Impact of Climate Change on Temperate and Alpine Grasslands in China during 1982–2006

Shen, Xiangjin; Liu, Binhui; Li, Guangdi; Zhou, Daowei

doi:https://doi.org/10.1155/2015/180614

Advances in Meteorology

On this page

Abstract Introduction Materials and Methods Results and Discussion Conclusions Disclosure References Copyright Related Articles

Research Article | Open Access

Volume 2015 | Article ID 180614 | https://doi.org/10.1155/2015/180614

Impact of Climate Change on Temperate and Alpine Grasslands in China during 1982–2006

Xiangjin Shen,^1,2Binhui Liu,³Guangdi Li,⁴and Daowei Zhou¹

Academic Editor: Harry D. Kambezidis

Received17 Jan 2015

Revised28 Apr 2015

Accepted30 Apr 2015

Published25 May 2015

Abstract

Based on GIMMS NDVI and climate data from 1982 to 2006, this study analyzed the impact of climate change on grassland in China. During the growing season, there were significant effects of precipitation on the growth of all the grassland types (), except for meadow vegetation. For the air temperatures, there existed asymmetrical effects of maximum temperature () and minimum temperature () on grassland vegetation, especially for the temperate grasslands and alpine steppe. The growing season NDVI correlated negatively with but positively with for temperate grasslands. Seasonally, these opposite effects were only observed in summer. For alpine steppe, the growing season NDVI correlated positively with but negatively with , and this pattern of asymmetrical responses was only obvious in spring and autumn. Under the background of global asymmetric warming, more attention should be paid to this asymmetric response of grassland vegetation to daytime and night-time warming, especially when we want to predict the productivity of China’s grasslands in the future.

1. Introduction

Vegetation is regarded as the Earth’s natural linkage of soil, atmosphere, and moisture [1]. Changes in vegetation activity are linked with climate changes [2, 3]. Investigating the impact of climate change on the terrestrial ecosystems is critical for understanding the interactions between vegetation and climate [4] and becomes an interdisciplinary research effort for international scholars [5, 6]. Remote sensing provides systematic and consistent observations of vegetation on large temporal and spatial scales. Amongst remotely sensed data from satellites, Normalized Difference Vegetation Index (NDVI) has been widely used to investigate the relationship between vegetation growth and climate variation [7–9].

As one of the most widespread vegetation types in the world, grassland covers large and continuous areas in temperate and tropic regions [10]. China’s grasslands, being the world’s third largest, cover nearly 1/4 of the entire territory of the country [11, 12]. Grasslands in China mainly include temperate grasslands and alpine grasslands (Chinese Academy of Sciences, 2001), which are located in arid/semiarid regions and the Tibetan Plateau, respectively.

Many studies have investigated the relationships between temperature, precipitation, and NDVI of grasslands in China [1, 2, 13–20], but most of these studies focused only on temperature or alpine grasslands. Differences in the dominant environmental factor will lead to different relationships between vegetation growth and climate change in different regions [21]. Fewer studies have compared the possibly different responses of temperature and alpine grasslands of China to climate changes. In addition, information on the distribution of grasslands in previous studies was usually obtained from a grassland map or land cover map of China in a given year [1, 2, 13, 15, 19–21]. However, the distribution of grasslands may change over time because of land use/cover change mainly caused by human activity [22, 23]. Many previous studies have explored the effect of average temperature on NDVI of grasslands, but the relationships between NDVI and the maximum temperature () or the minimum temperature () have seldom been discussed. It is well known that have increased at a faster rate than in most parts of the world since the 1950s, resulting in the decrease of diurnal temperature range (DTR) [24–27]. Considering the potential effects of asymmetric daytime and night-time warming, Peng et al. [3] made an important finding that there existed opposite (asymmetric) effects of and on northern hemisphere vegetation. They found that the correlation between NDVI and was negative in dry temperate regions but positive in most cool and wet ecosystems over boreal regions. In contrast, the relationship between NDVI and was positive in arid and semiarid regions (particularly in temperate grassland region of China) but negative in boreal regions. In order to predict the effects of global warming on the growth of grasslands in China, it is important to investigate vegetation response to and , separately.

