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<p>The Ten Tributaries Basin is one of the most severely eroded regions in the Yellow River Basin, and its dynamic soil erosion processes are critical to regional ecological conservation. Based on remote sensing data from 1990 to 2023, this study integrated the Revised Universal Soil Loss Equation (RUSLE), the Optimal Parameter-based Geographical Detector (OPGD), and Partial Least Squares Structural Equation Modeling (PLS-SEM) to systematically analyze the spatiotemporal evolution of soil erosion and its multi-factor driving mechanisms. The results indicate that: (1) The basin-wide average soil erosion modulus decreased by 76.29%, with a fundamental structural shift from severe erosion to slight and light erosion grades; (2) Spatially, erosion intensity exhibited a stable “higher in the south, lower in the north” pattern, with high-intensity erosion concentrated in the southern Pisha sandstone area and demonstrating significant positive spatial autocorrelation (Global Moran’s I &gt; 0.80); (3) Natural factors dominated the spatial differentiation of erosion, with slope identified as the primary driving factor (q = 0.734). All interactive effects among factors were characterized as bi-enhancement or nonlinear enhancement, with the slope–soil erodibility interaction exerting the strongest explanatory power (q = 0.83), which was of the bi-enhancement type; (4) Topographic factors exerted a significant positive direct effect on erosion (path coefficient = 0.93), while soil properties and vegetation exhibited significant negative effects. These findings indicate that, at the current stage, soil erosion is contingent upon the synergistic coupling of topographic, edaphic, and vegetation conditions. This integrated understanding moves beyond the identification of isolated factors to establish a systematic framework for comprehending erosion regulation in ecologically fragile regions.</p>

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Spatiotemporal changes and driving mechanisms of soil erosion in the Yellow River Basin: a case study of the Ten Tributaries

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月4日

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  • Yanhong Huang,
  • Fangfang Qiang,
  • Guangquan Liu,
  • Zuhao Zhou,
  • Hongwu Zhang,
  • Ning Ai

摘要

The Ten Tributaries Basin is one of the most severely eroded regions in the Yellow River Basin, and its dynamic soil erosion processes are critical to regional ecological conservation. Based on remote sensing data from 1990 to 2023, this study integrated the Revised Universal Soil Loss Equation (RUSLE), the Optimal Parameter-based Geographical Detector (OPGD), and Partial Least Squares Structural Equation Modeling (PLS-SEM) to systematically analyze the spatiotemporal evolution of soil erosion and its multi-factor driving mechanisms. The results indicate that: (1) The basin-wide average soil erosion modulus decreased by 76.29%, with a fundamental structural shift from severe erosion to slight and light erosion grades; (2) Spatially, erosion intensity exhibited a stable “higher in the south, lower in the north” pattern, with high-intensity erosion concentrated in the southern Pisha sandstone area and demonstrating significant positive spatial autocorrelation (Global Moran’s I > 0.80); (3) Natural factors dominated the spatial differentiation of erosion, with slope identified as the primary driving factor (q = 0.734). All interactive effects among factors were characterized as bi-enhancement or nonlinear enhancement, with the slope–soil erodibility interaction exerting the strongest explanatory power (q = 0.83), which was of the bi-enhancement type; (4) Topographic factors exerted a significant positive direct effect on erosion (path coefficient = 0.93), while soil properties and vegetation exhibited significant negative effects. These findings indicate that, at the current stage, soil erosion is contingent upon the synergistic coupling of topographic, edaphic, and vegetation conditions. This integrated understanding moves beyond the identification of isolated factors to establish a systematic framework for comprehending erosion regulation in ecologically fragile regions.