The Study on Cover-Management Factor in USLE and RUSLE: A Review

Abstract

The Universal Soil Loss Equation (USLE) and the Revised Universal Soil Loss Equation (RUSLE) are two most widely soil erosion prediction models worldwide. In both of these models, C factor, i. e., the Cover鄄Management Factor, quantifies the effects of vegetation cover and management practices on soil erosion, which is the most important factor in USLE / RUSLE models that can be easily controlled to alleviate soil erosion. The authors reviewed the evolvement of C factor and concluded various estimation methods for C factor at different scales by integrating recent domestic and international developments in this field. Therefore, five methods to quantify C factor were summarized at plot, hillslope and small watershed scales: 1) Obtaining C factor values from USLE / RUSLE handbooks; 2) Estimating C factor values based on the definition of C factor; 3) Computing C factor values by its sub鄄factors; 4) Calculating C factor values using inverse method based on the USLE / RUSLE (C = A / (R•K•L•S•P) ; 5) Assessing C factor with linear and nonlinear models between C factor and vegetation coverage. Currently, estimation of C factor mainly depended on field experiments and observations at plot, hillslope and small watershed scales. Thus, the consistency of experimental conditions among different experiments, especially the consistency of standard runoff plots, is important and a prerequisite for comparability of C factor values. Once again, five methods to quantify C factor at watershed, region scales were summarized as well: 1) Assigning C鄄factors values from reported values in literature according to land鄄cover / land鄄use categories; 2) Estimating C factor values at larger scales by equations based on relationships between C factor values and vegetation coverage acquired at smaller scales; 3) Performing direct regression between image bands ratios / vegetation indices and C factor values determined in the field; 4) http: / / www.ecologica.cn Estimating C factor values by linear Spectral Mixture Analysis; 5) Improving in mapping of C factor values by geostatistical methods with remote sensing images. In general, at watershed and region scales, C factor values were usually estimated by using remote sensing images, The development of remote sensing technology promoted the evolvement of C factor estimation methods, which makes C factor mapping more precise. However, full interpretation of C factor values by using remote sensing date remains a major challenge, requiring further research on C factor. In conclusion,the authors summarized 10 ways to estimate C factor values, their advantages and disadvantages and also their applicable conditions in detail. Furthermore, the authors emphasized the important aspects of further research on C factor in future and wished extensive involvement of more researchers in this field.

Global Analysis of the Cover-Management Factor for Soil Erosion Modeling

“…A simple and widely used method is referencing studies that have reported C-factor values for similar land cover. In addition, many studies have reported various formulas for deriving C-factor values [6,16,19,[21][22][23]. In general, the methods for calculating C-factor values can be classified into six categories: Following the RUSLE handbook, the C-factor is computed of five subfactors [9] as following Equation ( 2…”

The synergistic effects of afforestation and the construction of check‐dams on sediment trapping: Four decades of evolution on the Loess Plateau, China

“…For example, Cai, Ding, Shi, Huang, and Zhang (2000) used the runoff and soil loss amount from the experimental plots to generate C and P factors. Feng et al, (2014) reviewed the developing progress of C factor, and compared the methods of estimating C value at different scales. All these studies provide a solid basis for this work; thus, we applied these experimental-based C values in this study.…”

Spatial Quantification of Cropland Soil Erosion Dynamics in the Yunnan Plateau Based on Sampling Survey and Multi-Source LUCC Data

“…Compared with field investigations, satellite remote sensing is characterized by the acquisition of timely, affordable data that are uniform over large areas, the capacity for real-time information acquisition and the use of a regular revisit wide-view field, and it has been widely applied in soil erosion modeling and mapping [37,38]. In particular, efforts have been made towards direct soil erosion detection [37,39], as well as the estimation of parameters of rainfall erosivity [40,41], soil-related property derivation [42], topographic factors extraction [43], cover management (C-factor) and support practices (P-factor) [44][45][46][47], the mapping of specific soil conservation measures using high-resolution images [48][49][50] and large-scale soil erosion assessments with raster layer operations [51,52]. The biggest advantage of the sampling survey approach is that it provides reliable soil erosion rates, and large-scale spatial patterns of soil erosion status can be derived by combination with statistical principles [53][54][55], but the field measurement and investigation of indices are labor-intensive and costly processes.…”