# Assessing Impacts of Climate Change and Sea-Level Rise on Seawater Intrusion in a Coastal Aquifer

^{1}

^{2}

^{*}

## Abstract

**:**

^{−1}, the largest salinity increase (40.3%) was simulated. We concluded that this study may provide a better understanding of the climate change impacts on seawater intrusion by considering both SLR and freshwater recharge rates.

## 1. Introduction

^{−2}over the 157-year record of sea level. They also used polynomial regression for the SLA estimation and found that 0.013 mm yr

^{−2}, −0.0006 mm yr

^{−2}, and −0.023 mm yr

^{−2}for the periods 1855–2011, 1900–2011, and 1925–2011, respectively. Eazer and Corlett [5] showed that SLA for Chesapeake Bay (USA) ranged from 0.05 to 0.10 mm yr

^{−2}using Empirical Mode Decomposition and Hilbert-Huang Transformation [6]. Kim and Cho [7] reported that the average SLR around the Korean peninsula over the study period was 2.57 mm yr

^{−1}and the average SLA 0.075 ± 0.026 mm yr

^{−2}based on EEMD. They also found that 2.603 ± 0.0266 mm yr

^{−1}and 0.114 ± 0.040 mm yr

^{−2}for SLR and SLA, respectively, using polynomial regressions. Yoon [8] found that SLR around the Korean peninsula was higher the global mean SLR using a regression approach from tide gauge data.

## 2. Materials and Methods

#### 2.1. Site Description and Data Collection

^{−1}. A Piper trilinear diagram [34] was applied for hydrogeochemical facies of the Byeonsan2 monitoring site and the results from the diagram showed that the dominant hydrogeocheincal facies were classified with Na-Cl type (data not shown).

#### 2.2. Sea-Level Rise Projection

#### 2.3. Seawater Intrusion Modeling

^{−3}), S

_{op}is the specific pressure storativity (ML

^{−1}T

^{−2})

^{−1}, t is the time (T), ε is the fractional porosity [1], U is the solute mass fraction (MM

^{−1}), k is the permeability tensor (L

^{2}), μ is the fluid viscosity (ML

^{−1}T

^{−1}), p is the fluid pressure (ML

^{−1}T

^{−2}), g is the gravity vector (MT

^{−2}), and Q

_{p}is the fluid mass source (ML

^{−3}T

^{−1}):

^{−1}):

_{m}is the coefficient of molecular diffusion in porous medium fluid (L

^{2}T

^{−1}), I is the identity tensor [1], D is the dispersion tensor (L

^{2}T

^{−1}), C is the concentration of solute (MM

^{−1}), and C* is the concentration of a fluid source (MM

^{−1}).

^{−4}m day

^{−1}and 0.67 m

^{2}day

^{−1}, respectively. Longitudinal dispersivity and transverse dispersivity were estimated through the SUTRA model by comparing the simulated salinity against the observed salinity converted from the observed EC. An observation node was inserted at the location of the EC sensor (i.e., 44 m below sea level). In this study, the observed salinity from 2005 to 2010 were selected for the calibration of the transport parameters (i.e., longitudinal dispersivity and transverse dispersivity), while those in 2011 to 2015 were selected for the validation of the SUTRA model. The performance of the SUTRA model was assessed with Mean Absolute Percentage Error (MAPE, Equation (4)). The simulation periods selected in this study were the baseline period (2005–2015), 2050s (2051–2060), and 2090s (2091–2100).

_{i}is the observed value and P

_{i}is the simulated value.

## 3. Results and Discussion

#### 3.1. Sea-Level Rise Projection

^{−1}(p-value < 0.0001), based on the linear regression, which is assumed as a constant rate of SLR. An acceleration of 0.08 mm yr

^{−2}(p-value < 0.001) was found over same period from the quadratic regression. This linear trend is in good agreement with that by Yoon [8]. Yoon [8] reported that a constant rate of 3.4 mm yr

^{−1}and 3.7 mm yr

^{−1}were estimated from the Gunsan tide gauge station data for the periods 1981–2014 and 1985–2014, respectively. The constant rate (3.45 ± 0.49 mm yr

^{−1}) in this study is slightly lower than that (3.53 ± 0.29 mm yr

^{−1}) at the Mokpo tide gauge station by [6] and very close to that (3.4 ± 0.4 mm yr

^{−1}) for the global mean sea level by [43], while the constant rate is higher than the average SLR (2.57 mm yr

^{−1}) from their study. The acceleration in this study is close to the average acceleration of SLR (0.075 yr

^{-2}) by [6]. They estimated the trend and acceleration of SLR at the five tide gauge stations around the Korean peninsula using the ensemble empirical mode decomposition (EEMD) approach.