In this study, we explored the interannual variation in the growth of temperate and alpine grasslands of China during the growing seasons from 1982 to 2006 and investigated the effects of climate factors including precipitation, , , and on NDVI for different grassland types. According to previous studies [3, 20], the growing season in our study was defined as April–October, with spring (April and May), summer (June–August), and autumn (September and October). To exclude the influence of land use/cover change on the distribution of grasslands in China, we extracted unchanged patches of the grasslands from 1980s to 2006 in the study area by comparing two-period land use maps.

2. Materials and Methods

2.1. Study Area

Temperate grasslands in China mainly include temperate meadow, temperate steppe, and temperate desert steppe, whereas alpine grasslands include alpine meadow and alpine steppe (Chinese Academy of Sciences, 2001). According to China’s vegetation regionalization, temperate and alpine grasslands mainly occur in temperate grassland region and alpine grassland region, respectively. Temperate grassland region in China is located in the Loess Plateau, Inner Mongolia Plateau, and Songliao Plain, with a small fraction located in Altay Mountains of Xinjiang, whereas alpine grassland region is mainly distributed in the Tibetan Plateau. In this study, we chose these two grassland regions (excluding the small part in Xinjiang) as our study area (Figure 1).

The climate in the temperate grassland region varies from the east wet, half humid monsoon climate to the west arid/semiarid climate. The annual mean temperature ranges from −5°C to 10°C and average annual precipitation from 35 to 530 mm [1]. The climate in the alpine grassland region is characterized by long durations of sunshine, strong solar radiation, low temperature, and large day-night temperature contrast [19, 28]. The annual mean temperature is below 0°C in most areas of alpine grassland region. The annual precipitation follows a gradient from over 1000 mm on the southeastern meadow area to 100 mm in the northwest desert area [29, 30].

2.2. Data and Method

Data used in this study consist of the following. (1) Monthly precipitation/surface air temperature (including , , and ) data were from China’s Ground Precipitation/Temperature 0.5° × 0.5° Gridded Dataset (V2.0). These datasets were created from observation data of about 2400 meteorological stations in China by using spatial interpolation method, provided by National Meteorological Information Center, available at China Meteorological Data Sharing Service System (CMDSSS). (2) The Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data from 1982 to 2006 was derived from Global Land Cover Facility, with a temporal resolution of 15 day and 8 km × 8 km spatial resolution (http://glcf.umd.edu/data/gimms/). The atmospheric, radiometric, and geometric corrections had been made for this dataset. (3) Two periods (1980s and 2005) of land use and land cover data of China were from 100 m × 100 m pixels land cover products of China (Chinese Academy of Sciences, 2006), provided by the National Earth System Science Data Sharing Platform. The land use map was classified into 6 first levels of land use categories (grassland, woodland, unused land, farmland, water and wetland, and construction land) and 25 second levels of land use categories.

In this study, the Mann-Kendall (MK) test and simple linear regression [31, 32] were applied into the trend analysis of NDVI, precipitation, and temperatures. The Maximum Value Compositing (MVC) method [33] was used to reconstruct the original NDVI data into the monthly NDVI dataset. In order to match the 8 km GIMMS NDVI dataset, the monthly temperature and precipitation data were resampled to a resolution of 8 km. Based on the land use map, we extracted monthly NDVI, temperature, and precipitation values for the grassland vegetation during the growing season from 1982 to 2006. The average values of NDVI, temperature, and precipitation for a particular grassland type were obtained from the averages of all grid cells belonging to the same grassland type. To assess the impact of climate change on vegetation growth spatially, Spearman’s rank correlation coefficient between NDVI and climatic variable (temperature or precipitation) was calculated at pixel scale [19].

3. Results and Discussion

3.1. Annual Changes in Mean Growing Season NDVI, Precipitation, and Temperature for Different Grassland Types

Figure 2 shows the changes in mean growing season NDVI, precipitation, and temperature (, , and ) during 1982–2006 for different grassland types. Mean growing season NDVI of temperate meadow, temperate steppe, and alpine meadow increased significantly () from 1982 to 2006. Among them, alpine meadow exhibited the most significant increasing trend, with an average annual increasing of 0.0013 ( = 0.717, ) (Figure 2(d)). For temperate desert steppe and alpine steppe, mean growing season NDVI increased weakly ( = 0.292, and = 0.205, , resp.). Our analysis results are generally consistent with those reported before for temperate grasslands [1, 2] and alpine grasslands [13, 18, 19, 34] in China.

