• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1126-1139
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• 2014

World is experiencing abnormal weather caused by urbanization and industrialization increasing greenhouse gas and one of these phenomenon domestically happening is flood and drought. The increase of green-house gases is due to urbanization and industrialization acceleration which are causing abnormal climate changes such as the El Nino and a La Nina phenomenon. It is expected that there will be many difficulties in water management, especially considering the topography and seasonal circumstances in Korea. Unlike in the past, a variety of water conservation initiatives have been undertaken like the river-management flow and water capacity expansion projects. To meet the increasing demand for water resources, new environmentally-friendly small and medium-sized dams have been built. Therefore, the development of a new paradigm for water resources management is essential. This study shows that additional security is needed for potential water resources through diversion tunnels and is very important to consider for future water supplies and situations. Using RCP 6.0 and RCP 8.5 in representative concentration pathway climate change scenario, specific hydrologic data of study basin was produced to analyze past observed basin rainfall tendency which showed both scenario 5%~9% range increase in rainfall. Through sensitivity analysis using objective function, population in highest goodness was 1000 and cross rate was 80%. In conclusion, it is expected that the results from this study will help to make long-term and stable water supply plans by using the potential water resource evaluation model which was applied in this study.

• Ecology and Resilient Infrastructure
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• v.6 no.1
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• pp.23-33
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• 2019

Naeseong Stream is a sand-bed river that flows through the northern area of Gyeongbuk province. It is characterized by dynamic sandy bedforms developed in response to the seasonal hydrological fluctuation and by its unique riverine landscape called "white river." However, changes including construction of Yeongju Dam from 2010 and the extensive vegetation establishment around 2015 occurred along the Naeseong Stream. This paper aims to analyze climate, hydrology, and water quality as factors and to examine the possibility of channel changes accordingly. The second least precipitation during the last 60 years happened in 2015, which led to the lowest peak discharge in 50 years. The sediment characteristics of Naeseong Stream were not significantly different along the upstream and downstream reaches, but it was confirmed that annual minimum water level of the stream decreased continuously regardless of the dam construction. This suggests that intermittent drought and change in water quality are likely to provide favorable conditions for riparian vegetation establishment and the resulting physical changes have affected riverbed degradation. Therefore, it is necessary to conduct diversified monitoring in connection with river vegetation change in order to analyze the causes of river changes.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.470-470
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• 2011

환경부 하수재이용사업은 2007년도 하수도법 개정을 통해 공공하수처리시설 처리수의 재이용 의무화를 시작으로 2010년도 "물의 재이용 촉진 및 지원에 관한 법률"의 시행으로 전국적으로 사업이 확대되고 있다. 제주도 월정하수처리장은 2009년도 하수재이용사업지구로 선정이 되어 2010년도부터 농업용목적의 재이용으로 구체적인 설계와 시공이 추진되고 있는 사업지구이다. 제주도에서는 지하수보존을 위해 대체수자원 개발 방안을 시급히 마련중에 있다. 특히, 제주도 농업용수 종합계획수립(제주도, 2004)에는 하수처리수를 농업용수로 이용하기 위한 계획을 수립할 정도로 지하수 사용량을 줄이기 위한 노력을 진행중에 있으며, 그 일환으로 하수재이용사업을 지속적으로 추진하고 있다. 하수처리수의 농업용수 재이용은 사용된 물을 재이용함으로써 물과 에너지를 절약할 수 있고, 유역 또는 해양으로 배출되는 오염원을 억제하는 장점이 있는 반면에 농산물 생육에 직접 영향을 줄뿐만 아니라 주변의 수질 생태 토양 환경 및 영농인의 보건에도 영향을 주기 때문에 장기적인 관찰과 검증작업이 필요하다. 이 에 서울대와 한국농어촌공사는 안전한 농업용수 재이용기술을 개발하기 위해 장기간 현장시험을 통해 재이용 재배기술과 함께 보건환경에 미치는 영향을 검증하였고 그 개발기술을 월정사업지구에 적용하게 되었다. 월정하수처리장이 위치한 제주 동부의 월정지역은 농지면적이 밭(374ha)과 과수(12ha)등 제주도의 전형적인 농촌마을으로 주요 재배작물은 마늘과 당근, 쪽파, 콩 등으로서 농업기반시설의 미비로 영농에 어려움을 호소하고 있으며, 2006년도에 발생한 가뭄으로 그 해 평균 수학량의 30%가 감소된 바 있는 지역이다. 제주도 농업용수 종합계획수립(2004, 제주도)에서는 10년에 한발을 기준 으로 $43,000m^3$/일의 용수가 부족할 것으로 분석하였으며 최근 $35,000m^3$/day 규모의 상수도 확보사업 계획 수립하였으나 여전히 농업용수가 부족하다. 방류수의 수질은 방류수수질기준을 만족하지만 염분함량이 높아 직접 농업용으로 사용하기에는 적당하기 않고, 농업용재이용방류수 수질기준에 맞도록 재이용시스템을 통해 재처리하여 농업용수로 사용해야 한다. 제주도에서는 이미 제주 서부하수처리장 농업용수 재이용사업(이하 판포재이용사업)'이 완료되어 재이용수를 농업용수로 공급하고 있으며 향후 지속적으로 하수재이용사업이 확대될 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.301-301
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• 2021

