Soil erosion is a well-known global environmental problem (Belyaev et al., 2005; Deng et al., 2009). It not only seriously threatens natural resources, infrastructure construction, and agricultural production (Pimentel et al., 1995; Lal, 1998; Park et al., 2011; Sharda et al., 2013), but also directly affects human safety and quality of life. Water erosion is one of the most serious types of soil erosion. Water erosion can result in land degradation by removing fertile topsoil layers, create negative downstream effects by depositing soil materials in rivers or reservoirs, and cause non-point pollution by washing pollutants attached to soil particles into natural waters (Vrieling et al., 2008; Morgan, 2005; Wang et al., 2013). Therefore, soil conservation is considered important for the protection of the environment and the economy. China has suffered some of the most severe soil erosion on the planet. Between 2005 and 2007, almost 14% of the total soil erosion area in the world occurred in China (Li et al., 2009). Water erosion constitutes the primary type of soil erosion in China and accounts for 45% of the total soil erosion area (Li et al., 2008). Although the Chinese government has undertaken numerous soil conservation projects, the overall efficacy at improving environmental conditions is low (Zhang et al., 2010). One reason for this low efficacy is that the allocation of limited human and financial resources for soil conservation is made based on the size of the watershed rather than the erosion conservation prioritization (Zhang et al., 2002; Fan et al., 2008). The identification of soil erosion risk can help map and monitor the spatial dynamics of erosion and conservation prioritization. Therefore, it is important to identify dynamic soil erosion risk to effectively use limited resources to control soil erosion in China. Soil erosion is related to precipitation, land use, soil taxa, vegetation fractional coverage (VFC), and slope (Beskow et al., 2009; Tian et al., 2009). Water erosion is caused by rain and runoff, and its intensity can be expressed as the annual amount of surface soil loss (MWRC, 1997). Currently, both quantitative and qualitative methods are available for the identification of soil erosion. Among the quantitative methods, the experienced statistical universal soil loss equation (USLE) (Wischmeier and Smith, 1965), revised universal soil loss equation (RUSLE) (Renard et al., 1991), and process-based physical water erosion prediction project model (WEPP) (Baffalt et al., 1996) are widely used to simulate and estimate soil loss. Although quantitative methods can calculate absolute soil erosion amounts, their outcomes are generally applied qualitatively (Vrieling et al., 2008). It is difficult to calculate soil erosion over long time periods or over large regions using quantitative methods because sufficient and accurate field validation measurements are time-consuming and expensive, and standard validation equipment is not easy to obtain (Stroosnijder, 2005;Vrieling et al., 2008). In addition, the complexities in model structure, parameters, and scale effect also cause the calculated and measured results to differ (Boardman, 2006; Ni et al., 2008).