Analysis of soil erosion dynamics and its driving factors in the Qilian Mountains of Qingdong
Mingyuan Li
A # , Xiaohuang Liu B C # , Jianli Ding A * , Wenbo Zhang B C , Ran Wang B C , Xinping Luo B C , Liyuan Xing B C , Chao Wang C and Honghui Zhao C
A
B
C
Handling Editor: Abdul Mouazen
Abstract
The conservation of soil and water has become an important foundational project of worldwide social and economic development in the 21st century, especially for the protection and development of critical ecological function areas in Western China.
To clarify the current status of soil erosion and its drivers in the alpine temperate forest-grass subregion of Qilian Mountains in Qingdong (ATFSQMQ).
Based on GIS technology, the Universal Soil Loss Model (RUSLE) and Geographical detector were used to simulate the extent of soil erosion and assess the drivers of soil erosion in the ATFSQMQ from 2001 to 2020, and the Patch-generating Land Use Simulation (PLUS) model and Coupled Model Intercomparison Project Phase 6 (CMIP6) model were used to predict the future soil erosion in the study area.
(1) The soil erosion modulus of the ATFSQMQ decreased going from northwest to southeast, and soil erosion increased during the 2001–2020 period, and the average soil erosion modulus increasingly fluctuated. (2) Micro-erosion is the main form of soil erosion; from 2001 to 2020, regions with micro-erosion and mild erosion decreased, while those with moderate, strong, solid, and severe erosion increased slightly. (3) Vegetation cover is the dominant factor affecting soil erosion, and the synergistic effect of vegetation cover and precipitation has the highest explanatory power.
The soil erosion modulus fluctuated and increased from 2001 to 2020, but will gradually improve in the future.
The analyses in this paper can shed light on the current state of soil erosion and the drivers behind it, enabling the government to target soil erosion area management.
Keywords: CMIP6, driving factors, geographical detector, PLUS model, Qingdong Qilian mountains, RUSLE model, soil erosion, spatio-temporal analysis.
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