Why the universal soil loss equation and the revised version of it do not predict event erosion well

School of Resource, Environmental and Heritage Sciences, University of Canberra, Canberra ACT 2601 Australia
Hydrological Processes (Impact Factor: 2.68). 02/2005; 19(3):851 - 854. DOI: 10.1002/hyp.5816

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    • "This improves the sediment yield prediction, eliminates the need for delivery ratios, and allows the equation to be applied to individual storm events. By using the runoff index sediment yield prediction has been improved because runoff is a function of antecedent moisture condition as well as rainfall energy (Williams 1975a; Williams, Berndt 1977; Kinnell 2005; Zhang et al. 2009). "

    Full-text · Dataset · Jul 2014
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    • "These models have been used widely for the prediction of the average annual soil loss from upland fields (Mihara et al. 2005), especially the combination of the sediment delivery ratio and flow transport capacity with the USLE or RUSLE (Balamurugan 1991; Hrissanthou 2005; Krasa et al. 2005; Zhou and Wu 2008). Although certain limits and criticisms for the USLE/RUSLE have been discussed by Risse et al. (1993), Rapp et al. (2001), Šúri et al. (2002), Shrestha et al. (2004), and Kinnel (2005), the USLE or RUSLE is selected from the applicable models because of its very simple structure, the connection between the required inputs, and the available data and the investigation scale (Wischmeier and Smith 1958, 1965, 1978; Wu and Wang 2007; Terranova et al. 2009). "
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    ABSTRACT: Soil erosion can lead to an increase in the concentration of sediment in the runoff and the surplus floodwater during flood season, which increases the likelihood of a flood disaster. To analyze the relationship between the risk of soil erosion and the surplus floodwater during flood season, a case study of the Jinghe River Basin located in the middle Loess Plateau of China was performed. A measure of the soil erosion risk R-e was presented, which combined the five factors in universal soil loss equation (USLE) with information entropy theory. The results show that the northern watershed features both high and severe levels of soil erosion risk, especially the watershed controlled by the Qingyang (QY) station, whereas the risk level is low or slight in the southern Jinghe basin, the Ziwuling Mountains in the east, and the Liupanshan Mountains in the west. Compared with the USLE, the R-e measure can better reflect the spatial distribution of soil erosion risk and identify the areas corresponding to different soil erosion levels. Data for the sediment yield rate from 37 subbasins also prove the correctness of the R-e measure. The results from a sensitivity analysis indicate that the same amount of factor variability led to a larger soil erosion risk increment in 1986, followed by those of 2000 and 1995. The magnitude of the influences of the R, C, P, and LS factors on the soil erosion risk features a descending order of R > C > P > LS. The regression analysis reveals a statistically significant linear relationship between the coefficient of surplus floodwater and the level of soil erosion risk. The higher level of soil erosion risk can cause more surplus floodwater downstream when the sediment concentration is smaller than the limit of the sediment concentration for river water use. The limit also has important influences on the amount of surplus floodwater during flood season. (C) 2014 American Society of Civil Engineers.
    Full-text · Article · Jul 2014 · Journal of Hydrologic Engineering
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    • "Therefore, the unusual application of the MUSLE model, i.e. for estimation of soil erosion (Sadeghi et al. 2004, Esmali and Abedini 2009) or nutrient loss (Noor et al. 2010) provides inappropriate predictions at the watershed scale, or even at the plot scale (Sadeghi 2004, Kinnell 2005, 2010, Khaledi Darvishan 2009). However, an accurate estimation of sediment yield requires a sufficient number of samples or sedimentgraph preparation to give an appropriate basis for comparison and model calibration (Cordova 1981, Smith et al. 1984, Jackson et al. 1987, Banasik et al. 1988, Epifanio et al. 1991, McConkey et al. 1997, Santos and Canino 1997, Erskine et al. 2002, Khajehie et al. 2002, Mahmoudzadeh et al. 2002, Rezaiifard et al. 2002, Cambazoglu and Gogos 2004, Chen and Mackay 2004, Sadeghi and Mahdavi 2004, Sarkhosh et al. 2004, Basson 2005, Kinnell 2005, Porabdullah 2005, Appel et al. 2006, Ma 2006, Varvani et al. 2006, Abdulla and Eshtawi 2007, Arekhi 2007, Jaramillo 2007, Sadeghi et al. 2007a, 2007b, 2008, Khaledi Darvishan et al. 2009, Kinnell 2010, Noor et al. 2010). Although the MUSLE model has provided good results in some areas, review of the correct values and exact variables used and final conclusions of the application are strictly recommended in order to apply the MUSLE model correctly. "
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    ABSTRACT: The sediment yield model of the MUSLE (modified universal soil loss equation) is applied extensively throughout the world, but different performances have been reported of its success relative to measured data. A review of all the available literature is presented to assess the application of the model under different conditions and, ultimately, make a comprehensive judgement on the different aspects to allow readers to adjust their further research. A review of 49 papers showed the variable accuracy of the model, which depends on the manner of calculation and determination of the input and output, and the study time and space scales. There were differences in land use, in correspondence of the physiographic characteristics with those of the original conditions of model development, and even in the experience of researchers in applying the model. The results also show the need to consider the original application of the model, as proposed by its developers, to achieve comparable results. Key words: MUSLE Model; Sediment Yield; Storm Event; Soil Erosion Models; Model Goodness Of Fit.
    Full-text · Article · Feb 2014 · Hydrological Sciences Journal/Journal des Sciences Hydrologiques
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