• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.33-33
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• 2011

현재 우리나라 기상청에서는 단기, 중기 및 장기 예보자료를 생산하고 있으나, 이들 자료는 단순히 일기 예보에 치중되어 생산되고 있어 강우-유출해석에 직접 적용하기에는 시 공간 해상도가 크고 정량적 강수예측의 정확도가 미흡하다. 이에 기상 및 수자원분야에서는 정확도 개선을 위해서 관측강우와 예측강우의 비교 분석을 통해 편차를 산정하여 예측강수를 보정하는 기법을 적용하고 있다. 다만, 기존의 편차보정방법은 보정인자로 강수량만을 고려하기 때문에 정확도 개선에는 한계가 존재한다. 따라서 본 연구에서는 수자원분야의 수치예보자료의 정확도를 향상시키기 위해 규모, 발생영역에 대한 강수의 특성을 고려한 강수예측자료의 편차보정 방법을 제안하고 이를 강우-유출모델에 적용하여 개선정도를 평가하고자 한다. 이에 적용유역을 춘천댐상류유역으로 선정하고 국내 기상청의 RDAPS(Regional Data Assimilation and Prediction System)수치예보자료, 지점강우자료, radar자료의 수문기상자료와 지형자료를 수집하였다. 화천, 평화의 댐 일부 미계측유역의 관측자료로 radar자료를 이용하였다. 이상의 자료를 토대로 강우강도 및 규모, 영향범위를 고려한 예측강우의 편차를 산정하여 RDAPS 수치예보자료의 정확도를 개선하고 평가하였다. 이는 해당 유역뿐만 아니라 주변 유역의 정보를 이용하여 예측강우의 발생위치에 대한 오차를 고려한 방법으로, 각 영역별로 예측강우의 편차보정계수를 산정하여 적용하였다. 또한, 이전시간대의 강우 편차에 대한 오차를 줄이기 위해 정규분포방법을 이용한 Ensemble 편차보정계수를 산정하고 최근 생산된 수치예보자료에 적용하여 확률예측강우를 산정하였다.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.1
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• pp.69-78
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• 2008

An integration study of watershed model(HSPF, Hydological Simulation program-Fortran) and reservoir water quality model (CE-QUAL-W2) was performed for the evaluation of turbid water management in Yongdam reservoir. The watershed model was calibrated and analyzed for flow and suspended solid concentration variation during rainy period, their results were inputted for reservoir water quality model as time-variable water temperature and turbidity. Results of the watershed model showed a good agreement with the field measurements of flow and suspended solid. Also, results of the reservoir water quality model showed a good agreement with the filed measurements of water balance, water temperature and turbidity using linkage of the watershed model results. Integration of watershed and reservoir model is an important in turbid water management because flow and turbidity in stream and high turbidity layer in reservoir could be predicted and analyzed. In this study, the integration of HSPF and CE-QUAL-W2 was applied for the turbid water management in Yongdam reservoir, where it is evaluated to be appliable and important.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• Journal of Korean Society of Environmental Engineers
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• v.37 no.2
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• pp.126-135
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• 2015

Increase of delivery effect of pollutant loads and surface runoff due to urbanization of catchment area results in serious environmental problems in receiving urban streams. This study aims to develop integrated stormwater management system to assist efficient urban stream flow and water quality control using information from the Storm Water Management Model (SWMM), real time water level and quality monitoring system and remote or automatic treatment facility control system. Based on field observations in the study site, most of the pollutant loads are flushed within 4 hours of the rainfall event. SWMM simulation results indicates that the treatment system can store up to 6 mm of cumulative rainfall in the study catchment area, and this means any type of normal rainfall situation can be treated using the system. Relationship between rainfall amount and fill time were developed for various rainfall duration for operation of stormwater treatment system in this study. This study can further provide inputs of river water quality model and thus can effectively assist integrated water resources management in urban catchment and streams.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.67-74
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• 2024

In this paper, we present a study aimed at analyzing how different rainfall measurement methods affect the performance of reservoir water level predictions. This work is particularly timely given the increasing emphasis on climate change and the sustainable management of water resources. To this end, we have employed rainfall data from ASOS, AWS, and Thiessen Network-based measures provided by the KMA Weather Data Service to train our neural network models for reservoir yield predictions. Our analysis, which encompasses 34 reservoirs in Jeollabuk-do Province, examines how each method contributes to enhancing prediction accuracy. The results reveal that models using rainfall data based on the Thiessen Network's area rainfall ratio yield the highest accuracy. This can be attributed to the method's accounting for precise distances between observation stations, offering a more accurate reflection of the actual rainfall across different regions. These findings underscore the importance of precise regional rainfall data in predicting reservoir yields. Additionally, the paper underscores the significance of meticulous rainfall measurement and data analysis, and discusses the prediction model's potential applications in agriculture, urban planning, and flood management.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1644-1648
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• 2008

