• Journal of Korea Water Resources Association
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• v.52 no.4
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• pp.235-243
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• 2019

Recently, urban inundation was frequently occurred due to the intensive rainfall exceeding marginal capacity of the flood control facility. Furthermore, needs for the underground storage facilities to mitigate urban flood are increasing according to rapidly accelerating urbanization. Thus, in this study, drainage efficiency in drain tunnel connecting to underground storage was investigated from the air-core measurements in the drop shaft against two types of inlet structure. In case of the spiral inlet, the multi-stage structure is introduced at the bottom of the inlet to improve the vortex induction effect at low inflow discharge (multi-stage spiral inlet). The average cross-sectional area of the air-core in the multi-stage spiral inlet is 10% larger than the tangential inlet, and show the highly air emission effect and the highly inflow efficiency at the high inflow discharge. In case of the tangential inlets, the air emission effect decreased after exceeding the maximum inflow discharge, which is required to maintain the inherent performance of the tangential inlet. From the measurements, the empirical formula for the cross-sectional area of the air-core according to locations inside the drop shaft was proposed in order to provide the experimental data available for the inlet model used in experiments.

• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.443-452
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• 2021

The authors obtained the discrete hedging rule for a reservoir's water supply operation by applying mixed-integer programming to save more water by earlier rationing of water supply for a drought period. The 'water supply adjustment guide' is the current operational method applied to the multipurpose reservoirs, and it was derived by a simulation method. Applying the two rules to the Hapcheon multipurpose dam's reservoir simulations with the inflow record from 2003 to 2018, the water supply deficit occurred for the long drought from 2015 to 2018. Especially, the no water supply or intermittent water supply persisted for the second half of 2017. The water supply adjustment guide had the 'normal water supply recovery threshold on storage,' which resulted in the water supply being unavailable in July 2017; then, the water supply suspension occurred until January 2018, when the reservoir storage was greater than the normal water supply recovery threshold. Despite the storage increasing due to the inflow of water into the reservoir, the suspension occurrence needs to be improved in practice. The current water supply adjustment guide and the discrete hedging rule for a reservoir's water supply operation are useful and realistic as the reservoir operation guide, which shows the concept of reducing water supply during the drought phase as scientific figures. However, to improve the reservoir simulation results, which do not provide any or intermittent water for several months, it is necessary to increase the current water supply reduction for drought phases.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1203-1210
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• 2020

Drought is caused by a combination of various hydrological or meteorological factor, so it is difficult to accurately assess drought event, but various drought indices have been developed to interpret them quantitatively. However, the drought indexes currently being used are calculated from the lack of a single variable, which is a problem that does not accurately determine the drought event caused by complex causes. Shortage of a single variable may not be a drought, but it is judged to be a drought. On the other hand, research on developing indices using unstructured data, which is widely used in big data analysis, is being carried out in other fields and proven to be superior. Therefore, in this study, we intend to calculate the drought index by combining unstructured data (news data) with weather and hydrologic information (rainfall and dam inflow) that are being used for the existing drought index, and to evaluate the utilization of drought interpretation through verification of the calculated drought index. The Clayton Copula function was used to calculate the joint drought index, and the parameter estimation was used by the calibration method. The analysis showed that the drought index, which combines unstructured data, properly expresses the drought period compared to the existing drought index (SPI, SDI). In addition, ROC scores were calculated higher than existing drought indices, making them more useful in drought interpretation. The joint drought index calculated in this study is considered highly useful in that it complements the analytical limits of the existing single variable drought index and provides excellent utilization of the drought index using unstructured data.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.421-435
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• 2022

As natural disasters have been increasing due to climate change, sustainable solutions are in need to alleviate the degree of drought hazard, assess and project the drought influence based on future climate change scenarios. In assessing drought risk, socio-economic factors of the region must be considered along with meteorological factors. This study categorized drought hazard, exposure, and vulnerability as three major components of drought risk according to the Intergovernmental panel on Climate Change (IPCC) risk assessment framework, and selected indices for each component to quantify the drought risk in South Korea according to the mid-size basins. Combinations of climate scenarios (Representative Concentration Pathway; RCP 2.6 and RCP 8.5) and socio-economic scenarios (Shared Socio-economic Pathways; SSP 1, SSP2 and SSP3) for the near future (2030-2050) ant the far future (2080-2099) were utilized in drought risk analysis, and results were compared with the historical data (1986-2005). In general, the drought risks for all scenarios shows large increases as time proceeds to the far furture. In addition, we analyzed the rank of drought hazard, exposure, vulnerability for drought risk, and each of their contribution. The results showed that the drought hazard is the most contributing component to the increase of drought risk in future and each basin shows varying contributing components. Finally, we suggested countermeasures for each basin according to future climate change scenarios, and thus this study provides made the basis for establishing drought management measures.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.707-721
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• 2022

In this study, a state-of-the-art review on urban inundation simulation technology was presented summarizing major achievements and limitations, and future research recommendations and challenges. More than 160 papers published in major domestic academic journals since the 2000s were analyzed. After analyzing the core themes and contents of the papers, the status of technological development was reviewed according to simulation methodologies such as physically-based and data-driven approaches. In addition, research trends for application purposes and advances in overseas and related fields were analyzed. Since more than 60% of urban inundation research used Storm Water Management Model (SWMM), developing new modeling techniques for detailed physical processes of dual drainage was encouraged. Data-based approaches have become a new status quo in urban inundation modeling. However, given that hydrological extreme data is rare, balanced research development of data and physically-based approaches was recommended. Urban inundation analysis technology, actively combined with new technologies in other fields such as artificial intelligence, IoT, and metaverse, would require continuous support from society and holistic approaches to solve challenges from climate risk and reduce disaster damage.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1183-1193
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• 2021

