• Journal of Environmental Science International
• /
• v.26 no.10
• /
• pp.1135-1146
• /
• 2017

At riverbank filtration sites, groundwater levels of alluvial aquifers near rivers are sensitive to variation in river discharge and pumping quantities. In this study, the groundwater level fluctuation, pumping quantity, and streamflow rate at the site of a riverbank filtration plant, which produces drinking water, in the lower Nakdong River basin, South Korea were interrelated. The relationship between drawdown ratio and river discharge was very strong with a correlation coefficient of 0.96, showing a greater drawdown ratio in the wet season than in the dry season. Autocorrelation and cross-correlation were carried out to characterize groundwater level fluctuation. Autoregressive model analysis of groundwater water level fluctuation led to efficient estimation and prediction of pumping for riverbank filtration in relation to river discharge rates, using simple inputs of river discharge and pumping data, without the need for numerical models that require data regarding several aquifer properties and hydrologic parameters.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.39 no.6
• /
• pp.88-98
• /
• 1997

In this study, pumping capacity in the area of natural river estuary is estimated by the quantitative analysis using finite element method. The study area is Iga-ri pumping station in the estuary of Seojung-chun which runs to the East coast. First of aH, hydraulic properties and effects of different seawater levels are analyzed in this area. Variations of groundwater level caused by pumping and properties of seawater intrusion are analyzed, then compared the case of reinforcing the existing intake weir with the case of setting up an weir at the upper stream. The observed data of groundwater drawdown caused by pumping during drought period and seawater intrusion are compared with results of the analysis done by groundwater model using finite element method, and it is found that both are similar. Accordingly, groundwater model used in this study reflects well the variation of groundwater level caused by pumping.

• Journal of Environmental Science International
• /
• v.18 no.3
• /
• pp.315-323
• /
• 2009

Hydraulic gradient of the landfill soils is estimated by Devlin (2003) method, and its variation characteristics from rainfall and permeability of the aquifer material are analyzed. The study site of 18 m $\times$ 12 m is located in front of the Environment Research Center at the Pukyong National University, and core logging, slug/bail test and groundwater monitoring was performed. The sluglbail tests were performed in 9 wells (except BH9 well), and drawdown data with elapsed time for bail tests were analyzed using Bouwer-Rice and Hvorslev methods. The average hydraulic conductivity estimated in each of the test wells was ranged $1.991{\times}10^{-7}{\sim}4.714{\times}10^{-6}m/sec$, and the average hydraulic conductivity in the study site was estimated $2.376{\times}10^{-6}m/sec$ for arithmetic average, $1.655{\times}10^{-6}m/sec$ for geometric average and $9.366{\times}10^{-7}m/sec$ for harmonic average. The permeability of landfill soils was higher at the east side of the study site than at the west side. Groundwater level in 10 wells was monitored 44 times from October 2 to November 7, 2007. The groundwater level was ranged 1.187$\sim$1.610 m, and the average groundwater level range in each of the well showed 1.256$\sim$1.407 m. The groundwater level was higher at the east side than at the west side of the study site, and this distribution is identify to it of hydraulic conductivity. The hydraulie gradient and the major flow direction for 10 wells were estimated 0.0072$\sim$0.0093 and $81.7618{\sim}88.0836^{\circ}$, respectively. Also, the hydraulic gradient and the major flow direction for 9 wells were estimated 0.0102$\sim$0.0124 and $84.6822{\sim}89.1174^{\circ}$, respectively. The hydraulic gradient of the study site increased from rainfall (83.5 mm) on October 7, causing by that the groundwater level of the site with high permeability was higher. The hydraulic gradient estimated on and after October 16 was stable, due to almost no rainfall. Thus, it was confirmed that the variation of the hydraulic gradient in the landfill soils was controlled by the rainfall.

• Journal of Soil and Groundwater Environment
• /
• v.27 no.6
• /
• pp.22-36
• /
• 2022

Water resources in Jeju Island are dependent virtually entirely on groundwater. For groundwater resources, drought damage can cause environmental and economic losses because it progresses slowly and occurs for a long time in a large area. Therefore, this study quantitatively evaluated groundwater level fluctuations using principal component and cluster analyses for 42 monitoring wells in Jeju Island, and further identified the types of groundwater fluctuations caused by drought. As a result of principal component analysis for the monthly average groundwater level during 2005-2019 and the daily average groundwater level during the dry season, it was found that the first three principal components account for most of the variance 74.5-93.5% of the total data. In the cluster analysis using these three principal components, most of wells belong to Cluster 1, and seasonal characteristics have a significant impact on groundwater fluctuations. However, wells belonging to Cluster 2 with high factor loadings of components 2 and 3 affected by groundwater pumping, tide levels, and nearby surface water are mainly distributed on the west coast. Based on these results, it is expected that groundwater in the western area will be more vulnerable to saltwater intrusion and groundwater depletion caused by drought.

• Journal of Soil and Groundwater Environment
• /
• v.25 no.3
• /
• pp.1-11
• /
• 2020

In this study, leakage ratios of Jeju Island's public agricultural groundwater were calculated by utilizing field measurements of groundwater level and surface reservoir water level. The average leakage ratios were 75.6% at groundwater well A and 57.5% at well B, with the ratio inversely proportional to agricultural water usage. The level of agricultural reservoirs varied at constant intervals at night, and the amount of water leakage associated with the variation was estimated as 0.1 - 16.3 ㎥/h. The leakage ratio was also influenced by pipeline length, average slope, and number of farmhouses. Currently, the estimation of agricultural water leakage on Jeju Island is based upon field inspection which is very labor- and cost intensive. The leakage ratio estimated by monitoring the reservoirs associated with the well A and B were 73.3 and 54.7%, respectively, consistent with the values obtained by field measurements.

