• 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.

• Magazine of the Korean Society of Agricultural Engineers
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• v.31 no.4
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• pp.72-80
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• 1989

Various analyses were made to investigate the stochastic structure of the daily rainfall in Korea. Records of daily rainfall amounts from 1951 to 1984 at Chinju Metesrological Station were used for this study. Obtained results are as follows : 1. Time series of the daily rainfall at Chinju were positively, serially correlated for the lag as large as one day. 2. Rainfall events, defined as a sequence of consecutive wet days separated by one or more dry days, showed a seasonal variation in the occurrence frequency. 3. The marginal distribution of event characteristics of each month showed significant dif- ferences each other. Events occurred in summer had longer duration and higher magnitude with higher intensity than those of events occurred in winter. 4. There were significant positive correlations among four event characteristics ; dura- tion, magnitude, average intensity, and maximum intensity. 5. Correlations among the daily rainfall amounts within an event were not significant in general. 6. There were no consistant significancy in identity or difference between the distribu- tions of daily rainfall amounts for different days within events. 7. Above mentioned characteristics of daily rainfall time series must be considered in building a stochastic model of daily rainfall.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2B
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• pp.145-152
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• 2006

In the present study, a nonlinear model of rainfall-runoff process using Artficial Neural networks(ANNs) which have no consideration on the physical parameter for the basin was developed at Naju station which is the main stream of Yeongsan-river, and Sunam station which is the main stream of Hwangryong-river. The result from the model of ANN_NJ_9 at the Naju station revealed the best result of the rainfall-runoff process, while the model of ANN_SA_9 for the Sunam station. Also, GUI_FFS developed in the research showed the $R^2$ of more than 0.98 between the observed and predicted values using the rainfall and runoff in the respective stations. Therefore, the GUI_FFS might be expected that it can play a role for the high reliability to operate and manage the water resources and the design of river plan more efficiently in the future.

• Journal of the Korean Society for Railway
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• v.8 no.4
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• pp.308-313
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• 2005

In order to successfully propel the stormwater management program, administration plan of stormwater management is enacted in Spring, 2005. Hereafter, in a newly developing area such as a railway station, the best management practices should be established to control the nonpoint pollutant. Construction of a railway station means the increasing of imperviousness rate and lots of nonpoint pollutant emissions during rainfall. Therefore this research was performed to determine the pollutant EMC and to suggest the possible best management practice for treating nonpoint pollutants from a railway station. The $95\%$ confidence intervals of pollutant EMC were ranged to 69.4-115.2mg/L for TSS, 132.7-190.4mg/L for COD, 5.4-15.1ug/L for Oil & Grease, 4.9-12.4mg/L for TKN and 568.4-620.1ug/L for TP. The first flush criteria was ranged to 5-10mm accumulated rainfall using dynamic EMCs, Also laboratory reactor testing was performed. It shows that Zeolite media is useful for removing the washed-off pollutants from a railway station, especially for metal ions.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.2
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• pp.55-63
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• 2018

This study analyzed the applications of near real-time drought monitoring using satellite rainfall for the Korean Peninsula and un-gaged basins. We used AWS data of Yongdam-Dam, Hoengseong-Dam in Korea area, the meteorological station of Nakhon Rachasima, Pak chong for test-bed to evaluate the validation and the opportunity for un-gaged basins. In addition, we calculated EDI (Effective doought index) using the stations and co-located PERSIANN-CDR, TRMM (Tropical Rainfall Measurement Mission) TMPA (The TRMM Multisatellite Precipitation Analysis), GPM IMERG (the integrated Multi-satellitE Retrievals for GPM) rainfall data and compared the EDI-based station data with satellite data for applications of drought monitoring. The results showed that the correlation coefficient and the determination coefficient were 0.830 and 0.914 in Yongdam-dam, and 0.689 and 0.835 in Hoengseng-Dam respectively. Also, the correlation coefficient were 0.830, 0.914 from TRMM TMPA datasets and compasion with 0.660, 0.660 based on PERSIANN-CDR and TRMM data in nakhon and pakchong station. Our results were confirmed possibility of near real-time drought monitoring using EDI with daily satellite rainfall for un-gaged basins.