(a)

(b)

(c)

(d)

(e)

Growing season precipitation increased slightly for alpine steppe and decreased slightly for other grassland types (Figure 2). But these changes were not significant for all the grassland types: temperate meadow ( = 0.295, ), temperate steppe ( = 0.200, ), temperate desert steppe ( = 0.046, ), alpine meadow ( = 0.090, ), and alpine steppe ( = 0.394, ). Growing season mean temperature increased significantly for all the grassland types, with the largest increase (0.072°C per year) for temperate desert steppe and the smallest increase (0.033°C per year) for alpine steppe. Consistent with the changes of mean temperature, both and increased significantly for all the grassland types. The increase of was faster than for temperate meadow and temperate steppe but slower than for other grassland types. However, the change of DTR (the difference between and ) was only significant for alpine steppe ( = −0.028, ). It indicates that the asymmetric daytime and night-time warming continues in the regions of alpine steppe.

The interannual variation in growing season NDVI corresponded closely to those of precipitation for temperate steppe and temperate desert steppe (Figure 2). For temperate steppe, maximum precipitation values occurred in 1985, 1990, 1994, 1998, and 2003, which corresponded to high NDVI values in the same years (except for in 1985). Low NDVI values in 1982, 1989, and 2000 accordingly corresponded with low precipitation values. For temperate desert steppe, peak NDVI and precipitation values were reached in 1992, 1996, and 2003, and low values appeared in 1982, 1989, 2001, and 2005. Fluctuations in temperature did not obviously correspond with fluctuations in NDVI for all the vegetation types with the exception of alpine meadow. The growing season temperature and NDVI of alpine meadow peaked in 1998, 1999, and 2005 and reached their minimum in 1997.

3.2. Correlation between Growing Season NDVI, Precipitation, and Temperature

Figure 3 shows the spatial distributions of correlation coefficients between the growing season NDVI and climate variables. The growing season precipitation had a positive relationship with the NDVI for large areas of temperate grassland region, especially over temperate steppe and desert steppe regions (Figure 3(a)). Negative correlations between growing season precipitation and NDVI occurred mainly in the northwestern areas of the Tibetan Plateau, which are dominated by alpine steppe (Figure 3(a)). For temperature, it is interesting that the spatial relationship of NDVI with was very similar to that with over temperate grassland region but was similar to that with over alpine grassland region (Figures 3(b)–3(d)). It seems that () plays a more important role in temperate grassland (alpine grassland) vegetation growth during the growing season. Growing season maximum temperature showed a negative impact in large parts of temperate grassland region but had a positive impact on vegetation in the alpine grassland region (Figure 3(c)). In contrast to , however, an opposite pattern was observed in correlations between the NDVI and (Figure 3(d)); that is, a positive correlation appears to dominate in temperate grassland region, and a negative correlation concentrates in alpine steppe region.

(a)

(b)

(c)

(d)

To further analyze the relationship between growing season NDVI and climatic variables (precipitation and temperature), we calculated the correlation coefficients between them for different grassland types. The effects of growing season precipitation and temperature on NDVI differed in different grassland types (Table 1). For temperate steppe and temperate desert steppe, growing season NDVI correlated strongly with precipitation ( = 0.565, ; = 0.661, , resp.), suggesting that precipitation in growing season is critical for the growth of these two vegetation types. For alpine steppe, precipitation had a moderate negative effect on NDVI during the growing season ( = −0.218, ). For temperate meadow and alpine meadow, the correlations between growing season NDVI and precipitation were weak ( = −0.057, ; = 0.002, , resp.). Compared with other grassland types, the meadows are usually located under conditions with abundant precipitation [2, 19]; thus the effect of precipitation on vegetation growth is not obvious.

For temperatures, there were no significant effects of growing season temperatures on NDVI for all the vegetation types, with the exception of alpine meadow (Table 1). For alpine meadow, growing season NDVI correlated significantly with ( = 0.528, ), ( = 0.489, ), and ( = 0.512, ). It indicates that temperature is a key factor for growth of alpine meadow during the growing season. Unlike alpine meadow, we found opposite effects of and on NDVI for other grassland types, although all the correlation coefficients were not significant (Table 1). During the growing season, had a negative effect but had a positive effect on NDVI for temperate meadow, temperate steppe, and temperate desert steppe. Our analysis confirms the findings reported by Peng et al. [3] who discovered these opposite effects of growing season and on NDVI in temperate grassland regions of northern hemisphere. In addition, we found another asymmetrical response of vegetation to daytime versus night-time warming in alpine regions: had a positive effect but had a negative effect on NDVI for alpine steppe (Table 1). This pattern of asymmetrical response over alpine steppe region of the Tibetan Plateau was not reported in previous studies. In most of the Tibetan Plateau, Peng et al. [3] found that the correlation between NDVI and temperature (including both and ) was positive, which is probably due mainly to the significantly positive relationship between NDVI and temperature for alpine meadow (Table 1).

