• Water Engineering Research
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• v.1 no.2
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• pp.107-117
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• 2000

Streamflow patterns at two gauging stations in Korea, An-Dong dam and Chung-Ju dam, are statistically analyzed in relation to EI Nino/Southern Oscillation (ENSO). As a measure of ENSO, the Southern Oscillation Index (SOI) is used on a monthly and seasonal basis. The traditional correlation analysis shows that cross correlations of the SOI with the seasonal streamflow are generally weak. To investigate the relationship between the extreme values of the SOI, which represent the EI Nino and La Nina events, and the corresponding streamflow patterns, the composite analysis is employed in this study. The composite analysis demonstrates that when EI Nino occurs, seasonal streamflows at An-Dong and Chung-Ju dams during the period from September of the EI Nino year to February of the following year appear to be drier than their means.

• Journal of Korean Society on Water Environment
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• v.22 no.2
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• pp.244-249
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• 2006

Natural environment of Weolgokri watershed has been well preserved as a traditional agricultural watershed. A year record of streamflow, $NO_3-N$, T-N and T-P concentrations data (April 2004 - Mar. 2005) were examined to estimate annual and seasonal patterns of pollutant loads in streamflow and baseflow from the agricultural watershed. To estimate pollutant loads from baseflow, baseflow component was separated from streamflow using the digital filter method in the Web-based Hydrograph Analysis Tool system and loads of $NO_3-N$, T-N and T-P from streamflow and baseflow were evaluated. The $NO_3-N$, T-N, and T-P loads from streamflow were 13.85 kg/ha, 45.92 kg/ha and 1.887 kg/ha, respectively, while corresponding loads from baseflow were 7.43 kg/ha, 24.70 kg/ha, 0.582 kg/ha, respectively. It was found that $NO_3-N$ and T-N loads were contributed slightly more by the baseflow (53% and 53% of Total-loads) than by the direct runoff (47% and 47% of Total loads). However, only 30% of total T-P load was contributed by the baseflow. It is recommended that one needs to assess pollutant load contribution by the baseflow to identify appropriate pollution control strategies for an effective watershed management.

• Journal of Korean Society on Water Environment
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• v.29 no.1
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• pp.88-96
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• 2013

Much attention has been needed in water resource management at the watershed due to drought and flooding issues caused by climate change in recent years. Increase in air temperature and changes in precipitation patterns due to climate change are affecting hydrologic cycles, such as evaporation and soil moisture. Thus, these phenomena result in increased runoff at the watershed. The Soil and Water Assessment Tool (SWAT) model has been used to evaluate rainfall-runoff at the watershed reflecting effects on hydrology of various weather data such as rainfall, temperature, humidity, solar radiation, wind speed. For bias-correction of RCP data, at least 30 year data are needed. However, for most gaging stations, only precipitation data have been recorded and very little stations have recorded other weather data. In addition, the RCP scenario does not provide all weather data for the SWAT model. In this study, two scenarios were made to evaluate whether it would be possible to estimate streamflow using measured precipitation and long-term average values of other weather data required for running the SWAT. With measured long-term weather data (scenario 1) and with long-term average values of weather data except precipitation (scenario 2), the estimate streamflow values were almost the same with NSE value of 0.99. Increase/decrease by ${\pm}2%$, ${\pm}4%$ in temperature and humidity data did not affect streamflow. Thus, the RCP precipitation data for Hongcheon watershed were bias-corrected with measured long-term precipitation data to evaluate effects of climate change on streamflow. The results revealed that estimated streamflow for 2055s was the greatest among data for 2025s, 2055s, and 2085s. However, estimated streamflow for 2085s decreased by 9%. In addition, streamflow for Spring would be expected to increase compared with current data and streamflow for Summer will be decreased with RCP data. The results obtained in this study indicate that the streamflow could be estimated with long-term precipitation data only and effects of climate change could be evaluated using precipitation data as shown in this study.

• 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.

• Journal of Korean Society on Water Environment
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• v.28 no.6
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• pp.819-832
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• 2012

HSPF model based on BASINS was applied for the Hwaseong Reservoir watershed (HRW) to evaluate the feasibility of water quality management. The watershed was divided into 45 sub-basins considering various watershed environment. Streamflow was calibrated based on the measured meteorological data, discharge data of treatment plants and observed streamflow data for 2010 year. Then the model was calibrated against the field measurements of water qualities, including BOD, T-N and T-P. In most cases, there were reasonable agreements between observed and predicted data. The validated model was used to analyze the characterization of pollutant load from study area. As a result, Non-point source pollutant loads during the rainy season was about 66~78% of total loads. In rainy-season, water quality parameters depended on precipitation and pollutant loads patterns, but their concentration were not necessarily high during the rainy season, and showed a decreasing trend with increasing water flow. As another result of evaluation for load duration curves, in order to improve water qualities to the satisfactory level, the watershed managements considering both time-variant and pollution sources must be required in the HRW. Overall, it was found that the model could be used conveniently to assess watershed characteristics and pollutant loads in watershed scale.