• Journal of Soil and Groundwater Environment
• /
• v.19 no.4
• /
• pp.12-22
• /
• 2014

This study evaluated the pattern of groundwater fluctuation in cyrstalline rock using time series and factor analyses. From the results, groundwater level for the 18 wells was classified into 4 types reflecting the hydrogeological properties and rainfall event. Type 1 (DB1-5, DB1-6, DB2-2, KB-10, KB-13) was significantly influenced by groundwater flow through water-conducting features, whereas type 2 (DB1-3, DB1-7, KB-1~KB-3, KB-7, KB-11, KB-14, KB-15) was affected by minor fracture network as well as rainfall event. Type 3 (DB1-1, DB1-2) was mainly influenced by surface infiltration of rainfall event. Type 4 (DB1-8, KB-9) was reflected by the irregular variation of groundwater level caused by anisotropy and heterogeneity of crystalline rock.

• Journal of Environmental Science International
• /
• v.17 no.2
• /
• pp.257-267
• /
• 2008

Groundwater recharge characteristics in a fractured granite area, Mt. Geumjeong, Korea. was interpreted using bedrock groundwater and wet-land water data. Time series analysis using autocorreclation, cross-correlation and spectral density was conducted for characterizing water level variation and recharge rate in low water and high water seasons. Autocorrelation analysis using water levels resulted in short delay time with weak linearity and memory. Cross-correlation function from cross-correlation analysis was lower in the low water season than the high water season for the bedrock groundwater. The result of water level decline analysis identified groundwater recharge rate of about 11% in the study area.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.33 no.6
• /
• pp.2285-2291
• /
• 2013

In this study, daily based groundwater recharge was estimated under unsteady state by using groundwater table fluctuation model developed by Kim et al. (2013). This technique analyzes groundwater variation characteristics by using reaction factor related with groundwater flow and specific yield related with recharge. For the application of this model, measured groundwater level at JD Yongdam 1 and JW Konghang for 5 years were used. This model can estimate daily based groundwater recharge and the computed groundwater levels showed good agreement with measured groundwater levels. At JD Yongdam 1 and JW Konghang, the estimated recharges (rates) were 520.4~904.0mm (32.7~61.8%) and 447.4~633.4mm (24.0~45.1%), respectively. The developed model can be suggested as an efficient and precise method to estimate daily based groundwater recharge by using groundwater level data.

• Journal of Korea Water Resources Association
• /
• v.53 no.4
• /
• pp.273-283
• /
• 2020

Quantitative forecasting of groundwater levels for the assessment of groundwater variation and vulnerability is very important. To achieve this purpose, various time series analysis and machine learning techniques have been used. In this study, we developed a prediction model based on LSTM (Long short term memory), one of the artificial neural network (ANN) algorithms, for predicting the daily groundwater level of 11 groundwater wells in Hankyung-myeon, Jeju Island. In general, the groundwater level in Jeju Island is highly autocorrelated with tides and reflected the effects of precipitation. In order to construct an input and output variables based on the characteristics of addressing data, the precipitation data of the corresponding period was added to the groundwater level data. The LSTM neural network was trained using the initial 365-day data showing the four seasons and the remaining data were used for verification to evaluate the fitness of the predictive model. The model was developed using Keras, a Python-based deep learning framework, and the NVIDIA CUDA architecture was implemented to enhance the learning speed. As a result of learning and verifying the groundwater level variation using the LSTM neural network, the coefficient of determination (R²) was 0.98 on average, indicating that the predictive model developed was very accurate.

• The Journal of Engineering Geology
• /
• v.18 no.2
• /
• pp.167-176
• /
• 2008

The variation characteristics of groundwater level with distance from shoreline at Jeju Island was investigated using groundwater level data monitored from 257 wells for dry season (December 1998) and wet season (July 1997), respectively. Groundwater levels of the dry season were $7.46{\sim}203.8\;m$ with an average of 60.49 m, while those of the wet season were $4.01{\sim}204.10\;m$ with an average of 57.66 m. Groundwater level of the dry season was higher than that of the wet season, which was caused by heavy rains between June and October, 1998 at the Jeju Island. Correlation coefficients between altitude and groundwater level for dry and wet seasons were above 0.86, and those between dry season and wet season groundwater levels were very high above 0.95. 257 groundwater level data were classified at an interval of 500 m. Average values for altitude, groundwater levels and distance from shoreline were calculated for 17 intervals. Altitude and groundwater level fur dry and wet seasons at $0{\sim}4\;km$ intervals were increased with distance from shoreline, but those at $4{\sim}9\;km$ intervals were irregularity. Linear functions of the groundwater level for dry and wet seasons as distance from shoreline were estimated, and the coefficient of determinant at $0{\sim}4\;km$ interval data was higher than it at $0{\sim}9\;km$ interval data. Increasing rate of groundwater level at $0{\sim}4\;km$ intervals was more 2 times than it at $0{\sim}9\;km$ intervals. This results are caused by linear increase of groundwater level to 4 km from shoreline and by irregularity of groundwater level at the $4{\sim}9\;km$ intervals.