Article

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: 2.5). 02/2005; 19(3):851 - 854. DOI: 10.1002/hyp.5816
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    ABSTRACT: Predicting event runoff and soil loss under different land covers is essential to quantitatively evaluate the hydrological responses of vegetation restoration in the Loess Plateau of China. The Soil Conservation Service Curve Number (SCS-CN) and Revised Universal Soil Loss Equation (RUSLE) models are widely used in this region to this end. This study incorporated antecedent moisture condition (AMC) in runoff production and initial abstraction of the SCS-CN model, and considered the direct effect of runoff on event soil loss by adopting a rainfall-runoff erosivity factor in the RUSLE model. The modified SCS-CN and RUSLE models were coupled to link rainfall-runoff-erosion modeling. The effects of AMC, slope gradient and initial abstraction ratio on curve number of SCS-CN, as well as those of vegetation cover on cover-management factor of RUSLE were also considered. Three runoff plot groups covered by sparse young trees, native shrubs and dense tussock, respectively, were established in the Yangjuangou catchment of Loess Plateau. Rainfall, runoff and soil loss were monitored during the rainy season in 2008-2011 to test the applicability of the proposed approach. The original SCS-CN model significantly underestimated the event runoff, especially for the rainfall events that have large 5-day antecedent precipitation, whereas the modified SCS-CN model could predict event runoff well with Nash-Sutcliffe model efficiency (EF) over 0.85. The original RUSLE model overestimated low values of measured soil loss and under-predicted the high values with EF only about 0.30. In contrast to it, the prediction accuracy of the modified RUSLE model improved satisfactorily with EF over 0.70. Our results indicated that the AMC should be explicitly incorporated in runoff production, and direct consideration of runoff should be included in predicting event soil loss. Coupling the modified SCS-CN and RUSLE models appeared to be appropriate for runoff and soil loss simulation at plot scale in the Loess Plateau. The limitations and future study scopes of the proposed models were also indicated.
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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.
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    ABSTRACT: Predicting event runoff and soil loss under different land covers is essential to quantitatively evaluate the hydrological responses of vegetation restoration in the Loess Plateau of China. The Soil Conservation Service curve number (SCS-CN) and Revised Universal Soil Loss Equation (RUSLE) models are widely used in this region to this end. This study incorporated antecedent moisture condition (AMC) in runoff production and initial abstraction of the SCS-CN model, and considered the direct effect of runoff on event soil loss by adopting a rainfall-runoff erosivity factor in the RUSLE model. The modified SCS-CN and RUSLE models were coupled to link rainfall-runoff-erosion modeling. The effects of AMC, slope gradient and initial abstraction ratio on curve number of SCS-CN, as well as those of vegetation cover on cover-management factor of RUSLE, were also considered. Three runoff plot groups covered by sparse young trees, native shrubs and dense tussock, respectively, were established in the Yangjuangou catchment of Loess Plateau. Rainfall, runoff and soil loss were monitored during the rainy season in 2008-2011 to test the applicability of the proposed approach. The original SCS-CN model significantly underestimated the event runoff, especially for the rainfall events that have large 5-day antecedent precipitation, whereas the modified SCS-CN model was accurate in predicting event runoff with Nash-Sutcliffe model efficiency (EF) over 0.85. The original RUSLE model overestimated low values of measured soil loss and underpredicted the high values with EF values only about 0.30. In contrast, the prediction accuracy of the modified RUSLE model improved with EF values being over 0.70. Our results indicated that the AMC should be explicitly incorporated in runoff production, and direct consideration of runoff should be included when predicting event soil loss. Coupling the modified SCS-CN and RUSLE models appeared to be appropriate for evaluating hydrological effects of restoring vegetation in the Loess Plateau. The main advantages, limitations and future study scopes of the proposed models were also discussed.
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