plant characteristics in stressed systems favors conservative growth strategies. As a result, the biomass in cold, dry ecosystems may be less sensitive to temperature and precipitation variations than that in warmer, wetter areas. However, this suggestion is contradicted by the response of grassland to precipitation reported in the research of Huxman et al. (2004) and its subsequent studies, where the slope of response of the driest systems to precipitation was steeper than that of more mesic grasslands. Furthermore, grass productivity in different ecosystems is affected by temperature and precipitation in different months (Craine et al., 2012). Thus, it is important to assess the influence of climate variations on the primary productivity of grass in different ecosystems.
In this study, we applied the partial least squares regression (PLS) method to analyze the effect of mean monthly temperature and monthly total precipitation on aboveground biomass. Our objective was to identify when and to what degree annual temperature and precipitation changes might impact aboveground biomass in different grassland types.

2 Materials and methods

2.1 Study area and data

Twenty study sites were selected in Qinghai, on the central and eastern Qinghai-Tibet Plateau (32°-39°N, 92°-102°E, at elevations ranging from 2787 to 4533 m a.s.l.) (Table 1). Mean annual temperature (MAT) in the region ranges from -3.35°C to 5.39°C, monthly maximum temperature is 13.90°C in July, and monthly minimum temperature is -11.60°C in January. All sites receive mean annual total precipitation (MAP) of 332.98-757.34 mm, most (86.3%) of which is concentrated in the rainy season (May-September). Vegetation type at most of the study sites is alpine meadow. However, four of the study sites (Tianjun, Tongde, Tuole, Tuotuohe) are located in alpine steppe ecosystems, and two (Gangcha and Xinghai) in temperate steppe (Zhang et al., 2007) (Figure 1).
Table 1 Summary of location, mean annual temperature (MAT), mean annual total precipitation (MAP), and vegetation type at the 20 study sites of the Qinghai-Tibet Plateau
Sites Latitude (°N) Longitude
(°E)		 Elevation MAT MAP Vegetation type
(m) (°C) (mm)
Banma 100.74 32.93 3530.00 3.63 670.34 Alpine meadow
Dari 99.65 33.76 3967.50 0.23 584.90 Alpine meadow
Gande 99.89 33.96 4050.00 -1.44 554.14 Alpine meadow
Gangcha 100.14 37.33 3301.50 0.68 427.72 Temperate steppe
Haiyan 100.86 36.96 3140.00 1.51 431.21 Alpine meadow
Henan 101.60 34.73 3500.00 0.56 595.44 Alpine meadow
Jiuzhi 101.48 33.43 3628.50 1.83 757.34 Alpine meadow
Maduo 98.23 34.92 4272.30 -2.48 358.49 Alpine meadow
Maqin 100.24 34.48 3719.00 0.77 538.17 Alpine meadow
Nangqian 96.47 32.20 3643.70 5.39 568.79 Alpine meadow
Qilian 100.24 38.18 2787.40 2.02 443.79 Alpine meadow
Qingshuihe 97.13 33.80 4415.40 -3.35 555.90 Alpine meadow
Qumalai 95.80 34.12 4175.00 -0.76 455.44 Alpine meadow
Tianjun 99.02 37.30 3417.10 0.28 394.39 Alpine steppe
Tongde 100.60 35.24 3080.00 3.68 475.91 Alpine steppe
Tuole 98.42 38.81 3367.00 -1.59 340.48 Alpine steppe
Tuotuohe 92.44 34.22 4533.10 -2.71 332.98 Alpine steppe
Xinghai 99.98 35.59 3323.20 2.05 407.68 Temperate steppe
Zaduo 95.29 32.89 4066.40 1.83 546.60 Alpine meadow
Zeku 101.47 35.04 3662.80 -0.59 538.04 Alpine meadow
Figure 1 Geographical location of the study area in Qinghai on the central and eastern Qinghai-Tibet Plateau and vegetation types at the study sites
Data on aboveground biomass were collected at the 20 sites by personnel from the Qinghai Meteorological Administration. These data were measured at 20 long-term ecological monitoring stations established on natural grasslands that are representative of the topography, soil, and vegetation type in the region. Quadrats have been fenced to prevent grazing on the monitoring sites with a minimum area of 2500 m2 (50 m×50 m or 25 m×100 m) since the 1980s or 1990s. The quadrats were first divided into four sub-quadrats for replication, and then the sub-quadrats were further divided into four small observation quadrats, one of which was used each year in a four-year cycle. Aboveground biomass of all plants within 1 m ×1 m area was harvested and measured (fresh weight, g m-2) at the end of May, June, July, August, and September during 2003-2016 at each station. The peak production period in the area, when maximum production occurs, is during June-August.
Climate data for these 20 stations, including monthly mean temperature and total precipitation, were obtained from the China Meteorological Data Service Center (http://data.cma.cn/).

2.2 Methods

The responses of aboveground biomass to climate factors were evaluated by partial least square (PLS), a statistical analysis model for investigating the relations between matrices of response variable and independent variables (Jong, 1993; Luedeling and Gassner, 2012; Wang et al., 2015). A PLS model seeks to identify some linear combinations of the predictors that explain the maximum variance in the response matrix, and the regression equation consists of the most important combinations. Compared with standard regression, the PLS method is preferable for cases where multicollinearity exists among predictor variables (Luedeling and Gassner, 2012). The equations of the PLS model are:
${{\hat{X}}_{0}}={{X}_{S}}{{X}_{L}}$ (1)
${{\hat{Y}}_{0}}={{X}_{S}}{{Y}_{L}}$ (2)
${{X}_{S}}={{X}_{0}}W$ (3)
where X is an r-by-q matrix of predictor variables, whose rows correspond to observations and columns correspond to variables; Y is a r-by-1 matrix of response loadings; X0 or Y0 is the standardized X or Y on the basis of z-score method; ${{\hat{X}}_{0}}$ or ${{\hat{Y}}_{0}}$ is the PLS approximation to X0 or Y0; XS is an r-by-rcomp orthonormal matrix whose rows correspond to observations and columns correspond to factors; XL or YL, is a q-by-rcomp, or a 1-by-rcomp matrix, where the rows are the coefficients that determine a linear combination of PLS factors; and W is a q-by-rcomp matrix of PLS weights.
The parameters in the equations were estimated by the least squares method and two outputs were produced, i.e., the variable-importance-in-the-projection (VIP) and the model coefficients (Beta). The value of VIP was calculated as:
$VI{{P}_{j}}=\sqrt{p\sum\limits_{k=1}^{{{n}_{comp}}}{\text{(}P{{V}_{k}}*{{({{W}_{jk}}/||{{W}_{_{k}}}||)}^{2}})/\sum\limits_{k=1}^{{{n}_{comp}}}{\text{ }P{{V}_{k}}}}}$ (4)
where VIPj is the value of VIP for the j-th variable in the predictor matrix and PVk is the explanation of response matrix by the k-th factor. A variable in predictor matrix can be considered dominant and significantly explains the variation in the dependent variables if its VIP value exceeds 0.8 (Wold, 1995; Wang et al., 2015).
The value of Beta was calculated as:
${{Y}_{0}}={{X}_{0}}*Beta+RES$ (5)
where Beta is a p-by-1 matrix of PLS regression coefficients; and RES is the residuals. Any variable with a higher absolute value of Beta is considered to have a greater impact on the response variable.
In this study, we evaluated the response of aboveground biomass for three vegetation types to climate factors using the PLS model. The response variable Y in this case was a matrix with columns that included the peak aboveground biomass, calculated as the maximum produced from June to August at the 20 sites from 2003 to 2016. The predictor variable X was a matrix with 24 climate factors, including mean monthly temperature and monthly total precipitation from September in the year before the study year to August in the study year, since peak aboveground biomass in the region is statistically most likely to occur in August.
When we had identified the optimum months affecting aboveground biomass most significantly, we calculated the sensitivity for each vegetation type, i.e., the degree of aboveground biomass response to temperature or precipitation variations. We defined the sensitivity as the regression coefficient of the peak aboveground biomass against mean temperature or mean total precipitation during specific months.

3 Results

3.1 Variations in peak aboveground biomass, temperature, and precipitation

Peak aboveground biomass displayed a fluctuating increasing trend during 2003-2010 with the slope of 14.74 g m-2 yr-1 and a relative weaker decreasing trend of 6.27 g m-2 yr-1 after 2010 (Figure 2a). The highest value of mean peak aboveground biomass was 588.1±387.0 g m-2 in 2010 and the lowest was 408.4±281.1 g m-2 in 2015. Regarding climate factors, mean annual temperature slightly increased during the 14-year study period (slope=0.04°C yr-1), but annual precipitation decreased with the trend of 1.47 mm yr-1 (Figures 2b and 2c).
Figure 2 Inter-annual variation in peak aboveground biomass (a), and inter-annual variation in temperature (b) and total precipitation (c), averaged over all sites. Error bars indicate standard deviation between sites.

