• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.450-450
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• 2021

기후변화 보고서에 따르면 집중 호우의 강도 및 빈도 증가가 향후 몇 년동안 지속될 것이라 제시하였다. 이러한 집중호우가 빈번히 발생하게 된다면 강우 침식성이 증가하여 표토 침식에 더 취약하게 발생된다. Universal Soil Loss Equation (USLE) 입력 매개 변수 중 하나인 강우침식능인자는 토양 유실을 예측할때 강우 강도의 미치는 영향을 제시하는 인자이다. 선행 연구에서 USLE 방법을 사용하여 강우침식능인자를 산정하였지만, 60분 단위 강우자료를 이용하였기 때문에 정확한 30분 최대 강우강도 산정을 고려하지 못하는 한계점이 있다. 본 연구의 목적은 강우침식능인자를 이전의 진행된 방법보다 더 빠르고 정확하게 예측하는 머신러닝 모델을 개발하며, 총 월별 강우량, 최대 일 강우량 및 최대 시간별 강우량 데이터만 있어도 산정이 가능하도록 하였다. 이를 위해 본 연구에서는 강우침식능인자의 산정 값의 정확도를 높이기 위해 1분 간격 강우 데이터를 사용하며, 최근 강우 패턴을 반영하기 위해서 2013-2019년 자료로 이용했다. 우선, 월별 특성을 파악하기 위해 USLE 계산 방법을 사용하여 월별 강우침식능인자를 산정하였고, 국내 50개 지점을 대상으로 계산된 월별 강우침식능인자를 실측 값으로 정하여, 머신러닝 모델을 통하여 강우침식능인자 예측하도록 학습시켜 분석하였다. 이 연구에 사용된 머신러닝 모델들은 Decision Tree, Random Forest, K-Nearest Neighbors, Gradient Boosting, eXtreme Gradient Boost 및 Deep Neural Network을 이용하였다. 또한, 교차 검증을 통해서 모델 중 Deep Neural Network이 강우침식능인자 예측 정확도가 가장 높게 산정하였다. Deep Neural Network은 Nash-Sutcliffe Efficiency (NSE) 와 Coefficient of determination (R²)의 결과값이 0.87로서 모델의 예측성을 입증하였으며, 검증 모델을 테스트 하기 위해 국내 6개 지점을 무작위로 선별하여 강우침식능인자를 분석하였다. 본 연구 결과에서 나온 Deep Neural Network을 이용하면, 훨씬 적은 노력과 시간으로 원하는 지점에서 월별 강우침식능인자를 예측할 수 있으며, 한국 강우 패턴을 효율적으로 분석 할 수 있을 것이라 판단된다. 이를 통해 향후 토양 침식 위험을 지표화하는 것뿐만 아니라 토양 보전 계획을 수립할 수 있으며, 위험 지역을 우선적으로 선별하고 제시하는데 유용하게 사용 될 것이라 사료된다.

• Journal of Korean Society on Water Environment
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• v.26 no.5
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• pp.841-849
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• 2010

Soil erosion is an natural phenomenon. However accelerated soil erosion has caused many environmental problems. To reduce soil loss from a watershed, many management practices have been proposed worldwide. To develop proper and efficient soil erosion best management practices, soil erosion rates should be estimated spatially and temporarily. The Universal Soil Loss Equation (USLE) and USLE-based soil erosion and sediment modelling systems have been developed and tested in many countries. The Sediment Assessment Tool for Effective Erosion Control (SATEEC) system has been developed and enhanced to provide ease-of-use interface to the USLE users. However many researchers and decision makers have requested to enhance the SATEEC system for simulation of soil erosion and sediment reflecting effects of single storm event. Thus, the SATEEC R factors were estimated based on 5 day antecedent rainfall data. The SATEEC 2.1 daily R factor was applied to the study watershed and it was found that the R2 and EI values (0.776 and 0.776 for calibration and 0.927 and 0.911 for validation) with the daily R were greater than those (0.721 and 0.720 for calibration and 0.906 and 0.881 for validation) with monthly R, which was available in the SATEEC 2.0 system. As shown in this study, the SATEEC with daily R can be used to estimate soil erosion and sediment yield at a watershed scale with higher accuracy. Thus the SATEEC with daily R can be efficiently used to develop site-specific soil erosion best management practices based on spatial and temporal analysis of soil erosion and sediment yield at a daily-time step, which was not possible with USLE-based soil erosion modeling system.

• 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 the Korean Association of Geographic Information Studies
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• v.10 no.4
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• pp.122-131
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• 2007

This study estimates how soil loss in a basin has been occurred according to the change of land cover, and analyzes which type of land cover has the largest soil loss by classifying the land-cover type into each area and a whole basin. Musimcheon, the second branch stream of GeumGang, is chosen as a research area. The result of analysis shows that the average soil loss occurs most largely in a crop land and a paddy field. The yearly soil loss of watershed estimates approximately 14,000 ton/yr in case of using 100-year-frequency rainfall data. A forest area, which takes the largest area in watershed, shows the soil loss occurs approximately 1,000ton/yr. A crop field shows that soil loss increased most largely 4,900 ton/yr (34.6%) in 1985 to 8,100 ton/yr (56.1%) in 2000. The change of land cover in a crop land increased 8% to 14%, and this change influences on the increase of soil loss. As a result of analyzing the area over $200ton/km^2/yr$, the soil loss in a crop field accounts for 74% to 96%.

