• Korean Journal of Ecology and Environment
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• v.35 no.1 s.97
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• pp.36-44
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• 2002

The spatial and temporal trends of water qualities in Lake Soyang was statistically analyzed in this study. The water qualities include nutrients, ionic contents and chlorophyll-a (Chl-a) measured during 1993${\sim}$2000. The rainfall intensity and runoff from the catchment appeared to play an important role in water quality trends in the lake. According to seasonal Mann-Kendall test, conductivity, TP, and Ctl-a did not show any trends of increase or decrease over the 8 year period, while TN declined slightly. It was found that the variation of TP was a function of interannual inflow and rainfall. In the analyses of spatial trend, conductivity, based on the mean by site, showed a downlake decline over the eight year period. Minimum conductivity was found in the headwaters during summer monsoon of July to August and near the dam during October. This result indicates a time-lag phenomenon that the headwater is diluted by rainwater immediately after summer monsoon rain and then the lake water near the dam is completely diluted in October. During summer period, TP and TN had an inverse relation with conductivity values. Concentrations of TP peaked during July to September in the headwaters and during September in the downlake. Also, TN increase during the summer and was more than 1.5 mg/L regardless of season and location, indicating a consistent eutrophic state. Values of Chl-a varied depending on location and season, but peaked in the midlake rather than in the headwaters during the monsoon. Regression analyses of log-transformed seasonal Chl-a against TP showed that value of $R^2$ was below 0.003 in the premonsoon and monsoon seasons but was 0.82 during the postmonsoon, indicating a greater algal response to the phosphorus during the postmonsoon. In contrast, TN had no any relations with Chl-a during all seasons.

• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.503-514
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• 2018

In recent, the hydrological regime of the Mekong river is changing drastically due to climate change and haphazard watershed development including dam construction. Information of hydrologic feature like streamflow of the Mekong river are required for water disaster prevention and sustainable water resources development in the river sharing countries. In this study, runoff simulations at the Kratie station of the lower Mekong river are performed using SWAT (Soil and Water Assessment Tool), a physics-based hydrologic model, and LSTM (Long Short-Term Memory), a data-driven deep learning algorithm. The SWAT model was set up based on globally-available database (topography: HydroSHED, landuse: GLCF-MODIS, soil: FAO-Soil map, rainfall: APHRODITE, etc) and then simulated daily discharge from 2003 to 2007. The LSTM was built using deep learning open-source library TensorFlow and the deep-layer neural networks of the LSTM were trained based merely on daily water level data of 10 upper stations of the Kratie during two periods: 2000~2002 and 2008~2014. Then, LSTM simulated daily discharge for 2003~2007 as in SWAT model. The simulation results show that Nash-Sutcliffe Efficiency (NSE) of each model were calculated at 0.9(SWAT) and 0.99(LSTM), respectively. In order to simply simulate hydrological time series of ungauged large watersheds, data-driven model like the LSTM method is more applicable than the physics-based hydrological model having complexity due to various database pressure because it is able to memorize the preceding time series sequences and reflect them to prediction.

• Journal of Korea Water Resources Association
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• v.37 no.11
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• pp.897-913
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• 2004

There has been continuous efforts to manage the water resources for the required water quality criterion at river channel in Korea. However, we could not obtain the partial improvement only for the point source pollutant such as, wastewater from urban and industrial site through the water quality management. Therefore, it is strongly needed that the Best Management Practice(BMP) throughout the river basin for water quality management including non-point source pollutant loads. This problem should be resolved by recognizing the non-point source pollutant loads from upstream river basin to the outlet depends on the land use and soil type characteristic of the river basin using the computer simulation by distributed parameter model based on the detailed investigation and the application of Geographic Information System(GIS). Used in this study, Annualized Agricultural Non-Point Source Pollution (AnnAGNPS) model is a tool suitable for long term evaluation of the effects of BMPs and can be used for un gauged watershed simulation of runoff and sediment yield. Now applications of model are in progress. So we just describe the limited result. However If well have done modeling and have investigated of propriety of model, well achieve our final goal of this study.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.71-71
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• 2011

