• Journal of Korea Water Resources Association
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• v.57 no.9
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• pp.585-598
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• 2024

To mitigate natural disasters and efficiently manage water resources, it is essential to enhance hydrologic prediction while reducing model structural uncertainties. This study analyzed the impact of lumped and semi-distributed GR4J model structures on simulation performance and evaluated uncertainties with and without data assimilation techniques. The Ensemble Kalman Filter (EnKF) and Particle Filter (PF) methods were applied to the Namgang Dam basin. Simulation results showed that the Kling-Gupta efficiency (KGE) index was 0.749 for the lumped model and 0.831 for the semi-distributed model, indicating improved performance in semi-distributed modeling by 11.0%. Additionally, the impact of uncertainties in meteorological forcings (precipitation and potential evapotranspiration) on data assimilation performance was analyzed. Optimal uncertainty conditions varied by data assimilation method for the lumped model and by sub-basin for the semi-distributed model. Moreover, reducing the calibration period length during data assimilation led to decreased simulation performance. Overall, the semi-distributed model showed improved flood simulation performance when combined with data assimilation compared to the lumped model. Selecting appropriate hyper-parameters and calibration periods according to the model structure was crucial for achieving optimal performance.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.833-842
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• 2013

The quantile mapping is utilized to reproduce reliable GCM(Global Climate Model) data by correct systematic biases included in the original data set. This scheme, in general, projects the Cumulative Distribution Function (CDF) of the underlying data set into the target CDF assuming that parameters of target distribution function is stationary. Therefore, the application of stationary quantile mapping for nonstationary long-term time series data of future precipitation scenario computed by GCM can show biased projection. In this research the Nonstationary Quantile Mapping (NSQM) scheme was suggested for bias correction of nonstationary long-term time series data. The proposed scheme uses the statistical parameters with nonstationary long-term trends. The Gamma distribution was assumed for the object and target probability distribution. As the climate change scenario, the 20C3M(baseline scenario) and SRES A2 scenario (projection scenario) of CGCM3.1/T63 model from CCCma (Canadian Centre for Climate modeling and analysis) were utilized. The precipitation data were collected from 10 rain gauge stations in the Han-river basin. In order to consider seasonal characteristics, the study was performed separately for the flood (June~October) and nonflood (November~May) seasons. The periods for baseline and projection scenario were set as 1973~2000 and 2011~2100, respectively. This study evaluated the performance of NSQM by experimenting various ways of setting parameters of target distribution. The projection scenarios were shown for 3 different periods of FF scenario (Foreseeable Future Scenario, 2011~2040 yr), MF scenario (Mid-term Future Scenario, 2041~2070 yr), LF scenario (Long-term Future Scenario, 2071~2100 yr). The trend test for the annual precipitation projection using NSQM shows 330.1 mm (25.2%), 564.5 mm (43.1%), and 634.3 mm (48.5%) increase for FF, MF, and LF scenarios, respectively. The application of stationary scheme shows overestimated projection for FF scenario and underestimated projection for LF scenario. This problem could be improved by applying nonstationary quantile mapping.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2211-2221
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• 2013

During the four river restoration project, several weirs were constructed in the four rivers to prevent drought and flood, to improve water quality, and to manage water resources. However, due to the weir construction, bed changes are produced in the upstream channel of installed weirs because the incoming flow velocity is reduced and sediment transport capacity is also lowered. Especially, since the Haman Weir is located in the lowest downstream section among newly installed weirs in Nakdong River, bed change and sedimentation problems are expected due to the mild slope and reduced velocity. Therefore, numerical simulation was performed to analyze flow and bed changes in the upstream channel of Haman Weir and to evaluate quantitatively sediment control methods for bed stabilization using CCHE2D model. As a result of flow and bed change simulation after installation of Haman Weir, the flow velocity at the initial condition was faster than the final bed condition with the specific simulation time and it was represented that the locations where bed changes were great were identical for all modeling conditions of flow discharge. In case of 4.5 m of water level lowered from 5.0 m of the management water level at Haman Weir for bed stabilization, the flow velocity was generally faster than the case of the management water level and the continuous erosion was developed at the most narrow channel section as the applied discharge and simulation period were increased. The channel width extension at the most narrow channel section was proposed in this study to prevent and stabilize continuos bed erosion. As a result of numerical analysis, there was no bed erosion after channel width extension and it was presented that the channel geometry extension was effective for bed stabilization at Haman Weir.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.4
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• pp.81-94
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• 2006

