• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.104-104
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• 2018

이수안전도의 기준이 되는 갈수량에 대해 기후변화 시나리오에 따른 전망을 제시하였다. 충주 댐 유역을 대상으로 기준기간(1986~2000년)에서의 기상청의 관측 기상자료와 IPCC 보고서의 RCP 4.5/8.5 시나리오를 대상으로 CMIP5(Coupled Model Intercomparison Project Phase 5)에서 제공하는 기후변화 자료 중 5개의 모델(ACCESS1.3 CanESM2, CNRM-CM5, GFDL-ESM2G, HadGEM2-AO)의 기준기간과 미래기간(2011~2100년)의 기상자료를 수집하였다. 기후변화 자료는 정상성/비정상성 분위사상법과 베이지안 모델 평균기법을 통해 불확실성과 통계적 오차를 저감하였다. 미래기간에서, 강우는 RCP 4.5에서 1.74mm/year, RCP 8.5에서 3.22mm/year, 실제증발산은 RCP 4.5에서 1.09mm/year, RCP 8.5에서 1.78mm/year의 증가율을 보였다. 실제증발산을 입력자료로 활용할 수 있도록 IHACRES모델의 CMD(Catchment Moisture Deficit) 비선형 모듈의 매개변수를 변이하여 유효강우량 산정 과정을 개선하였다. 기준기간에서 관측유량자료와 IHACRES의 시뮬레이션을 통해 산정된 유량자료의 R-squared는 0.65이다. 기준기간에서의 매개변수를 고정하여 미래기간의 유량을 산정하고 유황분석을 통해 갈수량 전망하였다. 유량은 RCP 4.5에서 4.41MCM/year, RCP 8.5에서 9.66MCM/year의 증가율을 보였다. 갈수량은 RCP 4.5에서 0.30MCM/year, RCP 8.5에서 -0.47MCM/year의 증감율을 보였다. 연간 강수량 대비 실제증발산의 비율의 추세분석 결과, RCP 4.5에서는 홍수기에는 0.014%/year, 비홍수기에는 0.027%/year의 증가율을 보이며 거의 변화가 없는 추세를 확인할 수 있었다. RCP 8.5의 홍수기에는 -0.042%/year, 비홍수기에서는 0.167%/year의 증감율을 보이며 홍수기에는 실제증발산에 비해 강수량의 증가가 확연히 보였으며 비홍수기에는 강수량에 비해 실제증발산의 증가가 뚜렷이 확인되었다. RCP 8.5에서 비홍수기의 강수량 대비 실제증발산의 증가가 갈수량의 감소로 반영된 것을 확인할 수 있었다. 미래기간의 RCP 4.5/8.5에서 실제증발산의 증가로 인하여 강수량이 증가함에 따라 유입량이 증가함에도 불구하고 갈수량의 증가로 이어지지 않았다. 미래 갈수량의 감소는 하천의 건전성과 이수안전도의 위협이 될 수 있다.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.509-522
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• 2021

In many countries, including Korea, it has been challenging to understand flood-related social dynamics due to urbanization and climate change. In this regard, socio-hydrology has been proposed to consider the interaction between hydrologic systems, land-use change, and human activities. However, there is a general lack of understanding of the interactions of socio-hydrologicsystems. This study examines the interactions between human activities and hydrologic systems from a sociological perspective using a dynamic system model. In other words, this study aims to present a conceptualization model that considers the mutual interaction of flood and community from a socio-hydrologic perspective. Depending on the construction cost of the levee for the Yangjae River, this study considered three scenarios to simulate the interaction of socio-hydrologic systems. Socio-hydrologic interactions can effectively reproduce the changes in the Yangjae River. Moreover, It is expected that the proposed model can be further used to understand possible hydrologic changes and interaction with social systems in the future as a decision-making tool in water resources management.

• Journal of Korea Water Resources Association
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• v.51 no.spc
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• pp.1117-1126
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• 2018

Water supply system aims to meet the user's demand by securing water resources in a stable way. However, water supply failure sometimes happens because inflow decreases during drought period. Droughts induced by the lack of precipitation do not always lead to water supply failures. Thus, it is necessary to consider features of actual water shortage event when we evaluate a water supply risk. In this study, we developed a new drought index for drought management, i.e., Joint Drought Management Index (JDMI), using two water supply system performance indices such as reliability and vulnerability. Future data that were estimated from GCMs according to RCP 4.5 and 8.5 scenarios were used to estimate future water supply risk. After dividing the future period into three parts, the risk of water supply failure in the Nakdong River basin was analyzed using the JDMI. As a result, the risk was higher with the RCP 4.5 than the RCP 8.5. In case of RCP 4.5, W18 (Namgangdam) was identified as the most vulnerable area, whereas in case of RCP 8.5, W23 (Hyeongsangang) and W33 (Nakdonggangnamhae) were identified as the most vulnerable area.

• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.365-379
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• 2021

Hydrological model parameters are essential for model simulation and can vary over time due to topography, climatic conditions, climate change and human activity. Consequently, the use of fixed parameters can lead to inaccurate stream flow simulations. The aim of this study is to investigate an appropriate method of estimating time-varying parameters using stream flow observations, and how the simulation efficiency changes when stream flow data are assimilated into the model. The data assimilation method can be used to automatically estimate the parameters of a hydrological model by adapting to a variety of changing environments. Stream flow observations were assimilated into a two parameter monthly water balance model using a particle filter. The simulation results using the time-varying parameters by the data assimilation method were compared with the simulation results using the fixed parameters by the SCEM method. First, we conducted synthesis experiments based on various scenarios to investigate if the particle filter method can adequately track parameters that change over time. After that, it was applied to actual watersheds and compared with the predictive performance of stream flow when using parameters that change with time and fixed parameters. The conclusions obtained through this study are as follows: (1) The predictive performance of the overall monthly stream flow time series was similar between the particle filter method and the SCEM method. (2) The monthly runoff prediction performance in the period except the rainy season was better in the simulation by the periodically changing parameters using the data assimilation method. (3) Uncertainty in the observational data of stream flow used for assimilation played an important role in the predictive performance of the particle filter.

