• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2002.04a
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• pp.183-186
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• 2002

The objective of this study is to predict behavior of a contaminant plume and concentration of contaminants in soil through tile relations between the concentrations of contaminants in groundwater and in soil on the shallow sandy aquifer contaminated with petroleum hydrocarbons. The current state of the plume and its fate in the study area was simulated by using the MODFLOW-RT3D model and geochemical parameters of grounwater had been monitored and measured during 3 years (1999~2001). The relations between the concentrations of contaminants in each medium were taken from the investigation of site characterization conducted in 1999. Simulation results showed the center of the plume would migrate 407m twenty years later. At that time, the concentration would be decreased down to about 26 mg/$\ell$(93%). In comparison TEX concentration in the groundwater with that in the soil, the value of correlation coefficient (r=0.876) was as high as it could be used. Based on the high r-value, the linear equation was obtained from regression analysis. The results of model simulation by RT3D engine showed that the highest TEX concentration in the groundwater would be 58.8 mg/$\ell$ 16 years later, and then the TEX concentration in soil would be below the alarming level (80 mg/kg) of regulation criteria.

• Journal of Soil and Groundwater Environment
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• v.18 no.5
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• pp.65-76
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• 2013

The water-table fluctuation (WTF) method has been often used for estimating groundwater recharge by analysis of waterlevel measurements in observation wells. An important assumption inherent in the method is that the water level rise is solely caused by precipitation recharge. For the observation wells located near a stream, however, the water-level can be highly affected by the stream level fluctuations as well as precipitation recharge. Therefore, in applying the WTF method, there should be consideration regarding the effect of stream-aquifer interactions. Analysis of water-level hydrographs from the National Groundwater Monitoring Wells of Korea showed that they could be classified into three different types depending on their responses to either precipitation recharge or stream level fluctuations. A simple groundwater flow model was used to analyze the errors of the WTF method, which were associated with stream-aquifer interactions. Not surprisingly, the model showed that the WTF method could greatly overestimate recharge, when it was used for the observation wells of which the water-level was affected by streams. Therefore, in Korea, where most groundwater hydrographs are acquired from wells nearby a stream, more caution is demanded in applying the WTF method.

• Tunnel and Underground Space
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• v.11 no.2
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• pp.156-166
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• 2001

A stochastic continuum(SC) modeling technique was developed to simulate the groundwater flow pathway in fractured rocks. This model was developed to overcome the disadvantageous points of discrete fracture network(DFN) modes which has the limitation of fracture numbers. Besides, SC model is able to perform probabilistic analysis and to simulate the conductive groundwater pathway as discrete fracture network model. The SC model was formulated based on the discrete fracture network(DFN) model. The spatial distribution of permeability in the stochastic continuum model was defined by the probability distribution and variogram functions defined from the permeabilities of subdivided smaller blocks of the DFN model. The analysis of groundwater travel time was performed to show the consistency between DFN and SC models by the numerical experiment. It was found that the stochastic continuum modes was an appropriate way to provide the probability density distribution of groundwater velocity which is required for the probabilistic safety assessment of a radioactive waste disposal facility.

• Journal of the Korean Society of Groundwater Environment
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• v.2 no.2
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• pp.49-57
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• 1995

Areas of Chungyang in Chungcheongnamdo, Cheju island and Georgia in U.S.A. were selected to calculate their groundwater ages and dispersion parameters using tritium. Piston flow model, Completely mixing model and Dispersion binomial model which calculate men residence times, and Dispersion normal model and Dispersion model(C$\_$FF) which calculate mean residence times and dispersion parameters simultaneously were applied. Since the input data, tritium concentrations of rainwaters, lack in part, tritium input function was prepared using the correlation of tritium concentrations of rainwaters of Pohang, Korea and Ottawa, Canada. Similar results of PFM and DBM reflect the intrinsical similarity of two models, assumption of low dispersion. The ages of sites of showing relatively higher tritium concentrations than other sites in the sam, region were not calculated by CMM. Since the calculations of DNM and DM(C$\_$FF/) provide the combination of wide ranges of parameters and groundwater ages, the ranges of dispersion parameters were narrowed down under the assumption that ages calculated by PFM and DBM are correct. Since large variation of tritium concentrations of outflows in a same region may reflect the different characteristics of each groundwater flow regime, using only one specific model on a whole region is not recommended.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.697-723
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• 2022

Data-driven models to predict groundwater levels 30 days in advance were developed for 12 groundwater monitoring stations in the middle-Jeju watershed, Jeju Island. Stacked long short-term memory (stacked-LSTM), a deep learning technique suitable for time series forecasting, was used for model development. Daily time series data from 2001 to 2022 for precipitation, groundwater usage amount, and groundwater level were considered. Various models were proposed that used different combinations of the input data types and varying lengths of previous time series data for each input variable. A general procedure for deep-learning-based model development is suggested based on consideration of the comparative validation results of the tested models. A model using precipitation, groundwater usage amount, and previous groundwater level data as input variables outperformed any model neglecting one or more of these data categories. Using extended sequences of these past data improved the predictions, possibly owing to the long delay time between precipitation and groundwater recharge, which results from the deep groundwater level in Jeju Island. However, limiting the range of considered groundwater usage data that significantly affected the groundwater level fluctuation (rather than using all the groundwater usage data) improved the performance of the predictive model. The developed models can predict the future groundwater level based on the current amount of precipitation and groundwater use. Therefore, the models provide information on the soundness of the aquifer system, which will help to prepare management plans to maintain appropriate groundwater quantities.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.09a
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• pp.254-257
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• 2003