3.2 Response of peak aboveground biomass to monthly temperature and precipitation variations

The impact of mean monthly temperature and total precipitation in each month on peak aboveground biomass differed for the three vegetation types. Most VIP values for mean monthly temperature were greater than 0.8 (Figures 3a, 3c and 3e), suggesting that peak aboveground biomass correlated significantly with temperature. For the alpine meadow in particular, mean temperature in all 12 preceding months exerted a significant positive impact on peak aboveground biomass (Figure 3d). Similarly, for the alpine steppe, peak aboveground biomass showed a positive correlation with mean temperature in most months and the correlation was significant for five months (the preceding September, April to July), while temperature in January and March showed a significantly negative correlation with peak aboveground biomass (Figure 3b). However, for the temperate steppe, mean temperature in six months (the preceding October, February to June) exerted a significantly negative influence on peak aboveground biomass, while the temperature exerted a remarkably positive effect on peak aboveground biomass only in July (Figure 3f).
Figure 3 Response of peak aboveground biomass to mean monthly temperature (T, a-f) and monthly total precipitation (P, g-l) during preceding months, based on partial least squares (PLS) regression analysis for three vegetation types: alpine meadow (AM), alpine steppe (AS) and temperate steppe (TS). The left column shows the VIP values and the right column shows the correlation coefficients. The blue bars indicate VIP values greater than 0.8; the green and red bars indicate coefficients with significant VIP.
As regards to precipitation, most VIP values for monthly total precipitation were greater than 0.8 for the alpine ecosystems, but not for the temperate steppe (Figures 3g, 3i and 3k), indicating that the impact of precipitation on peak aboveground biomass varied among vegetation types. For the alpine meadow, peak aboveground biomass correlated positively with precipitation in all 12 preceding months (Figure 3j). In particular, it showed a significant positive correlation with monthly total precipitation during February-June and the preceding September-October (Figure 3j). For the alpine steppe, a more significant negative correlation was found between peak aboveground biomass and monthly precipitation, as was also observed for temperature (Figure 3h). For the temperate steppe, peak aboveground biomass was significantly positively correlated with monthly precipitation only in the preceding September, while it showed a significant and negative correlation with precipitation only in December (Figure 3l).

3.3 Sensitivity of peak aboveground biomass to climate factors

In the PLS analysis, peak aboveground biomass at most sites showed a significant correlation with mean temperature during most preceding months, while it also showed a significant correlation with total precipitation during the key period February-June (Figure 3). Therefore we calculated the response sensitivity of peak aboveground biomass to annual climate variability and to total precipitation change during February to June.
The results showed that the sensitivity of peak aboveground biomass of the alpine meadow was highest for the two precipitation variables, with a value of 1.69 g m-2 mm -1 for total annual precipitation (P<0.01), and a value of 3.17 g m-2 mm-1 for total precipitation in February-June (P<0.01) (Figures 4a and 4b). The temperature sensitivity of aboveground biomass of the alpine meadow (82.31 g m-2 °C -1) was also higher than that of the alpine steppe (P<0.01) (Figure 4c). This suggests that aboveground biomass of the alpine meadow would increase strongly if the climate became warmer and wetter. Similarly, for the alpine steppe, the sensitivity of peak aboveground biomass to the three factors studied was also significant and positive, but with a smaller value of 0.45 g m-2 mm-1 for total annual precipitation (P<0.05), 0.89 g m-2 mm-1 for total precipitation in February-June, and 16.91 g m-2 °C -1 for temperature (P<0.01) (Figures 4a, 4b and 4c). Unlike the alpine ecosystems, for the temperate steppe, the sensitivity of peak aboveground biomass to the two forms of precipitation was weak and non-significant, which confirmed the results in Figure 3l. However, the temperature sensitivity was high, being -105.02 g m-2 °C -1 (P<0.01) (Figure 4c), which is also consistent with the findings in Figure 3f. This indicates that aboveground biomass of the temperate steppe would decline with warmer temperatures.
Figure 4 Linear regression between peak aboveground biomass of alpine meadow (AM), alpine steppe (AS), and temperate steppe (TS), and a) total annual precipitation, b) total precipitation February-June, and c) mean annual temperature

4 Conclusions and discussion

4.1 Differences in the impacts of seasonal temperature and precipitation on peak aboveground biomass for three vegetation types

The impact of seasonal temperature and precipitation on peak aboveground biomass in August differed between the vegetation types. The key climate factors in the pre-season to which the response of aboveground biomass was most marked also differed for the three vegetation types. For example, mean temperature in all 12 months before the fast-growing season exerted a significantly positive influence on vegetation growth in the alpine meadow, confirming previous findings that temperature is the dominant factor controlling gross primary production in alpine meadow (Hollister et al., 2005; Wan et al., 2005). Notably, warmer temperatures during the peak growth period (July-August) played a consistently positive role in increasing aboveground biomass for all three vegetation types. Warmer temperatures not only promote aboveground grassland biomass production, but also increase the growth of rootstocks and replenish soil nutrient storage over time (Wang et al., 1988). However, when mean temperature in February to June increased, peak aboveground biomass of the temperate steppe in August declined significantly. This can be explained by drought events induced by fluctuating potential evapotranspiration during the growing season (Bai et al., 2011).
The water demands of grassland vary with the season (Knapp et al., 2001). In the present study, precipitation had an inconsistent influence on aboveground biomass, which is in line with findings in some earlier studies (Dukes et al., 2005; Trenberth and Shea, 2005; Wei et al., 2009; Craine et al., 2012; Thomey et al., 2015). The seasonal patterns and magnitude of rainfall events, particularly in the preceding autumn and spring, were critical in promoting the productivity of the alpine meadow and temperate steppe. This was probably because frozen soil water and snowpack that accumulated over autumn and winter increased the soil moisture content during the cold season and provided protection for buried plant parts, which was favorable for plant growth in the following spring or summer (Nandintsetseg and Shinoda, 2011; Richardson et al., 2013). Precipitation in early and mid-summer also played a significant role in peak biomass in August in the alpine meadow and alpine steppe, but in late summer the impact of precipitation was weak. This is highly consistent with previous findings in the Mongolia steppe region that normalized difference vegetation index (NDVI) in its yearly maximum phase (July-August) is most highly correlated with precipitation in the preceding months (June and July) (Miyazaki et al., 2004; Iwasaki, 2006).

4.2 Differences in the sensitivities of peak aboveground biomass to seasonal temperature and precipitation for three vegetation types

Aboveground biomass of the alpine meadow in this study showed a strong response to both temperature and precipitation variations, while the sensitivity of aboveground biomass of the alpine steppe to temperature and precipitation was relatively weak. However, aboveground biomass of temperate steppe was insensitive to precipitation, but strongly sensitive to temperature. Previous studies have found that the magnitude and direction of the response to climate factors varies between communities with different growth rates of biomass (Huxman et al., 2004; Wu et al., 2011). The aboveground biomass of communities in water-stressed areas with low production potential may be relatively insensitive to inter-annual variations in precipitation (Paruelo et al., 1999). Our results are also consistent with previous findings that an exponential relationship existed between ANPP and mean annual precipitation (Ma et al., 2008; Hu et al., 2010; Guo et al., 2012), whereby ANPP is insensitive to mean annual precipitation when primary production is low.
In addition, the low relative growth rates of the dominant plant species in the steppe might constrain the response (Paruelo et al., 1999), while the higher plant biodiversity in meadow can show a greater ANPP response to increasing precipitation, due to compensatory effects among species (Bai et al., 2004). Vegetation constraints might be more dominant than the impact of climate factors on ANPP in low-productivity areas. Abiotic factors should also be considered, such as soil moisture content (Shinoda and Nandintsetseg, 2011; Guo et al., 2012), soil nitrogen content (Sun et al., 2013), and soil texture (Yang et al., 2009). Better soil conditions in wetter areas are also reported to be beneficial to plant growth (Li et al., 2011).
The Qinghai-Tibet Plateau is becoming warmer and wetter (Qiu, 2014; Fang et al., 2016). Zhu et al. (2013) concluded that the average temperature of the Tibetan Plateau would increase by 0.6-0.9°C from 2015 to 2050, and the precipitation, in early summer and autumn in particular, would also increase due to earlier onset and later departure of the Asian summer monsoon. Future seasonal variations in temperature and precipitation would induce shifts in plant species distribution (Yang et al., 2011), species composition (Sherry et al., 2008), water and nutrient cycling, and trophic interactions (Zavaleta, 2006), ultimately influencing biomass production. For instance, a shift from grasses to forbs with decreased water availability in a subalpine steppe has been shown to cause an increase in biomass (Sebastià, 2007). Currently, vegetation in most areas of the plateau may no longer be the climax community of its environment, and it may take a long time to reach a new balance between climate and vegetation (Yu et al., 2012).
In conclusion, our study showed that aboveground biomass of the grasslands on the Qinghai-Tibet Plateau responded significantly to variations in monthly temperature and precipitation, but that the responses were not consistent for different grassland types. Aboveground biomass of both alpine meadow and alpine steppe would increase in warmer and wetter conditions, with alpine meadow showing greater increase, while aboveground biomass of temperature steppe would decrease with warmer temperature. This suggested that growth of grassland may be conservative in stressed ecosystems.

