Soil erosion assessment using RUSLE model and its validation by FR probability model

Abstract

The objective of the current study is to estimate the annual average soil loss through RUSLE model and furthermore assess the soil erosion risk and its distribution using frequency ratio (FR) probability algorithm. At first, soil erosion risk zones were identified using FR model by the consideration 14 soil erosion conditioning factors such as land use (LU/LC), slope, slope aspect, normalized difference vegetation index (NDVI), altitude, plan curvature, stream power index, distance from river, road, and lineament, soil types, rainfall erosivity, slope length and lineament density. Secondly, the spatial pattern of annual average soil loss rates was estimated using RUSLE model with consideration of five factors such as, rainfall erosivity (R), cover management (C), slope length (LS), soil erodability (K), and conservation practice factors (P). In order to map soil erosion susceptibility by the FR model, dataset divided randomly into parts 70/30 percent for training and validation purposes, respectively. Based on the FR value, the susceptibility map was reclassified into five different critical erosion probability zones. Among this, the severe and high erosion zones occupy 13.69% and 16.26%, respectively, of the total area, where as low and very low susceptibility zones together constitute 32.98% of the River Basin. The assessed high amount of average annual soil erosion (more than 100 t/ha/year) is occupied 9.55% of the total study area. It is conclude that high soil erosion susceptibility and yearly average soil loss were performed in this study area. Therefore, the produced soil erosion susceptibility maps and annual average soil erosion map can be very useful for primary land use planning and soil erosion hazard mitigation purpose for prioritizing areas.

Keywords:

• Frequency ratio
• RUSLE
• geo-environmental variables
• average annual soil loss

Acknowledgments

The authors would like to thank Editor Geocarto International (Prof. Lulla) and anonymous reviewer(s) for their helpful comments on the primary version of the manuscript. Amiya Gayen would like to sincere thanks to the Assistant Professor Dr. Swades Pal for his fair cooperation and cordial support to precede my research work. Dr. Hamid Reza Pourghasemi would like to thank the 2015 Jiangsu provincial key R & D Program (Social Development) (BE2015704), the National Natural Science Foundation of China (No. 41877522), for providing fellowship.

Disclosure statement

No potential conflict of interest was reported by the authors.