Abstract
Abstract. Soil erosion is a major problem around the world because of its effects on soil productivity, nutrient loss, siltation in water bodies, and degradation of water quality. By understanding the driving forces behind soil erosion, we can more easily identify erosion-prone areas within a landscape to address the problem strategically. Soil erosion models have been used to assist in this task. One of the most commonly used soil erosion models is the Universal Soil Loss Equation (USLE) and its family of models: the Revised Universal Soil Loss Equation (RUSLE), the Revised Universal Soil Loss Equation version 2 (RUSLE2), and the Modified Universal Soil Loss Equation (MUSLE). This paper reviews the different sub-factors of USLE and RUSLE, and analyses how different studies around the world have adapted the equations to local conditions. We compiled these studies and equations to serve as a reference for other researchers working with (R)USLE and related approaches. Within each sub-factor section, the strengths and limitations of the different equations are discussed, and guidance is given as to which equations may be most appropriate for particular climate types, spatial resolution, and temporal scale. We investigate some of the limitations of existing (R)USLE formulations, such as uncertainty issues given the simple empirical nature of the model and many of its sub-components; uncertainty issues around data availability; and its inability to account for soil loss from gully erosion, mass wasting events, or predicting potential sediment yields to streams. Recommendations on how to overcome some of the uncertainties associated with the model are given. Several key future directions to refine it are outlined: e.g. incorporating soil loss from other types of soil erosion, estimating soil loss at sub-annual temporal scales, and compiling consistent units for the future literature to reduce confusion and errors caused by mismatching units. The potential of combining (R)USLE with the Compound Topographic Index (CTI) and sediment delivery ratio (SDR) to account for gully erosion and sediment yield to streams respectively is discussed. Overall, the aim of this paper is to review the (R)USLE and its sub-factors, and to elucidate the caveats, limitations, and recommendations for future applications of these soil erosion models. We hope these recommendations will help researchers more robustly apply (R)USLE in a range of geoclimatic regions with varying data availability, and modelling different land cover scenarios at finer spatial and temporal scales (e.g. at the field scale with different cropping options).
Highlights
Soil erosion involves many processes, but the overall effect is of particles being transported and deposited from one location to another
The initial application of Revised Universal Soil Loss Equation (RUSLE) of Abu Hammad et al (2005) over-estimated soil loss by a factor of 3, but with adjustments to the subfactors based on local data on soil moisture, land cover, and support practices, the model error was reduced to 14 %
The combination of Compound Topographic Index (CTI) and the (R)Universal Soil Loss Equation (USLE) is a promising direction for including gully erosion, but care must be taken in coupling these models because both already account for upstream drainage area and slope
Summary
Soil erosion involves many processes, but the overall effect is of particles being transported and deposited from one location to another. The USLE is an empirical model used to estimate the annual average rate of soil erosion (tons per unit area) for a given combination of crop system, management practice, soil type, rainfall pattern, and topography It was originally developed at the plot scale for agricultural plots in the United States of America (Wischmeier and Smith, 1978). Extensive work by Naipal et al (2015) attempted to apply the (R)USLE at a coarse global scale (30 arcsec) by using USA and European databases to derive rainfall erosivity equations These equations use a combination of annual precipitation (millimetres), mean elevation (metres), and simple precipitation intensity index (millimetres per day) to calculate the R factor for different Köppen–Geiger climate classifications (Naipal et al, 2015). Several studies have published erosivity equations for tropical areas: da Silva (2004) for Brazil, Shamshad et al (2008) for Malaysia, and Jain and www.hydrol-earth-syst-sci.net/22/6059/2018/
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