• Journal of Korean Society on Water Environment
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• v.33 no.4
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• pp.394-402
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• 2017

In korea, TMDL is being implemented to manage nonpoint pollution sources as well as point pollution sources. LDC is being used for the planning of TMDL. In order to analyze the water quality using LDC, it is necessary to prepare FDC using the daily flow data. However, only the daily flow data is measured at the WAMIS branch, and 8days flow data and water quality data are measured at the monitoring Networks. So, in many researches, the water quality is being grasped by deriving the LDC using the 8days flow or the daily flow obtained by various methods. These fluctuations may lead to differences in determining whether the target load is achieved. In this study, each LDC was prepared using the 8day flow and the related daily flow. Then, the effect using different flow data on the achievement of target load was compared according to flow conditions. As a result, the difference ratio in the number of overloads under flow condition was showed 19% in high flows, 42% in moist conditions, 49% in mid-range flows, 41% in dry conditions, and 104% in low flows. In the top ten watershed with the highest difference ratio, the flow became lower the difference ration increases. These differences can cause uncertainty in assessing the achievement of target load using LDC. Therefore, in order to evaluate the water quality accurately and reliably using LDC, accurate daily flow data and water quality data should be secured through the installation of national nonpoint measurement network.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.519-519
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• 2017

우리나라는 국토의 65%가 산악지형으로서 유역경사가 급한 특성으로 인해, 강우발생 시 유하시간의 감소로 인한 홍수피해와 도시화와 산업화로 인한 수질오염에 매우 취약한 실정이다. 수질관리의 일환으로 환경부에서는 '4대강 물관리종합대책'과 '물환경관리 기본계획' 등을 발표하며, 유역단위의 통합적 물관리를 실시하고 있다. 물환경관리 기본계획의 단위유역 단위의 목표수질 달성을 위한 중요 정책적 실행수단으로서 수질오염총량제가 있으며, 관리대상 오염물질로서는 BOD, T-P만을 대상으로 하고 있다. 하지만 최근 환경부에서는 기존 BOD 중심의 유기물질 관리의 한계 극복과 환경기준 선진화의 일환으로서 수질 목표기준에 TOC를 설정하고 생활환경 기준에 질소의 도입을 추진하는 등의 경향을 볼 때, BOD와 T-P 중심의 목표수질 관리에는 한계가 있다고 판단되며, 이에 따라 현행 관리 대상물질 외 수질항목에 대한 다양한 분석방법이 필요하다. 본 연구에서는 청미천 본류의 환경부 물환경측정망 운영지점과 8개의 지류에 대해 2013년 6월부터 2015년 7월까지 8일 간격 수질 유량 자료에 대한 현장측정 자료를 이용하여 BOD, COD, SS 등 총 6개 수질항목에 대하여 FDC(Flow Duration Curve)와 초과율(Exceedance Rate) 분석을 실시하였다. 분석 결과를 바탕으로 각 지류에서 발생한 오염물질의 농도가 청미천 본류의 분류된 구간과 청미A 단위유역 말단에 미치는 영향에 대하여 분석하였다.

• Journal of Korean Society on Water Environment
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• v.33 no.1
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• pp.51-62
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• 2017

The purpose of this study is to evaluate on the applicability of Load Duration Curve (LDC) method using Maintenance of Variance Extension types 2 method and sampling data for efficient total maximum daily loads at the Nakbon-A unit watershed in Korea. The LDC method allows for characterizing water quality data such as BOD, TOC, T-N and T-P in this study at different flow regimes(or quarters). BOD usually exceeded the standard value (exceedance probability 50%) at low flow zone. On the other hand, TOC, T-N, T-P usually exceeded the standard value at dry and low flow zone. Seasonally all water quality variables usually exceeded the standard value at Q1(Jan-Mar) and Q2(Apr-Jun) zones. Improvement of effluent control from wastewater treatment plants are effective to improve BOD and T-P.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.3
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• pp.37-46
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• 2013

Similarity measure of catchments is essential for regionalization studies, which provide in depth analysis in hydrological response and flood estimations at ungauged catchments. However, this similarity measure is often biased to the selected catchments and is not clearly explained in hydrological sense. This study applied a type of hydrological similarity distance measure-Flood Estimation Handbook to 25 Geum River catchments, Korea. Three Catchment Characteristics, Area(A)-Annual precipitation(SAAR)-SCS Curve Number(CN), are used in Euclidian distance measures. Furthermore, six index of Flow Duration Curve are applied to clustering analysis of SPSS. The catchments' grouping of hydrological similarity measures suggests three groups (H1, H2 and H3) and the four catchments are not grouped in this study. The clustering analysis of FDC provides four Groups; F1, F2, F3 and F4. The six catchments (out of seven) of H1 are grouped in F1, while Sangyeogyo is grouped in F2. The four catchments (out of six) of H2 are also grouped in F2, while Cheongju and Guryong are grouped in F1. The catchments of H3 are categorized in F1. The authors examine the results (H1, H2 and H3) of similarity measure based on catchment physical descriptors with results (F1 and F2) of clustering based on catchment hydrological response. The results of hydrological similarity measures are supported by clustering analysis of FDC. This study shows a potential of hydrological catchment similarity measures in Korea.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of Environmental Science International
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• v.21 no.1
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• pp.31-39
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• 2012