• Spatial Information Research
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• v.17 no.3
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• pp.261-268
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• 2009

This study accomplished to draw a soil erosion map and a grade map of soil loss hazard in Korea. RUSLE and Rainfall-runoff (R) factor, which was estimated by using the rainfall data observed in 59 meteorological stations from 1977 to 2006 (for 30 years). FARD was used to analyze the frequency, and the whole country R factor was estimated according to the frequency. In the analysis of estimating the whole country R factor, Nakdong river has the smallest vaule, but Han river has the biggest value. According to the result of analyzing soil loss, soil loss occurred in a grass land, a bare land and a field in size order, and also approximately 17.2 ton/ha soil loss happened on the whole area. The average soil loss amount by the unit area takes place in a bare land and a grass land a lot. The total amount of soil loss in 5-year-frequency rainfall yields 15,000 ton and, what is more, a lot of soil loss happens in a paddy field, a forest and a crop field. The grade map of soil loss hazard is drawn up by classifying soil loss hazard grade by 5. As a result of analyzing soil loss, the moderate area which is the soil loss hazard grade 2 takes up the largest part, 72.8% of the total soil loss hazard area, on the contrary, the severe soil loss hazard area takes up only $1,038km^2$ (1.1%) of the whole area. The severe soil loss hazard area by land cover shows $93.5km^2$ in a bare land, $168.1km^2$ in a grass land and $327.4km^2$ in a crop field respectively.

• Journal of Korea Water Resources Association
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• v.55 no.4
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• pp.291-300
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• 2022

Artificial disturbance in mountainous areas increases the sensitivity to erosion by exposure of the subsoil with a low loam ratio to the surface. In this study, rainfall simulations were conducted to evaluate the erodibility of sand and loamy sand in the interrill erosion by the rainfall-induced sheet flow. The mean diameters of sand and loamy sand used in the experiment were 0.936 mm and 0.611 mm, respectively, and the organic matter content was 2.0% and 4.2%, respectively. In the experimental plot, the runoff coefficient of overland flow increased 1.16 times in loamy sand rather than sand. Mean sediment yields of loamy sand and sand by sheet erosion were 3.71kg/m²/hr and 1.13kg/m²/hr respectively. The erodibility, the rate of soil erosion for rainfall erosivity factor, was 3.65 times greater in loamy sand than in sand. As the gradient of the steep slope increased from 24° to 28°, the sediment concentration and the erodibility for two soils increased by about 20%. The erodibility factor K of sandy soils for small plots was overestimated compared to the measured erodibility. This means that RUSLE can overestimate the sediment yields by sheet erosion on sandy soils.

• Korean Journal of Soil Science and Fertilizer
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• v.37 no.4
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• pp.199-206
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• 2004

Factors of universal soil loss equation, USLE, and its revised version, RUSLE for Korean soils were reevaluated to estimate the national scale of soil loss based on digital soil maps. Rainfall erosivity factor, R, of 158 locations of cities and counties were spacially interpolated by the inverse distance weight method. Soil erodibility factor, K, of 1321 soil phases of 390 soil series were calculated using the data of soil survey and agri-environmental quality monitoring. Topographic factor, LS, was estimated using soil map of 1:25,000 scale with soil phase and land use type. Cover management factor, C, of major crops and support practice factor, P, were summarized by analyzing the data of lysimeter and field experiments for 27 years (1975-2001) in the National Institute of Agricultural Science and Technology. R factor varied between 2322 and 6408 MJ mm $ha^{-1}$ $yr^{-1}$ $hr^{-1}$ and the average value was 4276 MJ mm $ha^{-1}$ $yr^{-1}$ $hr^{-1}$. The average K value was evaluated as 0.027 MT hr $MJ^{-1}$ $mm^{-1}$. The highest K factor was found in paddy rice fields, 0.034 MT hr $MJ^{-1}$ $mm^{-1}$, and K factors in upland fields, grassland, and forest were 0.026, 0.019, and 0.020 MT hr $MJ^{-1}$ $mm^{-1}$, respectively. C factors of upland crops ranged from 0.06 to 0.45 and that of grassland was 0.003. P factor varied between 0.01 and 0.85.

• Korean Journal of Soil Science and Fertilizer
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• v.38 no.2
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• pp.59-65
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• 2005

This study was performed to estimate the soil loss on a national scale and grade regions with the potential risk of soil erosion. Universal soil loss equation (USLE) for rainfall and runoff erosivity factors (R), cover management factors (C) and support practice factors (P) and revised USLE for soil erodibility factors (K) and topographic factors (LS) were used. To estimate the soil loss, the whole nation was divided into 21,337 groups according to city county, soil phase and land use type. The R factors were high in the southern coast of Gyeongnam and Jeonnam and part of the western coast of Gyeonggi and low in the inland and eastern coast of Gyeongbuk. The K factors were higher in the regions located on the lower streams of rivers and the plain lands of the western coast of Chungnam and Jeonbuk. The average slope of upland areas in Pyeongchang-gun was the steepest of 30.1%. The foot-slope areas from the Taebaek Mountains to the Sobaek Mountains had steep uplands. Total soil loss of Korea was estimated as $50{\times}10^6Mg$ in 2004. The potential risk of soil erosion in upland was the severest in Gyeongnam and the amount of soil erosion was the greatest in Jeonnam. The regions in which annual soil loss was estimated over $50Mg\;ha^{-1}$ were graded as "the very severe" and their acreage was $168{\times}10^3ha$ in 2004. The soil erosion maps of city/county of Korea were made based on digital soil maps with 1:25,000 scale.

• 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.