Modeling of Soil Erosion using SWAT and Linking Land-Cover Pattern to Soil Erosion in Upper Krishna Sub-Basin

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-5
Year of Publication : 2022
Authors : Pravin V. Desai, Suresh K. Ukarande
DOI :  10.14445/22315381/IJETT-V70I5P218

Citation 

MLA Style: Pravin V. Desai, and Suresh K. Ukarande. "Modeling of Soil Erosion using SWAT and Linking Land-Cover Pattern to Soil Erosion in Upper Krishna Sub-Basin." International Journal of Engineering Trends and Technology, vol. 70, no. 5, May. 2022, pp. 159-172. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P218

APA Style:Pravin V. Desai, & Suresh K. Ukarande. (2022). Modeling of Soil Erosion using SWAT and Linking Land-Cover Pattern to Soil Erosion in Upper Krishna Sub-Basin. International Journal of Engineering Trends and Technology, 70(5), 159-172. https://doi.org/10.14445/22315381/IJETT-V70I5P218

Abstract
The soil erosion modeling in any watershed is controlled by the attribute related to hydro-meteorological and geospatial data. Following these principles, the study is conducted to simulate soil erosion through the Soil and Water Assessment Tool and topographic link pattern to soil erosion through Partial Least square regression (PLSR). The study involved a model run of 10-year calibration and 5-year validation in response to observed soil erosion. The sensitivity of the basin parameter was assisted through SIMCA-P and SUFI-2 algorithms. The soil erosion model performed well by displaying R²>0.70 for daily and monthly simulation. The catchment was delineated into 25 sub-basins and was further distributed in different erosion severity zones. The ranks are allotted to the SWAT parameters, which show higher sensitivity towards soil erosion through SUFI-2 analysis. In contrast, the sensitivity of parameters from PLSR towards soil erosion is evaluated through weight analysis. Identifying highly sensitive parameters towards soil erosion and finding the ability of landscape metrics to acquire a close association of soil erosion with the land cover pattern are the important findings of this study.

Keywords
Erosion modeling, GIS, PLSR, SWAT, Sensitivity Analysis.

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