Dynamic variation in the saltwater–freshwater transition zone below a seafront beach in South Korea was investigated with long-term monitoring of the groundwater in relation to the precipitation, wave height, and tide. Correlation, spectral analysis, and regression analysis of monitoring data were performed to deduce the relationships between these factors. The general shape of the transition zone was affected by the seasonal groundwater levels, but temporary fluctuations were predominantly affected by local rising-groundwater-level events. The distinct increases in the groundwater level were closely related to the wave height. Different patterns of electrical conductivity (EC) change were detected in the shallow and deep zones, and these differences indicated that the transition zone was highly dynamic. The EC values at shallow depths were temporarily increased by the wave setup and tidal fluctuations during the rising-groundwater events, but the EC at greater depths was reduced by the seaward or downward movement of the relative freshwater. In exceptional cases, during extreme increases in the groundwater level resulting from seawater flooding, the rapid downward flow of the flooding saltwater through the well bore caused synchronous EC fluctuations at all depths.

Freshwater–saline water interactions were evaluated in a coastal region influenced by external forces including tidal fluctuations and seasonal rainfall variations. Five different coastal zones were considered on Jeju Island, South Korea, and electrical conductivity (EC) profiles from the monitoring wells were examined to identify the configuration of the freshwater–saline water interface. There appeared to be discrepancies among EC profiles measured at different points in time. To analyze the dynamic behavior of freshwater–saline water interactions, groundwater level measurements and multi-depth EC and temperature probes were used to obtain time-series data; the data showed that water level, EC and temperature were influenced by both tidal fluctuations and heavy rainfall. The effects of oceanic tide on EC and temperature differed with depth due to hydraulic properties of geologic formations. A spectral filter was used to eliminate the effects of tidal forces and provide information on the influence of heavy rainfall on water level, EC and temperature. Heavy rainfall events caused different patterns and degrees of variation in EC and temperature with depth. The time-series data of EC and temperature in the subsurface at various depths enable greater understanding of the interaction processes between fresh and saline water.

The interactions between groundwater and surface water under a river island in South Korea were investigated. Sampling was focused on the western part of the island, where a groundwater heat pump system with groundwater monitoring wells has been installed. A riverside area was selected for the system because a high specific capacity was expected. However, surface-water intrusion remains a major concern, as the efficiency of the system can be impaired by groundwater temperature changes following surface-water infiltration. A combination of radon (²²²Rn) concentrations and microbial diversity was analyzed, along with hydraulic data. A process for estimating residence time based on ²²²Rn data and flow direction was developed to identify relationships with microbial diversity. The spatial distribution of ²²²Rn concentration was affected by fluctuations of river water levels caused by surrounding dam discharge and seasonal effects. Groundwater flowmeter data supported observations on these distributions. The estimated residence times indicated that river-water infiltration into the study site affected the groundwater flow direction. The microbial diversity, based on cluster analyses after pyrosequencing, showed significant variation with river flow direction and rainfall events, which was in accordance with the ²²²Rn tracer results. The combination of frequent ²²²Rn concentration measurements with microbial data allows better characterization of the dynamic interactions between groundwater and surface water.

Numerical groundwater models were used to assess groundwater sustainability on Jeju Island, South Korea, for various climate and groundwater withdrawal scenarios. Sustainability criteria included groundwater-level elevation, spring flows, and salinity. The latter was studied for the eastern sector of the island where saltwater intrusion is significant. Model results suggest that there is a need to revise the current estimate of sustainable yield of 1.77 × 10⁶m³/day. At the maximum extraction of 84 % of the sustainable yield, a 10-year drought scenario would decrease spring flows by 28 %, dry up 27 % of springs, and decrease hydraulic head by an island-wide average of 7 m. Head values are particularly sensitive to changes in recharge in the western parts of the island, due to the relatively low hydraulic conductivity of fractured volcanic aquifers and increased groundwater extraction for irrigation. Increases in salinity are highest under drought conditions around the current 2-m head contour line, with an estimated increase of up to 9 g/L under 100 % sustainable-yield use. The study lists recommendations towards improving the island’s management of potable groundwater resources. However, results should be treated with caution given the available data limitations and the simplifying assumptions of the numerical modeling approaches.

