1. Introduction
Coastal areas face rapid urbanization, population growth, and natural disasters that
degrade fresh groundwater resources. Seawater or saltwater intrusion (SWI) is a representative
disaster in coastal aquifers that induces groundwater salinization, reducing available
groundwater resources. Globally, the SWI of coastal groundwater resources occurs frequently
but remains dependent on local hydrological and topographical characteristics (Ranjan et al., 2006; Werner et al., 2013).
SWI occurs as a natural disaster in coastal aquifers. Because of the high concentration
of dissolved salt in seawater, seawater flows into the fresh groundwater aquifer,
creating a seawater-freshwater wedge and mixing zone. Anthropogenic factors, such
as changes in land use, artificial infrastructure, and excessive abstraction of coastal
groundwater, control SWI. These natural and anthropogenic drivers that contribute
to SWI in residential, agricultural, and natural areas have become increasingly serious
issues in resource management and groundwater environmental fields. Analytical solutions
and numerical analyses based on various mathematical models have been widely used
in coastal groundwater management and SWI studies to simulate the conditions of coastal
groundwater, considering geological diversity and topographic complexity. Prominent
research groups worldwide have conducted coastal groundwater research for several
decades (Goswami and Clement, 2007; Robinson et al., 2006; Oude Essink, 2001; El-Kadi et al., 2014; Werner et al., 2011).
Uncertainty in the prediction of water resources due to extreme climatic conditions
due to climate change, such as sea level rise (SLR) and reduced recharge due to extreme
drought, exacerbates the vulnerability of water resources. New diagnostic tools and
evaluation methods for protecting coastal water resources and ensuring sustainability
are continuously being advanced.
Fig. 1. Schematic Diagram of Coastal Aquifers and the Corresponding Indicators
Fig. 1 shows a conceptual cross-sectional diagram describing the groundwater flows that
occur near the sea. The figure shows widely used SWI indicators, including the salt
wedge, salt wedge toe, and groundwater discharge flow. This analysis of SWI indicators
excludes from discussion water table elevation, a common indicator in groundwater
studies. Initially, analytical and numerical research was mainly performed using lateral
movement as a measure of SWI by analyzing the cross-section of the coastal aquifer.
A salt wedge generally refers to a 50 % isochlor salt concentration. The 10 % and
90 % isochlors were used as SWI metrics when dispersion effects were considered. The
extent of SWI is defined as the horizontal length of a salt wedge toe. Salt wedges
in coastal aquifers divide the groundwater flow into two distinct regions: a freshwater
region above the wedge and a saline region below the wedge. Freshwater transport above
the wedge is much faster than that below the wedge. Groundwater discharge leaving
the aquifer from the coastal aquifer includes fresh local groundwater mixed with saline
seawater that has penetrated the aquifer. The SWI creates a typical circulation pattern
below the interface.
The vulnerability index describes the risk or impact of SWI on coastal aquifers using
a calculated index. By visualizing the vulnerability index, quantitative and qualitative
analyses were carried out to see how regional characteristics affected the vulnerability
assessment results. Vulnerability index methods using analytical solutions have been
widely employed. The analysis method assessed the vulnerability to SWI based on the
freshwater-saltwater interface derivation equation using the Ghyben-Herzberg ratio.
For example, SWI vulnerability indicators for freshwater lenses on strip islands in
Australia were assessed (Morgan and Werner, 2014). In the Netherlands, coastal groundwater vulnerability was assessed using six criteria
(sea-level-based freshwater head distribution, presence or absence of agriculture,
aquifer thickness, groundwater cultivation capacity, groundwater discharge area, and
soil characteristics) to supply fresh water actively and with a focus on vulnerable
areas. The GALDIT method, developed by Chachadi (2001), comprehensively identifies the seawater penetration vulnerability index by considering
aquifer characteristics, hydraulic conductivity characteristics, groundwater level,
distance from the coast, severity of current seawater penetration, and aquifer thickness.
