• Journal of Korea Water Resources Association
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• v.45 no.12
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• pp.1321-1330
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• 2012

In this study, the daily inflow at the basin of Chungju dam is predicted using wavelet-artificial neural network for nonlinear model. Time series generally consists of a linear combination of trend, periodicity and stochastic component. However, when framing time series model through these data, trend and periodicity component have to be removed. Wavelet transform which is denoising technique is applied to remove nonlinear dynamic noise such as trend and periodicity included in hydrometeorological data and simple noise that arises in the measurement process. The wavelet-artificial neural network (WANN) using data applied wavelet transform as input variable and the artificial neural network (ANN) using only raw data are compared. As a results, coefficient of determination and the slope through linear regression show that WANN is higher than ANN by 0.031 and 0.0115 respectively. And RMSE and RRMSE of WANN are smaller than those of ANN by 37.388 and 0.099 respectively. Therefore, WANN model applied in this study shows more accurate results than ANN and application of denoising technique through wavelet transforms is expected that more accurate predictions than the use of raw data with noise.

• Journal of Wetlands Research
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• v.15 no.1
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• pp.125-137
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• 2013

Global warming and climate change have influence on abnormal weather pattern and the rainstorm has a localized and intensive tendency in Korea. IPCC(2007) also reported the rainstorm and typhoon will be more and more stronger due to temperature increase during the 21st century. Flood Estimation Handbook(Institute of Hydrology, 1999) published in United Kingdom, in the case that the data period is shorter than return period, recommends the regional frequency analysis rather than point frequency analysis. This study uses Regional Climate Model(RCM) of Korea Meteorological Administration(KMA) for obtaining the rainfall and for performing the regional frequency analysis. We used the rainfall data from 58 stations managed by KMA and used L-moment algorithm suggested by Hosking and wallis(1993) for the regional frequency analysis considering the climate change. As the results, in most stations, the rainfall amounts in frequencies have an increasing tendency except for some stations. According to the A1B scenario, design rainfall is increased by 7~10% compared with the reference period(1970-2010).

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.46-46
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• 2015

강우가 지역별 계절별로 편중되어 있는 우리나라는 수자원의 안정적인 확보와 이용을 위해 다양한 형태의 댐을 건설하여 운영하고 있다. 그러나 대부분의 댐건설을 통해 형성된 저수지들은 탁수 장기화 및 녹조 발생 등의 환경, 생태적인 문제를 겪고 있으며, 그에 따른 사회적 우려로 인해 신규댐 건설을 통한 수자원확보는 더 이상 어려운 실정이다. 이러한 문제에 대응하기 위한 대안으로 기존 댐 저수지들(안동호-임하호)의 구조적 연계운영방안이 진행되고 있다. 본 연구의 목적은 2차원 CE-QUAL-W2모형을 활용하여 안동호와 임하호의 구조적 연결에 따른 탁수의 이동과 각 저수지 내에서의 유동 변화를 해석하는데 있다. 저수지 연계 시나리오는 EL. 138 m 위치에 길이 2 km, 직경 5.5 m 의 콘크리트관(마찰계수 0.05)이 안동호 좌안인 임동면 마리와 임하호 우안 망천리를 연결하는 것으로 가정하였다. 모델의 보정은 실측자료가 풍부한 2006년도 수문사상을 대상으로, 개별 저수지에 대해 수행하였고, 탁수 유동 시나리오 해석은 임하호에 심각한 탁수장기화 문제가 발생했던 2002년을 대상으로 댐 연계 탁수모의를 수행하였다. 안동호와 임하호의 댐 앞에서 모의값과 실측값을 오차를 분석한 결과 탁수예측오차는 AME 0.5~24 mg/L, RMSE 0.7~30.2mg/L의 범위로 비교적 실측값을 잘 반영한 것으로 나타났다. 임하댐의 경우 탁수층의 위치와 두께, 그리고 최고 탁도값을 적절히 재현 하였지만, 안동댐은 최고 탁도값 예측에서 다소 오차가 발생하는 것으로 나타났다. 안동호와 임하호 단독 운영시와 연계 운영시의 탁수변화 파악을 위해 초기 홍수사상이 발생한 8월 이후부터 저수지내의 TSS농도 분포를 비교하였다. 안동호의 경우 댐앞지점의 탁수분포는 수온성층구조에 영향을 받아, 단독 운영시(EL. 130 m)보다 연계운영시(EL. 140 m)에 탁수의 중심이 높은 위치에 형성되었다. 단독 운영시 10월 이후에 전도현상으로 인해 침강되지 않은 잔류 탁수층이 저수지 하부로 확산되었지만, 연계 운영시에는 재부상 되어 상층으로 확산되는 것으로 모의되었다. 또한 연계운영시 유량이동으로 인해 안동호의 탁수 댐앞 도달시간이 짧아지는 것으로 나타났다. 반면 임하호는 연계 운영시 안동댐으로 유출이 생기면서 중층에서 탁수량이 저감되는 것으로 모의되었다. 저수지 내 탁수량 분석을 위해 SS 15 mg/L 이상의 잔류 탁수량을 분석한 결과, 연계운영시 안동호의 평균 잔류탁수량 비율은 11.8% 증가, 임하호의 경우 11.7% 감소하였다. 또한, 탁수의 댐하류 방류일수도 SS 15 mg/L 기준 임하호 9일 저감, 안동호는 70일 증가하여 임하호의 탁수가 안동호의 탁수 장기화에 영향을 주는 것으로 나타났다.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.281-281
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• 2016