Statistical analysis is performed to estimate the correlations between geological or geographical factor and groundwater inflow rates in the Seoul subway system. Correlation analysis shows that among several geological and geographical factors fractures and streams have most strong effects on inflow rate into tunnels. In particular, subway line 5∼8 are affected more by these factors than subway line 1∼4. Time series analysis is carried out to forecast groundwater inflow rate. Time series analysis is a useful empirical method for simulation and forecasts in case that physical model can not be applied to. The time series of groundwater inflow rates is calculated using the observation data. Transfer function-noise model is applied with the precipitation data as input variables. For time series analysis, statistical methods are performed to identify proper model and autoregressive-moving average models are applied to evaluation of inflow rate. Each model is identified to satisfy the lowest value of information criteria. Results show that the values by result equations are well fitted with the actual inflow rate values. The selected models could give a good explanation of inflow rates variation into subway tunnels.

• Journal of Environmental Science International
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• v.28 no.4
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• pp.423-433
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• 2019

This study was carried out to identify the problems of the underground watersheds on Jeju Island, and to establish the hydraulic groundwater basin to be used as basis for the analysis of the groundwater model. In order to evaluate the adequacy of the groundwater basin on Jeju Island, a correlation analysis between elevation and groundwater level was conducted using data from 125 observation wells. The analysis, conducted with an elevation step of 100 m, exhibited values of R2 in the range 0.1653-0.8011. No clear correlation was observed between elevation and groundwater level. In particular, the eastern and western areas showed an inverse proportionality between elevation and groundwater level. The Kriging technique was used to analyze the underground water level data and to define the equipotential lines for all areas of Jeju Island. Eight groundwater watersheds were delineated by considering the direction of groundwater flow, the positions of the observation wells, and the long and short axes of the watersheds.

• Journal of Korea Water Resources Association
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• v.37 no.6
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• pp.509-515
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• 2004

In this study, comprehensive evaluation on the fully coupled SWAT-MODFLOW model is performed. Since combined model can consider the spatially varied daily recharge rate, groundwater modeling would be greatly enhanced. Also, combined model has been able to generate the distribution of groundwater heads with time, surface-subsurface flow modeling would be greatly advanced. River-aquifer interaction is well established in the combined model considering two-way interactions. Consequently, the reliability of groundwater discharge and total runoff of watershed would be greatly enhanced when combined model is used.

• Journal of Korea Water Resources Association
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• v.50 no.6
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• pp.387-395
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• 2017

Sea level rise, accompanied by climate change, is expected to exacerbate seawater intrusion in the coastal groundwater system. As the salinity of saturated groundwater increases, salinity can increase even in the unsaturated soil above the groundwater surface, which may cause crop damage in the agricultural land. The other adverse impact of sea level rise is reduced unsaturated soil thicknesses. In this study, a composite model to assess impacts of sea level rise in coastal agricultural land is proposed. The composite model is based on the combined applications of a three dimensional model for simulating saltwater intrusion into the groundwater and a vertical one dimensional model for simulating unsaturated zone flow and transport. The water level and salinity distribution of groundwater are calculated using the three dimensional seawater intrusion model. At some uppermost nodes, where salinity are higher than the reference value, of the 3D mesh one dimensional unsaturated zone modeling is conducted along the soil layer between the ground water surface and the ground surface. A particular location is judged salinized when the concentration at the root-zone depth exceeds the tolerable salinity for ordinary crops. The developed model is applied to a hypothetical agricultural reclamation land. IPCC RCP 4.5 and 8.5 scenarios were used as sea level rise data. Results are presented for 2050 and 2100. As a result of the study, it is predicted that by 2100 in the climate change scenario RCP 8.5, there will be 7.8% increase in groundwater saltwater-intruded area, 6.0% increase of salinized soil area, and 1.6% in increase in water-logging area.

• The Journal of Engineering Geology
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• v.29 no.4
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• pp.417-425
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• 2019

Agricultural groundwater use accounts for 51.8% of total groundwater use, so accurate estimation of groundwater use is important for efficient groundwater management. The purpose of this study is to develop a method for estimating the groundwater use of agricultural (rice farming and dry-field farming) wells using regression tree model based on the measured data of 370 wells. Three input variables of the model were evaluated as being significant: well depth, pipe diameter, and pump capacity, and the importance of each variable was 75% for well depth, 17% for pipe diameter, and 8% for pumping capacity. The daily usage of agricultural (rice farming and dry-field farming) wells by the regression tree model was estimated to be very similar to the actual usage, compared to the previous estimation method proposed by the Ministry of Construction and Transportation. In the future, it is expected that the reliability of the usage statistics will be improved if additional observed data is secured and this classification method is modified.