• Journal of Soil and Groundwater Environment
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• v.22 no.6
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• pp.29-36
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• 2017

Universal soil loss equation (USLE) has been frequently employed to estimate potential soil loss in land since it was developed based on the statewide data measured and collected in the United States. The equation is an empirical model mainly used for U.S. soil, thus it has been recently modified to reflect Korean soil conditions and named as Korean Soil Loss Equation (KORSLE). The modified equation was implemented in ArcGIS software, and used for estimation of potential soil loss from 2003 to 2016 in the thirty-eight Water Protection Districts. Five out of the thirty-eight districts were identified as the area of potential soil erosion most severly. In those five districts, potential soil erosion were estimated to be more than 50 Mg/ha/year that requires site investigation under supervision of the Korean Ministry of Environment. Distinctive site characteristics were found in the potential soil loss estimation such that the districts of low potential soil loss had low five factors in the aggregate. However, if one of more factors are dominantly large, the potential soil loss significantly increased. This study provides a useful tool to identify the potential areas for soil erosion and the important factors that play an important role in the estimation process.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.3
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• pp.29-42
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• 2023

Universal Soil Loss Equation (USLE) is suggested and employed in the policy to conserve soil resources and to manage the impact of development, since soil loss is very essential to nonpoint source pollution management. The equation requires only five factors to estimate average annual potential soil loss, USLE is simplicity provides benefits in use of the equation. However, it is also limitation of the model, since the estimated results are very sensitive to the five factors. There is a need to examine the application procedures. Three approaches to estimate potential soil loss were examined, In the first approach, all factors were prepared with raster data, soil loss were computed for each cell, and sum of all cell values was determined as soil loss for the watersheds. In the second approach, the mean values for each factor were defined as representing USLE factors, and then the five factors were multiplied to determine soil loss for the watersheds. The third approach was same as the second approach, except that the Vegetative and Mechanical measure was used instead of the Cover and management factor and Support practice factor. The approaches were applied in 38 watersheds, they displayed significant difference, moreover no trends were detected for the soil loss at watersheds with the approaches. Therefore, it was concluded that there is a need to be developed and provided a typical guideline or public systems so that soil loss estimations have consistency with the users.

• Journal of Industrial Technology
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• v.19
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• pp.147-154
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• 1999

The soil loss by rainfalls or runoffs has been one of the main environment problems in 20th century. The soil loss cause the various problems those are decreasing of the agricultural productivity, desolating of pasture land and disturbing of water flowing. Therefore, it is very important to measure properly various factors those are affecting to soil loss and to recognize a seriousness of soil loss problem. In this study, we use the USLE(Universal Soil Loss Equation) as a basic approaching way for soil loss analysis in a watershed, and the GSIS(Geo-Spatial Information System) technique is applied to evaluate for factors those are related to the USLE. The results of this study are consisted of three parts those are to build up the various topographical information that is needed for analysis of wide area soil loss by using the USLE, to evaluate the factors those are needed to the USLE, to estimate the soil loss condition of subbasin in the watershed.

• Journal of Korean Society on Water Environment
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• v.22 no.4
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• pp.619-625
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• 2006

Amount of soil loss is important information for the proper water quality management, In this research, annual average soil loss of the Geum River basin was estimated using RUSLE (Revised Universal Soil Loss Equation) and GIS (Geographic Information System). Input data were manipulated using ArcGIS ver. 8.3. From crop field which constitute 8.2% of the Geum River Basin, annual average soil loss was estimated as 53.6 ton/ha/year. From the rice paddy field which constitutes 20% of the Geum River Basin, soil loss was estimated as 33.5 ton/ha/year, In comparison, forestry area which constitutes 61.8% of the basin discharged 2.8 ton/ha/year, It could be known from this research that appropriate measures should be implemented to prevent excessive soil loss from the agricultural areas.

• Journal of Soil and Groundwater Environment
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• v.24 no.5
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• pp.31-41
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• 2019

In 2013, the Ministry of Environment in South Korea promulgated a new regulatory bulletin that contained revised enforcement ordinance on soil management protocols. The bulletin recommends the use of Universal Soil Loss Equation (USLE) for the soil erosion estimation, but USLE has limited applicability in prediction of soil erosion because it does not allow direct estimation of actual mass of soil erosion. Therefore, there is a great need of revising the protocol to allow direct comparison between the measured and estimated values of soil erosion. The Korean Soil Loss Equation (KORSLE) was developed recently and used to estimate soil loss in two fields as an alternative to existing USLE model. KORSLE was applied to estimate monthly rainfall erosivity indices as well as temporal variation in potential soil loss. The estimated potential soil loss by KORSLE was adjusted with correction factor for direct comparison with measured soil erosion. The result was reasonable since Nash-Stucliff efficiency were 0.8020 in calibration and 0.5089 in validation. The results suggest that KORSLE is an appropriate model as an alternative to USLE to predict soil erosion at field scale.