The authors have declared that no competing interests exist.

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[8]
Dukes J S, Chiariello N R, Cleland E E, et al., 2005. Responses of grassland production to single and multiple global environmental changes.PloS Biology, 3(10): e319.In this century, increasing concentrations of carbon dioxide (CO2) and other greenhouse gases in the Earth's atmosphere are expected to cause warmer surface temperatures and changes in precipitation patterns. At the same time, reactive nitrogen is entering natural systems at unprecedented rates. These global environmental changes have consequences for the functioning of natural ecosystems, and responses of these systems may feed back to affect climate and atmospheric composition. Here, we report plant growth responses of an ecosystem exposed to factorial combinations of four expected global environmental changes. We exposed California grassland to elevated CO2, temperature, precipitation, and nitrogen deposition for five years. Root and shoot production did not respond to elevated CO2 or modest warming. Supplemental precipitation led to increases in shoot production and offsetting decreases in root production. Supplemental nitrate deposition increased total production by an average of 26%, primarily by stimulating shoot growth. Interactions among the main treatments were rare. Together, these results suggest that production in this grassland will respond minimally to changes in CO2 and winter precipitation, and to small amounts of warming. Increased nitrate deposition would have stronger effects on the grassland. Aside from this nitrate response, expectations that a changing atmosphere and climate would promote carbon storage by increasing plant growth appear unlikely to be realized in this system.

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[9]
Fan Y, Li X Y, Wu X Cet al., 2016. Divergent responses of vegetation aboveground net primary productivity to rainfall pulses in the Inner Mongolian Plateau, China.Journal of Arid Environments, 129: 1-8.61We firstly explored responses of vegetation to rainfall in Inner Mongolian Plateau.61Gobi desert and Typical steppe responded faster to rainfall than Desert steppe.61Rainfall size, timing, soil texture and plant type were main factors for response.

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[10]
Fang J Y, Chen A P, Peng C Het al., 2001. Changes in forest biomass carbon storage in China between 1949 and 1998.Science, 292(5525): 2320-2322.

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[11]
Fang Y, Cheng W M, Zhang Y Cet al., 2016. Changes in inland lakes on the Tibetan Plateau over the past 40 year.Journal of Geographical Sciences, 26(4): 415-438.Inland lakes and alpine glaciers are important water resources on the Tibetan Plateau. Understanding their variation is crucial for accurate evaluation and prediction of changes in water supply and for retrieval and analysis of climatic information. Data from previous research on 35 alpine lakes on the Tibetan Plateau were used to investigate changes in lake water level and area. In terms of temporal changes, the area of the 35 alpine lakes could be divided into five groups: rising, falling-rising, rising-falling, fluctuating, and falling. In terms of spatial changes, the area of alpine lakes in the Himalayan Mountains, the Karakoram Mountains, and the Qaidam Basin tended to decrease; the area of lakes in the Naqu region and the Kunlun Mountains increased; and the area of lakes in the Hoh Xil region and Qilian Mountains fluctuated. Changes in lake water level and area were correlated with regional changes in climate. Reasons for changes in these lakes on the Tibetan Plateau were analyzed, including precipitation and evaporation from meteorological data, glacier meltwater from the Chinese glacier inventories. Several key problems, e.g. challenges of monitoring water balance, limitations to glacial area detection, uncertainties in detecting lake water-level variations and variable region boundaries of lake change types on the Tibetan Plateau were discussed. This research has most indicative significance to regional climate change.

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[12]
Gamon J A, Huemmrich K F, Stone R Set al., 2013. Spatial and temporal variation in primary productivity (NDVI) of coastal Alaskan tundra: Decreased vegetation growth following earlier snowmelt.Remote Sensing of Environment, 129(2): 144-153.78 Field NDVI sampling clearly resolved productivity patterns for arctic vegetation. 78 Reduced vegetation productivity (NDVI) occurred in years with earlier snowmelt. 78 Reduced precipitation and soil moisture best explained this reduced productivity. 78 MODIS could not resolve snowmelt dates, growing season length, or peak NDVI. 78 Field optical monitoring avoids cloud contamination that plagues satellite NDVI.

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[13]
Grime J P, 1977. Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory.American Naturalist, 111(982): 1169-1194.It is suggested that evolution in plants may be associated with the emergence of three primary strategies, each of which may be identified by reference to a number of characteristics including morphological features, resource allocation, phenology, and response to stress. The competitive strategy prevails in productive, relatively undisturbed vegetation, the stress-tolerant strategy is associated with continuously unproductive conditions, and the ruderal strategy is characteristic of severely disturbed but potentially productive habitats. A triangular model based upon the three strategies may be reconciled with the theory of r- and K-selection, provides an insight into the processes of vegetation succession and dominance, and appears to be capable of extension to fungi and to animals.

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[14]
Guo Q, Hu Z M, Li S Get al., 2012. Spatial variations in aboveground net primary productivity along a climate gradient in Eurasian temperate grassland: Effects of mean annual precipitation and its seasonal distribution.Global Change Biology, 18(12): 3624-3631.Concomitant changes of annual precipitation and its seasonal distribution within the context of global climate change have dramatic impacts on aboveground net primary productivity (ANPP) of grassland ecosystems. In this study, combining remote sensing products with in situ measurements of ANPP, we quantified the effects of mean annual precipitation (MAP) and precipitation seasonal distribution (PSD) on the spatial variations in ANPP along a climate gradient in Eurasian temperate grassland. Our results indicated that ANPP increased exponentially with MAP for the entire temperate grassland, but linearly for a specific grassland type, i.e. the desert steppe, typical steppe, and meadow steppe from arid to humid regions. The slope of the linear relationship appeared to be steeper in the more humid meadow steppe than that in the drier typical and desert steppes. PSD also had significant effect on the spatial variations in ANPP. It explained 39.4% of the spatial ANPP for the entire grassland investigated, being comparable with the explanatory power of MAP (40.0%). On the other hand, the relative contribution of PSD and MAP is grassland type specific. MAP exhibited a much stronger explanatory power than PSD for the desert steppe and the meadow steppe at the dry and wet end, respectively. However, PSD was the dominant factor affecting the spatial variation in ANPP for the median typical steppe. Our results imply that altered pattern of PSD due to climate change may be as important as the total amount in terms of effects on ANPP in Eurasian temperate grassland.

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[15]
IPCC. 2013. Summary for Policymakers. Climate Change 2013. The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. UK: Cambridge University Press, 1-1535.

[16]
Hollister R D, Webber P J, Tweedie C E, 2005. The response of Alaskan arctic tundra to experimental warming: Differences between short- and long-term responses.Global Change Biology, 11(4): 525-536.

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[17]
Hu Z, Yu G, Fan Jet al., 2010. Precipitation-use efficiency along a 4500-km grassland transect. Global Ecology & Biogeography, 19(6): 842-851.Aims Clarifying the spatiotemporal variations in precipitation-use efficiency (PUE), the ratio of vegetation above-ground productivity to annual precipitation, will advance our understanding of how ecosystems' carbon and water cycles respond to climate change. Our goal is to investigate the variations in PUE at both regional and site scales along a 4500-km climate-related grassland transect.Location The Inner Mongolian Plateau in northern China and the Qinghai-Tibetan Plateau.Methods We collected data on 580 sites from four data sources. The data were acquired through field surveys and long-term in situ observations. We investigated the relationships between precipitation and PUE at both regional and site scales, and we evaluated the effects of the main biotic and climatic factors on PUE at both spatial scales.Results PUE decreased with decreasing mean annual precipitation (MAP), except for a slight rise toward the dry end of the gradient. The maximum PUE showed large site-to-site variation along the transect. Vegetation cover significantly affected the spatial variations in PUE, and this probably accounts for the positive relationship between PUE and MAP. However, there was no significant relationship between inter-annual variations in precipitation or vegetation cover and PUE within given ecosystems along the transect.Conclusions The findings of this research contradict the prevailing view that a convergent maximum PUE exists among diverse ecosystems, as presented in previous reports. Our findings also suggest the action of distinct mechanisms in controlling PUE at different spatial scales. We propose the use of a conceptual model for predicting vegetation productivity at continental and global scales with a sigmoid function, which illustrates an increasing PUE with MAP in arid regions. Our approach may represent an improvement over use of the popular Miami model.