영산강 유역은 상류 급경사 지역에 있는 유역 내 댐들의 홍수조절용량과 평야지역에 있는 하굿둑의 홍수배제능력이 부족하여 나주시 등 하류부에 홍수피해가 빈번하였다. 영산강에서 4대강 사업의 주요내용은 하도내 퇴적토준설과 홍수조절지 건설, 농업용저수지 증고를 통한 홍수방어능력 증대, 다기능 보설치를 통한 용수확보, 하구지역 홍수배제능력 증대 등이었다. 동 사업에서 본류 하천의 홍수위 저감과 내수배제 개선을 위해 담양댐 하류부터 영암천 합류점까지 하도를 준설하고, 담양과 화순홍수조절지, 나주 강변저류지, 승촌보와 죽산보를 건설하고, 하굿둑 배수능력 증대와 농업용 댐 및 저수지들을 증고를 수행하였다. 4대강 사업이 준공된 2012년 이후로 2014년부터 5년간 가뭄이 지속되었고 큰 홍수가 없다가 2020년 8월에 장기간의 집중호우로 영산강 중상류인 광주시와 영산강 하류지역에서 큰 수해가 발생하였다. 따라서 시설물 운영 실적에 근거한 홍수저감 효과의 기술적 검토를 수행하였다. 사업전·후 수문관측자료와 하천시설 운영 실적에 근거한 홍수저감 효과를 분석하기 위해 사업전·후 유사 규모의 강우 발생 시 수위표 지점별로 계측된 첨두 수위 및 유량자료를 비교하여 홍수저감 효과를 분석하였다. 사업전·후 유사 규모 강우를 선정하기 위해 발생된 강우 사상 중 호우특보 발령 기준이상의 강우 사상에 대하여 총 강우량 및 강우의 지속시간, 시간 분포를 비교하여 유사 규모의 호우를 선정하였다. 사업전·후 유사 규모의 호우 사상 발생 시 계측된 홍수위와 홍수량 비교 결과 영산강 중·상류부와 중·하류부 수위표 지점(극락교,승용교,나주대교)에서 사업 시행 후 사업 전보다 첨두 수위가 1.36~2.81m 감소한 것으로 검토되었다. 이는 여러 가지 사업들의 복합적인 결과로 영산강유역의 홍수관리여건이 개선된 것으로 판단된다. 한편 2020년 8월7일~8일 발생한 호우에 의해 영산강 본류의 중·상류부와 중·하류부의 주요 수위표 지점에서는 200년빈도 계획홍수위를 초과한 홍수가 발생하였다. 상시개방과 철거로 처리방안이 결정된 승촌보와 죽산보의 여건을 반영하여 2개보 유무에 따른 홍수위 검토를 실시하였다. 홍수위 비교 결과보가 없을 경우 영산강 중·상류부(극락교,승용교)와 중·하류부(나주대교,영산교) 수위표 지점에서 홍수위가 0.01~0.07m 감소되는 것으로 검토되어 홍수시 보의 영향은 비교적 작은 것으로 나타났다. 홍수시 상류댐과 저수지, 홍수조절지, 하굿둑, 하천의 연계운영에 대해서는 추가연구가 필요하다.

• 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.