지난 세기동안 지구 평균 기온이 상승함에 따라 대기 중에 차지하는 수증기 함유량 또한 증가 추이를(7%/$^{\circ}C$) 보이고 있으며, 이는 전 세계적으로 수문 순환 패턴의 변화를 초래한다(IPCC, 2007). 그 중에서도 강수 특성의 변화는 궁극적으로 유출량의 변화를 초래하며, 이는 수자원 총량의 변화로 이어지게 된다. 특히, 여름철에 대부분의 강수 현상이 집중되는 우리나라의 경우 육지의 70% 정도가 산악 지형으로 이루어진 복잡한 지리적 영향으로 집중호우 시 홍수가 일시에 유출되어 이에 따른 인적 물적 피해가 해마다 되풀이 되고 있다. 수자원은 인간 생활과 밀접한 관계에 있기 때문에 이러한 극심한 기후변화에 의한 피해를 최소화하기 위해 수계단위의 효율적인 물관리가 필수적이다. 따라서 한반도 내 주요 강(한강, 금강, 영산강, 섬진강, 낙동강)을 중심으로 수계별 강수량 및 유출량의 장기 특성 변화를 살펴보고자 한다. 장기간의 자료를 보유하고 있는 기상청 산하 27개 지점의 시간 강수량 자료 및 국가 수자원관리 종합정보시스템에서 제공하는 장기유출 자료를 수집하여 수계 평균값을 산정하고, 각 수계별 강수량 및 유출량의 장기 추이 및 변동성, 상관도를 알아보고자 하였다. 최근 36년 동안(1973~2008년) 모든 수계에서 연총강수량이 증가하는 추이를 보였으며, 한강 수계에서 유의수준 5% 내에서 가장 높은 증가율(약 10 mm/yr)을, 섬진강 수계에서 가장 낮은 증가율(약 4 mm/yr)을 나타냈다. 여름철 집중호우(20 mm/hr 이상) 빈도 분석 결과, 모든 수계에서 호우 빈도의 증가 경향이 뚜렷함을 볼 수 있다. 특히, 최근 10년간(1999~2008) 호우빈도의 변화를 살펴보면 섬진강 수계의 경우 총 60번으로 가장 많았고 상대적으로 낙동강 수계에서 35번으로 가장 적었다. 여름철 무강수일수(강수량이 0.1 mm 미만인 일수)의 경우 모든 수계에서 거의 완만한 감소추세를 보임을 확인할 수 있었다. 1970~2001년간 연총유출량의 경우 한강 및 금강 수계의 경우 증가하는 경향을 나타내는 반면 섬진강 수계의 경우 오히려 감소하며, 영산강 및 낙동강 수계에서는 뚜렷한 변화를 볼 수 없었다. 월별 유출량의 경우 모든 수계에서 7월, 8월, 9월에 집중되며, 한강 수계에서 8월, 그 외 수계에서는 7월에 가장 높은 값을 보였다. 향후 장기적인 관점에서 바라 본 강수량과 유출량의 관계에 관한 추가적인 연구를 통하여 신뢰성 있는 기후변화에 따른 수자원 영향 평가에 기여할 수 있을 것으로 사료된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.

• Spatial Information Research
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• v.20 no.3
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• pp.39-50
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• 2012

The objective of this study is the quantitative analysis of climate change effects by performing several statistical analyses with hydrometeorological data sets for past 30 years in Geum river watershed. Temperature, precipitation, relative humidity data sets were collected from eight observation stations for 37 years(1973~2009) in Geum river watershed. River level data was collected from Gongju and Gyuam gauge stations for 36 years(1973~2008) considering rating curve credibility problems and future long-term runoff modeling. Annual and seasonal year-to-year variation of hydrometeorological components were analyzed by calculating the average, standard deviation, skewness, and coefficient of variation. The results show precipitation has the strongest variability. Run test, Turning point test, and Anderson Exact test were performed to check if there is randomness in the data sets. Temperature and precipitation data have randomness and relative humidity and river level data have regularity. Groundwater level data has both aspects(randomness and regularity). Linear regression and Mann-Kendal test were performed for trend test. Temperature is increasing yearly and seasonally and precipitation is increasing in summer. Relative humidity is obviously decreasing. The results of this study can be used for the evaluation of the effects of climate change on water resources and the establishment of future water resources management technique development plan.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1134-1138
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• 2010