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[18]
Huxman T E, Smith M D, Fay P Aet al., 2004. Convergence across biomes to a common rain-use efficiency.Nature, 429(6992): 651-654.

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[19]
Iwasaki H, 2006. Impact of interannual variability of meteorological parameters on vegetation activity over Mongolia.Journal of the Meteorological Society of Japan, 84(4): 745-762.

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[20]
Jiao C C, Yu G R, He N Pet al., 2017. Spatial pattern of grassland aboveground biomass and its environmental controls in the Eurasian steppe.Journal of Geographical Sciences, 27(1): 3-22.Vegetation biomass is an important component of terrestrial ecosystem carbon stocks. Grasslands are one of the most widespread biomes worldwide, playing an important role in global carbon cycling. Therefore, studying spatial patterns of biomass and their correlations to environment in grasslands is fundamental to quantifying terrestrial carbon budgets. The Eurasian steppe, an important part of global grasslands, is the largest and relatively well preserved grassland in the world. In this study, we analyzed the spatial pattern of aboveground biomass (AGB), and correlations of AGB to its environment in the Eurasian steppe by meta-analysis. AGB data used in this study were derived from the harvesting method and were obtained from three data sources (literature, global NPP database at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL), some data provided by other researchers). Our results demonstrated that: (1) as for the Eurasian steppe overall, the spatial variation in AGB exhibited significant horizontal and vertical zonality. In detail, AGB showed an inverted parabola curve with the latitude and with the elevation, while a parabola curve with the longitude. In addition, the spatial pattern of AGB had marked horizontal zonality in the Black Sea-Kazakhstan steppe subregion and the Mongolian Plateau steppe subregion, while horizontal and vertical zonality in the Tibetan Plateau alpine steppe subregion. (2) Of the examined environmental variables, the spatial variation of AGB was related to mean annual precipitation (MAP), mean annual temperature (MAT), mean annual solar radiation (MAR), soil Gravel content, soil pH and soil organic content (SOC) at the depth of 0 30 cm. Nevertheless, MAP dominated spatial patterns of AGB in the Eurasian steppe and its three subregions. (3) A Gaussian function was found between AGB and MAP in the Eurasian steppe overall, which was primarily determined by unique patterns of grasslands and environment in the Tibetan Plateau. AGB was significantly positively related to MAP in the Black Sea-Kazakhstan steppe subregion (elevation < 3000 m), the Mongolian Plateau steppe subregion (elevation < 3000 m) and the surface (elevation 4800 m) of the Tibetan Plateau. Nevertheless, the spatial variation in AGB exhibited a Gaussian function curve with the increasing MAP in the east and southeast margins (elevation < 4800 m) of the Tibetan Plateau. This study provided more knowledge of spatial patterns of AGB and their environmental controls in grasslands than previous studies only conducted in local regions like the Inner Mongolian temperate grassland, the Tibetan Plateau alpine grassland, etc.

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[21]
Jong S D, 1993. SIMPLS: An alternative approach to partial least squares regression.Chemometrics and Intelligent Laboratory Systems, 18(3): 251-263.A novel algorithm for partial least squares (PLS) regression, SIMPLS, is proposed which calculates the PLS factors directly as linear combinations of the original variables. The PLS factors are determined such as to maximize a covariance criterion, while obeying certain orthogonality and normalization restrictions. This approach follows that of other traditional multivariate methods. The construction of deflated data matrices as in the nonlinear iterative partial least squares (NIPALS)-PLS algorithm is avoided. For univariate y SIMPLS is equivalent to PLS1 and closely related to existing bidiagonalization algorithms. This follows from an analysis of PLS1 regression in terms of Krylov sequences. For multivariate Y there is a slight difference between the SIMPLS approach and NIPALS-PLS2. In practice the SIMPLS algorithm appears to be fast and easy to interpret as it does not involve a breakdown of the data sets.

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[22]
Kato T, Tang Y, Gu Set al., 2006. Temperature and biomass influences on interannual changes in CO2 exchange in an alpine meadow on the Qinghai-Tibetan Plateau.Global Change Biology, 12(7): 1285-1298.

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[23]
Knapp A K, Beier C, Briske D Det al., 2008. Consequences of more extreme precipitation regimes for terrestrial ecosystems.Bioscience, 58(9): 811-821.Amplification of the hydrological cycle as a consequence of global warming is forecast to lead to more extreme intra-annual precipitation regimes characterized by larger rainfall events and longer intervals between events. We present a conceptual framework, based on past investigations and ecological theory, for predicting the consequences of this underappreciated aspect of climate change. We consider a broad range of terrestrial ecosystems that vary in their overall water balance. More extreme rainfall regimes are expected to increase the duration and severity of soil water stress in mesic ecosystems as intervals between rainfall events increase. In contrast, xeric ecosystems may exhibit the opposite response to extreme events. Larger but less frequent rainfall events may result in proportional reductions in evaporative losses in xeric systems, and thus may lead to greater soil water availability. Hydric (wetland) ecosystems are predicted to experience reduced periods of anoxia in response to prolonged intervals between rainfall events. Understanding these contingent effects of ecosystem water balance is necessary for predicting how more extreme precipitation regimes will modify ecosystem processes and alter interactions with related global change drivers.

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[24]
Knapp A K, Briggs J M, Koelliker J K, 2001. Frequency and extent of water limitation to primary production in a mesic temperate grassland.Ecosystems, 4(1): 19-28.

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[25]
Li G Y, Liu Y Z, Frelich L Eet al., 2011. Experimental warming induces degradation of a Tibetan alpine meadow through trophic interactions.Journal of Applied Ecology, 48(3): 659-667.1. It is well known that climate change alters abiotic factors (temperature and water availability) that directly affect ecosystem properties. However, less is known about the indirect impacts of climate change on ecosystem structure and function. Here, we show that experimental warming may deteriorate ecosystems via trophic interactions.2. In a Tibetan alpine meadow, plant species composition, size, coverage and above-ground biomass were investigated to reveal the effect of artificial warming (c. 1 C mean annual temperature at the soil surface), which was accomplished using warmed and ambient open top chambers. In addition, rodent damage to plants was assessed.3. The dicot forb silverweed Potentilla anserina increased significantly, while other species groups remained unchanged or decreased in plant community dominance rank after 2 years of artificial warming. The change in community structure was attributed to the difference in biomass allocation and growth form among species.4. In the third year, plateau zokors Myospalax fontanierii, a widespread rodent herbivore, damaged plants in the warmed chambers, while leaving plants in the ambient chambers mostly undamaged. Above-ground biomass was found to be smaller in the warmed chambers than the controls in the third year, in contrast to the trend of the first 2 years. In addition, zokor burrow density was positively correlated with silverweed biomass and its dominance within communities, which was consistent with findings of independent field investigations that silverweed-dominated plots were more likely to be visited and damaged by the zokors than sites-dominated by grass species.5. Synthesis and applications. The top-down negative effect of zokor damage on above-ground biomass in the warmed chambers was induced by the bottom-up effect of changes in species composition and community structure on zokor foraging behaviour, which were driven by artificial warming. Such trophic interactions may invalidate some predictions of ecological effects by current species-climate envelope models. Furthermore, because management measures including increasing the water table, planting grass and moderate cattle grazing may prevent silverweed dominance, we suggest that these interventions could be employed to control zokor damage in alpine meadows that are predicted to be drier and warmer in the future.