The method expresses vulnerabilities through arithmetic calculations after assigning
weights to each vulnerability and ranking them using decision-making methods. Geographic
information systems (GIS) software was used to map parameter data and check distribution.
The overall trends in SWI and research trends have been reviewed by country (Barlow and Reichard, 2010; Custodio, 2010; Jeen et al., 2021; Werner, 2010). The study characteristics were classified according to the method, code, or area
of application. This review examined representative studies from a new perspective,
focusing on the indicators used to characterize outputs. To reveal the significance
and contributions of each SWI study, we focused on implementing the SWI indicators
to analyze the results. The objective of this review is to understand the contributions
of previous studies on SWI dynamics. A lot of case studies with conceptual models
and discussions of site studies in coastal regions are covered. Each section discusses
the SWI analysis method using analytical and numerical models, research trends related
to climate change and future research, and Korean studies related to the SWI. Groundwater
indicators. This review focused only on quantitatively measured SWI metrics and excluded
chemical and geochemical indicators.
2. Diagnosis Using SWI Indicators
2.1 Indicators Used in Analytical Analysis
The mathematical expression of the sharp interface model and related analytical solutions
are based on the Ghyben- Herzberg principle. When computing the SWI process, mixing
is commonly ignored, and a sharp interface between the saltwater and freshwater zones
is assumed. Most analytical models adopt a simplified, sharp interface approximation
(Strack, 1976). The equation for the sharp interface model is a single-potential analysis and was
derived from a study by Custodio and Bruggeman (Custodio and Bruggeman, 1987). In early studies, Tamai and Shima (Tamai and Shima, 1967) developed an approximate solution based on the Duipuit assumption to predict the
transient movement of an intrusion wedge toe. The authors compared the mathematically
predicted results to the experimental results and observed a good match when the experiment
was conducted using coarse sand. Vappicha and Nagaraja (1976) developed a similar model (Bear and Dagan, 1964), which adopted indicators such as the rate of movement of the interface and the length
of intrusion. They compared the mathematically predicted results with the experimental
results and observed a good match when the experiment was conducted with coarse sand.
Chesnaux and Allen (2008) used closed-form analytical solutions for circular and strip islands, applied the
Ghyben-Herzberg relation, and compared the location of the water table and the time
of groundwater transport. Werner and Simmons (2009) used analytical models to classify the extent and nature of SWI in aquifers with
immediate SLR according to the type of freshwater inflow. To evaluate the SWI extent,
they adapted an equation representing the salt wedge toe, originally developed by
Strack (1976) and Custodio (1987). The analytical index, considering the representative parameters of the Nile Delta
region, showed results consistent with the numerical solution of Sherif and Singh (1999). Nevertheless, the authors recommended using it to manage coastal aquifers impacted
by SLR separately rather than proposing a replacement with a numerical solution. Werner et al. (2011) improved the existing analytical solutions to devise new SWI vulnerability indicators
using two partial derivatives: 1) the rate of change in the wedge toe and 2) the rate
of change in seawater volume when the toe of the wedge and the volume of water are
expressed in the analytical solution. The stressors affecting the vulnerability of
the SWI include SLR, changes in recharge due to climate change, and changes in seaward
discharge. Based on these SWI vulnerability indicators, a national inventory of SWI
vulnerability for Australia was introduced based on 28 coastal aquifers in Australia
(Morgan and Werner, 2015). As a useful MODFLOW family tool, the SWI package was developed by Bakker et al. (2013). This package is based on sharp interface approximations and is more suitable for
modeling systems with negligible dispersion effects.
Recently, researchers have begun to consider uncertainties when studying the extent
of SWIs. Ferguson and Gleeson (2012) investigated the extent of the SWI based on the impact of SLR and saltwater inundation.