• The Journal of Engineering Geology
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• v.30 no.3
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• pp.269-278
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• 2020

In this study, the effects of groundwater artificial recharge through vertical wells in the upper small basin are preliminarily evaluated by using field injection test and a 3-D numerical model. The injection rate per well in a model is set to 20, 37.5, 60, and 75 ㎥/day based on the results of field injection test, groundwater levels, and hydraulic conductivities estimated from particle size analysis, and a numerical model using MODFLOW is conducted for 28 cases, which have diverse injection intervals, in order to estimated the changes of groundwater level and water balance after injection. Groundwater level after injection does not show a linear relationship with the injection rate per well, and the cumulative effect of artificial recharge decreases and the timing of maximum water level rise is shortened as the injection interval becomes longer. In four cases of continuous injection with total injection rate of 1,200 ㎥, it is revealed that the recharge effect is analyzed as 36.5~65.3% of the original injection rate. However, it will be more effective if the artificial recharge system combined with underground barrier is introduced for the longer pumping during a long and severe drought. Additionally, it will be possible to build a stable artificial recharge system by an establishment of efficient scenario from recharge to pumping as well as an optimization of recharge facilities.

• Journal of Wetlands Research
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• v.17 no.4
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• pp.348-358
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• 2015

Impacts of climate change are being observed in the globe as well as the Korean peninsula. In the past 100 years, the average temperature of the earth rose about 0.75 degree in celsius, while that of Korean peninsula rose about 1.5 degree in celsius. The fifth Assessment Report of IPCC(Intergovermental Panel on Climate Change) predicts that the water pollution will be aggravated by change of hydrologic extremes such as floods and droughts and increase of water temperature (KMA and MOLIT, 2009). In this study, future runoff was calculated by applying climate change scenario to analyze the future water quality for each targe period (Obs : 2001 ~ 2010, Target I : 2011 ~ 2040, Target II : 2041 ~ 2070, Target III : 2071 ~ 2100) in Hongcheon river basin, Korea. In addition, The future water quality was analyzed by using multiple linear regression analysis and artificial neural networks after flow-duration curve analysis. As the results of future water quality prediction in Hongcheon river basin, we have known that BOD, COD and SS will be increased at the end of 21 century. Therefore, we need consider long-term water and water quality management planning and monitoring for the improvement of water quality in the future. For the prediction of more reliable future water quality, we may need consider various social factors with climate components.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.1-8
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• 2024

Persistent droughts due to climate change will intensify water shortage problems in Korea. According to the 1st National Water Management Plan, the shortage of domestic and industrial waters is projected to be 0.07 billion m³/year under a 50-year drought event. A long-term prediction of water demand is essential for effectively responding to water shortage problems. Unlike industrial water, which has a relatively constant monthly usage, domestic water is analyzed on monthly basis due to apparent monthly usage patterns. We analyzed monthly water usage patterns using water usage data from 2017 to 2021 in Chungcheong, South Korea. The monthly water usage rate was calculated by dividing monthly water usage by annual water usage. We also calculated the water distribution rate considering correlations between water usage rate and climate variables. The division method that divided the monthly water usage rate by monthly average temperature resulted in the smallest absolute error. Using the division method with average temperature, we calculated the water distribution rates for the Chungcheong region. Then we predicted future water usage rates in the Chungcheong region by multiplying the average temperature of the SSP5-8.5 scenario and the water distribution rate. As a result, the average of the maximum water usage rate increased from 1.16 to 1.29 and the average of the minimum water usage rate decreased from 0.86 to 0.84, and the first quartile decreased from 0.95 to 0.93 and the third quartile increased from 1.04 to 1.06. Therefore, it is expected that the variability in monthly water usage rates will increase in the future.

• Journal of Korea Water Resources Association
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• v.51 no.1
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• pp.11-18
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• 2018

The climate change impacts on hydrological components and water balance in Jeju Island were evaluated using multiple climate models and watershed model, SWAT-K. To take into account the uncertainty of the future forecast data according to climate models, climate data of 9 GCMs were utilized as weather data of SWAT-K for future period (2010-2099). Using the modeling results of the past (1992-2013) and the future period, the hydrological changes of each year were analyzed and the precipitation, runoff, evapotranspiration and recharge were increasing. Compared with the past, the change in the runoff was the largest (up to 50% increase) and the evapotranspiration was relatively small (up to 11% increase). Monthly results show that the amount of evapotranspiration and the amount of recharge are greatly increased as the amount of precipitation increases in August and September, while the amount of evapotranspiration decreases in the same period. January and December showed the opposite tendency. As a result of analyzing future water balance changes, the ratio of runoff, evapotranspiration, and recharge to rainfall did not change much, but compared to the past, the runoff rate increased up to 4.3% in the RCP 8.5 scenario, while the evapotranspiration rate decreased by up to 3.5%. Based on the results of other researchers and this study, it is expected that rainfall and runoff will increase gradually in the future under the assumption of present climate change scenarios. Especially summer precipitation and runoff are expected to increase. As a result, the amount of groundwater recharge in Jeju Island will increase.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.