# Characterising Bedrock Aquifer Systems in Korea Using Paired Water-Level Monitoring Data

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## Abstract

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## 1. Introduction

## 2. Data Acquisition and Methods

- Considering spatial variations of precipitation and its impacts on groundwater levels, the AWS closest to the NGMN stations in the catchment were selected from the coordinates (Figure 2).
- To analyse the temporal variations, both precipitation and groundwater monitoring data were processed to the same temporal unit of daily data.
- Using spectral analysis, the principal periodic components were extracted from the time-series data. This analysis used the hourly groundwater-level data during the dry season from February to April 2010 to minimise the noise caused by precipitation. Then, using the components, the characteristics of the aquifers were classified based on specific frequency domains, which are shown in the unconfined, semi-confined, and confined conditions [33]. In the study of water-level fluctuation, five major harmonic components of tidal potential are identified: S2, M2, N2, K1, and O1 [28,36] (Table 2).In an ideal unconfined condition, the response of the aquifer is the same as the effect from atmospheric pressure, and because the aquifer is not under pressure, earth tide signals or atmospheric signals do not exist [28,29]. However, if a vadose zone exists on top of the water table, lags can occur as the atmospheric changes pass through the vadose zone. The atmospheric signals of S2 and K1 occur by such lags [29], and the lack of an M2 signal can indicate an unconfined condition of an aquifer [33]. As the thickness of the vadose zone increases, the aquifer becomes contained by greater pressure and exhibits an M2 earth tide signature. In other words, even when an S2 signal is predominant and a K1 signal still exists, a weak M2 signal can lead to the interpretation of a semi-confined aquifer [32]. If the aquifer is confined under an impermeable layer, groundwater would be pressurized and a strong M2 signal, the earth tidal force, would be observed [28].
- Using the daily average water-level data from the paired monitoring wells, the following statistical analyses were carried with the SPSS statistics program (Version 24, IBM
^{®}Corp., Armonk, NY, USA): (1) a principal component analysis (PCA) to characterize water-level fluctuation types of different aquifer conditions; (2) a cross-correlation analysis to analyse quantitatively the agreement of water-level fluctuations between the shallow and deep groundwater in paired wells with different aquifer conditions; (3) a lag-time analysis between precipitation and water-level responses of different aquifer types [37]; and (4) a linear regression analysis to confirm the fluctuation types of the two aquifers.

_{xy}(k) is the correlation coefficient at k, and the parameters x and y are the arithmetic means of length N, and N is the number of measurements. By calculating the value of k that maximises the correlation between the two time-series x and y, the lag-time can be determined.

## 3. Results

#### 3.1. Analysis of Aquifer Type through Spectral Analysis

^{®}program (R2015a, Mathworks Inc., Natick, MA, USA), spectral analysis was conducted to classify the frequency domains and to analyse the strengths of the signals that were derived from the respective water-level data (Table S1). To minimise the influence of precipitation, which can act as noise, dry-season data from February to April 2010 were selected. Because the FFT method has a fundamental limitation in the number of time-series data for the analysis (being 2

^{n}), this study tried to use at least 2048 data points for processing. The spectral analysis could identify aquifer conditions of 93 and 68 monitoring wells (50.6% of the total 318 wells) for bedrock and alluvial aquifers, respectively (Table 3). For the rest, the signals were not clear enough to determine aquifer type, probably due to disturbances in the data continuity that caused by possible mechanical errors of monitoring equipment and the quarterly water-quality sampling processes.

- Component 1 for the unconfined aquifer: Seasonal changes between the dry season and the wet season are obvious. Water levels rise during precipitation events and decline after the wet season, following the form of an exponential function (Figure 5a).
- Component 1 for the semi-confined aquifer: Seasonal changes appear between the dry season and the wet season, but the degree of water-level rise or decline is sharper than in PC1 of unconfined aquifer. The hydrograph pattern showed the intermediate form of the first component of unconfined and confined aquifer (Figure 5b).
- Component 1 for the confined aquifer: The magnitude of the factor scores was two times greater than unconfined and semi-confined aquifer during the wet season, but the deviation of fluctuation pattern was less than one during the dry season. The hydrograph peak appeared after main precipitation events (Figure 5c).

#### 3.2. Cross-Correlation Analysis

## 4. Discussion

- Type I (77.8%, 49 paired wells): Characterized by identical water-level fluctuation patterns in both shallow and deep groundwater, high correlation coefficients, and indistinguishable aquifer characteristics. Both alluvial and bedrock aquifers could be under the same unconfined or confined aquifer conditions (Figure 6).
- Type II (9.5%, 6 paired wells): Characterized by low similarity between water-level fluctuations, low correlation coefficients, and clearly different aquifer characteristics. This implies that the shallow and deep aquifers are separated with different aquifer conditions. In this case, groundwater recharge could occur along different flow paths. That is, the water level in the deep bedrock wells could respond to horizontal recharge from up-gradient areas through fractures developed in the bedrock aquifer (Figure 7).
- Type III (12.7%, 8 paired wells): As exceptions, three paired water-levels were similar in terms of high correlation coefficients with different periodic components, implying different aquifer characteristics. Even five paired data with low correlations, water-level fluctuations could become similar temporarily. This implies that groundwater flow pathways for recharge could vary depending on precipitation events (Figure 8).

## 5. Conclusions

## Supplementary Materials

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Locations and schematic diagrams of the National Groundwater Monitoring Stations (modified from [2], www.gims.go.kr).

**Figure 2.**Locations of the 159 well pairs among the National Groundwater Monitoring Stations (NGMS) and adjacent automatic weather stations (AWS) in the Korean Peninsula.

