Soil erosion occurs when water or wind breaks up soil particles and transports them to a new location. Soil erosion has become a global concern due to both intended and unintended on the economy and society. The land use and land cover processes are gradually degraded as a consequence of soil erosion. Understanding the relationships between LULC, topographical aspects of the landscape, and land use management is critical for applying optimization techniques for effectively reducing soil erosion. Soil erosion's potential as a severe danger to soil sustainability has been recognized in recent decades (Khademalrasoul and Amerikhah 2020). As a result, understanding soil management scenarios and implementing practical conservation strategies can help to protect the soil from erosive forces. However, being the most common kind of soil degradation, soil erosion pretends a constant threat to the soil and degrades its quality. All around the world, the soil is the primary source of production. As a result, evaluating various effective soil erosion factors is critical to selecting and implementing the optimal management techniques.
However, as a predetermined outcome in soil erosion elements, vegetation cover can conserve soil against erosive agents and control soil deterioration (Duchemin and Hogue 2009; Wang et al. 2016). Human involvement, such as constructional activities, land use changes, deforestation, and farming practices, speeds up the rate of soil deterioration. Water erosion affects 1094 million hectares of land worldwide, with 751 million hectares severely damaged, and the world's rivers are expected to transfer 15e30 billion tonnes of silt into the ocean each year (Walling and Webb 1996, Lal 2003). In India, approximately 91 percent of the entire geographical area is subject to possible soil deterioration rates ranging from 5 to 40 tones ha/year, so there is a huge need to adopt a variety of conservation strategies to avoid soil erosion (Sharda et al. 2013). Even if slow and gradual geologic erosion is required for pedogenesis and fast soil erosion should be avoided to avoid negative repercussions for soil fertility, soil quality, and agricultural production (Blanco-Canqui and Lal 2008).
A variety of soil deterioration and sediment transport models have been created to estimate soil loss and sedimentation at various scales (Lorup and Styczen 1990; Avwunudiogba and Hudson 2014). However, a number of factors impact which model is optimal, including the intended goal, catchment characteristics, and input data availability, among others (Ranzi et al. 2012). Due to their simple and resilient model structure and integration with GIS, USLE and RUSLE have gained incredible global acceptance for predicting soil loss at diverse geographical scales (Mallick et al. 2014). Because of its versatility and data availability, RUSLE, designed for land use management by the agriculture department in the US, is widely utilized (Renard et al. 1997, Alewell et al. 2019). The RUSLE model has five input components: R, K, LS, C, and P. The C-factor depicts the influence of vegetation and soil cover among these five input elements (Karaburun 2010). Human activities have a significant impact on the C-factor, which is calculated as the proportion of harvested land soil degradation to the resulting loss from farmed land in the barren environment (Das et al. 2018). The R stands for rainfall erosivity factors, and soil erosion patterns are varied from one place to another because of rainfall erosivity factors variation as it is the primary determinant of vegetation cover (Talchabhadel et al. 2020). Whereas the letter K stands for soil erodibility factors, LS stands for slope angle and angle of inclination. Factors and the letter P stand for the factor of conservational works. The RUSLE model is a versatile tool for management that may be applied at multiple scales, including landscape and watershed. According to the researchers, is it the RUSLE model can anticipate the map about the spatial pattern of erosion and soil loss when paired with a geographical information system (GIS) (Amsalu and Mengaw 2014). As a result, using the GIS technique, this model can be used by managers to build practical conservational strategies for soil loss mapping. The RUSLE model can also be used to detect areas at soil erosion threat and offer spatial distributions of soil loss in various locations within eroding areas (Ashiagbor et al. 2013).
The BRB is very much susceptible to soil erosion. Several works were previously done to intend to show the amount of soil loss in the region, viz., De (1998), Lama (2003), Tamang (2013). De (1998) quantitatively assesses the soil erosion rate of the BRB. Using rainfall, soil, and topographic factor, the highest potential soil loss of the BRB is observed > 8000 t ha− 1 year− 1, mainly in the hilly central part. The highest predicted or actual soil loss of the BRB using rainfall, soil, topographic, and biological factors found > 1000 t ha− 1 year− 1. Another author Lama (2003), has a detailed analysis on the different aspects of soil erosion in the BRB through the USLE method. The study computed the maximum potential soil loss in the hilly areas around Ambotia is > 10000 t ha− 1 year− 1, while the predicted soil loss is > 1000 t ha− 1 year− 1. Tamang (2003) focuses on the causes of bank failure of BRB, mainly in the plain areas. The studies also suggest proper land use planning and conservational measures to mitigate the issues related to soil erosion.
The primary goal of this research is to apply the RUSLE model to estimate the actual or predicted and potential soil loss in order to better understand the regional distribution of soil runoff in the area under study.