The water quality impacts of forest fragmentation at the Doam-dam watershed were evaluated in this study. For this ends, the watershed scale model, Soil and Water Assessment Tool (SWAT) model was utilized. To exclude the effects of different magnitude and patterns in weather, the same weather data of 1985 was used because of significant differences in precipitation in year 1985 and 2000. The water quality impacts of forest fragmentation were analyzed temporarily and spatially because of its nature. The flow rates for Winter and Spring has increased with forest fragmentations by $8,366m^3/month$ and $72,763m^3/month$ in the S1 subwatershed, experiencing the most forest fragmentation within the Doam-dam watershed. For Summer and Fall, the flow rate has increased by $149,901m^3/month$ and $107,109m^3/month$, respectively. It is believed that increased flow rates contributed significant amounts of soil erosion and diffused nonpoint source pollutants into the receiving water bodies. With the forest fragmentation in the S1 watershed, the average sediment concentration values for Winter and Spring increased by 5.448mg/L and 13.354mg/L, respectively. It is believed that the agricultural area, which were forest before the forest fragmentation, are responsible for increased soil erosion and sediment yield during the spring thaw and snow melts. For Spring and Fall, the sediment concentration values increased by 20.680mg/L and 24.680mg/L, respectively. Compared with Winter and Spring, the increased precipitation during Summer and Fall contributed more soil erosion and increased sediment concentration value in the stream. Based on the results obtained from the analysis performed in this study, the stream flow and sediment concentration values has increased with forest fragmentation within the S1 subwatershed. These increased flow and soil erosion could contribute the eutrophication in the receiving water bodies. This results show that natural functionalities of the forest, such as flood control, soil erosion protection, and water quality improvement, can be easily lost with on-going forest fragmentation within the watershed. Thus, the minimize the negative impacts of forest fragmentation, comprehensive land use planning at watershed scale needs to be developed and implemented based on the results obtained in this research.

• Journal of the Korean Society for Marine Environment & Energy
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• v.19 no.1
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• pp.74-85
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• 2016

For the assessment of seawater exchange rates in Danghangpo bay, Dangdong bay, Wonmun bay, Gohyunsung bay, and Masan bay, which are small-scale inner bays of Jinhae bay, an EFDC model was used to reproduce the seawater flow of the entire Jinhae bay, and Lagrange (particle tracking) and Euler (dye diffusion) model techniques were used to calculate the seawater exchange rates for each of the bays. The seawater exchange rate obtained using the particle tracking method was the highest, at 60.84%, in Danghangpo bay, and the lowest, at 30.50%, in Masan bay. The seawater exchange rate calculated based on the dye diffusion method was the highest, at 45.40%, in Danghangpo bay, and the lowest, at 34.65%, in Masan bay. The sweater exchange rate was found to be the highest in Danghangpo bay likely because of a high flow velocity owing to the narrow entrance of the bay; and in the case of particle tracking method, the morphological characteristics of the particles affected the results, since once the particles get out, it is difficult for them to get back in. Meanwhile, in the case of the Lagrange method, when the particles flow back in by the flood current after escaping the ebb current, they flow back in intact. However, when a dye flows back in after escaping the bay, it becomes diluted by the open sea water. Thus, the seawater exchange rate calculated based on the dye diffusion method turned out to be higher in general, and even if a comparison of the sweater exchange rates calculated through two methods was conducted under the same condition, the results were completely different. Thus, when assessing the seawater exchange rate, more reasonable results could be obtained by either combining the two methods or selecting a modeling technique after giving sufficiently consideration to the purpose of the study and the characteristics of the coastal area. Meanwhile, through a comparison of the degree of closure and seawater exchange rates calculated through Lagrange and Euler methods, it was found that the seawater exchange rate was higher for a higher degree of closure, regardless of the numerical model technique. Thus, it was deemed that the degree of closure would be inappropriate to be used as an index for the closeness of the bay, and some modifications as well as supplementary information would be necessary in this regard.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.2
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• pp.53-69
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• 2020

