• 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 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.172-172
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• 2020

In recent years, soil erosion has come to be regarded as an essential environmental problem in human life. Soil erosion causes various on- and off-site problems such as ecosystem destruction, decreased agricultural productivity, increased riverbed deposition, and deterioration of water quality in streams. To solve these problems caused by soil erosion, it is necessary to quantify where, when, how much soil erosion occurs. Empirical erosion models such as the Universal Soil Loss Equation (USLE) family models have been widely used to make spatially distributed soil erosion vulnerability maps. Even if the models detect vulnerable sites relatively well by utilizing big data related to climate, geography, geology, land use, etc. within study domains, they do not adequately describe the physical process of soil erosion on the ground surface caused by rainfall or overland flow. In other words, such models remain powerful tools to distinguish erosion-prone areas at the macro scale but physics-based models are necessary to better analyze soil erosion and deposition and eroded particle transport. In this study, the physics-based Surface Soil Erosion Model (SSEM) was upgraded based on field survey information to produce sediment yield at the watershed scale. The modified model (hereafter MoSE) adopted new algorithms on rainfall kinematic energy and surface flow transport capacity to simulate soil erosion more reliably. For model validation, we applied the model to the Doam dam watershed in Gangwon-do and compared the simulation results with the USLE outputs. The results showed that the revised physics-based soil erosion model provided more improved and reliable simulation results than the USLE in terms of the spatial distribution of soil erosion and deposition.

• Journal of Korea Water Resources Association
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• v.46 no.9
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• pp.885-896
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• 2013

SEMMA (Soil Erosion Model for Mountain Areas) should be revised to apply on mountain watershed of large scale. In this study, the basic structure of original SEMMA and methods to calculate main parameters are reviewed and the revised parameters are presented to expand a range of application. SEMMA-Ic is new model revised by a rate of vegetation cover which is substituted for index of vegetation structure to use specially NDVI for large scale areas. The correlation coefficient and the Nash-Sutcliffe simulation efficiency for the revised model decreased rather than those of original model. However the evaluation of the revised model on watershed showed the approximate simulation with measured sediment yield and the underestimated simulation when sediment yield is large. The additional research for channel erosion is needed so that soil erosion model for hillslopes is used to estimate sediment yield from a watershed.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.357-360
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• 2005

Soil loss is widely recognized as a threat to farm livelihoods and ecosystem integrity worldwide. Soil loss prediction models can help address long-range land management planning under natural and agricultural conditions. Even though it is hard to find a model that considers all forms of erosion, some models were developed specifically to aid conservation planners in identifying areas where introducing soil conservation measures will have the most impact on reducing soil loss. Revised Universal Soil Loss Equation (RUSLE) computes the average annual erosion expected on hillslopes by multiplying several factors together: rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and support practice (P). The value of these factors is determined from field and laboratory experiments. This study calculated soil erosion using GIS-based RUSLE model in Imha basin and examined soil erosion source area using SPOT 5 high-resolution satellite image and land cover map. As a result of analysis, dry field showed high-density soil erosion area and we could easily investigate source area using satellite image. Also we could examine the suitability of soil erosion area applying field survey method in common areas (dry field & orchard area) that are difficult to confirm soil erosion source area using satellite image.

• 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.

• Spatial Information Research
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• v.14 no.4 s.39
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• pp.363-377
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• 2006

Mean annual soil loss was calculated and critical soil erosion areas were identified for the Congaree River Basin in South Carolina, USA using the Revised Universal Soil Loss Equation (RUSLE) model. In the RUSLE model, the mean annual soil loss (A) can be calculated by multiplying rainfall-runoff erosivity (R), soil erodibility (K), slope length and steepness (LS), crop-management (C), and support practice (P) factors. The critical soil erosion areas can be identified as the areas with soil loss amounts (A) greater than the soil loss tolerance (T) factor More than 10% of the total area was identified as a critical soil erosion area. Among seven subwatersheds within the Congaree River Basin, the urban areas of the Congaree Creek and the Gills Creek subwatersheds as well as the agricultural area of the Cedar Creek subwatershed appeared to be exposed to the risk of severe soil loss. As a prototype model for examining future effect of human and/or nature-induced changes on soil erosion, the RUSLE model customized for the area was embedded into ESRI ArcGIS ArcMap 9.0 using Visual Basic for Applications. Using the embedded model, users can modify C, LS, and P-factor values for each subwatershed by changing conditions such as land cover, canopy type, ground cover type, slope, type of agriculture, and agricultural practice types. The result mean annual soil loss and critical soil erosion areas can be compared to the ones with existing conditions and used for further soil loss management for the area.