• Korean Journal of Hydrosciences
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• v.4
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• pp.105-116
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• 1993

Areal Reduction Factor(ARF) has been developed and used to convert point Rainfall intensity-Duration-Frequency(I-D-F) to areal I-D-F in many countries. In Korea, though ARF was estimated in Han river basin by several researchers, it has some limitations to apply to other regions due to low denisity of rainfall gauging station and shortage of data. In this study ARF has been developed in area of relatively high density of rainfall gauging station, i.e., Pyungchang river(Han river), Wi stream(Nakdong river), and Bochung stream(Guem river) basin by geographically fixed-area method. And coefficient of variation of mean annual precipitation was presented to use ARE in other areas and its applicability was analyzed.

• Journal of Korea Water Resources Association
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• v.33 no.5
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• pp.569-580
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• 2000

To account for the influence of heavy storm in Korea by climate change like global warming, the frequency analyses for annual maximum rainfall sequence in 12 rainfall gauge stations are carried out. In order to analyze the temporal change, the rainfall quantile of each station is estimated by the 30-yr data period being moved from 1954 to 1998 with 1-yr lag. Through the analysis for l00-yr rainfall quantile it has been shown that the recent heavy storms increase comparing with storms in the past. From the additional estimating of the rainfall quantile of each station by the 30-yr data period being cumulated from 1954 to 1998 with 1-yr, the change of the probable rainfall by including the heavy storm duration is realized. When the hydraulic structures are determined, it is important to select the data size and necessary to reestimate the flood prevention capacity in existing river systems.ystems.

• Journal of Wetlands Research
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• v.8 no.2
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• pp.75-81
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• 2006

In this study, runoff simulation was carried out in order to derive operational improvement of small urban storm water pumping station under heavy storm rainfall conditions. The flood inflow hydrograph of Guri city heavy storm in July, 2001 was successfully simulated by HEC-HMS, a GIS-based runoff simulation model. For the runoff simulation, ArcView, as an effective GIS tool, was used to provide input data of the model such as land use data, soil distribution data and SCS runoff curve number.

• Journal of Korean Society on Water Environment
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• v.23 no.4
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• pp.543-550
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• 2007

In this study, combined sewer overflows (CSOs) from five independent rainfall events in rural city area were collected and investigated. First flush effect in sewage pumping station located near the WWTP was retarded 30 to 60 minutes from booster pumping station. The ratios between SS, COD and TP concentrations prior to rainfall and peak concentrations during the period of rainfall were highly increased but nitrogen was relatively constant, which indicates that it is not associated with particles washed off from the surface of watershed. Mass balance results show that 30% of CSO was generated from booster pump station and 66.5% of CSO was from the whole runoff area. In the area of newly constructed sewer system, CSO problem was related with pump and sewer capacities, but in other old sewer system equipped area, it was due to the collection efficiency. Finally, Log-Log pollutant rating equations were suggested.

• Journal of Wetlands Research
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• v.20 no.1
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• pp.94-104
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• 2018

In general, the rainfall-runoff simulation is performed using rainfall data from meteorological and observational rain gauge stations. However, if we only use rainfall data from meteorological and observational rain gauge stations for runoff simulation of a large watershed, the problem in the reliability of the simulated runoff can be occurred. Therefore, this study examined the influence of the rainfall data on the simulated runoff volume by a Semi-distributed model. For this, we used rainfall data from meteorological stations, meteorological and observational stations, and a spatially distributed rainfall data from hypothetical stations obtained by kriging method. And, we estimated the areal rainfall of each sub-basin. Also the estimated areal rainfall and the observed rainfall were compared and we compared the simulated runoff volumes using SWAT model by the rainfall data from meteorological and observational rain gauge stations and runoff volume from the estimated areal rainfall by Kriging method were analyzed. This study was performed to examine the accuracy of calculated runoff volume by spatially distributed areal rainfall. The analysis result of this study showed that runoff volume using areal rainfall is similar to observed runoff volume than runoff volume using the rainfall data of weather and rain gauging station. this means that spatially distributed rainfall reflect the real rainfall pattern.