They concluded that saltwater inundation is more important than SLR inundation because
of the topographic gradient in low-lying coastal areas. By taking into account the
range of one standard deviation of hydraulic conductivity owing to various aquifer
types, from coarse-grained classic rocks to coarse-grained unconsolidated sediment,
the study included a statistical indicator, the uncertainty of the SWI. Representative
analytically modeled SWI studies are summarized in Table 1.
Table 1. Analytical Indicators Used for the Identification and Prediction of SWI
Relevant studies
|
Model description
|
Issues of the study (stressors)
|
Output indicators
|
Tamai and Shima (1967)
|
Conceptualized model compared to the laboratory experiment
|
Dupuit assumption for the movement of the intruding salt wedge toe
|
Movement of salt wedge toe
|
Chesnaux and Allen (2008)
|
Conceptualized unconfined island model
|
Analytical approach for groundwater travel time
|
Travel time
|
Werner and Simmons (2009)
|
Field-scale analytical model
|
Impact of hypothetical SLR
|
Change of salt wedge toe
|
Morgan and Werner (2015)
|
Selected Australian unconfined aquifers
|
SLR, recharge change and change in seaward groundwater flux
|
SWI vulnerability indicator:
1) The change of salt wedge toe to for the change of SLR
2) Recharge and seaward groundwater flux
|
Werner et al. (2011)
|
Adapted from the Gaza Strip (Moe et al., 2001); the Pioneer Valley aquifer (Werner and Gallagher, 2006); the Nile Delta; and the Madras aquifer (Sherif and Singh, 1999)
|
Analytical approach for SWI vulnerability assessment
|
SWI vulnerability indicator:
1) Rate of change in the salt wedge toe
2) Rate of change in seawater volume when the salt wedge toe and the volume of water
are expressed in the analytical solution according to 1) SLR, 2) change in recharge,
and 3) change in the seaward discharge
|
Ferguson and Gleeson (2012)
|
1,419 coastal watersheds, USA
|
Impact of SLR and saltwater inundation
|
1) Change of salt wedge toe,
2) Uncertainty considering the range of hydraulic conductivity
|
2.2 Indicators Used in Conceptualized Numerical Models
The extent of the SWI and movement of the mixing zone depend on the geography, hydrogeological
properties of the aquifer, and meteorological parameters, such as precipitation. Rectangular,
two-dimensional, and three-dimensional precise numerical simulations are used for
density-dependent groundwater flows. The advantage of more sophisticated numerical
models is that mixing effects due to dispersion can be simulated across the interface.
Numerical models are useful for modeling field-scale problems because they include
topographic complexity and geological variability (Dausman and Langevin, 2005; Oude Essink et al., 2010; Praveenaa et al., 2010; Vandenbohede and Lebbe, 2002). A particular set of benchmark problems is solved with tried-and-true analytical,
laboratory, and numerical solutions in order to validate a numerical model. The Henry
problem (Henry, 1964) is the only density coupling numerical example related to the saltwater-freshwater
interface and SWI for which a semi-analytical solution exists in a two-dimensional
flow. This solution defines the location and shape of the freshwater boundary, showing
that freshwater flow moves toward the ocean boundary. During the data validation process,
the isochore, which includes linearized points corresponding to 50 % concentration
of saltwater, was used as the main indicator defining the saltwater-freshwater interface.
After being introduced as a benchmark solution, the model design and numerical schemes
were comprehensively evaluated and modified by researchers. These studies were conducted
by verifying the model parameters and modifying a few according to Henry's solution.
Pinder and Cooper (1970), Lee and Cheng (1974), Segol and Pinder (1976), Frind (Frind, 1982), Huyakorn et al. (1987), and Voss and Souza (1987) developed several modified solutions. Croucher and O'Sullivan (1995) concluded that Henry's original interpretation may have been slightly erroneous.
Segol (1994) reexamined the errors related to Henry's solution and suggested a few corrections.
