• Journal of Wetlands Research
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• v.19 no.1
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• pp.30-36
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• 2017

SS(Suspended Solid) concentration by soil erosion into river at normal and flood season should be measured. However, to present the variation of SS due to various development project such as EIA(Environmental Impact Assessment), River Master Plan, and so on, it is necessary to estimate not measure SS, but there are not exist how to estimate SS. In the present study, therefore, we propose the hydrologic method of estimating SS concentration using the results of particular frequency flood discharge and sediment discharge by RUSLE method. SS consists of silty and clay soil and colloid particle etc. However, in the present study, silty and clay soils of sediment discharge except send set up SS standards. The flow discharge to estimate SS concentration are 1~2 years for normal season, 30~100 years for flood season. Meanwhile, analysis software for probable rainfall uses Fard2006, probable rainfalls under 2-year frequency are estimated using rainfall data and frequency factor of Gumbel distribution. The results of estimating SS concentration using runoff volume by sediment and flow discharges of silty and cray soils as above method show that reliable level of SS concentration is considered in predevelopment of natural condition and under development of barren condition. Especially, SS concentration takes notice that the value of sediment discharge makes a huge difference according to channel slope, it was confirmed that the value obtained by dividing the SS concentration by the channel slope is relatively constant even though the topographical factors are different. Therefore, if the present study will be proceeded for various watersheds, it will be developed as estimation method of SS concentration.

• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.905-916
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• 2018

The purpose of this study is to evaluate the climate change impact on watershed hydrology and flow duration in Geum River basin ($9,645.5km^2$) especially by extreme scenarios. The rainfall related extreme index, STARDEX (STAtistical and Regional dynamical Downscaling of EXtremes) was adopted to select the future extreme scenario from the 10 GCMs with RCP 8.5 scenarios by four projection periods (Historical: 1975~2005, 2020s: 2011~2040, 2050s: 2041~2070, 2080s: 2071~2100). As a result, the 5 scenarios of wet (CESM1-BGC and HadGEM2-ES), normal (MPI-ESM-MR), and dry (INM-CM4 and FGOALS-s2) were selected and applied to SWAT (Soil and Water Assessment Tool) hydrological model. The wet scenarios showed big differences comparing with the normal scenario in 2080s period. The 2080s evapotranspiration (ET) of wet scenarios varied from -3.2 to +3.1 mm, the 2080s total runoff (TR) varied from +5.5 to +128.4 mm. The dry scenarios showed big differences comparing with the normal scenario in 2020s period. The 2020s ET for dry scenarios varied from -16.8 to -13.3 mm and the TR varied from -264.0 to -132.3 mm respectively. For the flow duration change, the CFR (coefficient of flow regime, Q10/Q355) was altered from +4.2 to +10.5 for 2080s wet scenarios and from +1.7 to +2.6 for 2020s dry scenarios. As a result of the flow duration analysis according to the change of the hydrological factors of the Geum River basin applying the extreme climate change scenario, INM-CM4 showed suitable scenario to show extreme dry condition and FGOALS-s2 showed suitable scenario for the analysis of the drought condition with large flow duration variability. HadGEM2-ES was evaluated as a scenario that can be used for maximum flow analysis because the flow duration variability was small and CESM1-BGC was evaluated as a scenario that can be applied to the case of extreme flood analysis with large flow duration variability.

• Journal of the Korean Geographical Society
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• v.29 no.3
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• pp.233-252
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• 1994

One of major advantages of Lumped model is its ability to simulate extended flows. A further advantage is that it requires only conventional, readily available hydrological data (rainfall, evaporation and runoff). These two advantages commend the use of this type of model for the analysis of the hydrological effects of landuse change. Experimental Catchment(K11) of Kimakia site in Kenga experienced three phases of landuse change for sixteen and half years. The Institute of Hydrology offered the hydrological data from the catchment for this research. On basis of Blackie's(l972) 9-parameter model, a new model(R1131) was reorganized in consideration of the following aspects to reflect the hydrological characteristics of the catchment: 1) The evapotranspiration necessary for the landuse hydrology, 2) high permeable soils, 3) small catchment, 4) input option for initial soil moisture deficit, and 5) othel modules for water budget analysis. The new model is constructed as a 11-parameter, 3-storage, 1-input option model. Using a number of initial conditions, the model was optimized to the data of three landuse phases. The model efficiencies were 96.78%, 97.20%, 94.62% and the errors of total flow were -1.78%, -3.36%, -5.32%. The bias of the optimized models were tested by several techniques, The extended flows were simulated in the prediction mode using the optimized model and the data set of the whole series of experimental periods. They are used to analyse the change of daily high and low-flow caused by landuse change. The relative water use ratio of the clearing and seedling phase was 60.21%, but that of the next two phases were 81.23% and 83.78% respectively. The annual peak flows of second and third phase at a 1.5-year return period were decreased by 31.3% and 31.2% compared to that of the first phase. The annual peak flow at a 50-year return period in the second phase was an increase of only 4.8%, and that in the third phase was an increase of 12.9%. The annual minimum flow at a 1.5-year return period was decreased by 34.2% in the second phase, and 34.3% in the third phase. The changes in the annual minimum flows were decreased for the larger return periods; a 20.2% decrease in the second phase and 20.9% decrease in the third phase at a 50-year return period. From the results above, two aspects could be concluded. Firstly, the flow regime in Catchment K11 was changed due to the landuse conversion from the clearing and seedling phade to the intermediate stage of pine plantation. But, The flow regime was little affected after the pine trees reached a certain height. Secondly, the effects of the pine plantation on the daily high- and low-flow were reduced with the increase in flood size and the severity of drought.

