• Journal of Wetlands Research
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• v.14 no.4
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• pp.489-502
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• 2012

Biogeochemical processes in groundwater-stream water mixing zone are recently of great interest because biodegradation and natural attenuation of aquatic contaminants may occur through the processes. The objectives of this study are to investigate the hydrologic and biogeochemical processes at the groundwater-stream water mixing zone through which surface water-driven nitrate may be naturally attenuated, and to examine the effect of the vertical flow exchange flux on biogeochemical processes using correlation analysis. To examine the direction of vertical water flow in the zone, vertical hydraulic gradients were measured at several depths using mini-piezometers. Microbial populations in soil samples of the zone were also analyzed by means of the polymerase chain reaction (PCR) and Cloning methods. In addition, partial correlations among vertical flow exchange, nitrate concentration and microbial activity was investigated to examine their mutual interaction. The results showed the significant interaction among the three parameters, resulting in natural attenuation of nitrate. This study showed an example of the biogeochemical fuction of groundwater-stream water mixing zone, which can be predictable from the examination of the interaction among microbial activities, concentration of contamination and vertical flow exchange flux. temperature show a significant difference in adjacent streambed, Also, the results shows that distribution of temperature was more affected by groundwater direction than intensity of flux.

• Proceedings of the Korea Water Resources Association Conference
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• 1992.07a
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• pp.411-418
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• 1992

The purpose of this study is to develop dynamic-stochastic models that can forecast the inflow into reservoir during low/drought periods and flood periods. For the formulation of the models, the discrete transfer function is utilized to construct the deterministic characteristics, and the ARIMA model is utilized to construct the stochastic characteristics of residuals. The stochastic variations and structures of time series on hydrological data are examined by employing the auto/cross covariance function and auto/cross correlation function. Also, general modeling processes and forecasting method are used the model building methods of Box and Jenkins. For the verifications and applications of the developed models, the Chungju multi-purpose reservoir which is located in the South Han river systems is selected. Input data required are the current and past reservoir inflow and Yungchun water levels. In order to transform the water level at Yungchon into streamflows, the water level-streamflows rating curves at low/drought periods and flood periods are estimated. The models are calibrated with the flood periods of 1988 and 1989 and hourly data for 1990 flood are analyzed. Also, for the low/drought periods, daily data of 1988 and 1989 are calibrated, and daily data for 1989 are analyzed.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.4
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• pp.105-114
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• 2012

Soil moisture plays a pivotal role in hydrological processes, especially in the forest which covers more than 64% of the national land. Soil moisture was monitored to analyze soil moisture change characteristics in terms of time and soil layers in this study. 2 Years soil moisture change data was obtained from the experimental nut pine forest and statistical analysis including auto-correlation and cross-corelation among soil moisture data from different soil layers was conducted. Using the monitored soil moisture data, a relationship between soil moisture change and precipitation was analyzed and seasonal soil moisture change characteristics were analyzed. From the result of inter-relationships among soil layers in terms of season and time lag, soil moisture change characteristics in the nut pine forest were upper soil layers were much sensitive than lowers, and seasonal variation if soil moisture for upper soil layers were bigger than lowers showing low correlation with precipitation in winter and spring due to freezing and snowfalls.

• Environmental Engineering Research
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• v.18 no.1
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• pp.9-20
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• 2013

In this paper, the rainfall elasticity of streamflow was estimated to quantify the effects of climate change on 5 river basins. Rainfall elasticity denotes the sensitivity of annual streamflow for the variations of potential annual rainfall. This is a simple, useful method that evaluates how the balance of a water cycle on river basins changes due to long-term climate change and offers information to manage water resources and environment systems. The elasticity method was first used by Schaake in 1990 and is commonly used in the United States and Australia. A semi-distributed hydrological model (SLURP, semi-distributed land use-based runoff processes) was used to simulate the variations of area streamflow, and potential evapotranspiration. A nonparametric method was then used to estimate the rainfall elasticity on five river basins of Korea. In addition, the A2 (SRES IPCC AR4, Special Report on Emission Scenarios IPCC Fourth Assessment Report) climate change scenario and stochastic downscaling technique were used to create a high-resolution weather change scenario in river basins, and the effects of climate change on the rainfall elasticity of each basin were then analyzed.