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[26]
Liu B, Zhao W Z, Wen Z J, 2012. Photosynthetic response of two shrubs to rainfall pulses in desert regions of northwestern China.Photosynthetica, 50(1): 109-119.Pulses of rainfall are particularly pivotal in controlling plant physiological processes in ecosystems controlled by limited water, and the response of desert plants to rainfall is a key to understanding the responses of desert ecosystems to global climatic change. We used a portable photosynthesis system to measure the responses of the diurnal course of photosynthesis, light-response curves, and CO 2 -response curves of two desert shrubs ( Nitraria sphaerocarpa Maxim. and Calligonum mongolicum Turcz) to a rainfall pulse in a desert-oasis ecotone in northwestern China. The photosynthetic parameters, light- and CO 2 -response curves differed significantly before and after the rainfall pulse. Their maximum net photosynthetic rate ( P N ) values were 23.27 and 32.92 μmol(CO 2 ) m 612 s 611 for N. sphaerocarpa and C. mongolicum , respectively, with corresponding maximum stomatal conductance ( g s ) values of 0.47 and 0.39 mol(H 2 O) m 612 s 611 . The P N of N. sphaerocarpa after the rainfall was 1.65 to 1.75 times the value before rainfall, whereas those of C. mongolicum increased to approximately 2 times the prerainfall value, demonstrating the importance of the desert plants response by improving their assimilation rate to precipitation patterns under a future climate.

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[27]
Luedeling E, Gassner A, 2012. Partial Least Squares Regression for analyzing walnut phenology in California.Agricultural and Forest Meteorology, 158/159: 43-52.Many biological processes produce only one quantitative outcome per year, resulting from temperatures and precipitation during hundreds of days leading up to the event. Traditional regression approaches incur problems in such a setting, because independent variables are highly autocorrelated and their number often greatly exceeds the number of observations. Partial Least Squares Regression (PLS), a statistical analysis tool developed to handle these situations and widely used in hyperspectral remote sensing, was tested for its usefulness for explaining the climate responses of biological processes, using walnut phenology in California as an example. Observations of first female bloom, first male bloom and leaf emergence of three walnut cultivars at Davis, CA were coupled with daily temperature data since 1951. The dataset was analyzed by PLS, using three temperature inputs: (1) daily mean temperatures, (2) 11-day running means of daily mean temperatures and (3) monthly mean temperatures. For all data constellations, the Variable-Importance-in-the-Projection (VIP) statistic indicated a number of periods, during which temperatures were important determinants of phenological events, and the model-coefficients-of-the-centered-and-scaled-data (MC) statistic showed the direction, in which high temperatures during these phases influenced walnut flowering and leaf emergence. In all analyses, a delaying effect of warm winters, and an advancing effect of warm springs were clearly visible. It was also possible to identify the transition between the chilling and forcing phases, and the VIP and MC plots indicated quantitative differences in the effectiveness of winter chill during different phases of the dormancy season. Such effects have not been captured in any phenology models currently applied to fruit trees, indicating that PLS has potential to help refine such models. PLS can also be used for guiding experimental research by pinpointing the parts of the season that are most important for the timing of budburst. Results suggested that more than 20 years of observed data were necessary for producing clearly recognizable temperature response patterns, limiting the applicability of PLS to long time series.

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[28]
Ma W H, Yang Y H, He J Set al., 2008. Above and belowground biomass in relation to environmental factors in temperate grasslands, Inner Mongolia.Science in China Series C, 51(3): 263-270.Above- and belowground biomasses of grasslands are important parameters for characterizing re-gional and global carbon cycles in grassland ecosystems. Compared with the relatively detailed in-formation for aboveground biomass (AGB), belowground biomass (BGB) is poorly reported at the re-gional scales. The present study, based on a total of 113 sampling sites in temperate grassland of the Inner Mongolia, investigated regional distribution patterns of AGB, BGB, vertical distribution of roots, and their relationships with environmental factors. AGB and BGB increased from the southwest to the northeast of the study region. The largest biomass occurred in meadow steppe, with mean AGB and BGB of 196.7 and 1385.2 g/m, respectively; while the lowest biomass occurred in desert steppe, with an AGB of 56.6 g/m and a BGB of 301.0 g/m. In addition, about 47% of root biomass was distributed in the top 10 cm soil. Further statistical analysis indicated that precipitation was the primary determinant factor in shaping these distribution patterns. Vertical distribution of roots was significantly affected by precipitation, while the effects of soil texture and grassland types were weak.

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[29]
Miyazaki S, Yasunari T, Miyamoto Tet al., 2004. Agrometeorological conditions of grassland vegetation in central Mongolia and their impact for leaf area growth.Journal of Geophysical Research, 109(D22106): 1-14.1] The long-term observation of surface heat and water budget and hydrometeorological elements has been carried out over a grassland site at Arvaikheer (46.2300°N, 102.8200°E) in central Mongolia as the framework of the GEWEX Asian Monsoon Experiment-Asian Automatic Weather Station Network (GAME-AAN). The purpose of this study is to clarify the relationship between vegetation and climate using long-term data (19820900092000) of satellite-derived leaf area index (LAI) and climatic data observed at Arvaikheer. Furthermore, we aimed to reveal physical process by comparing soil moisture and heat and water budgets in 1999 and 2000 as a case study of good and poor vegetation growth. Significant positive correlations with 99% confidence levels were found for July precipitation (P) and the LAI in July (0.538), August (0.826), and September (0.564). Composite analysis for five highest (H5) and lowest (L5) LAI years showed the significant positive anomalies of P in July and LAI in July and August for H5. In June and July 1999, soil moisture and P values were higher than values in 2000; this pattern was reversed in August and September. The mean LAI during the 1999 growing season (1.0) was about twice that of 2000 (0.6). In 1999 the ratio of evapotranspiration (ET) to P (ET/P) and change of stored soil moisture (0200W) to P (0200W/P) were 0.79 and 0.15, respectively. In 2000, ET/P and 0200W/P were 0.94 and 0.0, respectively. These results suggest that the P and 0200W before July had the most influent on grass growth in central Mongolia.

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[30]
Mokany K, Raison R J, Prokushkin A S, 2006. Critical analysis of root: Shoot ratios in terrestrial biomes.Global Change Biology, 12(1): 84-96.

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[31]
Nandintsetseg B, Shinoda M, 2011. Seasonal change of soil moisture in Mongolia: Its climatology and modelling.International Journal of Climatology, 31(8): 1143-1152.lt;P>Mongolia has an arid and cold climate due to its geographical settings of inland and mid-latitude highlands. The soil moisture varies seasonally, depending mainly on the balance of precipitation and evapotranspiration as well as on winter soil-freezing and spring snowmelt. Soil moisture climatology (1986 2005) for Mongolia is presented with a focus on three vegetation zones: the forest steppe, steppe, and Gobi Desert. For this purpose, we used soil moisture observations based on the gravimetric method for the top 50-cm soil layer from 26 grass-covered field sites during April ctober of the 20-year period. In general, there was a latitudinal gradient in soil moisture content, with the southwestern soils being drier than northeastern soils. The seasonal change in soil moisture was small and the seasonal pattern was similar throughout Mongolia. The seasonality was characterised by three major phases of the warm season: the spring drying, summer recharge, and autumn drying phases, although each phase differed somewhat in timing and length between zones. In the northernmost forest steppe zone, the recharge phase was longer than that in the southern steppe and Gobi Desert zones, while the two drying phases were shorter in the forest steppe zone. This difference had a significant effect on the plant phenological timings of Stipa spp.; these were earlier in the forest steppe zone and later in the Gobi Desert zone. A simple water balance model was developed to examine the observed soil moisture dynamics, which implicitly simulated snow accumulation and soil freezing. The model simulated the observed seasonal and inter-annual soil moisture variations reasonably well (r = 0.75, p < 0.05). The results demonstrated that the three phases of seasonal change were produced by a subtle balance between precipitation and evapotranspiration. This model will provide a useful tool for a reliable and timely monitoring of agricultural drought for decision-makers and herders in Mongolia. Copyright 2010 Royal Meteorological Society</P>

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[32]
Paruelo J M, Lauenroth W K, Burke I Cet al., 1999. Grassland precipitation-use efficiency varies across a resource gradient.Ecosystems, 2(1): 64-68.Aboveground net primary production (ANPP) is positively related to mean annual precipitation, an estimate of water availability. This relationship is fundamental to our understanding and management of grassland ecosystems. However, the slope of the relationship between ANPP and precipitation (precipitation-use efficiency, PUE) has been shown to be different for temporal compared with spatial precipitation series. When ANPP and precipitation are averaged over a number of years for different sites, PUE is similar for grasslands all over the world. Studies for two US Long Term Ecological Research Sites have shown that PUE derived from a longterm dataset (temporal model) has a significantly lower slope than the value derived for sites distributed across the US central grassland region (spatial model). PUE differences between the temporal model and the spatial model may be associated with both vegetational and biogeochemical constraints. Here we use two independent datasets, one derived from field estimates of ANPP and the other from remote sensing, to show that the PUE is low at both the dry end and the wet end of the annual precipitation gradient typical of grassland areas (200-1200 mm), and peaks around 475 mm. The intermediate peak may be related to relatively low levels of both vegetational and biogeochemical constraints at this level of resource availability.