In our study, the opposite effects of growing season and on NDVI can be explained by the different ecophysiological responses of vegetation to daytime and night-time warming. In dry temperate regions, photosynthetic activity is subject to water limitation but less subject to temperature limitation [3]. The increase of can limit vegetation growth by reducing soil water content and enhancing evaporation. Compared with daytime warming, the effect of night-time warming was complicated because it can not only reduce vegetation productivity by enhancing autotrophic respiration but also indirectly promote vegetation growth during the following daytime due to physiological regulatory mechanisms and decreased frost risk [3]. During warmer nights, leaf carbohydrates are consumed more quickly due to enhanced leaf respiration, which depletes carbohydrates in the leaf and may cause a rebound effect of compensatory stimulated photosynthesis in the next day. This mechanism partly accounts for the positive relationship between and NDVI over temperate grassland region. For alpine steppe, however, the negative correlation between and NDVI implies that the negative effect of enhanced autotrophic respiration is more dominant than the positive impact of physiological regulatory mechanisms or decreased frost risk.

In alpine grassland region, photosynthetic activity is mainly limited by temperature because of the low temperature on the Tibetan Plateau. The increase of will promote the growth of both alpine meadow and alpine steppe by enhancing photosynthetic enzyme activity [35], increasing soil nitrogen availability and mineralization [36]. In contrast to , the correlation between and NDVI was different for alpine meadow and alpine steppe, which is most probably associated with complicated effect of night-time warming on vegetation growth. Further studies are needed to explore the mechanism of these different responses. In this study, the asymmetric daytime and night-time warming was obvious in the regions of alpine steppe from 1982 to 2006. Thus, if this asymmetric warming observed over the past decades continues in the future, the models using average temperature to predict the effects of global warming on alpine steppe (or alpine grasslands) productivity may overestimate the productivity of alpine steppe in China.

3.3. Correlation between Seasonal NDVI, Precipitation, and Temperature

To detect seasonal differences in vegetation response to climate change by vegetation type, we calculated the correlation between seasonal NDVI, temperature, and precipitation for each grassland type (Table 2). Similar to the growing season results of correlation analysis, the remarkable effects of seasonal precipitation on NDVI were found in temperate steppe, temperate desert steppe, and alpine steppe. For temperate steppe and temperate desert steppe, NDVI correlated positively with precipitation in spring ( = 0.500, ; = 0.522, , resp.) and summer ( = 0.430, ; = 0.529, , resp.), implying that precipitation in spring and summer is critical for the growth of these two grassland types. For alpine steppe, however, precipitation had a significant negative effect on NDVI in spring ( = −0.450, ). It is probably because temperature in spring is low for alpine steppe and that the increase of precipitation will further decrease temperature, which prohibits the growth of alpine steppe (Table 2). For temperate meadow and alpine meadow, we found no significant correlations between precipitation and NDVI in each of the three seasons, further implying that the effects of precipitation were not obvious for meadows.

Compared to precipitation, temperature has complex impact on grasslands growth in different seasons. In spring, temperature correlated positively with NDVI for all the grassland types (with the exception of for alpine steppe). The result shows that the increase of spring temperature is beneficial for grassland vegetation growth, and the influence of spring is more obvious than that of spring especially for temperate steppe ( = 0.595, ) and temperate desert steppe ( = 0.397, ). In autumn, both and correlated negatively with NDVI for temperate desert steppe but correlated positively with NDVI for other vegetation types (with the exception of for alpine steppe). For temperate desert steppe, which is dominated under a dry climate and limited by precipitation, the increase of autumn temperature will likely prohibit vegetation growth through reduced soil water content and enhanced evaporation. The significant effect of average autumn temperature on NDVI was found in alpine meadow and alpine steppe ( = 0.563, ; = 0.417, , resp.), indicating that increased autumn temperature will promote alpine grassland growth.

