• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.3
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• pp.29-40
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• 2003

The objective of the paper is to compute the half-month rainfall-runoff erosivity factor for revised universal soil loss equation (RUSLE). RUSLE is being used to develop soil conservation programs and identify optimum management practices. Rainfall-runoff erosivity factor (R) is a key input parameter to RUSLE. Rainfall-runoff erosivity factor has been calculated for twenty six stations from the nationwide rainfall data from 1973 to 2002 in south Korea. The average annual Rainfall-runoff erosivity factor at the analyzed stations Is between 3,130 and 10,476 (MJ/ha)ㆍ(mm/h). According to the computation of the half-month Rainfall-runoff erosivity factor for locations, 66-85% of the average annual R value has occurred during the summer months, June-August. The half-month R values from this study can be used for RUSLE.

• 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.

• Korean Journal of Soil Science and Fertilizer
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• v.44 no.6
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• pp.1004-1009
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• 2011

Soil erosion in North Korea has been continued to accelerate by deterioration of topographical conditions. However, few studies have been conducted to predict the amount of soil loss in North Korea due to limited data so far. Rainfall erosivity is an important factor to predict the amount of long-term annual soil loss by USLE (universal soil loss equation). The purpose of this study is to investigate rainfall erosivity, which presented the potential risk of soil erosion by water, in North Korea. Annual rainfall erosivities for 27 stations in North Korea for 1983~2010 were calculated using regression models based on modified Institute of Agricultural Sciences (IAS) index in this study. The result showed that annual average rainfall erosivity in North Korea ranged from 2,249 to 7,526 and averaged value was $4,947MJmm\;ha^{-1}\;hr^{-1}\;yr^{-1}$, which corresponded to about 70% of annual average rainfall erosivity in South Korea. The finding was that the potential risk of soil erosion in North Korea has been accelerated by the increase of rainfall erosivity since the late 1990s.

• 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.

• 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.

• 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.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.13-20
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• 2018

Universal Soil Loss Equation (USLE) has been widely used to estimate potential soil loss because USLE is a simple and reliable method. The rainfall erosivity factor (R factor) explains rainfall characteristics. R factors, cited in the Bulletin on the Survey of the Erosion of Topsoil of the Ministry of Environment in the Republic of Korea, are too outdated to represent current rainfall patterns in the Republic of Korea. Rainfall datasets at one minute intervals from 2013 to 2017 were collected from fifty rainfall gauge stations to update R factors considering current rainfall condition. The updated R factors in this study were compared to the previous R factors which were calculated using the data from 1973 to 1996. The coefficient of determination between the updated and the previous R factors shows 0.374, which means the correlation is not significant. Therefore, it was concluded that the previous R factors might not explain current rainfall conditions. The other remarkable result was that regression equations using annual rainfall data might be inappropriate to estimate reasonable R factors because the correlation between annual rainfall and the R factors was generally unsatisfy.

• Journal of Korea Water Resources Association
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• v.33 no.5
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• pp.603-610
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• 2000

Climate factors such as rainfall, temperature, wind, humidity, and solar radiant heat affect soil erosion. Among those factors, rainfall influences soil erosion to the most extent. The kinetic energy of rainfall breaks away soil particles and the water flow caused by the rainfall entrains and transport them downstream. In order to estimate soil erosion, therefore, it is important to determine the rainfall erosivity. In this study, the annual average Rainfall Erosivity(R) in Korea, an important factor of the Universal Soil Loss Equation(USLE) and Revised Equation(RUSLE), has been estimated using the nationwide rainfall data from 1973 to 1996. For this estimation, hourly rainfall data at 53 meterological stations managed by the Meterological Agency was used. It has been found from this study that the newly computed values for R are slightly larger than the existing ones. It would be because this study is based on the range of rainfall data that is longer in period and denser in the number of gauging stations than what the existing result used. The final result of this study is shown in the form the isoerodent map of Korea.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.130-145
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• 2016

Soil erosion processes are affected by weather factors, such as rainfall, temperature, wind, and humidity. Among these factors, rainfall directly influences soil erosion by breaking away soil particles. The kinetic energy of rainfall and water flow caused by rain entrains and transports soil particles downstream. Therefore, in order to estimate soil erosion, it is important to accurately determine the rainfall erosivity factor(R) in RUSLE(Revised Universal Soil Loss Equation). The objective of this study is to evaluate the average annual R using 14 years(2002~2015) of 1 minute rainfall data from 55 KMA(Korea Meteorological Administration) weather stations. The R results from 1 min rainfall were compared with previous R studies using 1 h rainfall data. The determination coefficients($R^2$) between R calculated using 1 min rainfall data and annual rainfall were 0.70-0.98. The estimation of 30 min rainfall intensity from 1 min rainfall data showed better $R^2$ results than results from 1 h rainfall data. For estimation of physical spatial rain erosivity(R), distribution of annual rainfall was estimated by IDW(Inverse Distance Weights) interpolation, taking elevation into consideration. Because of the computation burden, the R calculation process was programmed using the python GUI(Graphical User Interface) tool.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.2
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• pp.47-56
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• 2004

The purpose of this study is to estimate soil loss amount according to the rainfall-runoff erosivity factor frequency and to analyze the hazard zone that has high possibilities of soil erosion in the watershed. RUSLE was used to analyze soil loss quantity. The study area is Gwanchon that is part of Seomjin river basin. To obtain the frequency rainfall-runoff erosivity factor, the daily maximum rainfall data for 39 years was used. The probability rainfall was calculated by using the Normal distribution, Log-normal distribution, Pearson type III distribution, Log-Pearson type III distribution and Extreme-I distribution. Log-Pearson type III was considered to be the most accurate of all, and used to estimate 24 hours probabilistic rainfall, and the rainfall-runoff erosivity factor by frequency was estimated by adapting the Huff distribution ratio. As a result of estimating soil erosion quantity, the average soil quantity shows 12.8 and $68.0ton/ha{\cdot}yr$, respectively from 2 years to 200 years frequency. The distribution of soil loss quantity within a watershed was classified into 4 classes, and the hazard zone that has high possibilities of soil erosion was analyzed on the basis of these 4 classes. The hazard zone represents class IV. The land use area of class IV shows $0.01-5.28km^2$, it ranges 0.02-9.06% of total farming area. Especially, in the case of a frequency of 200 years, the field area occupies 77.1% of total fanning area. Accordingly, it is considered that soil loss can be influenced by land cover and cultivation practices.