Modeling soil loss at plot scale with EUROSEM and RUSLE2 at stony soils of Khamesan watershed, Iran

Khaleghpanah, Naser; Shorafa, M; Asadi, Hossein; Gorji, M.; Davari, Masoud

doi:10.1016/j.catena.2016.08.039

Cited by 30 publications

(10 citation statements)

References 38 publications

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“…Moreover, USLE-M and, with reference to the low annual soil losses, USLE-MM are biased, and this model bias can be explained by their deterministic nature (Nearing, 1998). This bias was also detected by modelling plot soil loss with WEPP (Tiwari et al, 2000) or RUSLE2 (Foster, 2005;Khaleghpanah et al, 2016), which is the newest in the family of USLE models. The NSEI model efficiency index calculated for the USLE was relatively low (0·52) and in line with those obtained by previous plot studies aimed to evaluate USLE performance at annual scale.…”

Section: Modelling Evaluation At Annual Scalementioning

confidence: 96%

Applying the USLE Family of Models at the Sparacia (South Italy) Experimental Site

2016

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Soil erosion is a key process to understand the land degradation, and modelling of soil erosion will help to understand the process and to foresee its impacts. The applicability of the Universal Soil Loss Equation (USLE) at event scale is affected by the fact that USLE rainfall erosivity factor does not take into account runoff explicitly. USLE‐M and USLE‐MM, including the effect of runoff in the event rainfall–runoff erosivity factor, are characterized by a better capacity to predict event soil loss. The specific objectives of this paper were (i) to determine the suitable parameterization of USLE, USLE‐M and USLE‐MM by using the dataseries of Sparacia experimental site and (ii) to evaluate their performances at both event and annual scale. The measurements allowed to establish the relationships for calculating the factors of USLE, USLE‐M and USLE‐MM usable at the Sparacia experimental area. At first, for slope‐length values greater than 33 m, the calibration of USLE model at event scale pointed out that sediment delivery processes, that is processes involving deposition of the transported eroded soil particles, occur. The analysis showed that USLE and USLE‐M tend to overestimate low event soil losses, while for USLE‐MM, this tendency is less pronounced. However, the USLE‐MM performed better than USLE and USLE‐M and was able to reproduce better than other two models the highest soil loss values that are the most interesting from a practical point of view. The results obtained at annual scale were generally consistent with those obtained at event scale. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Modelling Evaluation At Annual Scalementioning

confidence: 96%

Applying the USLE Family of Models at the Sparacia (South Italy) Experimental Site

2016

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“…In Section AB of the curve (slope < 35°), the SEI increases with an increase in slope gradient; this is because the slope has a significantly positive effect on the process of soil loss through erosion (B. T. Liu et al, ). The greater the gradient and length of a slope, the larger the runoff energy and the greater the soil loss (L. Jiang et al, ; Khaleghpanah et al, ). However, in Section BC of the curve, there is a negative relationship between the erosion intensity and slope; this is because the process of soil formation is slow in steep slope zones and the soil layer is shallow (L. Q. Chen et al, ; Y. Q. Xu et al, ).…”

Section: Discussionmentioning

confidence: 99%

Section: Relationship Between Slope and Seimentioning

confidence: 99%

“…Substantial efforts have been made to develop quantitative assessment models of soil erosion, and those developed mainly belong to two categories, either on-site measurements that are often applied in small-scale regional or point experiments or off-site quantification models, which can be used to evaluate the erosion intensity of zones on a large scale (Ganasri & Ramesh, 2016;Tamene, Park, Dikau, & Vlek, 2006; L. F. Xu, Xu, & Meng, 2013). Models developed to date to quantify soil erosion such as the Universal Soil Loss Equation (USLE; Mattheus & Norton, 2015), the Water Erosion Prediction Project (Baigorria & Romero, 2007), the European Soil Erosion Model (Khaleghpanah, Shorafa, Asadi, Gorji, & Davari, 2016), and the Erosion for Agricultural Management System (Vanwalleghem et al, 2017) are considered to be the most scientific approaches for use in studies, although experimental measurements are still considered indispensable and fundamental (Brooks, Dobre, Elliot, Wu, & Boll, 2016;Kinnell, 2017).…”