호소의 수질오염 문제를 해결하기 위해선 우선 소하천에서 강우유출에 의한 비점오염물질이 어디서 얼마나 발생하는지에 대한 정량적인 조사가 필요하다. 그러나 유역의 오염원에 대한 정량적인 조사가 이루어지려면 많은 비용과 시간 그리고 노력이 필요하다. 따라서 본 연구에서는 대청호 상류유역의 소하천인 안내천을 대상으로 강우유출수 조사를 실시하고, 높은 예측 정확성 때문에 세계적으로 널리 쓰이고 있는 Long-Term Hydrologic Impact Assessment(L-THIA)을 이용하여 실측데이터와 L-THIA 모델의 결과를 비교하였다. 안내천의 유역면적은 16.5 $km^2$로 유역의 약 69.5%가 산림, 농업 및 초지지역이 25.3% 그리고 주거지역이 2.6%로 조사되었다. 수질분석을 위하여 자동수질시료채취기(ISCO sampler 6712)를 설치하여 시간단위의 시료를 채취한 뒤 수질농도를 측정하였다. 수질항목은 유기물질인 $BOD_5$, TOC, T-N, T-P 항목에 대하여 수질오염 공정시험법으로 분석하였다. WHAT 시스템을 이용하여 분리된 직접유출량은 315.5~161,835.1 $m^3$의 범위로 나타났다. 직접유출량을 이용하여 산정한 유역의 EMC 농도는 안내천 유역의 $EMC_{BOD}$는 1.0~2.4 mg/L, $EMC_{TOC}$는 1.429~5.491 mg/L, $EMC_{COD}$는 2.2~10.2 mg/L, $EMC_{TN}$은 2.906~10.864 mg/L, $EMC_{TP}$는 0.029~0.285 mg/L의 범위를 보였다. 또한 실측된 유량과 농도를 이용하여 산정한 오염부하는 안내천 유역이 $BOD_5$ 37.9~390.9 g, $COD_{Mn}$ 0.8~1,657.5 g, TOC 0.54~791.83 g, T-N 0.968~1,758.174 g, T-P 0.011~42.139 g의 범위로 나타났다. L-THIA 모델을 이용하여 직접유출량의 산정된 결과와 실측 결과를 비교 분석한 결과 결정계수와 유효지수가 0.95와 0.93으로 높게 나타나 대청호 상류유역에서 발생하는 유출량을 모의하는데 적절할 것으로 판단된다. 토지이용도와 토양도 그리고 일 강우자료만으로 구축되는 L-THIA 모델을 이용하여 대청호 상류의 소하천 유역에 대하여 비점오염원 유출특성을 해석하는 것이 가능 할 것으로 판단된다.

• Journal of Korea Water Resources Association
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• v.47 no.11
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• pp.1017-1026
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• 2014

To investigate the effect of upstream dam operation and river water use on the downstream flows, SWAT-K watershed model was applied to the Paldang Dam watershed of the Han River basin. Analysis results from 2001 to 2009 showed that outflows from the multi-purpose dams such as the Soyanggang Dam and Chungju Dam much have a strong influence on the downstream flows during both the low- and high-flow seasons. This resulted an increase of low-flow at the Paldang Dam, the end of Pukhangang, and the Yangpyeong stage station by $100.57m^3/s$, $33.01m^3/s$, and $49.66m^3/s$, respectively. Whereas, the impact of river water use was hardly found in the Pukhangang, and also was not significant in the (Nam)hangang. Therefore, the effect of small dam such as the Hoengseong Dam or river water use would be able be excluded for long-term runoff analysis. But, in the case of the areas with a large amount of water use, a sufficient information such water-intake and water movement also must be taken into account like this study.

• Korean Journal of Environmental Agriculture
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• v.30 no.3
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• pp.268-274
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• 2011

BACKGROUND: A large scale of sediment load delivered from watershed causes substantial waterway damages and water quality degradation. Controlling sediment loading requires the knowledge of the soil erosion and sedimentation. The various factors such as watershed size, slope, climate, land use may affect sediment delivery processes. Traditionally sediment delivery ratio prediction equations have been developed by relating watershed characteristics to measured sediment yield divided by predicted gross erosion. However, sediment prediction equations have been developed for only a few regions because of limited sediment data. Besides, little research has been done on the prediction of sediment delivery ratio for asia monsoon period in mountainous watershed. METHODS AND RESULTS: In this study Tank model was expanded and applied for estimating sediment yield to Oship River of east coast. The rainfall-runoff in 2006 was verified using the Tank model and we derived good result between observed and calculated discharge in 2009 at the same conditions. In relation to sediment yield, the sediment delivery rate of 2006 was very high than 2009 regardless of methods for estimating sediment load. It was thought to be affected by heavy rainfall due to the typhoon. CONCLUSION(s): For estimating sediment volume from watershed, long-term monitoring data on discharge and sediment is needed. This model will be able to apply to predict discharge and sediment yield simultaneously in ungauged area. This approach is more effective and less expensive method than the traditional method which needs a lot of data collection.