A modified CSLE for soil loss prediction under different vegetation patterns at slope scale in China

Vegetation plays a fundamental role in reducing soil erosion by shielding the soil surface from raindrop impact and runoff erosion, promoting water infiltration to decrease runoff, and enhancing soil stability. Beyond the extent of vegetation cover, its spatial distribution is critical for optimizin...

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Published inInternational Soil and Water Conservation Research
Main Authors Shi, Wenhai, Bao, Jiachi, Wang, Miaomiao, Chen, Zhongming, Yu, Jinle, Chen, Hongjun, Song, Wenyi, Xin, Yan
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.06.2025
Subjects
Chinese soil loss equation
Flow path length
Soil loss prediction
Vegetation patterns
Flow path length
Chinese soil loss equation
Vegetation patterns
Soil loss prediction
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Abstract Vegetation plays a fundamental role in reducing soil erosion by shielding the soil surface from raindrop impact and runoff erosion, promoting water infiltration to decrease runoff, and enhancing soil stability. Beyond the extent of vegetation cover, its spatial distribution is critical for optimizing erosion control and ensuring long-term ecological sustainability. Existing soil erosion models, such as the Chinese Soil Loss Equation (CSLE), predominantly focus on vegetation type and coverage while neglecting the spatial configuration of vegetation. This oversight can introduce uncertainties in predicting soil erosion. To overcome this shortcoming, this study proposes a new method by integrating vegetation spatial pattern indices, with particular emphasis on the mean flow path length index (MFLI), into the conventional CSLE framework. The MFLI effectively captures the positional distribution and spatial arrangement of vegetation, providing a more refined analysis of erosion dynamics at the slope scale. Using this index, a revised biological control factor (B) was developed. The proposed method was validated with data from 52 experimental plots across China and further tested with optimized parameters on five additional representative sites. Results demonstrated that the new approach substantially outperformed the conventional storm-based CSLE model, achieving model efficiencies of 0.686 and 0.636 during calibration and validation, respectively. In summary, the proposed method offers a more accurate and reliable prediction of soil erosion under diverse vegetation pattern conditions at the slope scale. By integrating spatial distribution characteristics of vegetation, it provides an improved tool for soil and water conservation, supporting more precise erosion prediction and mitigation strategies.
AbstractList Vegetation plays a fundamental role in reducing soil erosion by shielding the soil surface from raindrop impact and runoff erosion, promoting water infiltration to decrease runoff, and enhancing soil stability. Beyond the extent of vegetation cover, its spatial distribution is critical for optimizing erosion control and ensuring long-term ecological sustainability. Existing soil erosion models, such as the Chinese Soil Loss Equation (CSLE), predominantly focus on vegetation type and coverage while neglecting the spatial configuration of vegetation. This oversight can introduce uncertainties in predicting soil erosion. To overcome this shortcoming, this study proposes a new method by integrating vegetation spatial pattern indices, with particular emphasis on the mean flow path length index (MFLI), into the conventional CSLE framework. The MFLI effectively captures the positional distribution and spatial arrangement of vegetation, providing a more refined analysis of erosion dynamics at the slope scale. Using this index, a revised biological control factor (B) was developed. The proposed method was validated with data from 52 experimental plots across China and further tested with optimized parameters on five additional representative sites. Results demonstrated that the new approach substantially outperformed the conventional storm-based CSLE model, achieving model efficiencies of 0.686 and 0.636 during calibration and validation, respectively. In summary, the proposed method offers a more accurate and reliable prediction of soil erosion under diverse vegetation pattern conditions at the slope scale. By integrating spatial distribution characteristics of vegetation, it provides an improved tool for soil and water conservation, supporting more precise erosion prediction and mitigation strategies.
Author Chen, Hongjun
Wang, Miaomiao
Song, Wenyi
Xin, Yan
Chen, Zhongming
Shi, Wenhai
Bao, Jiachi
Yu, Jinle
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  email: xinyan@iwhr.com
  organization: Key Laboratory of Sediment Science and Northern River Training, The Ministry of Water Resources, China Institute of Water Resources and Hydropower Research, China
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ContentType Journal Article
Copyright 2025 International Research and Training Center on Erosion and Sedimentation, China Water and Power Press, and China Institute of Water Resources and Hydropower Research
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Keywords Flow path length
Chinese soil loss equation
Vegetation patterns
Soil loss prediction
Language English
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Snippet Vegetation plays a fundamental role in reducing soil erosion by shielding the soil surface from raindrop impact and runoff erosion, promoting water...
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SourceType Index Database
Publisher
SubjectTerms Chinese soil loss equation
Flow path length
Soil loss prediction
Vegetation patterns
Title A modified CSLE for soil loss prediction under different vegetation patterns at slope scale in China
URI https://dx.doi.org/10.1016/j.iswcr.2025.06.003
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