기후변화로 인한 기상학적 자연재해로부터 대비하고 안정적인 용수공급을 위해 유역의 다양한 수문 요소들에 대한 분석 필요성이 증가하고 있다. 계절적 강수량의 편차가 큰 우리나라는 유역 통합 물관리가 중요하며, 효율적 수자원 관리와 물안보 확보를 위해 유역내 물순환을 이해하는 것이 중요하다. 유역의 유출을 결정하는 요소들에는 강우, 증발산량, 토양 수분 및 지하수 등이 있으며, 시간적으로는 홍수와 같이 단기에 발생하는 유출과 장기적으로 발생하는 유출이 있다. 장기 유출은 단기 유출에 비해 토양내 수분량이 무시할 수 없을 정도로 영향을 미치게 되므로, 1년 이상의 장기 유출 해석을 위해서는 강우가 발생하지 않는 기간 동안의 토양 수분량 변화와 증발산 영향을 고려할 필요가 있다. K-water에서 자체 개발된 분포형 장단기유출 모델인 K-DRUM은 유역을 격자(grid)단위로 구분하고 각 셀들에 대한 매개변수는 흐름방향도, 표고분포도, 토지이용도, 토지피복도 등을 GIS처리하여 일괄 입력할 수 있도록 함으로써 매개변수 산정과정에서 문제가 되는 경험적인 요인을 제거하였다. 흐름의 구분은 얕은면 흐름, 지표하 흐름, 지하수 흐름으로 구분하여 운동파법과 선형저류법을 적용하였다. 또한 초기 토양함수 자동보정기법으로 실제의 기저유출량을 재현하여 전체적인 유출모의 정확도를 높였으며, FAO-56 Penman-Monteith법을 적용한 증발산량 산정모듈과 Sugawara et al.(1984)이 제안한 개념적 융설 및 적설모듈을 추가하였다. K-DRUM모형을 이용한 유출분석은 용담댐 시험유역을 대상으로 2013년도 1년간의 유출모의를 수행하였다. 입력자료는 용담댐 유역의 지형, 토양 및 토지특성 정보와 시단위 강우 및 기상정보(온도, 바람, 일사 등)를 활용하였다. 분석 결과, 총 관측유출량은 7,151 ㎥/s이고 총 계산유출량 $8,257m^3/s$이며, 관측유출량 대비 계산유출량은 약 115% 정도로 나타났다. 연간 총 강우량은 1303.5 mm로 유역면적 약 $930km^2$을 적용하여 유역 총 강우량을 산정하면 $14,030m^3/s$로서 관측유출량은 유역 총 강우량 대비 51%이고 계산유출량은 59% 정도로 나타났다. 즉 유역 유출율은 약 51% 수준으로 보통의 유역과 유사한 수준이다. 관측된 토양수분량과 K-DRUM 모형의 계산된 토양수분량을 비교하기 위하여 관측 토양수분량의 비율을 이용하여 비교하였다. 모의결과 토양수분은 강우에 의해 변화하며, 관측결과와 유사한 형태로 나타남을 알 수 있었다.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1217-1227
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• 2018

This paper aims to evaluate the applicability of dam inflow prediction model using recurrent neural network theory. To achieve this goal, the Artificial Neural Network (ANN) model and the Elman Recurrent Neural Network(RNN) model were applied to hydro-meteorological data sets for the Soyanggang dam and the Chungju dam basin during dam operation period. For the model training, inflow, rainfall, temperature, sunshine duration, wind speed were used as input data and daily inflow of dam for 10 days were used for output data. The verification was carried out through dam inflow prediction between July, 2016 and June, 2018. The results showed that there was no significant difference in prediction performance between ANN model and the Elman RNN model in the Soyanggang dam basin but the prediction results of the Elman RNN model are comparatively superior to those of the ANN model in the Chungju dam basin. Consequently, the Elman RNN prediction performance is expected to be similar to or better than the ANN model. The prediction performance of Elman RNN was notable during the low dam inflow period. The performance of the multiple hidden layer structure of Elman RNN looks more effective in prediction than that of a single hidden layer structure.

