Abstract
“Soil erosion” caused by water is one of the most severe aspects of land degradation processes. This type of problem is more acute in the “subtropical environment” than in other parts of the world. The erosion of the topsoil is influenced by rainfall variability and anthropogenic activities. Soil erosion is presently recognized as a separate field for large-scale land degradation caused by soil loss across the world. Many academics from several disciplines have emphasized the need of using multiple approaches to assess the quantity of soil erosion. The empirical model can estimate the amount of soil loss from ground-based observations of the field site, but it takes a long time and is expensive. Apart from this most of the areas of the world are ungauged in nature facing the challenges for incorporating the necessary relevant data. For this purpose, the use of this model in a “GIS” integrated platform is rapidly increasing. This integrated model can produce nearly identical results across a wide range of variables, making it a reliable predictor. In recent times, the use of multiple predictive models in identical work has increased significantly, with the aim of determining which one is more reliable in terms of incorporating primary data. In this study, the application of “Universal Soil Loss Equation (USLE)”, “Revised Universal Soil Loss Equation (RUSLE)” and “Modified Universal Soil Loss Equation (MUSLE)” has been done to estimate the annul “soil loss” in Bengal Basin. From the analysis, the “RUSLE” gives the better outcome in comparison with “USLE” and “MUSLE”.
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Pal, S.C., Chakrabortty, R. (2022). Soil Loss Estimation Using Different Empirical and Semi-empirical Models. In: Climate Change Impact on Soil Erosion in Sub-tropical Environment . Geography of the Physical Environment. Springer, Cham. https://doi.org/10.1007/978-3-031-15721-9_5
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