• Title/Summary/Keyword: Nakdonggang River Basin

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Estimating the Return Flow of Irrigation Water for Paddies Using Hydrology-Hydraulic Modeling (수리·수문해석 모델을 활용한 농업용수 회귀수량 추정)

  • Shin, Ji-Hyeon;Nam, Won-Ho;Yoon, Dong-Hyun;Yang, Mi-Hye;Jung, In-Kyun;Lee, Kwang-Ya
    • Journal of The Korean Society of Agricultural Engineers
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    • v.65 no.6
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    • pp.1-13
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    • 2023
  • Irrigation return flow plays an important role in river flow forecasting, basin water supply planning, and determining irrigation water use. Therefore, accurate calculation of irrigation return flow rate is essential for the rational use and management of water resources. In this study, EPA-SWMM (Environmental Protection Agency-Storm Water Management Model) modeling was used to analyze the irrigation return flow and return flow rate of each intake work using irrigation canal network. As a result of the EPA-SWMM, we tried to estimate the quick return flow and delayed return flow using the water supply, paddy field, drainage, infiltration, precipitation, and evapotranspiration. We selected 9 districts, including pumping stations and weirs, to reflect various characteristics of irrigation water, focusing on the four major rivers (Hangang, Geumgang, Nakdonggang, Yeongsangang, and Seomjingang). We analyzed the irrigation period from May 1, 2021 to September 10, 2021. As a result of estimating the irrigation return flow rate, it varied from approximately 44 to 56%. In the case of the Gokseong Guseong area with the highest return flow rate, it was estimated that the quick return flow was 4,677 103 m3 and the delayed return flow was 1,473 103 m3 , with a quick return flow rate of 42.6% and a delayed return flow rate of 13.4%.

The infestation states and changing patterns of human infecting metacercariae in freshwater fish in Kyongang-do and Kyonggi-do, Korea (식이성 윤충류질환의 관리전략수립을 위한 감염원의 역학 및 병원체의 생물학적 특성에 관한 조사연구 - 경상도내 3개 강 및 경기도내 4개 하천에서 채집한 민물어류의 인체기생 흡충류 피낭유충 감염상 및 변동)

  • 임한종;김기홍
    • Parasites, Hosts and Diseases
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    • v.34 no.2
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    • pp.95-106
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    • 1996
  • The infestation rates and abundances of human infecting metacercariae (Clonorchis sinensis, Metcgonimus spry. , Centrocestus crmatus, Echinostoma hortense, Echinochusnus japonicw, Clinostomum complanctum) in freshwater fish were investigated at the three river areas - Taewhagang (river), Hyongsangang (river), Nakdonggang (river) - in Kyongsang-do and at four streams - Yonpungchon, Munsanchon, Kyonganchon, and Konjiamchon- in Kyonggi-do, Korea in 1994-1995. The fish caught at Taewhagang were heavily infested with metacercariae of Clonorchis sinensis and Centrocestw armctus. At Hyongsangang, Zncco platypus and Z. temmincki were moderately infested with metacercariae of C. crmctus. Chomanpo, at the basin of Nakdonggang, was still endemic for C. sinensis. In the fish caught at four streams of Kyonggi-do, metacercariae of C. sinensis exhibited the highest infestation rate and intensity out of 6 species of metacercariae. The infestation intensity of C. sinensis metacercariae in fish flesh was markedly different according to each division of flesh. The cause of this difference was conjectured as a result of larval behavior. The metacercariae of C. omnt5 were found in almost all parts, except scales and fins, of fish. The infestation rates and intensities of C. sinensis and C. armntus metacercariae in Taewhagang greatly increased as compared with those of previous reports . RhinoBobius bmnneus and Aconthorhone5 macropterus are newly recorded intermediate hosts of Echinostonn hortense. The reason of large differences from previous data was discussed and the standard method of metacercaria examination was proposed.

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A study on the selection of priority management watershed for the restoration of water cycle (물순환 회복을 위한 우선관리유역 선정 방안에 대한 연구)

  • Kim, Jaemoon;Baek, Jongseok;Park, Jaerock;Park, Byungwoo;Shin, Hyunsuk
    • Journal of Korea Water Resources Association
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    • v.55 no.10
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    • pp.749-759
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    • 2022
  • The paradigm of water cycle management in the watershed is changing due to the increase in abnormal climate phenomena caused by climate change and the increase in impervious area due to urbanization. Research is continuously underway based on Low Impact Development technology that can suppress water cycle distortion. In this study, factors that can reflect water cycle distortion were selected before applying LID, and the PSR index for each 148 watershed was calculated for the the Nakdonggang River basin. As of 1975, the PSR index is calculated by calculating the pressure index P, which represents the rate of change in impervious surface area to 2019, the phenomenon index S, which represents the rate of change in water cycle for each subwatershed, and the Low Impact Development area countermeasure index R. The lower PSR index value, the higher the priority management watershed, and the water cycle recovery priority management watershed was calculated in the order of 1, 2, 87, 90, 91, and 147. It is expected that the efficient application of low-impact development factors in accordance with the order of priority management of water cycle by subwatershed in the large area will contribute to the recovery of water cycle distortion.

Application of deep learning method for decision making support of dam release operation (댐 방류 의사결정지원을 위한 딥러닝 기법의 적용성 평가)

  • Jung, Sungho;Le, Xuan Hien;Kim, Yeonsu;Choi, Hyungu;Lee, Giha
    • Journal of Korea Water Resources Association
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    • v.54 no.spc1
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    • pp.1095-1105
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    • 2021
  • The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.