The impact of climate change and anthropogenic activities on alpine grassland over the Qinghai-Tibet Plateau
Introduction
Both global climate change and anthropogenic activities are the main driving forces of terrestrial ecosystems (Esser, 1987, Field, 2001, Haberl, 1997). Commonly, regional ecosystem changes are the consequence of both climate change and local anthropogenic activities, but it is almost impossible to directly differentiate between these two factors (Wessels et al., 2007). Especially in arid and semi-arid areas, heightened anthropogenic activities can easily lead to the degradation of certain ecosystems, even causing serious ecological and economical losses (Harris, 2010, Wessels et al., 2004). With the increase in climate warming and intensified anthropogenic activities over the last century (Consortium, 2013, Raupach et al., 2013), socioeconomic drivers are beginning to overwhelm the great forces of nature for some selected processes regionally or even on the global scale (Erb et al., 2009, Luck, 2007, Rojstaczer et al., 2001). As a result, separating and quantifying the influence of climate change and human activities on ecosystems has great significance to ecosystem management and adaptation; Thus, mankind can choose different adaptive management strategies to adapt to climate change or to counteract the negative effect of anthropogenic activities on ecosystems (Aldous et al., 2011, Lawler, 2009, Vignola et al., 2009). Therefore, it is imperative to use an objective and reproducible method to discriminate between the effects of climate change and anthropogenic activities on ecosystems (Wessels et al., 2008).
At present, the ways for indirectly evaluating the regional influence of anthropogenic activities on ecosystems include the Normalized Difference Vegetation Index (NDVI) residual trend method (Bai et al., 2008, Li et al., 2012, Wessels et al., 2004), and human appropriation of net primary productivity (HANPP) method based on models (DeFries, 2002, Haberl et al., 2007, Rojstaczer et al., 2001, Xu et al., 2009). Some studies reported that the NDVI significantly correlated with rainfall in arid and semi-arid areas, so the deviation between the actual and simulated NDVI can be regarded as the human activities effect on the ecosystem (Prince et al., 2007, Wessels et al., 2004, Wessels et al., 2007, Wessels et al., 2008). Study on the temperate grassland in Inner Mongolia in China showed that the governmental ecological protection strategy applied after 2000 in this area significantly resulted in large-scale vegetation improvements (Li et al., 2012), but there are existing uncertainties about the rain–production relationship, and the climatic and anthropogenic influences on the ecosystem still cannot be fully differentiated. Otherwise, the alternative methods for simulating HANPP applied social statistical models (Krausmann et al., 2013, Rojstaczer et al., 2001, Vitousek et al., 1986) or required much social statistical data to simulate the human and actual productivity, which can easily lead to results uncertainties (DeFries, 2002, DeFries et al., 1999, Haberl et al., 2007, Xu et al., 2009). Another possible method is that comparing the process-based and remote-sensing ecosystem productivity models to simulate the human-induced production, which can be then used for trend analysis and avoiding uncertainties in some extent.
As the sole and largest geographical unit with the highest elevation on earth, the Qinghai–Tibet plateau (QTP) is called the “Third Pole” and acts as an important reservoir for water, regulating climate change and water resources in east Asia and even for the whole world (Qiu, 2008, Yang et al., 2011, Yao et al., 2012). The QTP also has a large variety of ecosystem types, from subtropical rain forest in southeast to alpine desert in the northwest. Among all types of land cover vegetation, alpine grassland is the dominant ecosystem over the QTP, covering more than 50% of the whole plateau area (Bartholomé and Belward, 2005, Gao et al., 2012). With both global climate warming and increasing anthropogenic activities, the QTP has experienced approximately a three times increase of the global warming rate over the last 50 years (Piao et al., 2011, Qiu, 2008). Meanwhile, natural grassland has been regionally degrading since the 1980s, which may be due to a combination of climate warming, increasing population, fast-growing grazing pressure and rodent damage (Harris, 2010, Liu et al., 2012, Wang et al., 2013, Yu et al., 2012). Regardless of the exact reason of ecosystem degrading, the local government is facing a serious issue in managing the vast grasslands in view of such complicated environmental problems (Du et al., 2004, Qiu, 2007, Yu et al., 2012). Although, recent studies have shown that the QTP has changed from a small or neutral carbon source to a carbon sink during the 20th century, and net primary production (NPP) simulation has been persistently enhanced over the last 50 years (Piao et al., 2012, Zhuang et al., 2010), but it is still hard to determine where and how serious anthropogenic activities influences grassland NPP in such a large scale. Nonetheless, alpine grassland is sensitive to climate warming and anthropogenic activities, so the QTP is the ideal place for studying the relationship between climate warming and the anthropogenic activities effects on alpine grassland.
In this study, our objective was to distinguish between the effects of climate change and anthropogenic activities on alpine grassland over the QTP, and trying to determine which is the main driving force for grassland ecosystem change over different time periods. We used grassland NPP as the index for measuring the degree of climate change and anthropogenic activities effects on the alpine grassland ecosystem, with a climate factor-driven model to simulate grassland potential NPP and a remote sensing model to simulate the actual NPP, which is affected by both of climate change and human activities, so the human-induced NPP is modelled as the difference of potential and actual NPP. For comparing the trend of the potential and human-induced NPP over periods, the main driving force to ecosystem change can be determined. This study could be used as a reproducible method for quantifying the impact of climate change and anthropogenic activities on terrestrial ecosystem NPP change and provide a theoretical basis for optimizing ecosystem management over rangelands.
Section snippets
Methods
The Terrestrial Ecosystem Model (TEM) was used to simulate the climatic potential NPP (NPPP), which is the maximum grassland NPP and only driven by climatic factors and unaffected by human activities. The Carnegie–Ames–Stanford Approach (CASA) model was used to simulate actual NPP (NPPA), which is the actual existing NPP that was influenced by both climate change and human activities, for the remote-sensing NDVI data represent the actual vegetation greenness. So, the human-induced NPP (NPPH)
Validation of NPP
We used the fenced grassland sample NPP data to calibrate the parameters of the TEM and validate the simulations, meanwhile the open grassland sample data was used to validate the CASA simulation. Comparisons of the results showed that the simulated NPP data matched well with the observed data from 2009 to 2011 (P < 0.001) and can be used in the following analysis. Fig. 1 a showed that the regression linear slope of the NPPP against sampled fenced grassland NPP is 0.952, with 43 points, and the
NPPP and climate change over the QTP
Simulated TEM NPPP prediction is only influenced by climatic factors and has been regarded as the maximum NPP of an ecosystem (Raich et al., 1991), which can be considered as the alpine grassland NPPP that is not disturbed by animals or anthropogenic activities over the QTP. However, most of the alpine grassland is distributed in semi-dry and dry areas over the QTP, and these areas are sensitive to precipitation and vulnerable to climate warming (Qin et al., 2013). In this study, the main
Conclusions
In this study, we discriminated and quantified the effects of climate change and anthropogenic activities on alpine grassland ecosystem over the QTP, finding the different driving forces for the actual NPPA consistently enhanced in the periods of 1982–2001 and 2001–2011. Under the influences of climate change and human activities, the prime determinants of the increase in NPPA in the two periods were changed. A warm-wet climate and less human activities caused a rapid increase in NPPP and a
Acknowledgements
This study was jointly supported by Chinese Academy of Sciences project (XDB03030400), the National Basic Research Program of China (No. 2010CB951704), and the National Sciences Foundation of China (41171044). The constructive comments and suggestions from anonymous reviewers are also highly appreciated.
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