The proposed correction is a 183-term truncation scheme. These solutions to the Henry
problem have mainly investigated the steady-state isochore distribution. A criterion
for determining the extent of SWI is the locations of additional isochores, like 25
%, 75 %, and 50 % isochores, because of the mixing zone in the domain of the numerically
simulated Henry problem. Simpson and Clement (2003) adopted a novel transient indicator for the Henry problem: the transient position
of the toe. Simpson and Clement proposed a 293-term truncation scheme (Simpson and Clement, 2004). When Segol (Segol, 1994) recalculated Henry’s solution using additional terms, the new solution differed slightly
from the original. With this new solution, Segol (1994) showed that numerical codes could reproduce the correct solutions for the Henry problem.
The coupling of the flow and transport based on the density difference owing to the
high concentration was a key factor in the numerical analysis. Simpson and Clement (2003) compared the isochores of domains for coupled versus uncoupled cases and reported
that the coupling process effect of the Henry problem was not evident when the default
parameter values were given. Based on these findings, Simpson and Clement (2003; 2004) discussed a modified Henry problem by reducing the dimensionless freshwater flux
among the input parameters. Abarca et al. (2007) investigated the heterogeneity of the Henry problem by considering a dimensionless
input parameter, and the results illustrated a dimensionless saltwater flux for dispersive
and diffusive cases. Kerrou and Jaouher (2010) investigated the two-dimensional and three-dimensional Henry problems by following
the dimensionless parameters of Abarca et al. (2007) to avoid scale-dependent analysis. The dimensionless penetration length of the saltwater
wedge and the dimensionless width of the mixing zone described the geometry of the
saltwater-freshwater interface. The amount of saltwater entering the aquifer system
is described by the dimensionless saltwater inflow flux. The results showed that in
two dimensions, anisotropy significantly affects the permeability coefficient; however,
in three dimensions, the degree of diffusion through the dispersion coefficient changes,
changing the length of the SWI. In addition to the dimensionless salt wedge toe, width
of the mixing zone, and saltwater flux, recent studies of the Henry problem considered
additional dimensionless indicators (Fahs et al., 2016; Fahs et al., 2018).
A laboratory-scale experimental setup was used to analyze the crosscut model in the
SWI. The experimental results enabled visualization of the SWI movement under the
surface that could not be observed in the field. Oostrom et al. (1992) considered a laboratory experimental setup that is widely used for SWI. In contrast
to the Henry problem, which involves a wide mixing zone, most laboratory experimental
results indicate a relatively narrow mixing zone. Goswami and Clement (2007) provided the first benchmark problem based on experimental results by specifying
the boundary conditions of the experimental model. The numerical simulations using
10 %, 50 %, and 90 % isochores for steady-state simulations were compared with the
experimental results. In addition, the authors used only 50 % of the isochores in
the transient simulations. An experimental dataset of horizontal and vertical distances
with respect to time was provided for use as a benchmark dataset. Abarca and Clement (2009) succeeded in visualizing only the mixing zone, including the freshwater interface,
in different colors by setting boundary conditions for freshwater and saltwater with
different pH values in a small-scale water tank experiment.
Most benchmark problems, including the Henry problem, exist only as numerical simulations
or semi-analytical solutions. Chang and Clement (2012) extracted and quantified the location of the salt wedge in a laboratory experiment
and quantified the velocity of the movement of the freshwater–saltwater interface
using an exponential function to describe a time-dependent equation. To quantify the
difference between the intruding and receding SWI rate, the term ∆XT(t) was newly
defined, which is the distance that remains to be traversed by a migrating saltwater
wedge toe to reach the steady-state condition at the end of the simulations. This
indicator showed that the wedge rapidly advanced during the early simulation period
when the boundary condition was changed and gradually slowed down. Chang and Clement (2013) investigated the movement of contaminants in freshwater and saltwater regions based
on the freshwater interface and reproduced the migration of slugs using numerical
simulations. This study was extended to a site scale of decades to simulate the movement
of a contaminant or nutrient plume. When comparing water circulation patterns between
the freshwater and saltwater regions, the results showed that the movement of contaminants
or nutrients in the saltwater area was relatively low. Oz et al. (2014) obtained experimental and numerical results from laboratory experiments. The results
compared the salinity values of the 40 measuring points; these points were divided
into three different ranges based on their salinity values. Sensitivity tests were
performed using three dimensionless parameters: density, thickness, and flux.