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[33]
Qiu J, 2014. Double threat for Tibet.Nature, 512(7514): 240-241.

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[34]
Richardson A D, Keenan T F, Migliavacca Met al., 2013. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system.Agricultural & Forest Meteorology, 169(3): 156-173.Vegetation phenology is highly sensitive to climate change. Phenology also controls many feedbacks of vegetation to the climate system by influencing the seasonality of albedo, surface roughness length, canopy conductance, and fluxes of water, energy, CO2 and biogenic volatile organic compounds. In this review, we first discuss the environmental drivers of phenology, and the impacts of climate change on phenology, in different biomes. We then examine the vegetation-climate feedbacks that are mediated by phenology, and assess the potential impact on these feedbacks of shifts in phenology driven by climate change. We finish with an overview of phenological modeling and we suggest ways in which models might be improved using existing data sets. Several key weaknesses in our current understanding emerge from this analysis. First, we need a better understanding of the drivers of phenology, particularly in under-studied biomes (e.g. tropical forests). We do not have a mechanistic understanding of the role of photoperiod, even in well-studied biomes. In all biomes, the factors controlling senescence and dormancy are not well-documented. Second, for the most part (i.e. with the exception of phenology impacts on CO2 exchange) we have only a qualitative understanding of the feedbacks between vegetation and climate that are mediated by phenology. We need to quantify the magnitude of these feedbacks, and ensure that they are accurately reproduced by models. Third, we need to work towards a new understanding of phenological processes that enables progress beyond the modeling paradigms currently in use. Accurate representation of phenological processes in models that couple the land surface to the climate system is particularly important, especially when such models are being used to predict future climate.

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[35]
Sebastià M T, 2007. Plant guilds drive biomass response to global warming and water availability in subalpine grassland.Journal of Applied Ecology, 44(1): 158-167.1. The consequences of global warming and changes in resource availability were investigated in subalpine grasslands in the Pyrenees. These communities are considered to be especially vulnerable to climate change because of their position at the south-western edge of the semi-natural grassland biome in Europe. 2. Changes in patterns of above- and below-ground biomass were assessed for different plant guilds in two experiments, in which turves were transplanted from upland to lowland locations. The first experiment aimed to evaluate general responses to warming and drought, and the second to disentangle the effects of possible underlying mechanisms through resource manipulation by means of a nitrogen x phosphorus fertilization experiment. 3. The increased above-ground biomass in grassland turves transplanted to lowlands suggested that biomass production was more temperature-limited than water-limited. The enhancement effect found in the upland turves following phosphorus addition supported the hypothesis of a strong limitation arising from reduced nutrient availability, confirming the central role played by phosphorus in these grasslands and its potential importance in the response to global change. 4. Nitrogen addition did not stimulate total biomass but affected guild composition. Grasses dominated the uplands and at high resource levels, while forbs dominated the lowlands and when water and nutrients decreased. The counterintuitive effect of increased biomass with decreased water in the lowlands was related to shifts in dominance from grasses to forbs, probably enabled by decreased nutrient availability under drought conditions. 5. Synthesis and applications. Environmental factors interacted in complex ways, producing changes in biomass distribution and guild proportions in subalpine grassland. In addition, the results suggested that the capability of high-altitude grasslands to provide quality forage in summer time could be threatened in the northern Mediterranean region under climate change conditions because of: (i) a decrease in their reliability as a result of complex biomass interactions with temperature, water and nutrient dynamics; (ii) expected feedback mechanisms; and (iii) compositional shifts.

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[36]
Sherry R A, Weng E, Arnone III J Aet al., 2008. Lagged effects of experimental warming and doubled precipitation on annual and seasonal aboveground biomass production in a tallgrass prairie.Global Change Biology, 14(12): 2923-2936.Global climate change is expected to result in a greater frequency of extreme weather, which can cause lag effects on aboveground net primary production (ANPP). However, our understanding of lag effects is limited. To explore lag effects following extreme weather, we applied four treatments (control, doubled precipitation, 4 C warming, and warming plus doubled precipitation) for 1 year in a randomized block design and monitored changes in ecosystem processes for 3 years in an old-field tallgrass prairie in central Oklahoma. Biomass was estimated twice in the pretreatment year, and three times during the treatment and posttreatment years. Total plant biomass was increased by warming in spring of the treatment year and by doubled precipitation in summer. However, double precipitation suppressed fall production. During the following spring, biomass production was significantly suppressed in the formerly warmed plots 2 months after treatments ceased. Nine months after the end of treatments, fall production remained suppressed in double precipitation and warming plus double precipitation treatments. Also, the formerly warmed plots still had a significantly greater proportion of C 4 plants, while the warmed plus double precipitation plots retained a high proportion of C 3 plants. The lag effects of warming on biomass did not match the temporal patterns of soil nitrogen availability determined by plant root simulator probes, but coincided with warming-induced decreases in available soil moisture in the deepest layers of soil which recovered to the pretreatment pattern approximately 10 months after the treatments ceased. Analyzing the data with an ecosystem model showed that the lagged temporal patterns of effects of warming and precipitation on biomass can be fully explained by warming-induced differences in soil moisture. Thus, both the experimental results and modeling analysis indicate that water availability regulates lag effects of warming on biomass production.

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[37]
Shinoda M, Nandintsetseg B, 2011. Soil moisture and vegetation memories in a cold, arid climate.Global & Planetary Change, 79(1/2): 110-117.78 We found new evidence of interseasonal moisture memory mediated by the land surface in Mongolia. 78 Soil moisture and vegetation interannual anomalies are maintained via the winter to the spring. 78 The cold-season climate acts to prolong the time scale of autumn soil moisture anomalies to 8.2 months. 78 The vegetation has a long memory of the similar decay time scale. 78 These findings will be used as early warning information for drought/dust emission.

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[38]
Sun J, Cheng G W, Li W P, 2013. Meta-analysis of relationships between environmental factors and aboveground biomass in the alpine grassland on the Tibetan Plateau.Biogeosciences, 10(3): 1707-1715.

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[39]
Thomey M L, Collins S L, Vargas Ret al., 2015. Effect of precipitation variability on net primary production and soil respiration in a Chihuahuan Desert grassland.Global Change Biology, 17(4): 1505-1515.Abstract Precipitation regimes are predicted to become more variable with more extreme rainfall events punctuated by longer intervening dry periods. Water-limited ecosystems are likely to be highly responsive to altered precipitation regimes. The bucket model predicts that increased precipitation variability will reduce soil moisture stress and increase primary productivity and soil respiration in aridland ecosystems. To test this hypothesis, we experimentally altered the size and frequency of precipitation events during the summer monsoon (July through September) in 2007 and 2008 in a northern Chihuahuan Desert grassland in central New Mexico, USA. Treatments included (1) ambient rain, (2) ambient rain plus one 20 mm rain event each month, and (3) ambient rain plus four 5 mm rain events each month. Throughout two monsoon seasons, we measured soil temperature, soil moisture content ( ), soil respiration ( R s), along with leaf-level photosynthesis ( A net), predawn leaf water potential ( pd), and seasonal aboveground net primary productivity (ANPP) of the dominant C4 grass, Bouteloua eriopoda . Treatment plots receiving a single large rainfall event each month maintained significantly higher seasonal soil which corresponded with a significant increase in R s and ANPP of B. eriopoda when compared with plots receiving multiple small events. Because the strength of these patterns differed between years, we propose a modification of the bucket model in which both the mean and variance of soil water change as a consequence of interannual variability from 1 year to the next. Our results demonstrate that aridland ecosystems are highly sensitive to increased precipitation variability, and that more extreme precipitation events will likely have a positive impact on some aridland ecosystem processes important for the carbon cycle.