Consistent with the growing season results, we also found the opposite effects of seasonal and on temperate meadow, temperate steppe, temperate desert steppe, and alpine steppe. However, these asymmetric effects of seasonal and on NDVI were only observed in summer for temperate grassland vegetation but in spring and autumn for alpine steppe (Table 2). Summer NDVI is negatively correlated with but positively associated with for temperate grassland vegetation, while NDVI correlated positively with but negatively with for alpine steppe in spring and autumn. Under the background of global warming, the heat conditions in summer are relatively sufficient for vegetation growth over the temperate grassland region, and high temperatures during the daytime lead to evaporation, which prohibits vegetation growth. This may partly account for our new finding that the asymmetric effect of and on NDVI for temperate grasslands is only observed in summer. For alpine meadow, which is located in southeast of the Tibetan Plateau under conditions of abundant precipitation but low temperatures, the increases of temperature are beneficial for vegetation growth (Table 2). For alpine steep, NDVI positively correlated with temperatures, except for in spring and autumn (Table 2). Thus, it seems that the positive effect of dominates in summer, but the negative effect of dominates in spring and autumn. Considering the complicated effects of night-time warming on vegetation productivity, further studies are still needed to explore the mechanism of these different responses for alpine steep.

3.4. Correlation between Seasonal NDVI and the Previous Season’s Precipitation and Temperature

Given the lag-time effect of climate change on vegetation growth [37, 38] we also calculated correlation coefficients between seasonal NDVI and the preceding season’s temperature and precipitation (Table 3). During the whole growing season, there were significant lag-time effects of the previous season’s precipitation on NDVI for temperate steppe and temperate desert steppe. And the lag-time effects of summer precipitation on autumn NDVI ( = 0.529, ; = 0.657, , resp.) were even more obvious than the effects of autumn precipitation ( = −0.199, ; = 0.085, , resp.), indicating that increased precipitation in summer is beneficial for the growth of temperate steppe and temperate desert steppe both in summer and in autumn. These lag-time effects are consistent with the findings by Piao et al. [2] who found that there existed 3-month lag time of NDVI response to precipitation for temperate steppe and temperate desert steppe in China. However, unlike our finding, they did not found obvious effect of spring precipitation on summer NDVI of these two grassland types. The different distributions of temperate grasslands among studies may account for this difference. Table 3 shows that there was significant lag-time effect of spring precipitation on summer NDVI for alpine steppe ( = 0.565, ). Although precipitation in spring was observed to have negative effect on the growth of alpine steppe during the same period (Table 2), the increased precipitation in spring was beneficial for vegetation growth in summer. For meadow vegetation (temperate meadow and alpine meadow), however, the lag-time effects of precipitation on NDVI were not obvious during the growing season.

For temperature, there existed significant lag-time effect of nongrowing season temperature (especially ) on spring NDVI for temperate steppe and temperate desert steppe (Table 3), implying that the increase of nongrowing season temperature especially is beneficial to the spring growth of temperate steppe and temperate desert steppe. The lag-time effects of temperature on NDVI in summer and autumn were only obvious for temperate meadow and alpine meadow, respectively. Spring negatively correlated with summer NDVI for temperate meadow ( = −0.538, ), while summer positively correlated with autumn NDVI for alpine meadow ( = 0.456, ). The different growth environments of two kinds of meadows may contribute to these differences. For temperate meadow, it is dominated under a semihumid climate and likely limited by precipitation especially in summer when the evapotranspiration is at its peak [39]. Higher spring temperature especially daytime will accelerate the evaporation process and further cause water scarcity in summer, which prohibits vegetation growth. For alpine meadow, however, which is under a cold climate, temperature is always the limiting factor for its vegetation growth during the growing season (Tables 1 and 2); thus the increase of summer temperature promotes vegetation growth in autumn. In addition, we found that the lag-time effects of and on NDVI were opposite for temperate steppe in summer and temperate desert steppe in both summer and autumn, although the correlation coefficients were not significant. This further proves that there exist asymmetric responses of grassland vegetation growth to and , especially for temperate grasslands. Our findings confirmed that the increase of summer could not only limit temperate grasslands growth in summer but also limit the growth of temperate steppe and temperate desert steppe in autumn by enhancing evaporation. Besides, due to the higher level of water limitation, the increase of spring can exert a negative impact on summer NDVI of temperate desert steppe.