Many studies have used the detailed vertical movement of the SWI in nearshore aquifers
using two-dimensional numerical models. Taniguchi et al. (2002) highlighted the relationship between the extent of SWI inland and the increase or
decrease in submarine groundwater discharge (SGD) and the importance of evaluating
SGD as a coastal water resource management issue. SGD contributes greatly to the discharge
and circulation of nutrients and pollutants near the coast and has attracted the interest
of groundwater experts and experts in the marine engineering field. Changes in sea-level
oscillations due to tides and waves can influence offshore and large-scale SGD patterns,
which can affect the aquifer-ocean interface in terms of the upper saline plume (USP)
and saltwater wedge (Li et al., 2008; Li and Jiao, 2003a; 2003b; Michael et al., 2005; Robinson et al., 2007a; 2007b). The shape of sloping beaches has been altered by ocean oscillations, and numerical
models have been used to study the effects of wave and tidal forcing. These models
have confirmed that surface changes in unconfined aquifers can affect beaches (Horn, 2006; Robinson et al., 2006). Robinson et al. (2006) extracted the vertical hydraulic gradient from numerical simulations to indicate
upward flow according to tidal cycles. This study further investigated the effects
of wave forcing on beach profile evolution using large-scale laboratory experiments
with numerical modeling (Bakhtyar et al., 2011) and groundwater dynamics. The tidal influence simulated by Kuan et al. (2012) is indicated by the toe and upper edge of the salt wedge, width of the freshwater
discharge zone, area of the USP, tide-induced circulation rate, and root mean square
error (RMSE). Here, the RMSE was calculated based on the difference between the x
coordinates of the interface given by the Glover solution and the simulation results
for a given z value. The aquifer response to waves simulated by Robinson et al. (Robinson et al., 2014) is indicated by 1) the total salt mass per unit width of the aquifer in the USP,
2) the x-coordinate of the centroid of the salt plume, and 3) the z-coordinate of
the salt plume centroid in the USP of a subterranean estuary. Boufadel (2000), Boufadel et al. (2006) mesured concentrations of salt water and pressure heads from laboratory-scale experiments
on a tidal-induced fresh brine interface.
One of the most intensively studied coastal aquifers in North America is the Biscayne
Aquifer in southeastern Florida, USA (Kohout, 1960). For example, Dausman and Langevin (2005) evaluated the relative importance of drought, well-field pumping, SLR, and canal
management in SWI. Ranjan et al. (2006) focused on changes in groundwater recharge and SLR due to the loss of fresh groundwater
resources in water- resource-stressed coastal aquifers. Masterson (2004) and Masterson and Garabedian (2007) simulated the underground water system of Cape Cod Island, Massachusetts, USA, which
is well-known for tourism. Due to the significant abstraction of groundwater from
the aquifer, the seawater-freshwater interface continues to move inland. Gingerich and Voss (2005) used the three-dimensional model SUTRA (Provost and Voss, 2019) to simulate SWI in Hawaii, USA. Oki (Oki, 2005) simulated SWI in Hawaii. Langevin (2001) investigated coastal groundwater problems in Biscayne Bay, Florida, USA. Werner and Simmons (2009) studied the SWI problem in Pioneer Valley, Australia. Table 2 provides an overview of representative numerically modeled SWI studies.
