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Spatiotemporal Uncertainty of Rainfall Erosivity Factor Estimated Using Different Methodologies

적용 기법에 따른 강우침식인자 산정 결과의 시공간적 불확실성

  • Hwang, Syewoon (Department of Agricultural Engineering (Institute of Agriculture & Life Sciences), Gyeongsang National University) ;
  • Kim, Dong-Hyeon (National Academy of Agricultural Science, Rural Development Administration) ;
  • Shin, Sangmin (Department of Agricultural Engineering (Institute of Agriculture & Life Sciences), Gyeongsang National University) ;
  • Yoo, Seung-Hwan (Department of Rural and Bio-Systems Engineering, Chonnam National University)
  • Received : 2016.09.28
  • Accepted : 2016.10.06
  • Published : 2016.11.30

Abstract

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.

Keywords

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