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.
    Hydrology and Earth System Sciences Discussions 03/2012; 9(3):4193-4233. · 3.59 Impact Factor
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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.
    Hydrology and Earth System Sciences 07/2012; 16(7):2347-2364. · 3.59 Impact Factor
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    ABSTRACT: Eiumnoh, A. 2010. Calibration and validation of the Modified Universal Soil Loss Equation for estimating sediment yield on sloping plots: A case study in Khun Satan catchment of Northern Thailand. Can. J. Soil Sci. 90: 585Á596. In this study, model testing, calibration, and validation of the Modified Universal Soil Loss Equation (MUSLE) model were carried out in Khun Satan catchment, Thailand, for the estimation of sediment yield in plots of different slopes using the S factor from the classic Universal Soil Loss Equation (USLE) and the McCool model, as the calibration parameter. In situ experimental plots were established with five different inclinations (9, 16, 25, 30, and 35%), with the other model parameters (e.g., erodibility, conservation practice, etc) being treated as constants. Sediment yields were recorded from 27 rainfall events between July and October 2003. It was found that both the classic USLE and the McCool models over-estimated sediment yields at all slope angles. However, the classic USLE produced a smaller relative error (RE) than the McCool model at plots with slopes of 9 and 16%, while the McCool model performed better at plots with slopes over 16% inclination. The calibration of the model using the S factor was then made for two slope range intervals, and the slope algorithm was later modified. The calibrated S factors were used in the prototype model for slope ranges of 9 to 16% using classic USLE and for slopes from 16 to 35% using the McCool model. The results revealed that an acceptable accuracy can be obtained through model calibration. The model validation based on paired t-test, on the other hand, showed that there was no difference (a 00.05) between measured and estimated sediment yield using both models. This result indicates that if data on various slope gradients are limited, MUSLE needs to be calibrated before application, especially with respect to topographic factors, in order to obtain an accurate estimate of the sediment yield from individual rainfall events.

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