The objective of this study is to provide pollutant loads delivery ratio for flow duration in Oenam-cheon watershed, which is upstream watershed of Juam Lake. To calculate the delivery ratio by flow duration, rating curves and discharge-loads curves using measured data were established, then Flow Duration Curve(FDC) and pollutant loads delivery ratio curves were constructed. The results show that the delivery ratios for $BOD_5$ for abundant flow($Q_{95}$), ordinary flow($Q_{185}$), low flow($Q_{275}$), and drought flow($Q_{355}$) were 23.9, 12.7, 7.1, and 2.9%, respectively. The delivery ratios of same flow regime for T-N were 58.4, 31.2, 17.2 and 7.1%, respectively. While, the delivery ratios T-P were 17.3, 7.5, 3.4, and 1.1% respectively. In general, delivery ratio of high flow condition showed higher value due to the influence of nonpoint source pollution. Based on the study results, generalized equations were developed for delivery ratio and discharge per unit area, which could be used for ungaged watershed with similar pollution sources.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.307-307
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• 2022

최근 기후변화와 댐 상류 토지이용 변화 등과 같은 다양한 원인에 의해 댐 유입량의 변동성이 증가하면서 댐 관리 및 운영조작 의사 결정에 어려움이 발생하고 있다. 따라서 이러한 댐 유입량의 변동 특성을 반영하여 댐 유입량을 정확하고 효율적으로 예측할 수 있는 방안이 필요한 실정이다. 머신러닝 기술이 발전하면서 Auto-ML(Automated Machine Learning)이 다양한 분야에서 활용되고 있다. Auto-ML은 데이터 전처리, 최적 알고리즘 선택, 하이퍼파라미터 튜닝, 모델 학습 및 평가 등의 모든 과정을 자동화하는 기술이다. 그러나 아직까지 수문 분야에서 댐 유입량을 예측하기 위한 모델을 개발하는데 있어서 Auto-ML을 활용한 사례는 부족하고, 특히 댐 유입량의 예측 정확성을 확보하기 위해 High-inflow and low-inflow 의 변동 특성을 고려한 하이브리드 결합 방식을 통해 Auto-ML 기반 앙상블 모델을 개발하고 평가한 연구는 없다. 본 연구에서는 Auto-ML의 패키지 중 Auto-sklearn을 통해 홍수기, 비홍수기 유입량 변동 특성을 반영한 하이브리드 앙상블 댐 유입량 예측 모델을 개발하였다. 소양강댐을 대상으로 적용한 결과, 하이브리드 Auto-sklearn 앙상블 모델의 댐 유입량 예측 성능은 R² 0.868, RMSE 66.23 m³/s, MAE 16.45 m³/s로 단일 Auto-sklearn을 통해 구축 된 앙상블 모델보다 전반적으로 우수한 것으로 나타났다. 특히 FDC (Flow Duration Curve)의 저수기, 갈수기 구간에서 두 모델의 유입량 예측 경향은 큰 차이를 보였으며, 하이브리드 Auto-sklearn 모델의 예측 값이 관측 값과 더욱 유사한 것으로 나타났다. 이는 홍수기, 비홍수기 구간에 대한 앙상블 모델이 독립적으로 구축되는 과정에서 각 모델에 대한 하이퍼파라미터가 최적화되었기 때문이라 판단된다. 향후 본 연구의 방법론은 보다 정확한 댐 유입량 예측 자료를 생성하기 위한 방안 수립뿐만 아니라 다양한 분야의 불균형한 데이터셋을 이용한 앙상블 모델을 구축하는데도 유용하게 활용될 수 있을 것으로 사료된다.

• Economic and Environmental Geology
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• v.56 no.2
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• pp.139-153
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• 2023

This study aimed to understand the spatiotemporal variations in nitrogen content in the Gyeongan stream along the main stream and at the discharge points of the sub-basins, and to identify the origin of the nitrogen. Field surveys and laboratory analyses, including chemical compositions and isotope ratios of nitrate and boron, were performed from November 2021 to November 2022. Based on the flow duration curve (FDC) derived for the Gyeongan stream, the dry season (mid-December 2021 to mid-June 2022) and wet season (mid-June to early November 2022) were established. In the dry season, most samples had the highest total nitrogen(T-N) concentrations, specifically in January and February, and the concentrations continued to decrease until May and June. However, after the flood season from July to September, the uppermost subbasin points (Group 1: MS-0, OS-0, GS-0) where T-N concentrations continually decreased were separated from the main stream and lower sub-basin points (Group 2: MS-1~8, OS-1, GS-1) where concentrations increased. Along the main stream, the T-N concentration showed an increasing trend from the upper to the lower reaches. However, it was affected by those of the Osan-cheon and Gonjiamcheon, the tributaries that flow into the main stream, resulting in respective increases or decreases in T-N concentration in the main stream. The nitrate and boron isotope ratios indicated that the nitrogen in all samples originated from manure. Mechanisms for nitrogen inflow from manure-related sources to the stream were suggested, including (1) manure from livestock wastes and rainfall runoff, (2) inflow through the discharge of wastewater treatment plants, and (3) inflow through the groundwater discharge (baseflow) of accumulated nitrogen during agricultural activities. Ultimately, water quality management of the Gyeongan stream basin requires pollution source management at the sub-basin level, including its tributaries, from a regional context. To manage the pollution load effectively, it is necessary to separate the hydrological components of the stream discharge and establish a monitoring system to track the flow and water quality of each component.