• Journal of Environmental Policy
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• v.8 no.1
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• pp.73-95
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• 2009

The fact that soil loss causing to increase muddy water and devastate an ecosystem has been appearing upon a hot social and environmental issues which should be solved. Soil losses are occurring in most agricultural areas with rainfall-induced runoff. It makes hydraulic structure unstable, causing environmental and economical problems because muddy water destroys ecosystem and causes intake water deterioration. One of three severe muddy water source areas in Soyanggang-dam watershed is Jawoon-ri region, located in Hongcheon county. In this area, many cash-crops are planted at illegally cultivated agricultural fields, which were virgin forest areas. The purpose of this study is to estimate soil loss with current land uses(including illegal cash-crop cultivation) and soil loss reduction with land use conversion from illegal cultivation back to forest. In this study, the Sediment Assessment Tool for Effective Erosion Control(SATEEC) ArcView GIS was utilized to assess soil erosion. If the illegally cultivated agricultural areas are converted back to forest, it would be expected to 17.42% reduction in soil loss. At the Jawoon-ri region, illegally cultivated agricultural areas located at over 30% and 15% slopes take 47.48 ha(30.83%) and 103.64 ha(67.29%) of illegally cultivated agricultural fields respectively. If all illegally cultivated agricultural fields are converted back to forest, it would be expected that 17.41% of soil erosion and sediment reduction, 10.86% reduction with forest conversion from 30% sloping illegally agricultural fields, and 16.15% reduction with forest conversion from 15% sloping illegally agricultural fields. Therefore, illegally cultivated agricultural fields located at these sloping areas need to be first converted back to forest to maximize reductions in soil loss reduction and muddy water outflow from the Jawoon-ri regions.

• Korean Journal of Environmental Agriculture
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• v.27 no.1
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• pp.35-43
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• 2008

The study was carried out to estimate runoff loads of heavy metals in the valley watershed at the middle of South Korea, during farming season. There were no other pollution sources except agricultural activity. From 27 April 2006 to 31 October 2007, water samples were collected using two methods. The first method was regular sampling wherein water samples were taken every two weeks; and the other method was through regular sampling when water were collected during each rainfall event. Results showed that heavy metals were found in the water from the regular samples, and were highest during May and June. It was presumed that this might have been contributed by farming activities. Heavy metal concentration of the irregular samples was lower than regular samples. The correlation coefficient between each heavy metal of the regular samples were as follows: Fe-Al>Cr-Al>Fe-Cr>Mn-Fe. The correlation coefficient of the irregular samples were the following: Fe-Al>Fe-Cu is positive; and Pb-Cu>Ni-Al is negative. Measured pollutant loads of heavy metals in the valley watershed were : 2.047 kg $day^{-1}$ of Al, 0.008 kg $day^{-1}$ of Cd, 0.034 kg $day^{-1}$ of Cr, 0.311 kg $day^{-1}$ of Cu, 0.601 kg $day^{-1}$ of Fe, and 0.282 kg $day^{-1}$ of Zn in 2006; while in 2007, the following were observed: 2.535 kg $day^{-1}$ of Al, 0.026 kg $day^{-1}$ of Cd, 0.055 kg $day^{-1}$ of Cu, 0.727 kg $day^{-1}$ of Fe, and 0.317 kg $day^{-1}$ of Zn. In the analysis of data gathered, the loading rates of effluents from the valley watershed during the rainy season were : 79.8% of Al, 69.1% of Cu, 82.5% of Fe, and 69.1% of Zn in 2006; while 69.9% of Al, 67.5% of Cu, 70.4% of Fe, and 67.5% of Zn in 2007.