• Journal of the Korean GEO-environmental Society
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• v.20 no.3
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• pp.5-14
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• 2019

This study attempted to study the soil erosion dynamic in the Bagmati Basin of Nepal. In this study, an inclusive methodology that combines Revised Universal Soil Loss Equation (RUSLE) and GIS techniques was adopted to determine the distribution of soil loss in the study basin. As well, this study attempts to study the intensity of soil erosion in the seven different land use patterns in the Bagmati Basin. Soil loss is an associated phenomenon of hydrologic cycle and this dynamic phenomenon possesses threats to sustainability of basin hydrology, agriculture system, hydraulic structures in operation and overall ecosystem in a long run. Soil conservation works, and various planning and design of watersheds works demands quantification of soil loss. The results of the study in Bagmati Basin shows the total annual soil loss in the basin is 22.93 million tons with an average rate of 75.83T/ha/yr. The computed soil loss risk was divided into five classes from tolerable to severe and the spatial pattern was mapped for easy interpretation. Also, evaluation of soil loss in different land use categories shows barren area has highest rate of soil loss followed by agriculture area. This is a preliminary work and provides erosion risk scenario in the basin. The study can be further used for strategic planning of land use and hydrologic conservation works in a basin.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.1
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• pp.112-117
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• 2012

The Revised Universal Soil Loss Equation (RUSLE) is a revision of the Universal Soil Loss Equation (USLE). However, changes for each factor of the USLE have been made in RUSLE which can be used to compute soil loss on areas only where significant overland flow occurs. RUSLE which requires standardized methods to satisfy new data requirements estimates soil movement at a particular site by utilizing the same factorial approach employed by the USLE. The rainfall erosivity in the RUSLE expressed through the R-factor to quantify the effect of raindrop impact and to reflect the amount and rate of runoff likely is associated with the rain. Calculating the R-factor value in the RUSLE equation to predict the related soil loss may be possible to analyse the variability of rainfall erosivity with long time-series of concerned rainfall data. However, daily time step models cannot return proper estimates when run on other specific rainfall patters such as storm and daily cumulative precipitation. Therefore, it is desirable that cross-checking is carried out amongst different time-aggregations typical rainfall event may cause error in estimating the potential soil loss in definite conditions.

• Korean Journal of Agricultural Science
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• v.47 no.2
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• pp.381-394
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• 2020

Accelerated soil wind erosion still remains to date to cause severe economic and environmental impacts. Revised and updated models to quantitatively evaluate wind induced soil erosion have been made for specific factors in the wind erosion equation (WEQ) framework. Because of increasing quantities of accumulated data, the WEQ, the revised wind erosion equation (RWEQ), the wind erosion prediction system (WEPS), and other soil wind erosion models have been established. These soil wind erosion models provide essential knowledge about where and when wind erosion occurs although naturally, they are less accurate than the field-scale. The WEQ was a good empirical model for comparing the effects of various management practices on potential erosion before the RWEQ and the WEPS showed more realistic estimates of erosion using easily measured local soil and climatic variables as inputs. The significant relationship between the observed and predicted transport capacity and soil loss makes the RWEQ a suitable tool for a large scale prediction of the wind erosion potential. WEPS developed to replace the empirical WEQ can calculate soil loss on a daily basis, provide capability to handle nonuniform areas, and obtain predictions for specific areas of interest. However, the challenge of precisely estimating wind erosion at a specific regional scale still remains to date.

• Korean Journal of Soil Science and Fertilizer
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• v.38 no.2
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• pp.66-71
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• 2005

Soil loss into stream and river by runoff shall be considered for non-point source pollution management as national land conservation. The purpose of this study was to develop the mathematical equation to predict soil loss from inclined uplands of maize cultivation due to the runoff by rainfall which mainly converges on July and August. Soil loss was concentrated on May because of low canopy over an entire field in 2002 and on June and July because of heavy rainfall in 2003. By regression analysis the relation between runoff and soil loss can be represented by a linear equation of y =1.5291x - 3.4933, where y is runoff ($Mg\;ha^{-1}$) and x is soil loss ($kg\;ha^{-1}$). The determination coefficient of this equation was 0.839 (P<0.001). Therefore, the mathematical equation derived from the practical experiment at the inclined upland can be applicable to predict soil loss accompanied by runoff due to periodic rainfall converging on short periods within a couple of months.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.55-65
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• 2019

Universal Soil Loss Equation (USLE) is to estimate potential soil loss and has benefit in use with its simplicity. The equation is composed of five factors, one of the factors is the slope length and steepness factor (LS factor) that is for topographic property of fields to estimate potential soil loss. Since the USLE was developed, many equations to compute LS was suggested with field measurement. Nowadays the factor is often computed in GIS software with digital elevation model, however it was reported that the factor is very sensitive to the resolution of digital elevation model. In addition, the digital elevation model of high resolution less than 3 meter is required in small field application, however these inputs are not associate with the empirical models' backgrounds since the empirical models were derived in 22.1 meter field measurements. In the study, four equation to compute LS factor and two approaches to determine slope length and steepness were examined, and correction factor was suggested to provide reasonable precision in LS estimations. The correction factor is computed with field area and cell size of digital elevation model, thus the correction factor can be adapted in any USLE-based models using LS factor at field level.