Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Soil erosion estimation using RUSLE: a case study of Bamnidhi sub basin
In the present work, four parametric combinations of LS and P factor have been adopted for assessment of RUSLE-based soil erosion in Bamnidhi subbasin of the upper Mahanadi basin. The basin was discretized of the basin into 98 grids where by applying the SEdiment Delivery Distributed (SEDD) model, total sediment yield from the catchment is computed as sum of the yields from each grid. Soil erosion maps generated are classified as low, moderate, and severe concerning soil erosion potential as < 5, 5–30, and > 30 t ha−1 year−1 respectively. It was observed that the percentage disagreement of erosion area for the three classes with 37%, 25% and 12% for combinations 1 and 4, whereas for combinations 2 and 3 the disagreement improves to 44%, 26%, and 18% respectively. Validation of the maps with satellite information-based FCC showed 75% of accuracy to identify points in moderate to severe zone. For development of MLR model to assess soil erosion, the best parameter set has been selected with ${R^2} = 0.75$ and slope is found to be the most influential factor for soil loss than LULC, rainfall, and soil type.
Soil erosion estimation using RUSLE: a case study of Bamnidhi sub basin
In the present work, four parametric combinations of LS and P factor have been adopted for assessment of RUSLE-based soil erosion in Bamnidhi subbasin of the upper Mahanadi basin. The basin was discretized of the basin into 98 grids where by applying the SEdiment Delivery Distributed (SEDD) model, total sediment yield from the catchment is computed as sum of the yields from each grid. Soil erosion maps generated are classified as low, moderate, and severe concerning soil erosion potential as < 5, 5–30, and > 30 t ha−1 year−1 respectively. It was observed that the percentage disagreement of erosion area for the three classes with 37%, 25% and 12% for combinations 1 and 4, whereas for combinations 2 and 3 the disagreement improves to 44%, 26%, and 18% respectively. Validation of the maps with satellite information-based FCC showed 75% of accuracy to identify points in moderate to severe zone. For development of MLR model to assess soil erosion, the best parameter set has been selected with ${R^2} = 0.75$ and slope is found to be the most influential factor for soil loss than LULC, rainfall, and soil type.
Soil erosion estimation using RUSLE: a case study of Bamnidhi sub basin
Bhuyan, Pritam Kumar (Autor:in) / Meher, Janhabi (Autor:in) / Mohanty, Laxmipriya (Autor:in)
ISH Journal of Hydraulic Engineering ; 30 ; 273-280
14.03.2024
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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