• Spatial Information Research
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• v.10 no.1
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• pp.139-152
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• 2002

The purpose of this study is to estimate temporal soil loss change according to long-term land cover changes using G1S and RS. Revised USLE(Universal Soil Loss Equation) factors were prepared by using point rainfall data, DEM(Digital Elevation Model), soil map and land cover map. During the past two decades, land cover changes were traced by using Landsat MSS and TM data. As a result, forest area in 2000 has decreased 25.3 $km^2$ compared with that in 1990. Soil loss has decreased 3751.2 tou/yr. On the other hand, upland area has increased 22.5 $km^2$. Soil loss of upland has increased 5395.4 to/yr. Therefore, soil loss in 2000 increased 6.3 kg/$m^2$/yr compared with that in 1990. This was mainly caused by the increased upland area.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1324-1328
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• 2008

RUSLE(Revised Universal Soil Loss Equation) is one of empirical models for estimating the soil loss effectively, when there is no measured data from the study areas. It has been researching into application and estimation of the RUSLE parameters in Korea. As one of the RUSLE parameters, the rainfall-runoff erosivity factor R, is closely connected hydrologic characteristics of the study areas. It requires a continuous record of rainfall measurement at a minute time step for each storm to calculate an accurate R factor by the RUSLE methodology and it takes a lot of time to analyze it. For the more simplified and reasonable estimation of the rainfall erosivity, this study researched for correlation between the rainfall erosivity and mean annual precipitation used 122 data from the existing studies in Korea. Considering hydrologic homogeneity, new regression equations are presented and compared with other annual erosive empirical index for the test of application. As the results, the study presents the isoerodent map at 59 sites in Korea, using annual rainfall data by the Korea Meteorological Administration from 1978 to 2007.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1997-2002
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• 2007

This study selected soil erosion source area of Dechung basin by soil erosion estimation model and field survey for effective soil conservation planning and management. First, unit soil erosion amount of Dechung basin is analyzed using RUSLE (Revised Universal Soil Loss Equation) model based on DEM (Digital Elevation Model), soil map, landcover map and rainfall data. Soil erosion model is difficult to analyze the tracing route of soil particle and to consider the characteristics of bank condition and the types of crop, multidirectional field survey is necessary to choice the soil erosion source area. As the result of analysis of modeling value and field survey, Mujunamde-, Wondang-, Geumpyong stream are selected in the soil erosion source area of Dechung basin. Especially, these areas show steep slope in river boundary and cultivation condition of crop is also weakness to soil erosion in the field survey.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.789-792
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• 2010

Major impacts of climate change expert that soil erosion rate may increase during the $21^{st}$ century. This study was conducted to assess the potential impacts of climate change on soil erosion by water in Korea. The soil loss was estimated for regions with the potential risk of soil erosion on a national scale. For computation, Universal Soil Loss Equation (USLE) with rainfall and runoff erosivity factors (R), cover management factors (C), support practice factors (P) and revised USLE with soil erodibility factors (K) and topographic factors (LS) were used. RUSLE, the revised version of USLE, was modified for Korean conditions and re-evaluate to estimate the national-scale of soil loss based on the digital soil maps for Korea. The change of precipitation for 2010 to 2090s were predicted under A1B scenarios made by National Institute of Meteorological Research in Korea. Future soil loss was predicted based on a change of R factor. As results, the predicted precipitations were increased by 6.7% for 2010 to 2030s, 9.5% for 2040 to 2060s and 190% for 2070 to 2090s, respectively. The total soil loss from uplands in 2005 was estimated approximately $28{\times}10^6$ ton. Total soil losses were estimated as $31{\times}10^6$ ton in 2010 to 2030s, $31{\times}10^6$ ton in 2040 to 2060s and $33{\times}10^6$ ton in 2070 to 2090s, respectively. As precipitation increased by 17% in the end of $21^{st}$ century, the total soil loss was increased by 12.9%. Overall, these results emphasize the significance of precipitation. However, it should be noted that when precipitation becomes insignificant, the results may turn out to be complex due to the large interaction among plant biomass, runoff and erosion. This may cause increase or decrease the overall erosion.

• Journal of Korean Society on Water Environment
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• v.33 no.6
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• pp.744-753
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• 2017

The sediment delivery ratio (SDR) is widely used to estimate sediment loads by multiplying soil loss through the Revised Universal Equation (RUSLE). In this study, the SDR equation was developed for the Lake Imha watershed using soil loss calculated by RUSLE and sediment loads by the calibrated Hydrological Simulation. Program Fortran (HSPF). The ratio of watershed relief and channel length ($R_f/L_{ch}$), the ratio of watershed relief and watershed length ($R_f/L_b$), curve number (CN), area (A), and channel slope ($SLP_{ch}$) demonstrated strong correlations with SDR. SDR equations were developed by a combination of subwatershed parameters by referring to the correlation analysis. The area based power functional SDR developed in this study showed significant errors at the point right after entering major tributaries, because SDR was unrealistically reduced when the watershed area increased significantly. The $SLP_{ch}$-based power functional SDR also showed extraordinary values when the channel slope was gradual. The SDR equation that showed the highest value of the coefficient of determination also presented unrealistic changes in the sediment loads within a relatively short river distance. The SDR equation $SDR=0.0003A^{0.198}R_f/L{_w}^{1.167}$ was recommended for application to the Lake Imha watershed. Using this equation, sediment loads at the outlet of the Lake Imha watershed were calculated, and the HSPF parameters related to sediment in the uncalibrated subwatersheds were determined by referring to the sediment loads calculated with the SDR equation.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.4
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• pp.24-36
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• 2003

