• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.327-327
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• 2020

Increase in Earth's surface temperature, higher rainfall intensity rate, and rapid changes in land cover are just some of the most evident effects of climate change. Flooding, and river sedimentation are two inevitable natural processes in our environment, and both issues poses great risks in the dam industry when not addressed properly. River sedimentation is a significant issue that causes reservoir deposition, and thus causes the dam to gradually lose its ability to store water. In this study, the long-term effects of climate change on the sediment discharge in Yongdam Dam watershed is analyzed through the utilization of SWAT, a semi-distributed watershed model. Based from the results of this study, an abrupt increase on the annual sediment inflow trend in Yongdam Dam watershed was observed; which may suggests that due to the effects of climate change, higher rainfall intensity, land use and land cover changes, the sedimentation rate also increased. An efficient sedimentation management should consider the increasing trend in sedimentation rate due to the effects of climate change.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.550-555
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• 1999

yongdam multipurpose is under construction to divert a part fo Geum riverlfow to Mankyung watershed and to supply the domestic water to the Chunju region and produce the hydro-electricity. Generally construction of dam by the method of inther-region water transfer affects the quantity and quality of water inthe down streams and reservoirs. The impact of operation plan of Yondgam dam on the quantity and quality of water in the Guem river and Daechung dam was investigated .It was recommended that the discharge of water transfer from one watershed to another should be minimized as much as possible.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1772-1776
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• 2006

The Ministry of Environment is determining reference flows and goal water qualities in many stations over all around riverbasin to control TMDL. Reference flow is now defined to 10 years averaged 275th minimum flow$(Q_{275})$. Dam operation takes direct effect on flows downstream. The Yongdam mutipurposed dam was constructed in 2002 and TMDL managing stations between the Daecheong dam and the Yongdam dam are the Geumbon B, C, D, E, and F in main stream of the Geum river. Geumbon F is the Daecheong dam site. Observed flows are ideal to be used to set reference flows, but simulated flows are more practical to be used to set reference flows from the cause of the Yongdam dam's operation. A system for simulating daily storages of the Yongdam dam was constructed and the DAWAST model was selected to simulate daily streamflows. Analysis period was selected for 10 years from 1996 to 2005. Scenario was set as follows; Firstly, observed outflows from the Yongdam dam are used from 2002 to 2005 and the Yongdam dam does not exist from 1995 to 2001. Secondly, the Yongdam dam existed also from 1995 to 2001 and simulated outflows from the Yongdam dam are used from 1996 to 2005 with provision of constant outflow of $7.0m^3/s$ and water supply to the Jeonju region outsided watershed of $900,000m^3/day$. In case of scenario 1 reference flows at the Geumbon B, C, D, E, F are 4.52, 6.69, 7.96, 11.17, and $13.21m^3/s$, respectively. And in case of scenario 2 reference flows at the Geumbon B, C, D, E, F are 6.27, 8.48, 9.58, 12.73, and $15.12m^3/s$, respectively.

• Journal of Korea Water Resources Association
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• v.35 no.5
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• pp.525-539
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• 2002

Effects of Yongdam Dam discharge conditions on water quality of the Keum River and Daechung Lake inflow were analyzed for various scenarios using WASP5 water quality model. Three different groups of scenarios were tested: 1) Two different weather conditions; the lowest flow year and the highest flow year since the beginning of Daechung Dam operation in 1981, 2) Fine discharge flow rates; 5.4, 8.9, 12.4, 16.4 ㎥/s and field observed flow during the study period, 3) Three conditions of discharge water quality; first grade, second grade by Korean water quality standard and field observed water quality. Effect of changes in Yongdam Dam discharges was greater for dry year. The increase of discharge from the Yongdam Dam will improve water quality of downstream areas only when the water quality of the discharge is equal or better than that of downstream areas. Field observed water qualify data show that BOD concentrations are lower than first grade level but TN and TP concentrations are exceeding 5th and 3rd grade level in Korean standard, respectively. Considering that nutrient control methods in watershed areas of Yongdam dam are limited, it is expected that nutrient concentrations from Yongdam Dam discharge will be higher than 2nd grade water quality standard level. Therefore, it would be important to develop practical management strategies in the watershed area of Yongdam Dam based on field conditions for conservation of water quality in downstream areas.

• Journal of Korea Water Resources Association
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• v.36 no.4
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• pp.647-659
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• 2003

The purpose of this study is to evaluate hydrological applicability of spatially observed rainfall distribution data by the TRMM/PR (Tropical Rainfall Measuring Mission / Precipitation Radar). For this study, firstly, TRMM/PR data (Y) of the Yongdam-Dam Watershed (930.38$km^2$) was extracted and secondly, TRMM/PR data and the rainfall data (X) by AWS (Automatic Weather Station) were compared by executing a correlation analysis. As a result, the regression equations were deduced as two parts (under 60mm/day : Y = 18.55X-0.53, over 60mm/day : Y = 3.11X+51.16). SCS runoff analysis was conducted using 7 rainfall events in 1999 for Yongdam-Dam watershed and the Cheon-Cheon subwatershed for the revised TRMM/PR data. TRMM/PR data showed relative errors ranging from 19.6％ ti 45.6％, and from 11.3％ to 38.9％ for Cheon-Cheon subwatershed and Yongdam-Dam watershed, respectively, AWS data showed relative errors ranging from 0.5％ to 12.8％, and from -1.6％ to -10.3％, for Cheon-Cheon subwatershed and Yongdam-Dam watershed, respectively. Futher researches are necessary to evaluate the relationship between TRMM/PR data and AWS data for practical hydrological applications.