The increase of the impermeable area due to industrialization and urban development distorts the hydrological circulation system and cause serious stream drying phenomena. In order to manage this, it is necessary to develop a technology for impact assessment of stream drying phenomena, which enables quantitative evaluation and prediction. In this study, the cause of streamflow reduction was assessed for dam and weir watersheds in the five major river basins of South Korea by using distributed hydrological model DrySAT-WFT (Drying Stream Assessment Tool and Water Flow Tracking) and GIS time series data. For the modeling, the 5 influencing factors of stream drying phenomena (soil erosion, forest growth, road-river disconnection, groundwater use, urban development) were selected and prepared as GIS-based time series spatial data from 1976 to 2015. The DrySAT-WFT was calibrated and validated from 2005 to 2015 at 8 multipurpose dam watershed (Chungju, Soyang, Andong, Imha, Hapcheon, Seomjin river, Juam, and Yongdam) and 4 gauging stations (Osucheon, Mihocheon, Maruek, and Chogang) respectively. The calibration results showed that the coefficient of determination (R²) was 0.76 in average (0.66 to 0.84) and the Nash-Sutcliffe model efficiency was 0.62 in average (0.52 to 0.72). Based on the 2010s (2006~2015) weather condition for the whole period, the streamflow impact was estimated by applying GIS data for each decade (1980s: 1976~1985, 1990s: 1986~1995, 2000s: 1996~2005, 2010s: 2006~2015). The results showed that the 2010s averaged-wet streamflow (Q95) showed decrease of 4.1~6.3%, the 2010s averaged-normal streamflow (Q185) showed decreased of 6.7~9.1% and the 2010s averaged-drought streamflow (Q355) showed decrease of 8.4~10.4% compared to 1980s streamflows respectively on the whole. During 1975~2015, the increase of groundwater use covered 40.5% contribution and the next was forest growth with 29.0% contribution among the 5 influencing factors.

• Journal of Korean Society of Disaster and Security
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• v.15 no.1
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• pp.1-12
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• 2022

An automatic optimization technique is used to estimate the optimal parameters of the hydrologic model, and different hydrologic response results can be provided depending on objective functions. In this study, the parameters of the event-based rainfall-runoff model were estimated using various objective functions, the reproducibility of the hydrograph according to the objective functions was evaluated, and appropriate objective functions were proposed. As the rainfall-runoff model, the storage function model(SFM), which is a lumped hydrologic model used for runoff simulation in the current Korean flood forecasting system, was selected. In order to evaluate the reproducibility of the hydrograph for each objective function, 9 rainfall events were selected for the Cheoncheon basin, which is the upstream basin of Yongdam Dam, and widely-used 7 objective functions were selected for parameter estimation of the SFM for each rainfall event. Then, the reproducibility of the simulated hydrograph using the optimal parameter sets based on the different objective functions was analyzed. As a result, RMSE, NSE, and RSR, which include the error square term in the objective function, showed the highest accuracy for all rainfall events except for Event 7. In addition, in the case of PBIAS and VE, which include an error term compared to the observed flow, it also showed relatively stable reproducibility of the hydrograph. However, in the case of MIA, which adjusts parameters sensitive to high flow and low flow simultaneously, the hydrograph reproducibility performance was found to be very low.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.931-939
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• 2022

In Korea, about two-thirds of the precipitation is concentrated in the summer season, so the problem of turbidity in the summer flood season varies from year to year. Concentrated rainfall due to abnormal rainfall and extreme weather is on the rise. The inflow of turbidity caused a sudden increase in turbidity in the water, causing a problem of turbidity in the dam reservoir. In particular, in Korea, where rivers and dam reservoirs are used for most of the annual average water consumption, if turbidity problems are prolonged, social and environmental problems such as agriculture, industry, and aquatic ecosystems in downstream areas will occur. In order to cope with such turbidity prediction, research on turbidity modeling is being actively conducted. Flow rate, water temperature, and SS data are required to model turbid water. To this end, the national measurement network measures turbidity by measuring SS in rivers and dam reservoirs, but there is a limitation in that the data resolution is low due to insufficient facilities. However, there is an unmeasured period depending on each dam and weather conditions. As a sensor for measuring turbidity, there are Optical Backscatter Sensor (OBS) and YSI, and a sensor for measuring SS uses equipment such as Laser In-Situ Scattering and Transmissometry (LISST). However, in the case of such a high-tech sensor, there is a limit due to the stability of the equipment. Therefore, there is an unmeasured period through analysis based on the acquired flow rate, water temperature, SS, and turbidity data, so it is necessary to develop a relational expression to calculate the SS used for the input data. In this study, the AEM3D model used in the Water Resources Corporation SURIAN system was used to improve the accuracy of prediction of turbidity through the turbidity-SS relationship developed based on the measurement data near the dam outlet.