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[40]
Trenberth K E, Shea D J, 2005. Relationships between precipitation and surface temperature. Geophysical Research Letters, 32(14): 129-142.The co-variability of monthly mean surface temperature and precipitation is determined globally for 1979-2002 from observationally-based analyses (ERA-40) for surface air temperature and the Global Precipitation Climatology Project (GPCP) version 2 for precipitation and compared with results from the NCAR Community Atmospheric Model version 3 (CAM3) and Community Climate System Model version 3 (CCSM3). Results are combined for the 5 months for northern winter (November to March) and summer (May to September). Over land, negative correlations dominate, as dry conditions favor more sunshine and less evaporative cooling, while wet summers are cool. At high latitudes in winter, positive correlations dominate as warm moist advection in extratropical cyclones favors precipitation and the water holding capacity of the atmosphere limits precipitation amounts in cold conditions. Where ocean conditions drive the atmosphere, higher surface air temperatures are associated with precipitation, as in El Ni帽o, but some areas, such as the western Pacific in northern summer, feature negative correlations indicating that the atmosphere determines the surface temperatures. In the CAM driven with observed sea surface temperatures and the CCSM in fully coupled mode the latter mechanism is largely absent, and correlations are generally much stronger than observed, indicating more local control. Neither temperature nor precipitation records should be interpreted without considering the strong covariability that exists.

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[41]
Wan S Q, Hui D F, Wallace Let al., 2005. Direct and indirect effects of experimental warming on ecosystem carbon processes in a tallgrass prairie.Global Biogeochemical Cycles, 19(2): 1-13.1] This study was conducted to examine direct and indirect impacts of global warming on carbon processes in a tallgrass prairie in the U.S. Great Plains. Infrared radiators were used to simulate global warming, and clipping was used to mimic hay mowing. Experimental warming caused significant increases in green biomass in spring and autumn and total biomass in summer on most of the measuring dates. Green aboveground biomass showed positive linear correlations with soil temperature in spring and autumn whereas total aboveground biomass in summer was negatively correlated with soil temperature. Experimental warming also affected aboveground biomass indirectly by extending the length of growing season and changing soil nitrogen process. Elevated temperature tended to increase net nitrogen mineralization in the first year but decrease it in the second year, which could be attributable to stimulated plant growth and belowground carbon allocation and consequently enhanced microbial nitrogen immobilization. Warming-induced changes in soil respiration were proportional to those of total aboveground biomass. Clipping significantly reduced aboveground biomass and increased root biomass, but had no effect on net nitrogen mineralization and annual mean soil respiration. The proportional changes in soil respiration to those of aboveground biomass indicate warming-stimulated ecosystem carbon uptake could be weakened by increased carbon release through soil respiration.

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[42]
Wang G X, Bai W, Li Net al., 2011. Climate changes and its impact on tundra ecosystem in Qinghai-Tibet Plateau, China.Climatic Change, 106(3): 463-482.lpine ecosystems in permafrost region are extremely sensitive to climate change. The headwater regions of Yangtze River and Yellow River of the Qinghai-Tibet plateau permafrost area were selected. Spatial-temporal shifts in the extent and distribution of tundra ecosystems were investigated for the period 1967-2000 by landscape ecological method and aerial photographs for 1967, and satellite remote sensing data (the Landsat's TM) for 1986 and 2000. The relationships were analyzed between climate change and the distribution area variation of tundra ecosystems and between the permafrost change and tundra ecosystems. The responding model of tundra ecosystem to the combined effects of climate and permafrost changes was established by using statistic regression method, and the contribution of climate changes and permafrost variation to the degradation of tundra ecosystems was estimated. The regional climate exhibited a tendency towards significant warming and desiccation with the air temperature increased by 0.4-0.67070705C/10a and relative stable precipitation over the last 45 years. Owing to the climate continuous warming, the intensity of surface heat source ( HI) increased at the average of 0.45 W/m per year, the difference of surface soil temperature and air temperature (DT) increased at the range of 4.1070705C-4.5070705C, and the 20-cm depth soil temperature within the active layer increased at the range of 1.1070705C-1.4070705C. The alpine meadow and alpine swamp meadow were more sensitive to permafrost changes than alpine steppe. The area of alpine swamp meadow decreased by 13.6-28.9%, while the alpine meadow area decreased by 13.5-21.3% from 1967 to 2000. The contributions of climate change to the degradation of the alpine meadow and alpine swamp was 58-68% and 59-65% between 1967 and 2000. The synergic effects of climate change and permafrost variation were the major drivers for the observed degradation in tundra ecosystems of the Qinghai-Tibet plateau.

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[43]
Wang L, Feng Z M, Yang Y Z, 2015. The change in population density from 2000 to 2010 and its influencing factors in China at the county scale.Journal of Geographical Sciences, 25(4): 485-496.Studying the change in population distribution and density can provide important basis for regional development and planning. The spatial patterns and driving factors of the change in population density in China were not clear yet. Therefore, using the population census data in 2000 and 2010, this study firstly analyzed the change of population density in China and divided the change in all 2353 counties into 4 types, consisting of rapid increase, slow increase, slow decrease and rapid decrease. Subsequently, based on the partial least square (PLS) regression method, we recognized the significant factors (among 11 natural and social-economic factors) impacting population density change for the whole country and counties with different types of population change. The results showed that: (1) compared to the population density in 2000, in 2010, the population density in most of the counties (over 60%) increased by 21 persons per km 2 on average, while the population density in other counties decreased by 13 persons per km 2 . Of all the 2353 counties, 860 and 589 counties respectively showed rapid and slow increase in population density, while 458 and 446 counties showed slow and rapid decrease in population density, respectively. (2) Among the 11 factors, social-economic factors impacted population density change more significantly than natural factors. The higher economic development level, better medical condition and stronger communication capability were the main pull factors of population increase. The dense population density was the main push factor of population decrease. These conclusions clarified the spatial pattern of population change and its influencing factors in China over the past 10 years and could provide helpful reference for the future population planning.

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[44]
Wang Q J, Yang F T, Shi S H, 1988. A preliminary study on formation of belowground biomass in a Kobresia Humilis meadow. In: Northwest Institute of Plateau Biology of the Chinese Academy of Sciences (eds.). The Proceedings of the International Symposium of Alpine Meadow Ecosystem. Beijing: Science Press, 73-81.

[45]
Wei Y L, Ma X H, Song L M, 2009. Soil moisture dynamic of natural meadow and its impacts on forage biomass in Qinghai Lake region.Pratacultural Science, 26(5): 76-80. (in Chinese)The dynamic changes of soil moisture on natural grassland and the relations between forage biomass and the soil moisture in Qinghai Lake region were analyzed.Results indicated that changes of the soil humidity in the Qinghai Lake region can be divided into four periods,such as the slowly lost period in spring,the slow-moving convalescent period in the end of spring and early summer,the fastly lost period in summer and the fast increment and imbursement period in autumn.The perpendicular variety of the soil moisture differed among different years.0-30 cm depth of soil moisture levels effected by factors such as weather and pasture growth,and the impact of wet and dry changed significantly,the changes in 30-50 cm depth of soil moisture levels were relatively stable.The forage biomass at the end of each month during the growth season(from May to September) was closely related to the soil humidity in its front period.The influence of the soil humidity in previous year on forage biomass was more obvious in the early period of forage grass growth,and it worn off gradually in late period.

[46]
Wu Z T, Dijkstra P, Koch G Wet al., 2011. Responses of terrestrial ecosystems to temperature and precipitation change: A meta-analysis of experimental manipulation.Global Change Biology, 17(2): 927-942.Global mean temperature is predicted to increase by 2鈥7 掳C and precipitation to change across the globe by the end of this century. To quantify climate effects on ecosystem processes, a number of climate change experiments have been established around the world in various ecosystems. Despite these efforts, general responses of terrestrial ecosystems to changes in temperature and precipitation, and especially to their combined effects, remain unclear. We used meta-analysis to synthesize ecosystem-level responses to warming, altered precipitation, and their combination. We focused on plant growth and ecosystem carbon (C) balance, including biomass, net primary production (NPP), respiration, net ecosystem exchange (NEE), and ecosystem photosynthesis, synthesizing results from 85 studies. We found that experimental warming and increased precipitation generally stimulated plant growth and ecosystem C fluxes, whereas decreased precipitation had the opposite effects. For example, warming significantly stimulated total NPP, increased ecosystem photosynthesis, and ecosystem respiration. Experimentally reduced precipitation suppressed aboveground NPP (ANPP) and NEE, whereas supplemental precipitation enhanced ANPP and NEE. Plant productivity and ecosystem C fluxes generally showed higher sensitivities to increased precipitation than to decreased precipitation. Interactive effects of warming and altered precipitation tended to be smaller than expected from additive, single-factor effects, though low statistical power limits the strength of these conclusions. New experiments with combined temperature and precipitation manipulations are needed to conclusively determine the importance of temperature recipitation interactions on the C balance of terrestrial ecosystems under future climate conditions.