해안지역은 해수욕, 어패류의 수집 등의 각종 레크레이션에 있어 많은 사람들이 이용하는 공간이며, 해수는 해안지역에서 각종 활동 중 섭취할 가능성이 있으므로, 수질이 매우 중요하다고 할 수 있다. 이에 본 연구에서는 실제 해수욕장의 수치 모의(수리, 수문, 수질)를 통하여 우수 및 오수가 지표를 통해 해안으로 유입될 경우의 해안지역의 수질에의 영향에 관하여 연구하였다. 지표에서의 우수 유출 및 오수의 흐름을 수치해석은 MOUSE 모델을 사용하였으며 해안지역의 수치해석은 MIKE 3 모델을 사용하였다. 또한 수질 분석을 위하여 미생물의 증감에 영향을 주는 해당 지역의 기온, 수온, 일조량 등의 각종 인자를 구성하여 MIKE 3의 ECOLAB 모듈을 통하여 생물학적 분석을 수행하였다. 그 결과, 해수의 오염이 발생하면, 해수욕이 가능한 기간을 위주로 확인하였을 시, 미생물이 해수에 존재하는 시간은 연간 총 200시간 가량인 것으로 나타났으며, 강우시 해수의 오염이 발생할 시, 강우가 그친 뒤에도 미생물이 완전히 사멸할 때 까지 $4{\sim}6$시간의 정화기간이 필요한 것으로 나타났다. 그리고 첨두 오염 부하량은 비가 그친 직후에 나타나는 것으로 나타났으며 미생물의 해수 유입은 5mm 이상의 강우일 경우에 기준치 이상의 미생물이 발생하며, 해당 지역에 합류식 하수관거가 있을 시에 더욱 많이 발생하는 것으로 나타났다.

• Journal of Wetlands Research
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• v.8 no.2
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• pp.75-81
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• 2006

In this study, runoff simulation was carried out in order to derive operational improvement of small urban storm water pumping station under heavy storm rainfall conditions. The flood inflow hydrograph of Guri city heavy storm in July, 2001 was successfully simulated by HEC-HMS, a GIS-based runoff simulation model. For the runoff simulation, ArcView, as an effective GIS tool, was used to provide input data of the model such as land use data, soil distribution data and SCS runoff curve number.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.57-57
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• 2022

국내의 댐·하천 설계기준은 다양한 수자원 시설물 설계 시에 활용되고 있으나, 강우사상에 대한 분석은 과거의 강우 사상에 대한 통계분석에 따라 수행되어 기후변화의 영향을 고려하지 않고 있다. 또한, 하천 설계기준에서는 홍수량 산정에 대한 방안을 명시한 바에 따르면, 홍수량 산정 표준지침에서 활용하는 빈도해석을 활용하는 방안 또는 강우-유출모형을 활용한 방안을 제시하고 있으나, 홍수량 산정 표준지침 역시 미래 강수 변화에 대한 구체적인 방안을 반영하지 않고 있는 실정이다. 전 세계적인 기후변화는 국내의 기후변동성을 증가시켜 극한강우사상의 빈도와 강도를 증대시키므로 이를 고려한 미래강우에 대한 분석이 필요한 시점이다. 일반적으로 기후 전망에 활용되는 전지구 모델(Global Climate Model; GCM)은 한반도의 복잡한 지형을 고려하기 어려우므로 지역적인 강제력을 보다 효과적으로 고려하기 위하여 지역기후모델(Regional Climate Model; RCM)을 사용하고 있다. 역학적으로 상세화 된 RCM은 비교적 고해상도의 자료를 제공하고 있으나, 강수량을 전반적으로 과소 추정하는 것으로 알려지고 있다. 본 연구에서는 지속시간 1-24시간 연최대 강우량(annual maximum rainfalls; AMRs)과 역학적 상세화 된 SSP 시나리오 일 자료를 활용하며, Copula 함수 기반의 상세화 모형을 통해 Sub-Daily 정보를 시간적으로 상세화 하였다. 최종적으로 이를 활용하여 미래 IDF 곡선을 유도하였다. 산정된 IDF 곡선 결과를 활용하여 기후변화의 영향을 고려한 설계강수량 변화량을 정량적으로 제시하고자 한다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1B
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• pp.29-39
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• 2012

Climate change has been obtained researchers' interest, especially in water resources engineering to adjust current conditions to the new circumstance influenced by climate change. In this study, WRF-ARW will be evaluated the capability to estimate distributed precipitation using global weather information instead of the data from rainfall observatory or radar. Cheongmi watershed is selected and adopted to generate a distributed rainfall-runoff model using ModClark. The results from the distributed model with precipitation data from WRF-ARW and the lumped model using observed precipitation data were compared to the observed discharge values. The final results showed that the distributed model, ModClark generated similar pattern of hydrograph to the observations in terms of the time and amount of peak discharge. In addition, the trend of hydrograph from the distributed model presented similar pattern to the observations.