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Assessing soil erosion risk in Meghalaya, India: integrating geospatial data with RUSLE model

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Abstract

Meghalaya is well-known for its fragile ecosystem; because of its undulating landscape and high-intensity rainfall, Meghalaya faces severe soil erosion issues. This study aims to conduct a quantitative analysis to understand the soil loss in the region. Using various datasets covering rainfall, soil properties, topography, and land cover, this study employs a Geographic Information System (GIS) framework to apply the Revised Universal Soil Loss Equation (RUSLE) for soil erosion loss estimation. The study estimates the region’s mean soil erosion at 14 t ha−1 y−1, subsequently causing an annual loss of 5871.32 t ha−1 y−1. The sorting of the area into six risk zones reveals that 66% experience slight to moderate erosion, 18% experience high to very high erosion, and 16% encounter severe erosion. Study findings reveal that the LS factor significantly influences soil erosion. Different physiographic regions show varying erosion rates: Khasi Hills show the highest (20.94 t. ha–1. y–1), trailed by Jaintia Hills (13.35) and Garo Hills (5.47). The research highlights open and degraded forest areas with the highest erosion rates, followed by agricultural lands, range land, and barren land. Definite terrain characteristics, such as slope angles within 0 to 15 degrees and elevations greater than 1000 m, appear as erosion-prone areas. This research highlights the critical requirement for targeted preservation efforts and ecologically sound land use practices in Meghalaya. The findings provide essential guidance and regulation for stakeholders, policymakers, land managers, and conservationists to implement effective erosion control measures and protect the region’s valuable soil resources.

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Data availability

The datasets generated or analysed during this study are available from the corresponding author; they can be provided upon reasonable request.

Abbreviations

ANOVA:

Analysis of Variance

CRU:

Climate Research Unit

DEM:

Digital Elevation Model

FAO-UNESCO:

Food and Agriculture Organization of the United Nations Educational, Scientific and Cultural Organization

GIS:

Geographic Information System

IDW:

Inverse distance weighted

LULC:

Land Use Land Cover

NDVI:

Normalized Difference Vegetation Index

RS:

Remote Sensing

RUSLE:

Revised Universal Soil Loss Equation

USLE:

Universal Soil Loss Equation

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  1. Department of Civil Engineering, National Institute of Technology Meghalaya, Shillong, Meghalaya, 793003, India

    Naveen Badavath, Smrutirekha Sahoo & Rasmiranjan Samal

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Badavath, N., Sahoo, S. & Samal, R. Assessing soil erosion risk in Meghalaya, India: integrating geospatial data with RUSLE model. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-04855-4

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