Table 2. Numerical Indicators Used for the Identification and Prediction of SWI
Relevant studies
|
Model description
|
Issues of the study (stressors)
|
Output indicators
|
Pinder and Cooper (1970),
Lee and Cheng (1974),
Segol and Pinder (1976),
Frind (1982),
Huyakorn et al. (1987),
Voss and Souza (1987),
Croucher and O'Sullivan (1995)
|
Conceptualized numerical model for the Henry problem
|
Numerical validation
|
1) The dimensionless salt wedge toe
2) The dimensionless spread of the concentration between 10 % and 90 % isochlors
|
Fahs et al. (2016)
|
Conceptualized numerical model for the Henry problem
|
New semi-analytical solution for velocity-dependent dispersion
|
1) The dimensionless salt wedge toe
2) The dimensionless spread of the concentration between 10 % and 90 % isochlors
3) The dimensionless width of the mixing zone
4) The dimensionless z coordinate of the adverse flow inflection point
5) The dimensionless saltwater flux
|
Fahs et al. (2018)
|
Conceptualized numerical model for the Henry problem
|
Stratification and anisotropy on the Henry problem
|
1) The dimensionless salt wedge toe
2) The dimensionless width of the mixing zone
3) The dimensionless saltwater flux
4) The dimensionless depth of the zone of groundwater discharge to the sea
|
Goswami and Clement (2007)
|
Laboratory experiment
|
Boundary condition
|
The steady-state and transient 10 %, 50 %, and 90 % isochlors
|
Kuan et al. (2012)
|
Conceptualized unconfined aquifer with a sloping beach
|
Tidal influence
|
1) Salt wedge toe
2) Upper edge of the salt wedge
3) Width of the freshwater discharge zone
4) Area of upper saline plume
|
Robinson et al. (2014)
|
Conceptualized unconfined aquifer with a sloping beach
|
Subterranean estuary driven by intensified wave conditions
|
1) The total salt mass per unit width of the aquifer in the USP
2) The x coordinate of the centroid of the salt plume
3) The z coordinate of the salt plume centroid in the USP of a subterranean estuary
|
Dausman and Langevin (2005)
|
Field-scale model for the Biscayne Aquifer in southern Florida
|
Drought, well-field pumping, SLR, and canal management
|
1) Salt wedge toe
2) Isochlors for 200, 1000, 5000, 10 000, 15 000, 17 000, 19 000 mg/L of chloride concentrations
|
2.3 Application of Index in Changing Future Environment
Climate change, which induces environmental changes in coastal aquifers, is a critical
issue that significantly increases uncertainty in water resource management due to
groundwater salinization (Green et al., 2011). In recent years, increased anthropogenic activities, extreme droughts, and SLR due
to climate change have emerged as issues for coastal groundwater management. Climate
change can affect long-term groundwater resources by affecting precipitation patterns
and recharge pathways. Long-term recharge changes can affect groundwater resources
by inducing drought or excessive water supply during rapidly changing or intensified
seasonality. Numerous modeling-driven coastal groundwater studies have investigated
the effects of climate change on future water resources. Future anthropogenic and
natural changes were incorporated into these scenarios. Nicholls et al. (2008) defined a scenario for climate change and coastal vulnerability as “a description
of potential future conditions developed to inform decision-making under uncertainty”
(Parson et al., 2007).
