• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.451-461
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• 2019

The dam reservoir inflow prediction is utilized to ensure for water supply and prevent future droughts. In this study, we predicted the dam reservoir inflow and analyzed how seasonal weather forecasting affected the accuracy of the inflow for even multi-purpose dams. The hindcast and forecast of GloSea5 from KMA were used as input for rainfall-runoff models. TANK, ABCD, K-DRUM and PRMS models which have individual characteristics were applied to simulate inflow prediction. The dam reservoir inflow prediction was assessed for the periods of 1996~2009 and 2015~2016 for the hindcast and forecast respectively. The results of assessment showed that the inflow prediction was underestimated by comparing with the observed inflow. If rainfall-runoff models were calibrated appropriately, the characteristics of the models were not vital for accuracy of the inflow prediction. However the accuracy of seasonal weather forecasting, especially precipitation data is highly connected to the accuracy of the dam inflow prediction. It is recommended to consider underestimation of the inflow prediction when it is used for operations. Futhermore, for accuracy enhancement of the predicted dam inflow, it is more effective to focus on improving a seasonal weather forecasting rather than a rainfall-runoff model.

• Journal of The Korean Society of Agricultural Engineers
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• v.48 no.2
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• pp.27-35
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• 2006

Large dams are managed with operation standard and flood forecasting systems, while small dams do not have management method generally. Shortage of water resources and natural disasters due to drought and flood raised public concerns for management of small dams. Most of small dams are irrigation dams, which need diversified water uses. However, the lack of systematic management of small dams have caused serious water wastage and increased natural disasters. Storage management method and system were developed to solve these problems in small dams. The system was applied to Seongju dam for effective management. The storage management method was established considering hydrology simulation and statistical analysis using the system. This method can bring additional available water, even in the same conditions of the water demand and the supply conditions of watershed. It can improve the flood control capacity and water utilization efficiency by' the flexible operation of storage space.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.81-86
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• 1999

A Web-based information system from the Korea Agricultural Water Use Laboratory AWUL, has been developed to provide with regional water management information and guidance for the operations of irrigation reservoirs through the World Wide Web(WWW). Twenty-six reservoirs are selected as the reference reservoirs for regional water management , and the real-time operation guide may be issued the grwoing seasons. The information available from the system includes the wether forecasting , drought analyses, and reservoir operation data for those reference sites. For a specific reservoir, the manager may access the system to obtain the water requriement, irrigation secheduling , and reservoir operations that fit best to the irrigation district.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.49-49
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• 2023

지구온난화의 영향으로 가뭄의 발생 빈도가 전 세계 곳곳으로 증가하고 있는 추세이다. 가뭄이란 강수량 혹은 가용 수자원 등이 평균적인 수준에 비해 지속해서 적게 유지되는 현상으로 다양한 분야(기상, 농업, 사회, 경제 등)에 피해를 발생시킨다. 가뭄이 지속되면 인간 사회 뿐만 아니라 동·식물이 서식하고 있는 생태계에도 영향을 미치게 된다. 우리나라에서도 2000년대 이후 주기적으로 발생한 가뭄으로 인해 가뭄 현상을 모니터링하고 예측, 전망하기 위한 다양한 연구가 진행되고 있으나 아직까지 환경생태가뭄에 대한 연구는 미흡한 실정이다. 본 연구에서는 가뭄으로 인해 환경생태계에 미치는 영향 중 수생태계에 초점을 맞춰 진행하였으며, 수생태계에 서식하는 동·식물 중 어류만을 대상으로 하였다. 생태가뭄을 빠르고 쉽게 예측하기 위해 Ecological Nomograph를 개발하여 가뭄에 따른 수생태계에 미치는 영향을 분석하고자 하였다. 본 연구에서 나온 결과를 바탕으로 환경가뭄을 감시하고 대응하기 위한 분석 방법으로 활용할 수 있을 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.304-304
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• 2022

가뭄은 인간 활동과 생태계의 다양한 측면에 영향을 미치는 중요한 자연재해 중 하나이다. 가뭄을 사전에 예측하여 필요한 완화 조치를 취하고 환경적 피해를 줄이는 것이 중요하다. 이에 따라 다양한 인공지능 기술을 이용한 가뭄 예측은 수문학, 수자원 관리, 농업 등의 분야에서 중요성이 커지고 있다. 최근에는 딥러닝 알고리즘을 기반으로 하는 중장기 강수예보를 위한 다양한 방법이 제시되고 있다. 이 논문의 목적은 가뭄 예보를 목적으로 월 강수량 예측을 위한 딥러닝 모델의 성능을 평가하는 것이다. 이를 위해 딥러닝 모델인 LSTM(Long Short-Term Memory)을 적용하였으며, 1981-2020년 기간의 월 강수 자료가 모델을 구축하기 위해 사용되었다. 관측자료를 기반으로 학습된 모델을 이용하여 테스트 기간에 대해 월 강수량을 예측하였다. 예측된 강수량을 통해 표준강수지수(Standardized Precipitation Index, SPI)을 산정하고, 예측 정확도를 분석하였다. 이 연구는 가뭄 예보를 위한 딥러닝 모델의 적용 가능성을 보여준다.

• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.395-405
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• 2021

When issuing forecasts and alerts for agricultural drought, the relevant ministries only rely on the observation data from the reservoirs managed by the Korea Rural Community Corporation, which creates gaps between the drought analysis results at the local (si/gun) governments and the droughts actually experienced by local residents. Closing these gaps requires detailed local geoinformation on reservoirs, which in turn requires the information on reservoirs managed by local governments across Korea. However, installing water level and flow measurement equipment at all of the reservoirs would not be reasonable in terms of operation and cost effectiveness, and an alternate approach is required to efficiently generate information. In light of the above, this study validates and calibrates the parameters of the TANK model for reservoir basins, divided them into groups based on the characteristics of different basins, and applies the grouped parameters to unmeasured local government reservoirs to estimate and assess inflow. The findings show that the average determinant coefficient and the NSE of the group using rice paddies and inclinations are 0.63 and 0.62, respectively, indicating better results compared with the basin area and effective storage factors (determinant coefficient: 0.49, NSE: 0.47). The findings indicate the possibility of utilizing the information regarding unmeasured reservoirs managed by local governments.

• Journal of Korea Water Resources Association
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• v.52 no.12
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• pp.1075-1086
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• 2019

Recently, as the incidence of climate warming and abnormal climate increases, the forecasting of hydrological factors such as precipitation and river flow is getting more complicated, and the risk of water shortage is also increasing. Therefore, this study aims to develop a model for predicting the amount of water intake in mid-term. To this end, the correlation between water intake and meteorological factors, including temperature and precipitation, was used to select input factors. In addition, the amount of water intake increased with time series and seasonal characteristics were clearly shown. Thus, the preprocessing process was performed using the time series decomposition method, and the support vector machine (SVM) was applied to the residual to develop the river intake prediction model. This model has an error of 4.1% on average, which is higher accuracy than the SVM model without preprocessing. In particular, this model has an advantage in mid-term prediction for one to two months. It is expected that the water intake forecasting model developed in this study is useful to be applied for water allocation computation in the permission of river water use, water quality management, and drought measurement for sustainable and efficient management of water resources.

• Journal of Korea Water Resources Association
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• v.37 no.1
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• pp.77-86
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• 2004

This paper presents a reservoir operation plan coupled with storage forecasting model to maintain a target storage and a critical storage. The observed storage data from 1990 to 2001 in the Geum-Gang agricultural reservoir in Korea have been applied to the low flow frequency analysis, which yields storage for each return period. Two year return period drought storage is then designated as the target storage and ten year return period drought storage as the critical storage. Storage in reservoir should be forecasted to perform reasonable reservoir operation. The predicted storage can be effectively utilized to establish a reservoir operation plan. In this study the autoregressive error (ARE) model and the ARIMA model are adopted to predict storage of reservoir. The ARIMA model poorly generated reservoir storage in series because only observed storage data were used, but the autoregressive error model made to enhance the reliability of the forecasted storage by applying the explanation variables to the model. Since storages of agricultural reservoir with respect to time have been affected by irrigation area, high or mean temperature, precipitation, previous storage and wind velocity, the autoregressive error model has been adopted to analyze the relationship between storage at a period and affecting factors for storage at the period. Since the equation for predicting storage at a period by the autoregressive error model is similar to the continuity equation, the predicting storage equation may be practical. The results from compared the actual storage in 2002 and the predicted storage in the Geum-Gang reservoir show that forecasted storage by the autoregressive error model is reasonable.

• The Journal of the Korea Contents Association
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• v.7 no.3
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• pp.140-151
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• 2007

In this study, we attempted to develop a watershed runoff index subject to main control points by dividing the Geum River basin into 14 sub-basins. The Yongdam multipurpose dam Daecheong multipurpose dam and Gongju gage station were selected to serve as the main control points of the Geum River basin, and the observed flow of each control point was calculated by the discharge rating curve, whereas the simulated flow was estimated using the Rainfall Runoff Forecasting System (RRFS), user-interfaced software developed by the Korea Water Corporation, based on the Streamflow Synthesis and Reservoir Regulation (SSARR) model developed by the US Army Corps of Engineers. This study consisted of the daily unit observed flow and the simulated flow of the accumulated moving average flow by daily, 5-days, 10-days, monthly, quarterly and annually, and normal monthly/annually flow. We also performed flow duration analysis for each of the accumulated moving average and the normal monthly/annually flows by unit period, and abundant flow, ordinary flow, low flow and drought flow estimated by each flow duration analysis were utilized as watershed runoff index by main control points. Further, as we determined the current flow by unit period and the normal monthly/annually flow through the drought and flood flow analysis subject to each flow we were able to develop the watershed runoff index in a system that can be used to determine the abundance and scarcity of the flow at the corresponding point.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.6
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• pp.1-9
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• 2020

Reservoir storage and water level information is essential for accurate drought monitoring and prediction. In particular, the agricultural drought has increased the risk of agricultural water shortages due to regional bias in reservoirs and water supply facilities, which are major water supply facilities for agricultural water. Therefore, it is important to evaluate the available water capacity of the reservoir, and it is necessary to determine the water surface area and water capacity. Remote sensing provides images of temporal water storage and level variations, and a combination of both measurement techniques can indicate a change in water volume. In areas of ungauged water volume, satellite remote sensing image acts as a powerful tool to measure changes in surface water level. The purpose of this study is to estimate of reservoir storage and level variations using satellite remote sensing image combined with hydrological statistical data and the Normalized Difference Water Index (NDWI). Water surface areas were estimated using the Sentinel-2 satellite images in Seosan, Chungcheongnam-do from 2016 to 2018. The remote sensing-based reservoir storage estimation algorithm from this study is general and transferable to applications for lakes and reservoirs. The data set can be used for improving the representation of water resources management for incorporating lakes into weather forecasting models and climate models, and hydrologic processes.