Path analysis model to identify and analyse the causes of aeolian desertification in Mu Us Sandy Land, China

• Published in: Ecological indicators Vol. 124; p. 107386
• Main Authors: Feng, Kun, Wang, Tao, Liu, Shulin, Yan, Changzhen, Kang, Wenping, Chen, Xiang, Guo, Zichen
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2021; Elsevier
• Subjects: Aeolian desertification; Driving factors; Mu Us Sandy Land; Path analysis model; Spatial-temporal dynamics; Path analysis model; Driving factors; Spatial-temporal dynamics; Mu Us Sandy Land; Aeolian desertification
• ISSN: 1470-160X; 1872-7034
• DOI: 10.1016/j.ecolind.2021.107386

[Display omitted] •Obtained a time-series data set of aeolian desertification.•Application of a quantitative method (Path Analysis Model) for identifying the cause of aeoian desertification.•A network of intercorrelations of human activities and climate in determining rarea is identified.•The influence of the interaction between human activities and climate change on aeolian desertification is obvious. As is a land degradation process caused by an uncoordinated Human-Earth relationship, aeolian desertification has threatened the safety of eco-environmental and the development of social economy in northern China. Although most studies focus on the causes or driving forces of aeolian desertification with climatic and anthropogenic factors, lack of the impact analysis of the relationship between climate change and human activities, the driving mechanism still remain unclear. In the present study, first the spatial patterns of aeolian desertification land from 1975 to 2015 were obtained by visual interpretation in Mu Us Sandy Land with satellite data. Then, climatic (i.e., temperature, precipitation, relative humidity, and wind speed), and socio-economic data (i.e., population density, livestock, woodland area, and farmland area) were combined with the aeolian desertification pattern to investigate the direct and indirect effects of climate change and human activities on the aeolian desertification via the path analysis model. The results are as follows: (1) During the past 4 decades, the aeolian desertification in Mu Us Sandy Land showed a trend of “violent development-slow reversal” with extremely severe aeolian desertified land as the main representative. The aeolian desertified land area reached 57,778.32 km2 by 2015. (2) Overall, human activities accounted for 50.96% of the relative influence on aeolian desertification land net growth rate, followed by climate 49.04%, with the temperature, livestock, and population as dominate factors. (3) Path analysis identified a network of inter-correlations of human activities and climate in determining aeolian desertification land net growth rate. (4) The direct correlation of aeolian desertification land net growth rate with human activities was significantly weakened if removing the effects of climate. These results reveal the relative importance of human activities and climate and their complex interconnections in regulating aeolian desertification process.