The SLR boundaries of SWI models were hypothetically simulated or exaggerated in early
studies on the effects of climate change on SWI. The results were primarily analyzed
using the salt wedge in the aquifer's crosscut or the salt concentration, measured
in terms of salinity or total dissolved solids (TDS). Meisler et al. (1984) numerically analyzed the effect of eustatic sea-level fluctuations on the transition
zone between freshwater and seawater in the Northern Atlantic coastal plain by lowering
the relative sea level from 0 to 150 m. Fesker (2007) simulated an SLR of 0.5 m per 100 years over a simulation time of 250 years by comparing
the salt wedge for these three cases. The results showed that a constantly rising
sea level resulted in a linear increase in the salt load in the model domain with
successive salinization of the marsh area from the coastline towards Geest, which
could represent the effect of SLR at the freshwater-saltwater interface. Oude Essink (2001) established three types of simplified SLR scenarios to perform numerical analysis
in aquifers in the Netherlands and concluded that a 0.5 m rise in sea level over 100
years would lead to an increase in salinity in all low-lying areas near the sea, providing
maps of the freshwater head and the chloride concentrations. By comparing the measured
and simulated water levels and the toe positions of 250 mg/L isochores, Dausman and Langevin (2005) concluded that the coastal aquifer of Broward County, Florida, could cause chloride
contamination if the sea level exceeded 48 cm over 100 years. By comparing the volumetric
loss of freshwater due to SLR in Israel, Melloul and Collin (2006) proved that 77 % of the loss was due to lateral movement and 23 % was due to head
change, assuming that the SLR is 0.5 m in the coastal aquifers. Navoy (1991) graphically compared the positions of sharp interfaces from a crosscut model to study
aquifer-estuary interactions based on the impact of several hypothetical SLRs and
various combinations of hydraulic conductivities in the coastal aquifer of New Jersey,
USA.
Aquifers are dynamically linked to the watershed hydrology and SWI studies have been
conducted to integrate detailed hydrological analysis results (Chang et al., 2016; El-Kadi et al., 2016). This makes it even more important to generate detailed projection
of groundwater recharge based on climatic future stresses. The climatic effect on
groundwater resources is considered dependent on local climatic conditions and topography
(Green et al., 2011; Ranjan et al., 2006). Ranjan et al. (2006) calculated freshwater loss by considering land-use changes and the hydrological behavior
of a catchment in Sri Lanka. Ranjan et al. studied the impact of climate change on
high- (A2) and low-emission scenarios (B2) and observed that it may result in a decrease
in semi-arid rainfall at the local scale, whereas increased temperatures are projected
to increase precipitation at the global scale (Ranjan et al., 2006). These two studies adopted an aridity index to study fresh groundwater loss. Green and MacQuarrie (2014) simulated the impact of climate change and groundwater abstraction in Atalatic, Canada
to complete a vulnerability assessment and quantified the impact of SLR and changes
after recharge on SWI in coastal aquifers.
Due to the effects of climate change, islands are significant research sites that
are greatly impacted by changes in the water resources that are available. Several
island regions have geological and terrestrial characteristics that make it difficult
to use surface water resources, and most groundwater recharge in these regions relies
on rainfall. Therefore, compared to coastal aquifers along the inland coast, groundwater
resources on islands are more susceptible to SWI, and the islands can be sensitive
to natural disasters depending on the island's hydrology, geology, topography, or
shape (van der Geest et al., 2020). Among the island regions, the most vulnerable to SWI are the atoll islands in the
Pacific and Indian Oceans, which are in the lowlands less than a few meters above
sea level and simultaneously have a relatively small area (Bailey et al., 2009; Kundzewicz and DÖll, 2009; White and Falkland, 2010). In contrast, the impact of groundwater recharge due to rainfall changes will have
a greater effect on the freshwater lenses of islands if shoreline erosion by SLR is
insignificant (White and Falkland, 2010). Studies have focused on SWIs impacted by climate-related stressors. The impact of
changes in precipitation and SLR was reported in the Special Report on Emission Scenario
(SRES) from the IPCC 4th report (IPCC, 2007), which showed the movement of the saltwater wedge and change in volumetric freshwater
lenses under both the most and least favorable scenarios applied to Shelter Island,
USA (Rozell and Wong, 2010). Mapping the water level distribution, impact of SLR, and impact of increased precipitation
were projected until 2100 based on the IPCC SRES A2 scenario on freshwater lenses
of the North Sea Island of Borkum, Germany (Sulzbacher et al., 2012). The calibration method was illustrated by comparing the measured TDS at several
monitoring wells to the computed results and the vertical profiles of electrical conductivities.