4. Conclusions

Based on GIMMS NDVI and climate data from 1982 to 2006, this study analyzed the impact of climate change on temperate and steppe grasslands in China during the growing seasons (April–October). In order to exclude the influence of land use/cover change on the distribution of grasslands in China, we extracted unchanged patches of the grasslands from 1980s to 2006 in the study area by comparing two-period land use maps. Results showed that average growing season NDVI increased significantly for temperate meadow, temperate steppe, and alpine meadow (≥0.001 per year) and moderately for temperate desert steppe and alpine steppe (0.001 per year). Alpine meadow exhibited the largest increase in growing season NDVI, with an average annual increasing of 0.0013 ( = 0.717, ). For all the grassland types, the mean growing season precipitation showed no significant changes () while air temperature increased significantly (). The increase of was faster than for temperate meadow and temperate steppe but was slower than for other grassland types.

The impact of precipitation or temperature change on grassland vegetation differed according to vegetation type and season. There were significant effects of precipitation in the same season and previous season on the growth of all the grassland types (), except for meadow vegetation. For temperate meadow, seasonal NDVI did not show a significant correlation with temperature during the same season (). However, there was a significant lag-time effect of spring temperate on summer NDVI ( = −0.422, ). For alpine meadow, temperature had a significantly positive effect on NDVI during the whole growing season ( = 0.528, ), and there also existed a significant lag-time effect of summer temperature on autumn NDVI ( = 0.456, ). For temperate steppe and temperate desert steppe, precipitation is critical to boost vegetation growth especially in spring and summer, and the lag lag-time effect of previous season’s precipitation on NDVI is obvious in all the three seasons. In addition, increased temperature in nongrowing season and spring is beneficial for the growth of these two grassland types in spring. For alpine steppe, the precipitation in spring and autumn has negative effects on vegetation growth during the same period but increased precipitation in spring was beneficial for vegetation growth in summer ( = 0.565, ).

The growing season NDVI correlated negatively with but positively with for temperate grasslands. Seasonally, these opposite effects were only observed in summer, and there also existed asymmetric lag-time effects of and on NDVI for temperate steppe and temperate desert steppe. In addition, we found that the growing season NDVI correlated positively with ( = 0.173, ) but negatively with ( = −0.140, ) for alpine steppe and this pattern of asymmetrical responses was only obvious in spring and autumn. Under the background of global asymmetric daytime and night-time warming, more attention should be given to these asymmetrical effects of daytime and night-time warming on grassland vegetation (especially temperate grasslands and alpine steppe) when we want to predict the productivity of China’s grasslands in the future.

Disclosure

Graham Centre for Agricultural Innovation is an alliance between NSW Department of Primary Industries and Charles Sturt University.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgment

The authors gratefully acknowledge the National Natural Science Foundation of China (Grant no. 41330640) for funding this work.