• Journal of Korea Water Resources Association
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• v.54 no.2
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• pp.105-119
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• 2021

An increase in the frequency and intensity of both floods and droughts has been recently observed due to an increase in climate variability. Especially, land-use change associated with industrial structure and urbanization has led to an imbalance between water supply and demand, acting as a constraint in water resource management. Accurate rainfall-runoff analysis plays a critical role in evaluating water availability in the water budget analysis. This study aimed to explore various continuous rainfall-runoff models over the Soyanggang dam watershed. Moreover, the ensemble modeling framework combining multiple models was introduced to present scenarios on streamflow considering uncertainties. In the ensemble modeling framework, rainfall-runoff models with fewer parameters are generally preferred for effective regionalization. In this study, more than 40 continuous rainfall-runoff models were applied to the Soyanggang dam watershed, and nine rainfall-runoff models were primarily selected using different goodness-of-fit measures. This study confirmed that the ensemble model showed better performance than the individual model over different flow regimes.

• 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.

• Ecology and Resilient Infrastructure
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• v.10 no.1
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• pp.11-19
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• 2023

Alien species are known to threaten regional biodiversity globally, which has increased global interest regarding introduction of alien species. The Ministry of Environment of Korea designated species that have not yet been introduced into the country with potential threat as alert alien species to prevent damage to the ecosystem. In this study, potential habitats of Esox lucius and Maccullochella peelii, which are predatory and designated as alert alien fish, were predicted on a national basis. Habitat suitability was evaluated using EHSM (Ecological Habitat Suitability Model), and water temperature data were input to calculate Physiological Habitat Suitability (PHS). The prediction results have shown that PHS of the two fishes were mainly controlled by heat or cold stress, which resulted in biased habitat distribution. E. lucius was predicted to prefer the basins at high latitudes (Han and Geum River), while M. peelii preferred metropolitan areas. Through these differences, it was expected that the invasion pattern of each alien fish can be different due to thermal preference. Further studies are required to enhance the model's predictive power, and future predictions under climate change scenarios are required to aid establishing sustainable management plans.

• Korean Journal of Plant Taxonomy
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• v.52 no.3
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• pp.184-195
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• 2022

The invasive alien species Brassica tournefortii Gouan (Brassicaceae) is herein reported for the first time in Korea, from Gunsan-si, Gochang-gun, and Jeju-si. Brassica tournefortii can easily be distinguished from B. juncea and B. napus by its dense stiff hairs at the base of the stem and leaves, basally and distally branched stems, partially dehiscent fruits, and seeds that become mucilaginous in the presence of moisture. Although some taxonomists have classified this species as belonging to Coincya Rouy based on its fruit and seed characteristics, the existence of one vein on the fruit valves and our maximum likelihood analysis using internal transcribed spacer sequences placed it in Brassica. Distribution data, photographs, and a description of B. tournefortii are presented herein. Moreover, potential changes in the distribution of B. tournefortii were predicted under different climate scenarios, but our analysis showed that the probability of the spreading of this species is low. Nevertheless, continuous monitoring is necessary for an accurate assessment. The results of the present study can be used to conduct an invasion risk assessment and can assist with the effective management of this invasive alien species.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.613-621
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• 2022

Due to the global climate change, the rainfall volume and frequency on the Korean Peninsula are predicted to increase at the end of the 21st century. In addition, impervious surface areas have increased due to rapid urbanization which has caused the urban water cycle to deteriorate. Green Infrastructure (GI) researches have been conducted to improve the water cycle soundness; the efficiency of this technique has been verified through various studies. However, there are still no suitable GI design guidelines for this aspect. Therefore, the rainfall scenarios are set up for each percentile (60, 70, 80, 90) based on the volume and frequency analysis using 10-year rainfall data (Busan Meteorological Station). After determining the GI areas for each scenario, the runoff reduction characteristics are analyzed based on Storm Water Management Model (SWMM) 10-year rainfall-runoff-simulations. The total runoff reduction efficiency for each GI areas are computed to have a range of 13.1~52.1%. As a results of the quantitative analysis, the design rainfall for GI is classified into the 80~85 percentile in the study site.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.563-574
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• 2023

Recently, climate change has affected functional responses of river basins to meteorological variables, emphasizing the importance of rainfall-runoff simulation research. Simultaneously, the growing interest in machine learning has led to its increased application in hydrological studies. However, it is not yet clear whether machine learning models are more advantageous than the conventional conceptual models. In this study, we compared the performance of the conventional GR6J model with the machine learning-based Random Forest model across 38 basins in Korea using both gauged and ungauged basin prediction methods. For gauged basin predictions, each model was calibrated or trained using observed daily runoff data, and their performance was evaluted over a separate validation period. Subsequently, ungauged basin simulations were evaluated using proximity-based parameter regionalization with Leave-One-Out Cross-Validation (LOOCV). In gauged basins, the Random Forest consistently outperformed the GR6J, exhibiting superiority across basins regardless of whether they had strong or weak rainfall-runoff correlations. This suggest that the inherent data-driven training structures of machine learning models, in contrast to the conceptual models, offer distinct advantages in data-rich scenarios. However, the advantages of the machine-learning algorithm were not replicated in ungauged basin predictions, resulting in a lower performance than that of the GR6J. In conclusion, this study suggests that while the Random Forest model showed enhanced performance in trained locations, the existing GR6J model may be a better choice for prediction in ungagued basins.