This study integrates the revised universal soil loss equation(RUSLE) with a grid-based GIS method to assess the potential risk of soil erosion at the watershed scale. Data used in this study to generate the RUSLE factors include several thematic maps such as land use, topographic and soil maps, together with tabular precipitation data. Based on the RUSLE estimation for all the grids(10m cells) in the corresponding watershed, a cumulative histogram for the annual soil loss can be constructed. As the results, it shows that the 83.5% value of the annual soil loss for the watershed is less than 1ton/ha. However, the above 30% of agricultural land is defined as a medium or very high-risk area(more than 10ton/ha/yr). So it is necessary to establish soil conservation practices to reduce soil erosion based on the field observations.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.4
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• pp.19-27
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• 2012

RUSLE(Revised Universal Soil Loss Equation) has been widely used to estimate the soil loss amount of watersheds from rainfall erosivity, soil erodibility, topographic features and cropping management condition. Rainfall erosivity is the most dominant and sensitive factor among these so that the determination of reliable rainfall erosivity is essential to estimate the soil loss of watershed. Since there has been no criterion to determine the rainfall erosivity in Korea, the empirical values, determined from the relation between the annual average rainfall and erosivity or suggested by TBR(Transport Research Board), have been used for designing the erosion control structure and controlling the soil erosion for watersheds. In this study, the procedure for estimating the rainfall erosivity using frequency analysis is proposed. The most fitted distribution function, with calculated rainfall erosivities with various frequencies and durations, has been also selected. The suggested procedure can be used to estimate the optimal value of rainfall erosivity for RUSLE in order to design soil erosion structures and control the soil erosion in watersheds effectively.

• Journal of Korea Water Resources Association
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• v.34 no.3
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• pp.199-207
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• 2001

Due to the nationwide development, sediment yield has to be estimated to design the sedimentation basin which is used to minimize the effects of construction or disturbing the natural soil condition. But there is no proved equation for the estimation of sediment yield in Korea. Therefore, applicability and the limitation of RUSLE (revised universal sediment loss equation) sediment yield equation is examined for the construction sites, where the rainfall and sediment data are available. General mistakes in estimation of the RUSLE parameters are also discussed. It is found that RUSLE could be applied in Korea as long as the sediment delivery ratio was considered. Appropriate estimation method of sediment delivery ratio are also proposed.

• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.6
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• pp.55-69
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• 2016

RUSLE (Revised Universal Soil Loss Equation) is the empirical formular widely used to estimate rates of soil erosion caused by rainfall and associated overland flow. Among the factors considered in RUSLE, rainfall erosivity factor (R factor) is the major one derived by rainfall intensity and characteristics of rainfall event. There has been developed various methods to estimate R factor, such as energy based methods considering physical schemes of soil erosion and simple methods using the empirical relationship between soil erosion and annual total rainfall. This study is aimed to quantitatively evaluate the variation among the R factors estimated using different methods for South Korea. Station based observation (minutely rainfall data) were collected for 72 stations to investigate the characteristics of rainfall events over the country and similarity and differentness of R factors calculated by each method were compared in various ways. As results use of simple methods generally provided greater R factors comparing to those for energy based methods by 76 % on average and also overestimated the range of factors using different equations. The variation coefficient of annual R factors was calculated as 0.27 on average and the results significantly varied by the stations. Additionally the study demonstrated the rank of methods that would provide exclusive results comparing to others for each station. As it is difficult to find universal way to estimate R factors for specific regions, the efforts to validate and integrate various methods are required to improve the applicability and accuracy of soil erosion estimation.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.70-74
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• 2005

Soil particles from rainfall flow into reservoir and give lots of influence In water quality because the geological conditions and landcover characteristics of imha basin have a weakness against soil loss. Especially, much soil particles induced to reservoir in shape of muddy water when it rains a lot because the geological characteristics of imha reservoir are composed of clay and shale layer. Therefore, field turbidity data can be Indirect-standards to estimate the soil erosion of imha basin. This study evaluated annual soil erosion using GIS-based RUSLE (Revised Universal Soil Loss Equation) and developed rainfall weighting value method using time-series rainfall data to estimate monthly soil erosion. In view of field turbidity data(2003 yr), we can find out monthly soil erosion with rainfall weighting value is more efficient than that with monthly rainfall data.