Scholarlink

<p>Soil erosion, influenced by agricultural practices and human activities, presents major challenges to both land productivity and long-term sustainability. Its complexity varies, complicating loss estimation and management. Models like RUSLE aid in assessment and are crucial for watershed conservation planning. This study combined the Revised Universal Soil Loss Equation (RUSLE) model with Geographic Information Systems (GIS) and Remote Sensing (RS) techniques to evaluate soil erosion risk areas in the Jia Bharali River Basin.The approach allowed for detailed mapping of erosion-prone areas by utilizing spatial data and remote sensing imagery, improving the accuracy of erosion risk assessments. The RUSLE model factors in variables such as conservation practices, land cover, rainfall intensity, slope characteristics, and soil erodibility to estimate soil erosion rates. The study found a significant increase in annual potential soil loss in the Jia Bharali River Basin, rising from 2,606,800 t/ha/yr in 2003 to 3,874,170 t/ha/yr in 2023. The basin was classified into five erosion severity levels, with the central, northern, and northwestern regions experiencing the highest levels of natural erosion. Spatial erosion maps, generated using a weighted overlay index, revealed that while most areas face low erosion risk, some zones exhibit severe erosion potential. These maps are critical for land management and planning strategies aimed at mitigating soil erosion in Assam’s Sonitpur District. In addition, the study employed remote sensing techniques, using Landsat satellite images to monitor changes in the Jia Bharali River’s channel between 1993 and 2023. Substantial modifications in the river’s course were observed, with key factors contributing to bank erosion and accretion including chute cutoffs, lateral channel migration, meander bend expansion, and channel widening due to shear failure and the liquefaction of bank materials.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Assessment of soil erosion and accretion process in the Jia Bharali River Basin, Sonitpur District, Assam, India using RUSLE model

  • 期刊论文

  • 发表日期：

    2025年6月17日

查看全文
  • Ollica S. Kiba,
  • Shehnaj Ahmed Pathan

摘要

Soil erosion, influenced by agricultural practices and human activities, presents major challenges to both land productivity and long-term sustainability. Its complexity varies, complicating loss estimation and management. Models like RUSLE aid in assessment and are crucial for watershed conservation planning. This study combined the Revised Universal Soil Loss Equation (RUSLE) model with Geographic Information Systems (GIS) and Remote Sensing (RS) techniques to evaluate soil erosion risk areas in the Jia Bharali River Basin.The approach allowed for detailed mapping of erosion-prone areas by utilizing spatial data and remote sensing imagery, improving the accuracy of erosion risk assessments. The RUSLE model factors in variables such as conservation practices, land cover, rainfall intensity, slope characteristics, and soil erodibility to estimate soil erosion rates. The study found a significant increase in annual potential soil loss in the Jia Bharali River Basin, rising from 2,606,800 t/ha/yr in 2003 to 3,874,170 t/ha/yr in 2023. The basin was classified into five erosion severity levels, with the central, northern, and northwestern regions experiencing the highest levels of natural erosion. Spatial erosion maps, generated using a weighted overlay index, revealed that while most areas face low erosion risk, some zones exhibit severe erosion potential. These maps are critical for land management and planning strategies aimed at mitigating soil erosion in Assam’s Sonitpur District. In addition, the study employed remote sensing techniques, using Landsat satellite images to monitor changes in the Jia Bharali River’s channel between 1993 and 2023. Substantial modifications in the river’s course were observed, with key factors contributing to bank erosion and accretion including chute cutoffs, lateral channel migration, meander bend expansion, and channel widening due to shear failure and the liquefaction of bank materials.