Change in groundwater level is predicted for a special site where transient natural factors affecting the groundwater level are mixed with very irregular anthropogenic influences. When there is not enough hydrogeological information about the area to be analyzed, an artificial neural network (ANN) is a powerful tool for groundwater level forecasting in highly irregular and uncertain groundwater systems. In this study, groundwater levels were predicted by using ANN models with input variables composed of one natural factor and two anthropogenic factors in Yangpyeong riverside area, South Korea. Complex and irregular change of the groundwater level was monitored due to the operation of a groundwater heat pump system and winter intensive pumping for water curtain cultivation (by which greenhouses are warmed). The prediction results showed good performance with root mean square errors of 3–6 cm when the average groundwater level is about 25.59 m, the correlation coefficient is >0.9 and the Nash–Sutcliffe efficiency is >0.75, indicating that the ANN models are well suited for assessing complex groundwater systems. Along with the prediction, an extraction method was devised to calculate contributions and relative impacts of the input variables in the time-series-based ANN models. As a result, it was proved that the river level dominantly affects the groundwater level fluctuation, and the contributions of each influencing factor were obtained reliably according to spatial distribution and temporal variance. This makes the scheme effective for managing and using groundwater resources with consideration of every crucial influencing factor of the groundwater level fluctuation.

An artificial neural network model (ANN) and a geographic information system (GIS) are applied to the mapping of regional groundwater productivity potential (GPP) for the area around Pohang City, Republic of Korea. The model is based on the relationship between groundwater productivity data, including specific capacity (SPC) and its related hydrogeological factors. The related factors, including topography, lineaments, geology, and forest and soil data, are collected and input into a spatial database. In addition, SPC data are collected from 44 well locations. The SPC data are randomly divided into a training set, to analyse the GPP using the ANN, and a test set, to validate the predicted potential map. Each factor’s relative importance and weight are determined by the back-propagation training algorithms and applied to the input factor. The GPP value is then calculated using the weights, and GPP maps are created. The map is validated using area under the curve analysis with the SPC data that have not been used for training the model. The validation shows prediction accuracies between 73.54 and 80.09 %. Such information and the maps generated from it could serve as a scientific basis for groundwater management and exploration.

The development and implementation of a hybrid discrete fracture network/equivalent porous medium (DFN/EPM) approach to groundwater flow at the Gyeong-Ju low- and intermediate-level radioactive waste (LILW) disposal site in the Republic of Korea is reported. The geometrical and hydrogeological properties of fractured zones, background fractures and rock matrix were derived from site characterization data and implemented as a DFN. Several DFN realizations, including the deterministic fractured zones and the stochastic background fractures, whose statistical properties were verified by comparison with in-situ fracture and hydraulic test data, were suggested, and they were then upscaled to continuums using a fracture tensor approach for site-scale flow simulations. The upscaled models were evaluated by comparison to in-situ pressure monitoring data, and then used to simulate post-closure hydrogeology for the LILW facility. Simulation results demonstrate the importance of careful characterization and implementation of fractured zones. The study highlighted the importance of reducing uncertainty regarding the properties and variability of natural background fractures, particularly in the immediate vicinity of repository emplacement.

The extent of denitrification in a small agricultural area near a river in Yangpyeong, South Korea, was determined using multiple isotopes, groundwater age, and physicochemical data for groundwater. The shallow groundwater at one monitoring site had high concentrations of NO₃-N (74–83 mg L⁻¹). The δ¹⁵N-NO₃ values for groundwater in the study area ranged between +9.1 and +24.6‰ in June 2014 and +12.2 to +21.6‰ in October 2014. High δ¹⁵N-NO₃ values (+10.7 to +12.5‰) in both sampling periods indicated that the high concentrations of nitrate in the groundwater originated from application of organic fertilizers and manure. In the northern part of the study area, some groundwater samples showed elevated δ¹⁵N-NO₃ and δ¹⁸O-NO₃ values, which suggest that nitrate was removed from the groundwater via denitrification, with N isotope enrichment factors ranging between −4.8 and −7.9‰ and O isotope enrichment factors varying between −3.8 and −4.9‰. Similar δD and δ¹⁸O values of the surface water and groundwater in the south appear to indicate that groundwater in that area was affected by surface-water infiltration. The mean residence times (MRTs) of groundwater showed younger ages in the south (10–20 years) than in the north (20–30 years). Hence, it was concluded that denitrification processes under anaerobic conditions with longer groundwater MRT in the northern part of the study area removed considerable amounts of nitrate. This study demonstrates that multi-isotope data combined with physicochemical data and age-dating information can be effectively applied to characterize nitrate contaminant sources and attenuation processes.