Low flow affects various fields such as river water supply management and planning, and irrigation water. A sufficient period of flow data is required to calculate the Flow Duration Curve. However, in order to calculate the Flow Duration Curve, it is essential to secure flow data for more than 30 years. However, in the case of rivers below the national river unit, there is no long-term flow data or there are observed data missing for a certain period in the middle, so there is a limit to calculating the Flow Duration Curve for each river. In the past, statistical-based methods such as Multiple Regression Analysis and ARIMA models were used to predict sulfur in the unmeasured watershed, but recently, the demand for machine learning and deep learning models is increasing. Therefore, in this study, we present the DNN technique, which is a machine learning technique that fits the latest paradigm. The DNN technique is a method that compensates for the shortcomings of the ANN technique, such as difficult to find optimal parameter values in the learning process and slow learning time. Therefore, in this study, the Flow Duration Curve applicable to the unmeasured watershed is calculated using the DNN model. First, the factors affecting the Flow Duration Curve were collected and statistically significant variables were selected through multicollinearity analysis between the factors, and input data were built into the machine learning model. The effectiveness of machine learning techniques was reviewed through statistical verification.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1137-1147
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• 2022

River and groundwater stages are the main elements in the hydrologic cycle. They are spatially correlated and can be used to evaluate hydrological and agricultural drought. Stochastic simulation is often performed independently on hydrological variables that are spatiotemporally correlated. In this setting, interdependency across mutual variables may not be maintained. This study proposes the Bayesian vector autoregression model (VAR) to capture the interdependency between multiple variables over time. VAR models systematically consider the lagged stages of each variable and the lagged values of the other variables. Further, an autoregressive model (AR) was built and compared with the VAR model. It was confirmed that the VAR model was more effective in reproducing observed interdependency (or cross-correlation) between river and ground stages, while the AR generally underestimated that of the observed.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1091-1104
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• 2022

Climate change is predicted to increase the frequency and intensity of rainfall worldwide, and the pattern is changing due to inundation damage in urban areas due to rapid urbanization and industrialization. Accordingly, the impact assessment of climate change is mentioned as a very important factor in urban planning, and the World Meteorological Organization (WMO) is emphasizing the need for an impact forecast that considers the social and economic impacts that may arise from meteorological phenomena. In particular, in terms of traffic, the degradation of transport systems due to urban flooding is the most detrimental factor to society and is estimated to be around £100k per hour per major road affected. However, in the case of Korea, even if accurate forecasts and special warnings on the occurrence of meteorological disasters are currently provided, the effects are not properly conveyed. Therefore, in this study, high-resolution analysis and hydrological factors of each area are reflected in order to suggest the depth of flooding of urban floods and to cope with the damage that may affect vehicles, and the degree of flooding caused by rainfall and its effect on vehicle operation are investigated. decided it was necessary. Therefore, the calculation formula of rainfall-immersion depth-vehicle speed is presented using various machine learning techniques rather than simple linear regression. In addition, by applying the climate change scenario to the rainfall-inundation depth-vehicle speed calculation formula, it predicts the flooding of urban rivers during heavy rain, and evaluates possible traffic network disturbances due to road inundation considering the impact of future climate change. We want to develop technology for use in traffic flow planning.

• Journal of Korea Water Resources Association
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• v.55 no.5
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• pp.345-356
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• 2022

A severe flooding occured at a small urban catchment in Daejeon-si South Korea on July 30, 2020 causing significant loss of property (inundated 78 vehicles and two apartments) and life (one casualty and 56 victims). In this study, a retrospective analysis of the inundation event was implemented using a physically-based urban flood model, H12 with high-resolution data. H12 is an integrated 1-dimensional sewer network and 2-dimensional surface flow model supported by hybrid parallel techniques to efficiently deal with high-resolution data. In addition, we evaluated the impact of the flooding barriers which were installed after the flood disaster. As a result, it was found that the inundation was affected by a combination of multiple components including the shape of the basin, the low terrain of the inundation area located in the downstream part of the basin, and lack of pipe capacity to drain discharge from the upstream during heavy rain. The impact of the flooding barriers was analyzed by modeling with and without barriers on the high-resolution terrain input data. It was evaluated that the flood barriers effectively lower the water depth in the apartment complex. This study demonstrates capability of high-resolution physically-based urban modeling to quantitatively assess the past inundation event and the impact of the reduction measures.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1167-1181
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• 2021

In relation to the algae bloom, four types of blue-green algae that emit toxic substances are designated and managed as harmful Cyanobacteria, and prediction information using a physical model is being also published. However, as algae are living organisms, it is difficult to predict according to physical dynamics, and not easy to consider the effects of numerous factors such as weather, hydraulic, hydrology, and water quality. Therefore, a lot of researches on algal bloom prediction using machine learning have been recently conducted. In this study, the characteristic importance of water quality factors affecting the occurrence of Cyanobacteria harmful algal blooms (CyanoHABs) were analyzed using the random forest (RF) model for Bohyeonsan Dam and Yeongcheon Dam located in Yeongcheon-si, Gyeongsangbuk-do and also predicted the occurrence of harmful blue-green algae using the machine learning and deep learning models and evaluated their accuracy. The water temperature and total nitrogen (T-N) were found to be high in common, and the occurrence prediction of CyanoHABs using artificial neural network (ANN) also predicted the actual values closely, confirming that it can be used for the reservoirs that require the prediction of harmful cyanobacteria for algal management in the future.