**Figure 4.**Locations of the groundwater monitoring wells and analysed aquifer types from the various signal components: (

**a**) bedrock aquifer; and (

**b**) alluvial aquifer.

**Figure 5.**Factor scores for the first principal component and groundwater hydrograph of the example site pairs of each aquifer characteristic: (

**a**) unconfined aquifer, GimpoGimpo; (

**b**) semi-confined aquifer, JecheonGoam; and (

**c**) confined aquifer, SeoulMagok.

**Figure 6.**Results of the correlation analysis and harmonic analysis of the paired water-level data from shallow and deep monitoring wells in 2010 for Type I with identical pattern, high correlation coefficient, and the same unconfined/confined aquifer: (

**a**) groundwater hydrograph; (

**b**) one-hour interval data for spectral analysis; (

**c**) signal components; and (

**d**) correlated data.

**Figure 7.**Results of the correlation analysis and harmonic analysis of the paired water-level data from shallow and deep monitoring wells in 2010 for Type II with low similarity between water-level fluctuation pairs, low correlation coefficients and different aquifer characteristics: (

**a**) groundwater hydrograph; (

**b**) one-hour interval data for spectral analysis; (

**c**) signal components; and (

**d**) correlated data.

**Figure 8.**Results of the correlation analysis and harmonic analysis of the paired water-level data from shallow and deep monitoring wells in 2010 for Type III with unmatched aquifer system pairs and correlation coefficients: (

**a**) the different aquifer characteristic but high correlation coefficient; and (

**b**) the same aquifer characteristic but low correlation coefficient.

River Basin | No. of Monitoring Stations | No. of Stations with Paired Alluvial and Bedrock Monitoring Wells | Total Number of Wells |
---|---|---|---|

Han River | 102 | 47 | 149 |

Geum River | 76 | 37 | 113 |

Nakdong River | 93 | 49 | 142 |

Yeongsan–Seomjin River | 52 | 26 | 78 |

Total | 323 | 159 | 482 |

Symbol (Signal Components) | Period | Confined | Semi-Confined | Unconfined | Description |
---|---|---|---|---|---|

M2 | 12.42 h | Present (dominant) | Present | Not present | Main lunar |

S2 | 12.00 h | Present | Present (dominant) | May be present | Main solar |

N2 | 12.66 h | Present | May be present | Not present | Elliptical lunar |

K1 | 23.93 h | Present | Present | May be present | Soli-lunar |

O1 | 25.82 h | Present | May be present | Not present | Main lunar |

**Table 3.**Aquifer characterisation for alluvial and bedrock monitoring wells based on the results of the spectral analysis.

Classification | Signal | Aquifer Characteristic | Monitoring Well | |
---|---|---|---|---|

Bedrock | Alluvial | |||

Clear | M2, K1, S2, (O1) | Confined | 9 (5.7%) | 4 (2.5%) |

K1, S2, M2, (O1) | Semi-confined | 34 (21.4%) | 17 (10.7%) | |

K1, S2 | Unconfined | 50 (31.4%) | 47 (29.6%) | |

Not clear | ND * | ND * | 66 (41.5%) | 91 (57.2%) |

Overall | - | - | 159 (100%) | 159 (100%) |

**Table 4.**Total variance of water-level data as explained by the PCA from the 63 pairs of monitoring wells which characterized aquifer conditions by the spectral analysis.

Component | Initial Eigenvalues | ||||||||
---|---|---|---|---|---|---|---|---|---|

Unconfined Aquifer | Semi-Confined Aquifer | Confined Aquifer | |||||||

Total | % of Variance | Cumulative% of Variance | Total | % of Variance | Cumulative% of Variance | Total | % of Variance | Cumulative% of Variance | |

1 | 54.689 | 55.805 | 55.805 | 27.911 | 54.727 | 54.727 | 7.266 | 55.896 | 55.896 |

2 | 15.612 | 15.930 | 71.735 | 8.050 | 15.783 | 70.510 | 1.675 | 12.884 | 68.779 |

3 | 5.307 | 5.415 | 77.150 | 5.563 | 10.908 | 81.418 | 1.192 | 9.171 | 77.950 |

4 | 4.321 | 4.409 | 81.560 | - | - | - | - | - | - |

Aquifer Type | Lag-Time (in Units of Days) | ||||
---|---|---|---|---|---|

Depth | Unconfined | Semi-Confined | Confined | Average | |

Shallow alluvial well | 1.47 ± 0.20 (n = 30) | 2.18 ± 0.78 (n = 10) | 2.75 ± 1.11 (n = 4) | 1.77 ± 0.24 | |

Deep bedrock well | 1.78 ± 0.31 (n = 23) | 1.94 ± 0.50 (n = 16) | 2.75 ± 1.11 (n = 4) | 1.93 ± 0.26 |

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**MDPI and ACS Style**

Lee, J.M.; Woo, N.C.; Lee, C.-J.; Yoo, K.
Characterising Bedrock Aquifer Systems in Korea Using Paired Water-Level Monitoring Data. *Water* **2017**, *9*, 420.
https://doi.org/10.3390/w9060420

**AMA Style**

Lee JM, Woo NC, Lee C-J, Yoo K.
Characterising Bedrock Aquifer Systems in Korea Using Paired Water-Level Monitoring Data. *Water*. 2017; 9(6):420.
https://doi.org/10.3390/w9060420

**Chicago/Turabian Style**

Lee, Jae Min, Nam C. Woo, Chan-Jin Lee, and Keunje Yoo.
2017. "Characterising Bedrock Aquifer Systems in Korea Using Paired Water-Level Monitoring Data" *Water* 9, no. 6: 420.
https://doi.org/10.3390/w9060420