• 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.

• 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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• 2021.06a
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• pp.150-150
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• 2021

Soil erosion due to climate change is one of the global environmental issues. Especially, Korea is vulnerable to soil erosion as the frequency of extreme rainfall events and rainfall intensity are increasing. Soil erosion causes various problems such as reduced farmlands, deterioration of water quality in rivers, etc. To these severe problems, understanding the process of soil erosion is the first process. Then, it is necessary to quantify and analyze soil ersoion using an erosion model. Soil erosion models are divided into empirical, conceptual, and physics-based models according to the structures and characteristics of models. This study used GSSHA (Gridded Surface Subsurface Hydrologic Analysis), the physics-based erosion model, running on WMS (Watershed Modeling System) to analyze soil erosion vulnerability of the CheonCheon watershed. In addition, we compared the six sediment transport capacity formulas provided in the model and evaluated the equations fir on this study site. Therefore, this result can be as a primary tool for soil conservation management.

• Korean Journal of Environmental Agriculture
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• v.27 no.4
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• pp.314-320
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• 2008

During the summer season, more than half of the annual precipitation in Korea occurs during the summer season due to the geographical location in the Asian monsoon belt. So, this causes severe soil erosion from croplands, which is directly linked to the deterioration of crop/land productivity and surface water quality. Therefore, much attention has been given to develop accurate estimation tools of soil erosion. The aim of this study is to assess the performance of using the empirical Universal Soil Loss Equation (USLE) and the physical-based model of the Water Erosion Prediction Project (WEPP) to quantify eroded amount of soil from agricultural fields. Input data files, including climate, soil, slope, and cropping management, were modified to fit into Korean conditions. Chuncheon (forest) and Jeonju (level-plain) were selected as two Korean cities with different topographic characteristics for model analysis. The results of this current study indicated that better soil erosion prediction can be achieved using the WEPP model since it has better power to illustrate a higher degree of spatial variability than USLE in topography, precipitation, soils, and crop management practices. These present findings are expected to contribute to the development of the environmental assessment program as well as the conservation of the agricultural environment in Korea.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.3
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• pp.77-85
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• 2007

Assessment of soil erosion is a cost and time-consuming task. There are many models developed to predict soil erosion from an area, but Universal Soil Loss Equation (USLE) is most widely used empirical equation for estimating annual soil erosion. Soil erosion depends upon-rainfall intensity, type of soil, land cover and land use, slope degree, slope length and soil conservation practice. All these parameters are have spatial distribution and hence satellite remote sensing and Geographic Information System (GIS) are applicable in the assessment of the influence on soil erosion. GIS has been integrated with the USLE (Universal Soil Loss Equation) model in identification of rainfall-based erosion to the Bocheong River which is the representative basin of IHP due to Typhoon Rusa. Similar studies are available in literature, ranging from those that use a simple model such as USLE to others of a more sophisticated nature.

• Journal of Soil and Groundwater Environment
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• v.22 no.6
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• pp.66-73
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• 2017

Empirical erosion models like Universal Soil Loss Equation (USLE) models have been widely used to make spatially distributed soil erosion vulnerability maps. Even if the models detect vulnerable sites relatively well 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 are still powerful tools to distinguish the erosion-prone areas at large scale, but physics-based models are necessary to better analyze soil erosion and deposition as well as the eroded particle transport. In this study a physics-based soil erosion modeling system was developed to produce both runoff and sediment yield time series at watershed scale and reflect them in the erosion and deposition maps. The developed modeling system consists of 3 sub-systems: rainfall pre-processor, geography pre-processor, and main modeling processor. For modeling system validation, we applied the system for various erosion cases, in particular, rainfall-runoff-sediment yield simulation and estimation of probable maximum sediment (PMS) correlated with probable maximum rainfall (PMP). The system provided acceptable performances of both applications.

• 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.

• 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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• 2010.05a
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• pp.774-778
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• 2010

Upland erosion pollutes surface waters and often causes serious problems when deposition occurs. This study builds a sediment rating curve using the measured sediment yield and the simulated soil erosion by a GIS-embedded empirical model. The coefficient of determination ($R^2$) between the simulated soil erosion and the measurement sediment yields with rainfall amount are 0.427 for Donghyang and 0.667 for Cheonchen, but the values with rainfall intensity are 0.873 and 0.927 respectively. The data are divided into two groups: one for calibration during 2002-2005 (48 months) and the other for estimation during 2006-2008 (36 months). The first data group (2002-2005) was used to derive the SDR with an aid of soil erosion calculated by the USLE and the measured sediment yield. The mean SDR with rainfall amount is 6.273 and 3.353, respectively, while 4.799 and 2.874 for rainfall intensity. But the standard deviation (STD) with rainfall intensity is 0.930 and 0.407, which is much less than that with rainfall amount (3.746 and 2.090) for both sites. The results show the derived SDR provides reasonable accuracy and rainfall intensity gives better performance in calculating soil erosion than rainfall amount.

• 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.