#### 3.2. Seawater Intrusion Modeling

^{−1}. For the 2050s and 2090s, annual mean precipitations were first calculated with RCP 4.5 and 8.5 climate change scenarios from the KGAWC and these annual mean precipitations were then used for freshwater recharge rates, respectively. There were four freshwater recharge rates except for the baseline period. However, it should be noted that this simple approximation of the freshwater recharge rate from annual mean precipitation may lead to inadequate inference of seawater intrusion investigation. For example, for the water-balance method, evapotranspiration, runoff, and precipitation should be considered to estimate groundwater recharge rates [45]. A further study is suggested for the accurate estimation of freshwater recharge rates. Four SLR scenarios, including the projections from the polynomial regressions and the projections for the West Sea under the two emission scenarios [2] were assumed for this study: 0.12 m by the year 2050 from the linear regression, 0.32 m by the year 2050 from the quadratic regression, 0.57 m under RCP 4.5 [2], and 0.72 m under RCP 8.5 [2]. A total of 15 scenarios were made considering these four freshwater recharge rates and SLR scenarios and the baseline. These scenarios in this study are summarized in Table 1. The lowest freshwater recharge rate (0.0549 kg s

^{−1}) was found in the 2090s under RCP 4.5 and the highest freshwater recharge rate (0.0694 kg s

^{−1}) was found in the 2090s under RCP 8.5.

_{in}were set to be 0.0 m and 0.00603 kg s

^{−1}, respectively. The longitudinal dispersivity and transverse dispersivity were estimated by comparing the observed and simulated salinities from the observation node (located at 44 m below sea level), based on the assumption of anisotropic and homogeneous domains for the transport parameters. The estimates of the longitudinal dispersivity and transverse dispersivity were 10.0 m and 0.1 m, respectively. The value of MAPE of the observed and simulated salinities for the calibration period was approximately 1.6%, while the MAPE value for the validation period was about 2.4%. The ratio of longitudinal dispersivity to transverse dispersivity was 100 for this site. This ratio is in substantial agreement with that reported by Anderson [46]. Anderson [46] found that the ratio of longitudinal dispersivity to transverse dispersivity ranged from 10 to 100. Gelhar et al. [47] reported a “scale effect” that transport parameters are generally proportional to the sizes of the study regions. Therefore, a further study on a tracer test is suggested to accurately determine transport parameters, including longitudinal dispersivity and transverse dispersivity.

^{−1}) and the results for the baseline and this scenario are displayed in Figure 6. A salinity change of only 7.7% increased with the case 2090RCP85_RR2090RCP85 (i.e., the highest SLR of 0.72 m and the highest freshwater recharge rate of 0.00694 kg s

^{−1}). These results imply that a freshwater recharge rate of 0.00694 kg s

^{−1}may largely offset the impact of SLR on seawater intrusion. This finding is in good agreement with that by Hussain et al. [30]. They found that salinity in the aquifer could be largely reduced by the artificial recharge application. These results suggest that to accurately assess the impacts of climate change on seawater intrusion in coastal groundwater systems, both SLR and freshwater recharge rates should be considered.

## 4. Conclusions

^{−1}), while the change in salinity increased by only 7.7% for case 2090RCP85_RR2090RCP85, even though the highest SLR (0.72 m) was assumed for this case. These findings indicate that a freshwater recharge rate of 0.00694 kg s

^{−1}may largely offset the impact of SLR on seawater intrusion at the Byeonsan2 groundwater monitoring well. These findings suggest that both freshwater recharge rate and SLR should be considered for the accurate assessment of climate change impacts on seawater intrusion in coastal groundwater systems. We concluded that this study may provide a better understanding of the climate change impacts on seawater intrusion in coastal groundwater systems by considering the climate change impacts on SLR and freshwater recharge rates.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**Figure 1.**Location map of the Gunsan tide gauge station (35.98° N 126.56° E), the Byeonsan2 groundwater monitoring well for seawater intrusion (35.64° N 126.48° E), and the Buan weather station (35.73° N 126.72° E).

