4.6 Article

Risk modelling of soil erosion in semi-arid watershed of Tamil Nadu, India using RUSLE integrated with GIS and Remote Sensing

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ENVIRONMENTAL EARTH SCIENCES
卷 80, 期 16, 页码 -

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SPRINGER
DOI: 10.1007/s12665-021-09800-6

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RUSLE; Soil erosion; CHIRPS; GIS and remote sensing; Amravati watershed

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The study estimated the average soil loss in the Amravati watershed of Tamil Nadu state in South India using the RUSLE model along with GIS and Remote Sensing techniques. The results showed that most of the watershed is at low risk of soil erosion, but there are also some areas with moderate to very high erosion risk.
Water-induced soil erosion is one of the challenging threats in various parts of the world, thus systematic investigations of soil loss risk are more crucial for sustainable agricultural production and water management. The present study was carried out to estimate the average soil loss in the Amravati watershed of Tamil Nadu state in South India using Revised Universal Soil Loss Equation (RUSLE) along with Geographical Information System (GIS) and Remote Sensing techniques. In this study, the rain gauge-based rainfall data were completely substituted with the CHIRPS datasets to compute the R factor, since it gives continuous surface rainfall data rather than location-based measurement. The estimated soil loss in the watershed was found to be in the range of 0-280.2 t/ha(-1)/yr(-1), in which about 64.7% of the watershed under very low erosion risk whilst about 12.9% of the area prone to moderately high-to-very high erosion risk. The maximum soil loss rate was identified in the Degraded forest with a mean loss of 59.51 t/ha(-1)/yr(-1) followed by the Degraded plantation (32.17 t/ha(-1)/yr(-1)), Scrubland/Wasteland (17.75 t/ha(-1)/yr(-1)), Current fallow (12.08 t/ha(-1)/yr(-1)), and Rainfed cropland (10.03 t/ha(-1)/yr(-1)). The study shows that the present land use / land cover and the landscape of the watershed have a great influence on soil loss. To check the efficiency of the RUSLE model for the assessment of soil erosion risk, the final derived output was validated with the use of 4k UHD Google Earth images.

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