• Journal of Korean Society on Water Environment
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• v.32 no.2
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• pp.173-182
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• 2016

The objective of this study is to quantitatively analyze climate change effects by using statistical trends and a watershed model in the Yongdam dam watershed. The annual average air temperature was found to increase with statistical significance. In particular, greater increases were observed in autumn. Also, this study was performed to evaluate the potential climate change in the streamflow and water temperature using a watershed model (HSPF) with RCP climate change scenarios. The streamflow of Geum river showed a decrease of 5.1% and 0.2%, respectively, in the baseline data for the 2040s and 2080s. The seasonal impact of future climate change on the streamflow showed a decrease in the summer and an increase in the winter. The water temperature of Geum river showed an average increase of 0.7~1.0℃. Especially, the water temperature of Geum river showed an increase of 0.3~0.5℃ in the 2040s and 0.5~1.2℃ in the 2080s. The seasonal impact of future climate change on the water temperature showed an increase in winter and spring, with a decrease in summer. Therefore, it was determined that a statistical analysis-based meteorological and quantitative forecast of streamflow and water temperature using a watershed model is necessary to assess climate change impact and to establish plans for future water resource management.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.467-470
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• 2003

The size, scale, and number of subwatersheds can affect a watershed modeling process and subsequent results. The objective of this study was to determine the appropriate level of subwatershed division for simulating streamflow. The Soil and Water Assessment Tool(SWAT) model with a GIS interface(BASINS SWAT) was applied to Yongdam Dam watershed. Daily output was analyzed from simulation, which was executed for 10 years using climate data representing the 1987 to 1996 period. The optimal number of subwatersheds and HRUs to adequately predict streamflow was found to be around 15, 174. Increasing the number of subwatersheds and HRUs beyond this level does not significantly affect the computed streamflow. this number of subwatersheds and HRUs can be used to optimize SWAT input data preparation requirements and simplify the interpretation of results without compromising simulation accuracy.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.3
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• pp.13-22
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• 2008

The objective of this study is to evaluate the applicability of extracted PRMS input parameters by KGIS-Hydrology over Yongdam-Dam watershed. KGIS-Hydrology is a system for automatic extraction and analysis of watershed characteristic data. Input parameters of PRMS were generated from GIS data (DEM, soil, forest type, etc.) using KGIS-Hydrology. Multi-temporal meteorological data from Jangsu station of KMA (Korea Meteorological Administration) were used for all simulation periods. Input parameters of PRMS were optimized using observed runoff data of Yongdam-Dam station (1966-2001) and validated using observed runoff data of Yongdam-Dam station (2002-2006, Yongdam-Dam watershed). The results showed that the simulated flows were much closed to the observed flows of Yongdam-Dam (2002-2006) and Donghyang (2001-2004) station by 0.49~0.83 and 0.57~0.75 model efficiencies, respectively.

• Journal of Korea Water Resources Association
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• v.53 no.3
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• pp.155-166
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• 2020

In this study, we applied the Radar-AWS Rainrates (RAR), weather radar-based quantitative precipitation estimations (QPEs), to the Yongdam study watershed in order to perform the flood runoff simulation and calculate the inflow of the dam during flood events using hydrologic model. Since the Yongdam study watershed is a representative area of the mountainous terrain in South Korea and has a relatively large number of monitoring stations (water level/flow) and data compared to other dam watershed, an accurate analysis of the time and space variability of radar rainfall in the mountainous dam watershed can be examined in the flood modeling. HEC-HMS, which is a relatively simple model for adopting spatially distributed rainfall, was applied to the hydrological simulations using HEC-GeoHMS and ModClark method with a total of eight independent flood events that occurred during the last five years (2014 to 2018). In addition, two NCL and Python script programs are developed to process the radar-based precipitation data for the use of hydrological modeling. The results demonstrate that the RAR QPEs shows rather underestimate trends in larger values for validation against gauged observations (R² 0.86), but is an adequate input to apply flood runoff simulation efficiently for a dam watershed, showing relatively good model performance (E_NS 0.86, R² 0.87, and PBIAS 7.49%) with less requirements for the calibration of transform and routing parameters than the spatially averaged model simulations in HEC-HMS.

• Journal of the Korean Geographical Society
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• v.46 no.3
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• pp.319-330
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• 2011

In this study, a soil moisture estimation model was proposed using the ground observation data of soil moisture, precipitation, surface temperature, MODIS NDVI and artificial neural networks. The model was calibrated and verified on the Yongdam dam watershed which has reliable ground soil moisture networks. The test statistics of calibration sites, Jucheon, Bugui, Sangjeon, showed that the correlation coefficients between observations and estimations are about 0.9353 and RMSE is about 1.4957%. Also that of the verification site, Cheoncheon2, showed that the correlation coefficient is about 0.8215 and RMSE is about 4.2077%. The soil moisture estimation model was applied to estimate the spatial distribution of soil moisture in the Yongdam dam watershed and results showed improved spatial soil moisture distribution since the model used satellite information of NDVI and artificial neural networks which can represent the nonlinear relationships between data well. The model should be useful to estimate wide range soil moisture information.