• Journal of Soil and Groundwater Environment
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• v.17 no.6
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• pp.119-128
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• 2012

The applicability and accuracy of Revised Universal Soil Loss Equation (RUSLE) model on the estimation of soil loss at impacted area of shooting range was tested to further the understanding of soil erosion at shooting ranges by using RUSLE. At a shooting range located in northern Kyunggi, the amount of soil loss was estimated by RUSLE model and compared with that estimated by Global Positioning System-Total Station survey. As results, the annual soil loss at a study site (202 m long by 79 m wide) was estimated to be 2,915 ton/ha/year by RUSLE and 3,058 ton/ha/year by GPS-TS survey, respectively. The error between two different estimations was less than 5%, however, information on site conditions should be collected more to adjust model coefficients accurately. At the study shooting range, sediments generated by rainfall was transported from the top to near the bottom of the sloping face through sheet erosion as well as rill erosion, forming a gully along the direction of the storm water flow. Coarser fractions of the sediments were redeposited in the limited area along the channel. Distribution characteristics of explosive compounds in soil before and after summer monsoon rainfall in the study area were compared with the erosion patterns. Soil sampling and analyses results showed that the dispersion of explosive compounds in surface soil was consistent with the characteristics of soil erosion and redeposition pattern of sediment movements after rainfalls.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.1
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• pp.27-37
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• 2001

Soil erosion by outflow of water or rainfall has caused many environmental problems as declining agricultural productivity, damaging pasture and preventing flow of water. As the interest in environment is increasing lately, soil erosion is considered as a serious problem, whereas the systematic regulation and analysis for that have not established yet. This research shows the method of extracting factor entered model which expects soil erosion by GSIS. There are several erosion model such as ANSWER, WEPP, RUSLE. The research used RUSLE erosion model which could expect general soil erosion connected easily with GSIS data. RUSLE's input factors are composed of rainfall runoff factor(R). soil erodibility factor(K), slope length factor(L), slope steepness factor(S), cover management factor(C) and support practice factor(P). The general equation used to extract L, S factor on the RUSLE to be oriented for agricultural area has some limitation to apply whole watershed. So, on this study we used a revised empirical equation applicable to the watershed by grid on the GSIS. Also, we analyzed RUSLE factors by watershed being analyzed with watershed extraction algorithm. Then we could calculate the minimum, maximum. mean and standard deviation of RUSLE factors by watershed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2D
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• pp.341-347
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• 2006

Geographically Imha basin is adjacent to Andong basin, but the occurrence of turbid water in each reservoir by storm events shows big differences. Hence, it is very important to identify the reason for these large differences. This study compared and analyzed soil erosion using the semi-empirical soil erosion model, RUSLE for both Imha and Andong basin, especially with emphasis on high-density turbid water. The agricultural district, which is the most vulnerable to soil erosion, was intensively analyzed based on land cover map produced by Ministry of Environment. As a result, the portion of the agricultural area is 11.88% for Andong basin, while it is 14.95% for Imha basin. Also all RUSLE factors excepts practice factor turned out to be higher for Imha basin. This means that the basin characteristics such as soil texture, terrain, and land cover for Imha basin is more vulnerable to soil erosion. Estimation of soil erosion by RUSLE for Andong and Imha basin is 1,275,806 ton and 1,501,608 ton, respectively, showing higher soil erosion by 225,802 ton for Imha basin.