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<p>Soil erosion represents a major environmental challenge in the Eastern Himalayan region, where steep terrain, intense monsoonal rainfall, and land-use changes accelerate land degradation and threaten ecosystem stability. Despite its ecological importance, spatially explicit erosion assessments in the Kamlang River watershed remain limited. This study presents a geospatial assessment of soil erosion in the Kamlang River watershed, Arunachal Pradesh, India, through the integration of the Revised Universal Soil Loss Equation (RUSLE), Remote Sensing (RS), and Geographic Information Systems (GIS). Multi-source datasets, including WorldClim precipitation data, SRTM Digital Elevation Model (DEM), FAO soil information, and IRS satellite imagery, were utilized to derive rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and support practice (P) factors. Rainfall erosivity was estimated from long-term average annual precipitation using an empirical rainfall–erosivity relationship because sub-hourly rainfall intensity data required for direct erosivity determination were unavailable. All RUSLE factor layers were harmonized to a common spatial resolution of 90&#xa0;m prior to modelling. The RUSLE–GIS framework was applied to estimate the spatial distribution of annual soil loss across the watershed. Results indicate that annual soil loss ranges from negligible values to 3295.23 Mg ha⁻¹ yr⁻¹, with a mean soil loss of 42.95 Mg ha⁻¹ yr⁻¹. Most of the watershed exhibits low erosion rates due to extensive forest cover and protective vegetation, whereas erosion hotspots are concentrated in steeply sloping areas characterized by sparse vegetation and higher rainfall erosivity. The findings highlight the dominant influence of topography, rainfall erosivity, and land cover on erosion dynamics in mountainous environments. The integration of multi-source geospatial datasets demonstrates the effectiveness of the RUSLE–GIS framework for erosion assessment in data-limited regions. The study contributes to watershed management and conservation planning by identifying priority erosion-prone areas and provides a transferable methodological framework for soil erosion assessment in other Himalayan and mountainous watersheds.</p>

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Geospatial assessment of soil erosion in the Kamlang River watershed using the RUSLE framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月26日

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  • Roshni Rai,
  • Suchitra S. Pardeshi,
  • Rocky Pebam

摘要

Soil erosion represents a major environmental challenge in the Eastern Himalayan region, where steep terrain, intense monsoonal rainfall, and land-use changes accelerate land degradation and threaten ecosystem stability. Despite its ecological importance, spatially explicit erosion assessments in the Kamlang River watershed remain limited. This study presents a geospatial assessment of soil erosion in the Kamlang River watershed, Arunachal Pradesh, India, through the integration of the Revised Universal Soil Loss Equation (RUSLE), Remote Sensing (RS), and Geographic Information Systems (GIS). Multi-source datasets, including WorldClim precipitation data, SRTM Digital Elevation Model (DEM), FAO soil information, and IRS satellite imagery, were utilized to derive rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and support practice (P) factors. Rainfall erosivity was estimated from long-term average annual precipitation using an empirical rainfall–erosivity relationship because sub-hourly rainfall intensity data required for direct erosivity determination were unavailable. All RUSLE factor layers were harmonized to a common spatial resolution of 90 m prior to modelling. The RUSLE–GIS framework was applied to estimate the spatial distribution of annual soil loss across the watershed. Results indicate that annual soil loss ranges from negligible values to 3295.23 Mg ha⁻¹ yr⁻¹, with a mean soil loss of 42.95 Mg ha⁻¹ yr⁻¹. Most of the watershed exhibits low erosion rates due to extensive forest cover and protective vegetation, whereas erosion hotspots are concentrated in steeply sloping areas characterized by sparse vegetation and higher rainfall erosivity. The findings highlight the dominant influence of topography, rainfall erosivity, and land cover on erosion dynamics in mountainous environments. The integration of multi-source geospatial datasets demonstrates the effectiveness of the RUSLE–GIS framework for erosion assessment in data-limited regions. The study contributes to watershed management and conservation planning by identifying priority erosion-prone areas and provides a transferable methodological framework for soil erosion assessment in other Himalayan and mountainous watersheds.