• Journal of Korean Society on Water Environment
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• v.26 no.5
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• pp.860-870
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• 2010

Long term monthly monitoring data showed that the water quality of streams flowing into Lake Paldang has been improved by various strategy for water. However, the effect of quality on Lake Paldang is still insufficient because of nonpoint source from watershed. In order to evaluate quantifying methods for pollution source and make a suggestion on improvements, Storm Water Management Model (SWMM) was constructed by using data set from the water quality and streamflow monitoring network in the Kyoungan watershed for Total Maximum Daily Loads (TMDLs). Load duration curve (LDC) based on the result of the Kyoungan watershed SWMM indicated that the water quality criterion on $BOD_5$ was often exceeded in up-stream than down-stream. From flowrate-load correlation curve, SS load significantly increased as streamflow increases. 75.3% of streamflow and 62.1% of $BOD_5$ loads is discharged especially in the zone of high flows, but monitoring data set didn't provide proper information about the conditions and the patterns associated with storm events. Therefore, it is necessary to acquire representative data set for comparing hydrograph and pollutograph through monitoring experimental watershed and to establish methods for quantifying point and nonpoint source pollutant loads.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.3
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• pp.19-29
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• 2014

This study was to evaluate the potential climate change impact on watershed hydrological components of evapotranspiration, surface runoff, lateral flow, return flow, and streamflow using Soil and Water Assessment Tool (SWAT). For Yongdam dam watershed (930 $km^2$), the SWAT model was calibrated for five years (2002-2006) and validated for three years (2004-2006) using daily streamflow data at three locations and daily soil moisture data at five locations. The Nash-Sutcliffe model efficiency (NSE) and coefficient of determination ($R^2$) were 0.43-0.67 and 0.48-0.70 for streamflow, and 0.16-0.65 and 0.27-0.76 for soil moisture, respectively. For future evaluation, the HadGEM3-RA climate data by Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios were adopted. The biased future data were corrected using 30 years (1982-2011, baseline period) of ground weather data. The HadGEM3-RA 2080s (2060-2099) temperature and precipitation showed increase of $+4.7^{\circ}C$ and +22.5 %, respectively based on the baseline data. The impacts of future climate change on the evapotranspiration, surface runoff, baseflow, and streamflow showed changes of +11.8 %, +36.8 %, +20.5 %, and +29.2 %, respectively. Overall, the future hydrologic results by RCP emission scenarios showed increase patterns due to the overall increase of future temperature and precipitation.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.580-585
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• 2005

Natural environment of Weolgok-ri watershed has been well preserved as a traditional agricultural watershed. A year record of streamflow, $NO_3-N$, T-N and T-P concentrations data(Apr, 2004-Mar. 2005) was examined to estimate annual and seasonal patterns of pollutnat loads in streamflow and baseflow from the agriculture watershed. To estimate pollutant loads from baseflow, baseflow component from total stream flow was seperated using digital filter method in the Web-based Hydrograph Analysis Tool system. Loads of $NO_3-N$, T-N and T-P from streamflow and baseflow were evaluated to investigate pollutant loads contribution by baseflow. The $NO_3-N$, T-N, and T-P loads from streamflow were 13.85 kg/ha, 45.92 kg/ha and 1.887 kg/ha, respectively. $NO_3-N$, T-N and T-P loads from baseflow were 7.43 kg/ha, 24.70 kg/ha, 0.582 kg/ha, respectively. It was found that $NO_3-N$ and T-N loads were contributed by the baseflow(53% and 53% of Total-loads) than the direct runoff(47% and 47% of Total loads). However, only 30% of total T-P was contributed by the baseflow. It is recommended that one needs to assess pollutant load contribution by the baseflow to identify appropriate control strategies for effective watershed management.

• Journal of Wetlands Research
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• v.21 no.3
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• pp.236-243
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• 2019

Hydrologic time series has been analyzed and forecasted by using classical linear models. However, there is growing evidence of nonlinear structure in natural phenomena and hydrologic time series associated with their patterns and fluctuations. Therefore, the classical linear techniques for time series analysis and forecasting may not be appropriate for nonlinear processes. Daily streamflow series at St. Johns river near Cocoa, Florida, USA showed an interesting result of a low dimensional, nonlinear dynamical system but daily inflow at Soyang reservoir, South Korea showed stochastic property. Based on the chaotic dynamical characteristic, DVS (deterministic versus stochastic) algorithm is used for short-term forecasting, as well as for exploring the properties of the system. In addition to the use of DVS algorithm, a neural network scheme for the forecasting of the daily streamflow series can be used and the two techniques are compared in this study. As a result, the daily streamflow which has chaotic property showed much more accurate result in short term forecasting than stochastic data.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.969-980
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• 2023

Since the 2000s, ensemble streamflow prediction (ESP) has been actively utilized in South Korea, primarily for hydrological forecasting purposes. Despite its notable success in hydrological forecasting, the original objective of enhancing water resources system management has been relatively overlooked. Consequently, this study aims to demonstrate the utility of ESP in water resources management by creating a simple hypothetical exercise for dam operators and applying it to actual multi-purpose dams in South Korea. The hypothetical exercise showed that even when the means of ESP are identical, different costs can result from varying standard deviations. Subsequently, using sampling stochastic dynamic programming (SSDP) and considering the capacity-inflow ratio (CIR), optimal release patterns were derived for Soyang Dam (CIR = 1.345) and Chungju Dam (CIR = 0.563) based on types W and P. For this analysis, Type W was defined with standard deviation equal to the mean inflow, and Type P with standard deviation ten times of the mean inflow. Simulated operations were conducted from 2020 to 2022 using the derived optimal releases. The results indicate that in the case of Dam Chungju, more aggressive optimal release patterns were derived under types with smaller standard deviations, and the simulated operations demonstrated satisfactory outcomes. Similarly, Soyang Dam exhibited similar results in terms of optimal release, but there was no significant difference in the simulation between types W and P due to its large CIR. Ultimately, this study highlights that even with the same mean values, the standard deviation of ESP impacts optimal release patterns and outcomes in simulation. Additionally, it underscores that systems with smaller CIRs are more sensitive to such uncertainties. Based on these findings, there is potential for improvements in South Korea's current operational practices, which rely solely on single representative values for water resources management.