• Journal of Korea Water Resources Association
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• v.52 no.9
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• pp.647-655
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• 2019

Although problems such as river management and flood control have occurred continuously in the Imjin and Bukhan river basin, which are shared by South and North Korea, efforts to manage the basin have not been carried out consistently due to limited cooperation. As the magnitude and frequency of hydrologic phenomena are changing due to global climate change, it is necessary to prepare countermeasures for the rainfall variation in the shared river basin area. Therefore, this study was aimed to project future changes in extreme precipitation in South-North Korea shared river basin by applying 13 Global Climate Models (GCM). Results showed that the probability rainfall compared to the reference period (1981-2005) of the shared river basin increased in the future periods of 2011-2040, 2041-2070 and 2071-2100 under the Representative Concentration Pathways (RCP)4.5 and RCP8.5 scenarios. In addition, the rainfall frequency over the 20-year return period was increased in all periods except for the future periods of 2041-2070 and 2071-2100 under the RCP4.5 scenario. The extreme precipitation in the shared river basin has increased both in magnitude and frequency, and it is expected that the region will have a significant impact from climate change.

• Ecology and Resilient Infrastructure
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• v.9 no.1
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• pp.24-35
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• 2022

For water quality management, it is necessary to continuously improve the forecasting by analyzing the past water quality, and a Data-driven model is emerging as an alternative. Because the Data-driven model is built based on a wide range of data, it is essential to apply the correlation analysis method for the combination of input variables to obtain more reliable results. In this study, the Gamma Test was applied as a preceding step to build a faster and more accurate data-driven water quality prediction model. First, a physical-based model (HSPF, EFDC) was operated to produce daily water quality reflecting the complexity of the watershed according to various hydrological conditions for Paldang Dam. The Gamma Test was performed on the water quality at the water quality prediction site (Paldangdam2) and major rivers flowing into the Paldang Dam, and the method of selecting the optimal input data combination was presented through the analysis results (Gamma, Gradient, Standar Error, V-Ratio). As a result of the study, the selection criteria for a more efficient combination of input data that can save time by omitting trial and error when building a data-driven model are presented.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.529-541
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• 2023

Due to the growing concern over forecasting extreme weather events such as droughts caused by climate change, there has been a rising interest in seasonal meteorological forecasts that offer ensemble predictions for the upcoming seven months. Nonetheless, limited research has been conducted in South Korea, particularly in assessing their effectiveness at the catchment-scale. In this study, we assessed the accuracy of ECMWF's seasonal forecasts (including precipitation, temperature, and evapotranspiration) for the period of 2011 to 2020. We focused on 12 multi-purpose reservoir catchments and compared the forecasts to climatology data. Continuous Ranked Probability Skill Score method is adopted to assess the forecast skill, and the linear scaling method was applied to evaluate its impact. The results showed that while the seasonal meteorological forecasts have similar skill to climatology for one month ahead, the skill decreased significantly as the forecast lead time increased. Compared to the climatology, better results were obtained in the Wet season than the Dry season. In particular, during the Wet seasons of the dry years (2015, 2017), the seasonal meteorological forecasts showed the highest skill for all lead times.

• Journal of Korea Water Resources Association
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• v.56 no.3
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• pp.173-184
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• 2023

Due to climate change, algal blooms frequently occur not only in Korea but also around the world, and the risk of toxicity of harmful algae has recently been issued. It is known that the representative harmful algae, cyanobacteria, are caused by the intersection of three factors: water temperature, residence time, and nutrients. In this study, water quality simulation was carried out using EFDC, a three-dimensional numerical model, to analyze the variations in water quality due to the decrease of residence time according to the opening of Yeongju Dam in Naeseong-Cheon. In fact, the concentration of chlorophyll-a in Yeongju Dam in the summer of 2021 was significant, exceeding the 'algae warning' for a long time based on the previous algae warning system. On the other hand, as a result of performing the simulation under the condition that the dam gate was completely opened, the concentration of chlorophyll-a was mostly reduced below the 'algae warning' level during the simulation period. It was confirmed that reducing the residence time by restoring the flow of Naeseong-Cheon is a way to immediately reduce algae in Yeongju Dam.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.562-575
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• 2023

In an effort to improve water quality, the South Korean government has implemented measures to increase seawater circulation in Saemangeum Lake. We analyzed the effect of increasing the frequency of seawater circulation based on salinity levels and bottom water exchange in the lake, using an environmental fluid dynamics code model. When the sluice gate opening and shutting frequency increased from once to twice per day, the internal water level of Saemangeum Lake increased by up to ~0.7 m. The salinity increased by 2.12 psu near the western breakwater and decreased by 1.18 psu near the freshwater inlet. We analyzed the extent of bottom water exchange using a particle tracing method and observed that the residual rate of particles shallower than 5 m in water depth decreased by 2.52% in Case 2 (opening and shutting twice per day) compared to Case 1 (opening and shutting once per day). This indicates that increasing the frequency of sluice gate opening and shutting would promote enhanced bottom water exchange. Consequently, the increased salinity and bottom water exchange associated with increased seawater circulation are expected to improve water quality in Saemangeum Lake.