The impact of climate change on aeolian desertification in northern China: Assessment using aridity index
Introduction
Global climate change has caused a series of ecological issues, such as forest degradation, biodiversity loss, land degradation, soil erosion, ecosystem degradation, and vegetation belt evolution (IPCC 2013). In China, the surface air temperature increased by 1.1 °C, with a mean warming rate of 0.22 °C/ decade over the period 1950–2000 (Ding 2006). Although mean annual precipitation for China followed a generally increasing trend between 1961 and 2018, there were significant regional differences; i.e., annual precipitation increased significantly in most of western China, and increased to some extent in northeastern China and Inner Mongolian, whereas it decreased in the north and west parts of northwestern China, and the southern part of northeastern China (China Meteorological Administration, 2019). The dynamics of aeolian desertification are closely related to climate change (Grainger et al., 2000, Wang et al., 2017, Xu et al., 2019).
Desertification is defined as land degradation caused by both climate change and human intervention in arid, semi-arid, and dry sub-humid areas (UNCCD 1994). However, at present, the contributions of these two drivers of aeolian desertification remain under debate and there is no consensus as to what degree climate change drives the variations seen in desertification (Herrmann and Hutchinson, 2005, Wang et al., 2006, Xu et al., 2010, Feng et al., 2015). Some researchers believe that climate change plays an important role in desertification (Charney 1975), whereas others have pointed out that human activities such as over-cutting, over-reclamation, and over-grazing are the main causes of desertification development (Bo et al., 2013, Danfeng et al., 2006, Zhang and Deng, 2020) and restoration was the main cause of desertification reversion (Feng et al. 2015).
Several methods have been used to discriminate between the contributions of climate change and human factors to desertification. For example, Wessels et al., 2004, Wessels et al., 2007 distinguished human induced land degeneration based on negative trends in the differences between the observed and potential normalized difference vegetation index (NDVI). Deviations in plant biomass estimated from the NDVI–climate relationship that was expressed as residuals, had been successfully used to estimate the human-induced contribution (Evans and Geerken, 2004). Xu et al., 2019, Xu et al., 2010 selected potential net primary production (NPP), and the difference between potential and actual NPP, as indicators to assess the relative roles of climate change and human activity in sandy desertification. This method has been widely used by other researchers (e.g., Pan et al., 2016, Zhang et al., 2011, Zhou et al., 2015).
At present, the methods used to distinguish between the climatic and human factors that influence desertification still have some limitations. For example, the variations of desertification show temporal and spatial changes, consequently, the main causes of desertification should be discussed within a specific spatiotemporal context (Wang et al., 2021). Furthermore, land degradation tends to be determined using indicators including NDVI, NPP, or vegetation cover when discussing the desertification causes, but the results are not compared with existing desertification monitoring data because the index system and classification criteria are different (Li et al., 2016). In this paper, we use the aridity index (AI), defined as the ratio of precipitation (P) to potential evapotranspiration (PET), as an indicator of the climate dryness (Middleton and Thomas, 1992, Mortimore, 2009). As a widely recognized indicator, the AI has been successfully used in previous assessments of regional climate change (Asadi Zarch et al., 2015, Costa and Soares, 2012, Huo et al., 2013, Middleton and Thomas, 1992).
Section snippets
Methods
Aeolian desertification datasets derived from Wang et al. (2011), additional details of the processing have been described in Zhang et al. (2020). Climatology data (maximum and minimum temperature, precipitation, atmospheric pressure, sunshine duration, relative humidity, and wind speed) from 349 stations in northern China for the period 1975–2010 were downloaded from the China Meteorological Administration Information Center. PET was calculated based on FAO Penman-Monteith equation (Allen, 1998
Desertification scenarios
Three different desertification scenarios were set up on the basis of possible combinations of variations induced by human intervention and climate factors (Xu et al., 2010, Zhang et al., 2011, Zhang et al., 2020).
Scenario I SAI > 0 indicates the climate is getting wetter and the natural factor favors the reversal of aeolian desertification in the whole region. Natural factor is the main or at least one cause of observed reversal of desertification, and human factors is the main cause of
Average AI distribution across China for the period 1975–2010
The mean annual AI for the period 1975–2010 was 0.34 for the whole of northern China, with a range of 0–1.2 (Fig. 1). Areas with an aridity index below 0.2 were located mainly in regions with arid climates, including the Taklimakan Desert, Kumtag Desert, Hashun Gobi, Central Gobi, and the Badain Jaran Desert and its adjacent areas. These areas are all located in the northwest of China and have the most arid climates in China. Areas with an aridity index between 0.2 and 0.5 were located in
Discussions
The traditional separation of the impacts of human activity and climate change focused mainly on the contribution of anthropogenic impacts to desertification (Wessels et al. 2004). In the current study, the aridity index, an integrated indicator of water input and output, was calculated to assess the contribution of climate change to desertification in northern China over different periods.
According to remote sensing data, desertification was not reversed until 2000, and some researchers
Conclusions
Based on the reclassified aeolian desertification datasets from 1975 to 2010, we defined the ratio between the input and loss of water as the aridity index (AI). Three desertification scenarios were set up on the basis of various combinations of potential human and climate change induced variations. In addition, the impacts of natural factors on desertification in China were evaluated. Our results indicate that climate change accounted for 64%, 28%, 83%, and 70% of the total reversion area for
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This work was supported by a grant from the National Key Research and Development Program of China (2020YFA0608404) and the National Nature Science Foundation of China (41101006).
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