**Figure 2.**The observed sea level data at the Gunsan tide gauge data: (

**a**) hourly, daily, and monthly sea level data; (

**b**) monthly seal level data; (

**c**) seasonal cycle of sea level; and (

**d**) monthly sea level data with no seasonal components. SSH is the sea surface height.

**Figure 3.**Initial and boundary conditions and finite-element meshes for the simulation domain. ρ

_{sea}= 1024.99 kg m

^{−}

^{3}; H = depth (m); g = 9.81 (m s

^{−}

^{2}); P = hydrostatic seawater pressure; Q

_{in}= freshwater recharge rate (kg m

^{−}

^{2}); C

_{in}= 0.0 (kg kg

^{−}

^{1}); C

_{sea}= 0.0357 (kg kg

^{−}

^{1}). A total of 4637 nodes and 4539 elements were generated for the simulation domain.

**Figure 4.**Sea level (black curve), fitted straight line (blue line, linear trend), and fitted quadratic line (green curve, quadratic trend) at the Gusan tide gauge station.

**Figure 5.**Salinity changes (%) relative to the baseline at the observation node for the 14 scenarios.

**Figure 6.**(

**a**) Salinity for the baseline and (

**b**) salinity differences between the baseline and the scenario 2090RCP45_RR2090RCP45 2090RCP45_RR2090RCP45 (0.57 m SLR with a freshwater recharge rate of 0.0058 kg s

^{−1}) in × 10

^{−3}(kg-dissolved solids) (kg-seawater)

^{−1}.

Cases | SLR (m) | Freshwater Recharge Rate (kg s^{−1}) | Descriptions |
---|---|---|---|

Baseline | 0.0 | 0.00603 | 2005–2015 |

RR2050RCP45 | 0.0 | 0.00627 | 2050s, RCP4.5, Precipitation (1) |

RR2050RCP85 | 0.0 | 0.00580 | 2050s, RCP8.5, Precipitation (2) |

RR2090RCP45 | 0.0 | 0.00549 | 2090s, RCP4.5, Precipitation (3) |

RR2090RCP85 | 0.0 | 0.00694 | 2090s, RCP8.5, Precipitation (4) |

2050L | 0.12 | 0.00603 | Linear trend by 2050 (5) |

2050C | 0.32 | 0.00603 | Quadratic trend by 2050 (6) |

2090RCP45 | 0.57 | 0.00603 | SLR of the West sea under RCP4.5 (7) |

2090RCP85 | 0.72 | 0.00603 | SLR of the West sea under RCP8.5 (8) |

2050L_RR2050RCP45 | 0.12 | 0.00627 | (1) and (5) |

2050L_RR2050RCP85 | 0.12 | 0.00580 | (2) and (5) |

2050C_RR2050RCP45 | 0.32 | 0.00627 | (1) and (6) |

2050C_RR2050RCP85 | 0.32 | 0.00580 | (2) and (7) |

2090RCP45_RR2090RCP45 | 0.57 | 0.00549 | (3) and (7) |

2090RCP85_RR2090RCP85 | 0.72 | 0.00694 | (4) and (8) |

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**MDPI and ACS Style**

Chun, J.A.; Lim, C.; Kim, D.; Kim, J.S.
Assessing Impacts of Climate Change and Sea-Level Rise on Seawater Intrusion in a Coastal Aquifer. *Water* **2018**, *10*, 357.
https://doi.org/10.3390/w10040357

**AMA Style**

Chun JA, Lim C, Kim D, Kim JS.
Assessing Impacts of Climate Change and Sea-Level Rise on Seawater Intrusion in a Coastal Aquifer. *Water*. 2018; 10(4):357.
https://doi.org/10.3390/w10040357

**Chicago/Turabian Style**

Chun, Jong Ahn, Changmook Lim, Daeha Kim, and Jin Sung Kim.
2018. "Assessing Impacts of Climate Change and Sea-Level Rise on Seawater Intrusion in a Coastal Aquifer" *Water* 10, no. 4: 357.
https://doi.org/10.3390/w10040357