References

X. W. Chuai, X. J. Huang, W. J. Wang, and G. Bao, “NDVI, temperature and precipitation changes and their relationships with different vegetation types during 1998–2007 in Inner Mongolia, China,” International Journal of Climatology, vol. 33, no. 7, pp. 1696–1706, 2013.
View at: Publisher Site | Google Scholar
S. Piao, A. Mohammat, J. Fang, Q. Cai, and J. Feng, “NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China,” Global Environmental Change, vol. 16, no. 4, pp. 340–348, 2006.
View at: Publisher Site | Google Scholar
S. Peng, S. Piao, P. Ciais et al., “Asymmetric effects of daytime and night-time warming on Northern Hemisphere vegetation,” Nature, vol. 501, no. 7465, pp. 88–92, 2013.
View at: Publisher Site | Google Scholar
S. Horion, Y. Cornet, M. Erpicum, and B. Tychon, “Studying interactions between climate variability and vegetation dynamic using a phenology based approach,” International Journal of Applied Earth Observation and Geoinformation, vol. 20, no. 1, pp. 20–32, 2012.
View at: Publisher Site | Google Scholar
M. Rees, R. Condit, M. Crawley, S. Pacala, and D. Tilman, “Long-term studies of vegetation dynamics,” Science, vol. 293, no. 5530, pp. 650–655, 2001.
View at: Publisher Site | Google Scholar
W. Xu, S. Gu, X. Zhao et al., “High positive correlation between soil temperature and NDVI from 1982 to 2006 in alpine meadow of the Three-River Source Region on the Qinghai-Tibetan Plateau,” International Journal of Applied Earth Observation and Geoinformation, vol. 13, no. 4, pp. 528–535, 2011.
View at: Publisher Site | Google Scholar
C. J. Tucker, D. A. Slayback, J. E. Pinzon, S. O. Los, R. B. Myneni, and M. G. Taylor, “Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999,” International Journal of Biometeorology, vol. 45, no. 4, pp. 184–190, 2001.
View at: Publisher Site | Google Scholar
R. R. Nemani, C. D. Keeling, H. Hashimoto et al., “Climate-driven increases in global terrestrial net primary production from 1982 to 1999,” Science, vol. 300, no. 5625, pp. 1560–1563, 2003.
View at: Publisher Site | Google Scholar
B. F. Zaitchik, J. P. Evans, R. A. Geerken, and R. B. Smith, “Climate and vegetation in the Middle East: interannual variability and drought feedbacks,” Journal of Climate, vol. 20, no. 15, pp. 3924–3941, 2007.
View at: Publisher Site | Google Scholar
J. Ni, “Estimating net primary productivity of grasslands from field biomass measurements in temperate northern China,” Plant Ecology, vol. 174, no. 2, pp. 217–234, 2004.
View at: Publisher Site | Google Scholar
G. F. Liao and Y. L. Jia, Rangeland Resources of China, China Science & Technology Press, Beijing, China, 1996.
Y. Yang, J. Fang, W. Ma et al., “Soil carbon stock and its changes in northern China's grasslands from 1980s to 2000s,” Global Change Biology, vol. 16, no. 11, pp. 3036–3047, 2010.
View at: Publisher Site | Google Scholar
L. Zhong, Y. Ma, M. S. Salama, and Z. Su, “Assessment of vegetation dynamics and their response to variations in precipitation and temperature in the Tibetan Plateau,” Climatic Change, vol. 103, no. 3, pp. 519–535, 2010.
View at: Publisher Site | Google Scholar
M. Q. Hu, F. Mao, H. Sun, and Y. Y. Hou, “Study of normalized difference vegetation index variation and its correlation with climate factors in the three-river-source region,” International Journal of Applied Earth Observation and Geoinformation, vol. 13, no. 1, pp. 24–33, 2011.
View at: Publisher Site | Google Scholar
G. Zhang, X. Xu, C. Zhou, H. Zhang, and H. Ouyang, “Responses of grassland vegetation to climatic variations on different temporal scales in Hulun Buir Grassland in the past 30 years,” Journal of Geographical Sciences, vol. 21, no. 4, pp. 634–650, 2011.
View at: Publisher Site | Google Scholar
H. Yu, J. Xu, E. Okuto, and E. Luedeling, “Seasonal response of grasslands to climate change on the Tibetan Plateau,” PLoS ONE, vol. 7, no. 11, Article ID e49230, 2012.
View at: Publisher Site | Google Scholar
S. Mu, H. Yang, J. Li et al., “Spatio-temporal dynamics of vegetation coverage and its relationship with climate factors in Inner Mongolia, China,” Journal of Geographical Sciences, vol. 23, no. 2, pp. 231–246, 2013.
View at: Publisher Site | Google Scholar
L. Zhang, H. Guo, L. Ji et al., “Vegetation greenness trend (2000 to 2009) and the climate controls in the Qinghai-Tibetan Plateau,” Journal of Applied Remote Sensing, vol. 7, no. 1, Article ID 073572, 2013.
View at: Publisher Site | Google Scholar
L. Zhang, H. Guo, C. Wang et al., “The long-term trends (1982–2006) in vegetation greenness of the alpine ecosystem in the Qinghai-Tibetan Plateau,” Environmental Earth Sciences, vol. 72, no. 6, pp. 1827–1841, 2014.
