• Magazine of the Korean Society of Agricultural Engineers
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• v.42
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• pp.24-30
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• 2000

A system for evaluating streamflow data (KORSAS) was developed, and is operated using PC based Windows to help the hydrological observation practitioner's working in Korea Water Resources Corporation (KOWACO). This system has modules including; DB access and data management, flow measurement arranging, H-Q relation deriving, area rainfall calculating, flow calculating, and flow evaluating modules. Evaluation of observed streamflow is accomplished through the following processes. First, hourly streamflow data is calculated from water level data stored in a DB server by applying the rating relationship between water level and flow rates derived from the past flow measurements. Second, hourly areal rainfal data is calculated from point data stored in the DB server by applying Thiessen networks. Third, hydrographs are displayed on a daily, weekly, monthly, or seasonal duration basis, and are compared to hydrographs of reservoir inflow, hydrographs at water level observation stations and hydrographs derived from simulated results using models.

• Water for future
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• v.18 no.4
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• pp.317-325
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• 1985

In the analysis, monthly streamflow records atthe gauging station in Nakdong, Han and Geum river were used. Also, the fitness of monthly streamflow to Gamma and Long-normal distribution was tested by Kolomogorv-Smirnov test. The results obtained in this study can be summarized as follws (1) The fitness of monthly streamflow to two-parameter Gamma distribution was tested by Kolomorov-Smirnov test, which fits well to this Gamma distribution (2) The Run-length and Run-sum were simulated by the Gamma model. In this result, run-length and Run-sum of monthly streamflow were fit for Gamma model (3) The mean decreases (increases) the expected surplus (deficit) Run-Sum of the monthly streamflow. The higher the truncation level of negative Run-length and Run-sum the larger is the effect of mean.

• Journal of Korea Water Resources Association
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• v.47 no.5
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• pp.491-500
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• 2014

To estimate the severity of streamflow drought, this study introduced the concept of streamflow drought index based on threshold level method and Seomjingang Dam inflow was applied. Threshold levels used in this study are fixed, monthly and daily threshold, The $1^{st}{\sim}3^{rd}$ analysis results of annual drought, the severe hydrological droughts were occurred in 1984, 1988 and 1995 and the drought lasted for a long time. Annual compared to extreme values of total water deficit and duration, the drought occurred in 1984, 1988, 1995 and 2001 was serious level. In the results of study, because a fixed threshold level is not reflect seasonal variability, at least the threshold under seasonal level was required. Threshold levels determined by the monthly and daily were appropriate. The proposed methodology in this study can be used to forecast low-flow and determine reservoirs capacity.

• 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.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.04a
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• pp.451-454
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• 2004

The study aims to assess the quality of bank filtrate in relation to streamflow and physico-chemical properties of the stream. Turbidity, pH, temperature and dissolved oxygen (DO) of Nakdong River and riverbank filtrate were statistically analyzed. The physico-chemical properties of riverbank filtrate were measured from irregularly different seven pumping wells every day. Autocorrelation analyses were conducted to the qualities of stream water and bank filtrated water. Temperature, pH and DO of streamflow shows strong linearity and long memory effect, indicating the effect of seasonal air temperature and rainy season. Temperature of riverbank filtrate shows weak linearity and weak memory, indicating differently from the trend of stream temperature. Turbidity of steramflow shows strong linearity and long memory effect, while turbidity of riverbank filtrate indicates weak linearity and weak memory. Cross-correlation analysis shows low relation between turbidity, pH, temperature and DO of riverbank filtrate and those of streamflow. Turbidity of streamflow was largely affected by the streamflow rate, showing a similar trend with autocorrelation function of streamflow rate. The turbidity of riverbank filtrate has a lag time of 25 hours. This indicates that turbidity of streamflow in a dry season has very low effect on the turbidity of riverbank filtrate, and a high turbidity of the stream in a rainy season has a fairly low effect on the turbidity of riverbank filtrate.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.

• Journal of Korea Water Resources Association
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• v.45 no.10
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• pp.1051-1067
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• 2012

Increased use of water curtain facilities to keep green house warm during winter cultivation has been known to cause excessive groundwater ion which might lead to decline of groundwater level, resulting in streamflow depletion. Therefore it is required to quantitatively assess the effects of groundwater ion on the streamflow depletion such as magnitude and extent. The objective of this study is to assess the change of stream-aquifer interaction according to groundwater ion near stream. To this end, a green house cultivation land in Sooha-ri, Sindun-myun, Icheon-si, Gyonggi-do was selected as a field experimental site, and monitoring wells were established near and within stream to observe the water level and temperature changes over a long period of time. From the observed water level and temperature data, it was found that the river reach of interest changed to a losing stream pattern during the winter cultivation season due to groundwater level decline around pumping wells near the stream. The continuous exchange rates between stream and aquifer were estimated by plugging the observed water level data series into the experimental relation between head difference and exchange rate, showing the streamflow depletion by 16% of the groundwater pumping rate in Feb, 2011.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1998.10a
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• pp.87-91
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• 1998

Unused irrigation water due to delivery losses and overflow from paddies in an irrigation system, and groundwater releases from infiltration are eventually returned to stream. The estimate of irrigation returnflow is important to streamflow modeling and water resources planning. This study was to field monitor the irrigation water use, streamflow, lateral inflow and ground water level, and to determine the return flow of irrigation water

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.6
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• pp.169-176
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• 2012

This study describes the estimation of upstream ungauged watershed streamflow using downstream discharge data. For downstream Dongchon (DC) and upstream Kumho (KH) water level stations in Kumho river basin ($2,087.9km^2$), three methods of Soil and Water Assessment Tool (SWAT) modeling, drainage-area ratio method and regional regression equation were evaluated. The SWAT was calibrated at DC with the determination coefficient ($R^2$) of 0.70 and validated at KH with $R^2$ of 0.60. The drainage-area ratio method showed $R^2$ of 0.93. For the regional regression, the watershed area, average slope, and stream length were used as variables. Using the derived equation at DC, the KH could estimate the flow with maximum 41.2 % error for the observed streamflow.