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Dynamic monitoring of soil erosion in the upper Minjiang catchment using an improved soil loss equation based on remote sensing and geographic information system

et al. 2018

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The upper Minjiang catchment has suffered intensive soil erosion due to frequent geologic hazards and its fragile ecosystem zone with steep slopes. This study fully considers the topographic features and surface cover and introduces improved methods of K, LS, and C to establish a soil loss equation for the upper Minjiang catchment based on remote sensing and geographic information system. Spatial–temporal change patterns of soil erosion intensity and its driving mechanisms are then analyzed and discussed. Results show that (a) the soil erosion modulus of the upper Minjiang catchment for 2005 and 2015 were 1,577.29 t km−2 a−1 and 1,619.77 t km−2 a−1, both of which belong to a level of mild erosion. The slight and mild erosion zones covered the largest area and were widely distributed in the northern part of the study area, whereas zones of intensive and severe erosion were mostly concentrated in Wenchuan County and the lower reaches of the Heishui and Zagunao Rivers. (b) During 2005–2015, changes in erosion intensity showed a trend of “overall stability, local deterioration.” Zones of mild, moderate, and intensive increase were mainly concentrated in the Longmen Mountain Fault Zone, such as southern Maoxian and western Wenchuan Counties. (c) Returning cultivated land to forest and grasslands greatly reduced the erosion intensity and erosion amount, whereas geologic hazards aggravated the soil erosion condition. Zones with a slope of <35° had a positive relationship with soil erosion intensity; these areas are crucial control areas for soil preservation and containing soil loss. In addition, grassland is more effective in conserving soil and water than forestland in the upper Minjiang catchment in areas of steep terrain. These results provide an important reference for estimating soil loss intensity in southwest mountainous regions of China, particularly in the Hengduan Mountains, and greatly contribute to the planning of soil and water conservation.

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“…Other enhancements included in RUSLE2 were designed to accommodate the specific needs of conservation planners to deal with situations not included in the original USLE database [69]. RUSLE2 is associated with an extensive database in the USA, but has been used in countries such as China [70], Spain [71], Nigeria [72], and Iran [73].…”

Section: What Next?mentioning

confidence: 99%

A Review of the Science and Logic Associated with Approach Used in the Universal Soil Loss Equation Family of Models

Kinnell

2019

Soil Systems

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Soil erosion caused by rain is a major factor in degrading agricultural land, and agricultural practices that conserve soil should be used to maintain the long-term sustainability of agricultural land. The Universal Soil Loss Equation (USLE) was developed in the 1960s and 1970s to predict the long-term average annual soil loss from sheet and rill erosion on field-sized areas as an aid to making management decisions to conserve soil. The USLE uses six factors to take account of the effects of climate, soil, topography, crops, and crop management, and specific actions designed to conserve soil. Although initially developed as an empirical model based on data from more than 10,000 plot years of data collected in plot experiments in the USA, the selection of the independent factors used in the model was made taking account of scientific understanding of the drivers involved in rainfall erosion. In addition, assumptions and approximations were needed to make an operational model that met the needs of the decision makers at that time. Those needs have changed over time, leading to the development of the Revised USLE (RUSLE) and a second version of that, the Revised USLE, Version 2 (RUSLE2). While the original USLE model was not designed to predict short-term variations in erosion well, these developments have involved more use of conceptualization in order to deal with the time-variant impacts of the drivers involved in rainfall erosion. The USLE family of models is based on the concept that the “unit” plot, a bare fallow area 22.1 m long on a 9% slope gradient with cultivation up and down the slope, provides a physical situation where the effect of climate and soil on rainfall erosion can be determined without the need to consider the impact of the four other factors. The science and logic associated with this approach is reviewed. The manner by which the soil erodibility factor is determined from plot data ensures that the long-term average annual soil loss for the unit plot is predicted well, even when the assumption that event soil loss is directly related to the product of event rainfall energy, and the maximum 30-min intensity is not wholly appropriate. RUSLE2 has a capacity to use CLIGEN, the weather generator used in WEPP, and so can predict soil losses based on individual storms in a similar way to WEPP. Including a direct consideration of runoff in determining event erosivity enhances the ability to predict event soil losses when runoff is known or predicted well, but similar to more process-based models, this ability is offset by the difficulty in predicting runoff well.

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Modeling soil loss at plot scale with EUROSEM and RUSLE2 at stony soils of Khamesan watershed, Iran

Cited by 30 publications

References 38 publications

Applying the USLE Family of Models at the Sparacia (South Italy) Experimental Site

Applying the USLE Family of Models at the Sparacia (South Italy) Experimental Site

Dynamic monitoring of soil erosion in the upper Minjiang catchment using an improved soil loss equation based on remote sensing and geographic information system

A Review of the Science and Logic Associated with Approach Used in the Universal Soil Loss Equation Family of Models

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