A framework for dynamic assessment of soil erosion and detection of driving factors in alpine grassland ecosystems using the RUSLE-InVEST (SDR) model and Geodetector: A case study of the source region of the Yellow River
Understanding the spatiotemporal patterns and key drivers of soil erosion in alpine grassland regions is crucial for developing effective conservation and restoration strategies in ecologically vulnerable areas. This study focuses on the source region of the Yellow River (SRYR). It evaluated the spa...
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Elsevier
2025-03-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954124004709 |
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| author | Hucheng Li Jianjun Chen Ming Ling Zizhen Chen Yanping Lan Qinyi Huang Xinhong Li Haotian You Feng Wang Xiaowen Han Guoqing Zhou |
| author_facet | Hucheng Li Jianjun Chen Ming Ling Zizhen Chen Yanping Lan Qinyi Huang Xinhong Li Haotian You Feng Wang Xiaowen Han Guoqing Zhou |
| author_sort | Hucheng Li |
| collection | DOAJ |
| description | Understanding the spatiotemporal patterns and key drivers of soil erosion in alpine grassland regions is crucial for developing effective conservation and restoration strategies in ecologically vulnerable areas. This study focuses on the source region of the Yellow River (SRYR). It evaluated the spatiotemporal trends of soil erosion intensity, as well as identified erosion-prone areas, by combining the Sediment Delivery Ratio (SDR) model and the Revised Universal Soil Loss Equation (RUSLE). The Theil-Sen trend analysis, Mann-Kendall test, and Hurst exponent were used to assess the significance and persistence of soil erosion trends. Additionally, the Geodetector was used to analyze the driving effects of natural factors and human activities on the changes in soil erosion rate. The results indicated that: (1) From 2000 to 2020, the soil erosion intensity in the SRYR exhibited an overall declining trend, despite some fluctuations, with areas of slight erosion (< 5 t/(hm2·a)) accounting for over 78 % of the region; (2) The multi-year average SDR was below 15 %, with erosion-prone areas primarily located in the central region of the SRYR and topographically complex edge regions; (3) The areas where soil erosion had significantly improved were mainly located in the western half of the SRYR. It is expected that future soil erosion conditions will continue to follow historical trends, with approximately 30 % of the area possibly continuing to improve; (4) The interaction of vegetation cover, slope, and temperature was the main natural factor affecting the interannual variation of soil erosion rates, while the impact of rainfall remained relatively stable. Currently, the influence of human activities is limited. This study revealed the spatiotemporal characteristics and driving factors of soil erosion in the SRYR, providing a scientific basis for evaluating the effectiveness of ecological protection and restoration measures and offering a reference framework for assessing soil erosion conditions in similar regions. |
| format | Article |
| id | doaj-art-5551ce27ea49452292850883c6ae303b |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-5551ce27ea49452292850883c6ae303b2025-01-19T06:24:34ZengElsevierEcological Informatics1574-95412025-03-0185102928A framework for dynamic assessment of soil erosion and detection of driving factors in alpine grassland ecosystems using the RUSLE-InVEST (SDR) model and Geodetector: A case study of the source region of the Yellow RiverHucheng Li0Jianjun Chen1Ming Ling2Zizhen Chen3Yanping Lan4Qinyi Huang5Xinhong Li6Haotian You7Feng Wang8Xiaowen Han9Guoqing Zhou10College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China; Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, China; Corresponding author at: College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China.College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China; Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China; Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China; Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China; Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, ChinaUnderstanding the spatiotemporal patterns and key drivers of soil erosion in alpine grassland regions is crucial for developing effective conservation and restoration strategies in ecologically vulnerable areas. This study focuses on the source region of the Yellow River (SRYR). It evaluated the spatiotemporal trends of soil erosion intensity, as well as identified erosion-prone areas, by combining the Sediment Delivery Ratio (SDR) model and the Revised Universal Soil Loss Equation (RUSLE). The Theil-Sen trend analysis, Mann-Kendall test, and Hurst exponent were used to assess the significance and persistence of soil erosion trends. Additionally, the Geodetector was used to analyze the driving effects of natural factors and human activities on the changes in soil erosion rate. The results indicated that: (1) From 2000 to 2020, the soil erosion intensity in the SRYR exhibited an overall declining trend, despite some fluctuations, with areas of slight erosion (< 5 t/(hm2·a)) accounting for over 78 % of the region; (2) The multi-year average SDR was below 15 %, with erosion-prone areas primarily located in the central region of the SRYR and topographically complex edge regions; (3) The areas where soil erosion had significantly improved were mainly located in the western half of the SRYR. It is expected that future soil erosion conditions will continue to follow historical trends, with approximately 30 % of the area possibly continuing to improve; (4) The interaction of vegetation cover, slope, and temperature was the main natural factor affecting the interannual variation of soil erosion rates, while the impact of rainfall remained relatively stable. Currently, the influence of human activities is limited. This study revealed the spatiotemporal characteristics and driving factors of soil erosion in the SRYR, providing a scientific basis for evaluating the effectiveness of ecological protection and restoration measures and offering a reference framework for assessing soil erosion conditions in similar regions.http://www.sciencedirect.com/science/article/pii/S1574954124004709Sediment delivery ratioSpatiotemporal dynamicsGeodetectorSoil erosionRUSLE |
| spellingShingle | Hucheng Li Jianjun Chen Ming Ling Zizhen Chen Yanping Lan Qinyi Huang Xinhong Li Haotian You Feng Wang Xiaowen Han Guoqing Zhou A framework for dynamic assessment of soil erosion and detection of driving factors in alpine grassland ecosystems using the RUSLE-InVEST (SDR) model and Geodetector: A case study of the source region of the Yellow River Ecological Informatics Sediment delivery ratio Spatiotemporal dynamics Geodetector Soil erosion RUSLE |
| title | A framework for dynamic assessment of soil erosion and detection of driving factors in alpine grassland ecosystems using the RUSLE-InVEST (SDR) model and Geodetector: A case study of the source region of the Yellow River |
| title_full | A framework for dynamic assessment of soil erosion and detection of driving factors in alpine grassland ecosystems using the RUSLE-InVEST (SDR) model and Geodetector: A case study of the source region of the Yellow River |
| title_fullStr | A framework for dynamic assessment of soil erosion and detection of driving factors in alpine grassland ecosystems using the RUSLE-InVEST (SDR) model and Geodetector: A case study of the source region of the Yellow River |
| title_full_unstemmed | A framework for dynamic assessment of soil erosion and detection of driving factors in alpine grassland ecosystems using the RUSLE-InVEST (SDR) model and Geodetector: A case study of the source region of the Yellow River |
| title_short | A framework for dynamic assessment of soil erosion and detection of driving factors in alpine grassland ecosystems using the RUSLE-InVEST (SDR) model and Geodetector: A case study of the source region of the Yellow River |
| title_sort | framework for dynamic assessment of soil erosion and detection of driving factors in alpine grassland ecosystems using the rusle invest sdr model and geodetector a case study of the source region of the yellow river |
| topic | Sediment delivery ratio Spatiotemporal dynamics Geodetector Soil erosion RUSLE |
| url | http://www.sciencedirect.com/science/article/pii/S1574954124004709 |
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