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[47]
Xu J C, Grumbine R E, Shrestha Aet al., 2009. The melting Himalayas: Cascading effects of climate change on water, biodiversity, and livelihoods.Conservation Biology, 23(3): 520-530.Abstract: The Greater Himalayas hold the largest mass of ice outside polar regions and are the source of the 10 largest rivers in Asia. Rapid reduction in the volume of Himalayan glaciers due to climate change is occurring. The cascading effects of rising temperatures and loss of ice and snow in the region are affecting, for example, water availability (amounts, seasonality), biodiversity (endemic species, predator rey relations), ecosystem boundary shifts (tree-line movements, high-elevation ecosystem changes), and global feedbacks (monsoonal shifts, loss of soil carbon). Climate change will also have environmental and social impacts that will likely increase uncertainty in water supplies and agricultural production for human populations across Asia. A common understanding of climate change needs to be developed through regional and local-scale research so that mitigation and adaptation strategies can be identified and implemented. The challenges brought about by climate change in the Greater Himalayas can only be addressed through increased regional collaboration in scientific research and policy making.

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[48]
Xu M H, Liu M, Xue Xet al., 2016. Warming effects on plant biomass allocation and correlations with the soil environment in an alpine meadow, China.Journal of Arid Land, 8(5): 773-786.Alpine meadow ecosystem is fragile and highly sensitive to climate change. An understanding of the allocation of above- and below-ground plant biomass and correlations with environmental factors in...

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[49]
Yang H J, Wu M Y, Liu W Xet al., 2011. Community structure and composition in response to climate change in a temperate steppe.Global Change Biology, 17(1): 452-465.Abstract Climate change would have profound influences on community structure and composition, and subsequently has impacts on ecosystem functioning and feedback to climate change. A field experiment with increased temperature and precipitation was conducted to examine effects of experimental warming, increased precipitation and their interactions on community structure and composition in a temperate steppe in northern China since April 2005. Increased precipitation significantly stimulated species richness and coverage of plant community. In contrast, experimental warming markedly reduced species richness of grasses and community coverage. Species richness was positively dependent upon soil moisture (SM) across all treatments and years. Redundancy analysis (RDA) illustrated that SM dominated the response of community composition to climate change at the individual level, suggesting indirect effects of climate change on plant community composition via altering water availability. In addition, species interaction also mediated the responses of functional group coverage to increased precipitation and temperature. Our observations revealed that both abiotic (soil water availability) and biotic (interspecific interactions) factors play important roles in regulating plant community structure and composition in response to climate change in the semiarid steppe. Therefore these factors should be incorporated in model predicting terrestrial vegetation dynamics under climate change.

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[50]
Yang Y H, Fang J Y, Pan Y Det al., 2009. Aboveground biomass in Tibetan grasslands.Journal of Arid Environment. 73(1): 91-95.This study investigated spatial patterns and environmental controls of aboveground biomass (AGB) in alpine grasslands on the Tibetan Plateau by integrating AGB data collected from 135 sites during 2001–2004 and concurrent enhanced vegetation index derived from MODIS data sets. The AGB was estimated at 68.8 g m 612, with a larger value (90.8 g m 612) in alpine meadow than in alpine steppe (50.1 g m 612). It increased with growing season precipitation (GSP), but did not show a significant overall trend with growing season temperature (GST) although it was negatively correlated with GST at dry environments (<200 mm of GSP). Soil texture also influenced AGB, but the effect was coupled with precipitation; increased silt content caused a decrease of AGB at small GSP, and generated a meaningful increase under humid conditions. The correlation between AGB and sand content indicated an opposite trend with that between AGB and silt content. An analysis of general linear model depicted that precipitation, temperature, and soil texture together explained 54.2% of total variance in AGB. Our results suggest that moisture availability is a critical control of plant production, but temperature and soil texture also affect vegetation growth in high-altitude regions.

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[51]
Yu H Y, Xu J C, Okuto Eet al., 2012. Seasonal response of grasslands to climate change on the Tibetan Plateau.Plos One, 7(11): e49230.Monitoring vegetation dynamics and their responses to climate change has been the subject of considerable research. This paper aims to detect change trends in grassland activity on the Tibetan Plateau between 1982 and 2006 and relate these to changes in climate. Grassland activity was analyzed by evaluating remotely sensed Normalized Difference Vegetation Index (NDVI) data collected at 15-day intervals between 1982 and 2006. The timings of vegetation stages (start of green-up, beginning of the growing season, plant maturity, start of senescence and end of the growing season) were assessed using the NDVI ratio method. Mean NDVI values were determined for major vegetation stages (green-up, fast growth, maturity and senescence). All vegetation variables were linked with datasets of monthly temperature and precipitation, and correlations between variables were established using Partial Least Squares regression. Most parts of the Tibetan Plateau showed significantly increasing temperatures, as well as clear advances in late season phenological stages by several weeks. Rainfall trends and significant long-term changes in early season phenology occurred on small parts of the plateau. Vegetation activity increased significantly for all vegetation stages. Most of these changes were related to increasing temperatures during the growing season and in some cases during the previous winter. Precipitation effects appeared less pronounced. Warming thus appears to have shortened the growing season, while increasing vegetation activity. Shortening of the growing season despite a longer thermally favorable period implies that vegetation on the Tibetan Plateau is unable to exploit additional thermal resources availed by climate change. Ecosystem composition may no longer be well attuned to the local temperature regime, which has changed rapidly over the past three decades. This apparent lag of the vegetation assemblage behind changes in climate should be taken into account when projecting the impacts of climate change on ecosystem processes.

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[52]
Zavaleta E S, 2006. Shrub establishment under experimental global changes in a California grassland.Plant Ecology, 184(1): 53-63.Accelerating invasion of grasslands by woody species is a widespread global phenomenon. The native shrub Baccharis pilularis has recently increased in abundance in some California grasslands, with large local community and ecosystem effects. I investigated potential contributions of (1) future global climate and atmospheric changes and (2) variation in moisture and nutrient availability to increased Baccharis germination and early establishment rates. I examined responses of Baccharis seeds and seedlings to simulated warming (+65161202°C) and elevated CO 2 (+6530002ppm) in a 2-year field experiment. Warming and CO 2 treatments were applied at ambient and increased water and nitrogen levels chosen to simulate future increases in precipitation (+6550%) and N deposition (+65702gN 02m 612 y 611 ). Elevated CO 2 and water addition each increased or accelerated germination. Herbivory strongly reduced seedling populations during the winter wet season; drought further reduced seedling survival in the spring. Overall Baccharis survivorship was extremely low (<0.1%) across all treatments, complicating the interpretation of global change effects.

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[53]
Zhang X S, 2007. Vegetation Map of China Committee, Chinese Academy of Sciences. Vegetation map of China and its geographic pattern: Illustration of the vegetation map of the People’s Republic of China (1:1,000,000). Beijing: Geographical Publishing House. (in Chinese)

[54]
Zhu X H, Wang W Q, Fraedrich K, 2013. Future climate in the Tibetan Plateau from a statistical regional climate model.Journal of Climate, 26(24): 10125-10138.The authors use a statistical regional climate model [Statistical Regional Model (STAR)] to project the Tibetan Plateau (TP) climate for the period 2015-50. Reanalysis datasets covering 1958-2001 are used as a substitute of observations and resampled by STAR to optimally fit prescribed linear temperature trends derived from the Max Planck Institute Earth System Model (MPI-ESM) simulations for phase 5 of the Coupled Model Intercomparison Project (CMIP5) under the representative concentration pathway 2.6 (RCP2.6) and RCP4.5 scenarios. To assess the related uncertainty, temperature trends from carefully selected best/worst ensemble members are considered. In addition, an extra projection is forced by observed temperature trends in 1958-2001. The following results are obtained: (i) Spatial average temperature will increase by 0.6 degrees-0.9 degrees C; the increase exceeds 1 degrees C in all months except in boreal summer, thus indicating a reduced annual cycle; and daily minimum temperature rises faster than daily maximum temperature, resulting in a narrowing of the diurnal range of near-surface temperature. (ii) Precipitation increase mainly occurs in early summer and autumn possibly because of an earlier onset and later withdrawal of the Asian summer monsoon. (iii) Both frost and ice days decrease by 1-2 days in spring, early summer, and autumn, and the decrease of frost days on the annual course is inversely related to the precipitation increase. (iv) Degree-days increase all over the TP with peak amplitude in the Qaidam Basin and the southern TP periphery, which will result in distinct melting of the local seasonal frozen ground, and the annual temperature range will decrease with stronger amplitude in south TP.

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