• Proceedings of the Korea Water Resources Association Conference
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• 1997.05a
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• pp.192-198
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• 1997

The spatial variation of saturation tendency can be calculated from the Digital Elevation Model (DEM) employing the multiple flow direction algorithm on the platform of Geographic Resources Support Analysis System (GRASS). It is expected that a bettter understanding of dynamical runoff processes in hillslope hydrological scale is obtained through tracing various runoff path such as infiltration excess overland flow component, strutation excess overland flow component and subsurface runoff component. A procedure is suggested to consider the effect of a tile system on calculating the topographic index. A small agricultural subwatershed (3.4 km2) is used for this study.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.7-7
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• 2011

On March 11 in 2011, off the Pacific coast of Tohokua huge earthquake of Magnitude 9.0 occurred. This presentation reports the earthquake, the ensuing tsunami and the devastating damages caused by them. The epicenter was approximately 72 km east of the Oshika Peninsula of Tohoku, with the hypo-center at an underwater depth of approximately 32 km. Owing to this earthquake, strong quakes were observed in eastern Japan with the levels 6 and 7 on the Japanese scale. The earthquake triggered extremely destructive tsunami wave, which attacked the very wide range of eastern Japan coast. The earthquake and ensuing tsunami caused severe damage to levees and embankment along the coasts and rivers. Those water-related damages are reported in this presentation. The Fukushima No. 1 Nuclear Power Plant was also damaged by the earthquake and ensuing tsunami. From the crippled nuclear power plant, appreciable quantities of radioactive material were emitted to the surrounding environment. Those substances which emitted to air may fall on the ground together with raindrops and runoff to rivers. Elucidation of those processes is the task which our hydrological society should undertake.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.193-193
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• 2017

Trend analysis can enhance our knowledge of the dominant processes in the area and contribute to the analysis of future climate projections. The results of previous studies in South Korea showed that southeast regions of Korea had the highest value of evapotranspiration. Thereby, it is of interest to determine the trend analysis in hydrological variables in this area. In this study, the recent 35 year trends of precipitation, reference evapotranspiration, and aridity index in monthly and annual time scale will be analyzed over three stations (Pohang, Daegu, and Pusan) of southeast Korea. After removing the significant Lag-1 serial correlation effect by pre-whitening, non-parametric statistical Mann-Kendall test was used to detect the trends. Also, the slope of trend of the Mann-Kendall test was determined by using Theil-Sen's estimator. The results of the trend analysis of reference evapotranspiration on the annual scale showed the increasing trend for the three mentioned stations, with significant increasing trend for Pusan station. The results obtained from this research can guide development if water management practices and cropping systems in the area that rely on this weather stations. The approaches use and the models fitted in this study can serve as a demonstration of how a time series trend can be analyzed.

• Journal of Environmental Science International
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• v.28 no.1
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• pp.125-135
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• 2019

For the purposes of enhancing usability of Numerical Weather Prediction (NWP), the quantitative precipitation prediction scheme by machine learning has been proposed. In this study, heavy rainfall was corrected for by utilizing rainfall predictors from LENS and Radar from 2017 to 2018, as well as machine learning tools LightGBM and XGBoost. The results were analyzed using Mean Absolute Error (MAE), Normalized Peak Error (NPE), and Peak Timing Error (PTE) for rainfall corrected through machine learning. Machine learning results (i.e. using LightGBM and XGBoost) showed improvements in the overall correction of rainfall and maximum rainfall compared to LENS. For example, the MAE of case 5 was found to be 24.252 using LENS, 11.564 using LightGBM, and 11.693 using XGBoost, showing excellent error improvement in machine learning results. This rainfall correction technique can provide hydrologically meaningful rainfall information such as predictions of flooding. Future research on the interpretation of various hydrologic processes using machine learning is necessary.

• Journal of The Geomorphological Association of Korea
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• v.23 no.1
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• pp.77-85
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• 2016

To understand the difference of runoff discharge processes between Gwangneung deciduous and coniferous forest catchments, we collected hydrological data (e.g., precipitation, soil moisture, runoff discharge) and conducted hydrochemical analyses in the deciduous and coniferous forest catchments in Gwangneung National Arboretum in the northwest part of South Korea. Based on the end-member mixing analysis of the three storm events during the summer monsoon in 2005, the hillslope runoff in the deciduous forest catchment was higher 20% than the coniferousforest catchment during the firststorm event. Howerver, hillslope runoff increased from the second storm event in the coniferous catchment. We conclude that low soil water contents and topographical gradient characteristics highly influence runoff in the coniferous forest catchment during the first storm events. In general, coniferous forests are shown high interception loss and low soil moisture compared to the deciduous forests. It may also be more likely to be a reduction in soil porosity development when artificial coniferous forests reduced soil biodiversity. The forest soil porosity is an important indicator to determine the water recharge of the forest. Therefore, in order to secure the water resources, it should be managed coniferous forests for improving soil biodiversity and porosity.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.