View at: Publisher Site | Google Scholar
S. Piao, M. Cui, A. Chen et al., “Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang Plateau,” Agricultural and Forest Meteorology, vol. 151, no. 12, pp. 1599–1608, 2011.
View at: Publisher Site | Google Scholar
G. Bao, Z. Qin, Y. Bao, Y. Zhou, W. Li, and A. Sanjjav, “NDVI-based long-term vegetation dynamics and its response to climatic change in the mongolian plateau,” Remote Sensing, vol. 6, no. 9, pp. 8337–8358, 2014.
View at: Publisher Site | Google Scholar
R. T. Conant, K. Paustian, and E. T. Elliott, “Grassland management and conversion into grassland: Effects on soil carbon,” Ecological Applications, vol. 11, no. 2, pp. 343–355, 2001.
View at: Publisher Site | Google Scholar
Z. Wang, K. Song, B. Zhang et al., “Shrinkage and fragmentation of grasslands in the West Songnen Plain, China,” Agriculture, Ecosystems and Environment, vol. 129, no. 1-3, pp. 315–324, 2009.
View at: Publisher Site | Google Scholar
T. R. Karl, R. W. Knight, K. P. Gallo et al., “A new perspective on recent global warming: asymmetric trends of daily maximum and minimum temperature,” Bulletin of the American Meteorological Society, vol. 74, no. 6, pp. 1007–1023, 1993.
View at: Publisher Site | Google Scholar
B. Liu, M. Xu, M. Henderson, Y. Qi, and Y. Li, “Taking China's temperature: daily range, warming trends, and regional variations, 1955–2000,” Journal of Climate, vol. 17, no. 22, pp. 4453–4462, 2004.
View at: Publisher Site | Google Scholar
M. Wild, A. Ohmura, and K. Makowski, “Impact of global dimming and brightening on global warming,” Geophysical Research Letters, vol. 34, no. 4, Article ID L04702, 2007.
View at: Publisher Site | Google Scholar
X. Shen, B. Liu, G. Li et al., “Spatiotemporal change of diurnal temperature range and its relationship with sunshine duration and precipitation in China,” Journal of Geophysical Research: Atmospheres, vol. 119, no. 23, pp. 13163–13179, 2014.
View at: Publisher Site | Google Scholar
M. Dong, Y. Jiang, C. Zheng, and D. Zhang, “Trends in the thermal growing season throughout the Tibetan Plateau during 1960–2009,” Agricultural and Forest Meteorology, vol. 166-167, pp. 201–206, 2012.
View at: Publisher Site | Google Scholar
S. Q. Zhao, Geography of China: Environment, Resources, Population and Development, John Wiley & Sons, New York, NY, USA, 1994.
D. Zhao, S. Wu, Y. Yin, and Z.-Y. Yin, “Vegetation distribution on Tibetan Plateau under climate change scenario,” Regional Environmental Change, vol. 11, no. 4, pp. 905–915, 2011.
View at: Publisher Site | Google Scholar
H. B. Mann, “Nonparametric tests against trend,” Econometrica, vol. 13, pp. 245–259, 1945.
View at: Publisher Site | Google Scholar | MathSciNet
M. G. Kendall, Rank Correlation Measures, Charles Griffin, London, UK, 1975.
B. N. Holben, “Characteristics of maximum-value composite images from temporal AVHRR data,” International Journal of Remote Sensing, vol. 7, no. 11, pp. 1417–1434, 1986.
View at: Publisher Site | Google Scholar
J. Peng, Z. Liu, Y. Liu, J. Wu, and Y. Han, “Trend analysis of vegetation dynamics in Qinghai-Tibet Plateau using Hurst Exponent,” Ecological Indicators, vol. 14, no. 1, pp. 28–39, 2012.
View at: Publisher Site | Google Scholar
M. H. Turnbull, R. Murthy, and K. L. Griffin, “The relative impacts of daytime and night-time warming on photosynthetic capacity in Populus deltoides,” Plant, Cell & Environment, vol. 25, no. 12, pp. 1729–1737, 2002.
View at: Publisher Site | Google Scholar
J. M. Melillo, P. A. Steudler, J. D. Aber et al., “Soil warming and carbon-cycle feedbacks to the climate system,” Science, vol. 298, no. 5601, pp. 2173–2176, 2002.
View at: Publisher Site | Google Scholar
B. H. Braswell, D. S. Schimel, E. Linder, and B. Moore III, “The response of global terrestrial ecosystems to interannual temperature variability,” Science, vol. 278, no. 5339, pp. 870–872, 1997.
View at: Publisher Site | Google Scholar
S. O. Los, G. J. Collatz, L. Bounoua, P. J. Sellers, and C. J. Tucker, “Global interannual variations in sea surface temperature and land surface vegetation, air temperature, and precipitation,” Journal of Climate, vol. 14, no. 7, pp. 1535–1549, 2001.
View at: Publisher Site | Google Scholar
G. G. Burba and S. B. Verma, “Seasonal and interannual variability in evapotranspiration of native tallgrass prairie and cultivated wheat ecosystems,” Agricultural and Forest Meteorology, vol. 135, no. 1–4, pp. 190–201, 2005.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2015 Xiangjin Shen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

2736

Downloads

1173

Citations