The impact of a 25 % increase in the recharge rate was reported in Malaysia (Praveena and Aris, 2010), and the impact of future climate and land-use changes during 2070-2099 under the
SRES A2 scenario on the entire Jeju Island of South Korea was studied (El-Kadi et al., 2014). Babu et al. (2018) simulated a reduction in freshwater volume on Tongatapu Island under recharge reduction
and increased pumping scenarios. Chang et al. (2016) examined the effects of anthropogenic activities and recharge scenarios caused by
climate change based on the IPCC SRES on Dauphin Island, USA, and indicated a volumetric
reduction in freshwater by indicating changes in the freshwater head, salt wedge in
the vertical crosscut of the model, and volumetric changes in freshwater resources.
Loáiciga et al. (2011) simulated SLR scenarios for the city of Monterey, California, USA. The study illustrated
numerically simulated locations of the vertically averaged 1000 and 10,000 mg/L isosalinity
lines in the plan view of the model, where predictions of mean SLR are expected to
range from 0.10 to 0.90 m. The simulation analysis included a detailed resolution
of SWI, and its causes were quantified via numerical simulation under scenarios of
changes in both groundwater extraction and SLR in the 21st century. One of the most
recent studies was conducted by Chun et al. (2018) who simulated SLR and precipitation scenarios under the Representative Concentration
Pathway (RCP) 4.5 and 8.5 scenarios based on the 5th IPCC report. The results showed
the highest salinity increase of 40 % in the case of SLR and a less favorable freshwater
recharge rate under RCP 4.5.
Recently, studies have considered more sophisticated climate change scenarios that
consider recharge and SLR based on projected changes in precipitation and air temperature
from global climate models and emission scenarios. In addition, interdisciplinary
research has been conducted to map the SWI vulnerability in groundwater aquifers that
are impacted by climate change (Klassen and Allen, 2017; Werner et al., 2013) and to inform the status of groundwater resources using indexed equations, various
hydrogeological data, and GIS techniques. Various indicators have been developed in
field research to assess the severity of SWI. Giambastiani et al. (2007) conducted a numerical study to investigate SWI in an unconfined coastal aquifer in
Ravenna, Italy. By indicating the salt concentration distribution in the aquifer,
the results showed that the mixing zone between fresh and saline groundwater shifted
800 m farther inland for an SLR of 0.475 m per century. In addition, the impact of
the SLR was illustrated by indicating the seepage rate and salt load. Representative
SWI studies related to changing environments are summarized in Table 3.
Table 3. Description of Indicators Used to Identify and Predict SWI Related to Changing Environments
Relevant studies
|
Model description
|
Issues of the study (Stressors)
|
Output indicators
|
Ranjan et al. (2006)
|
Field-scale numerical model for a catchment of Sri Lanka
|
Change of recharge, land use, and hydrologic soil condition
|
Volume of freshwater loss
|
Sulzbacher et al. (2012)
|
Field-scale numerical model for freshwater lenses of the North Sea Island
|
SLR, and the increase in precipitation under IPCC SRES A2 scenario
|
1) the measured TDS at monitoring well positions
2) the vertical profiles of electrical conductivities
|
Chang et al. (2016)
|
Field-scale numerical model for Dauphin Island, USA
|
Land use, land-use change, and recharge based on IPCC SRES scenarios
|
1) Salt wedge toe
2) Volumetric reduction of freshwater
|
Chun et al. (2018)
|
Field-scale numerical model, Korea
|
SLR and future precipitation under RCP 4.5 and 8.5 scenarios
|
Salinity
|
Giambastiani et al. (2007)
|
Field-scale numerical model for Ravenna, Italy
|
SLR with uncertainty based on IPCC scenarios
|
1) Salt wedge toe
2) Seepage rate
3) Salt load
|
2.4 Recent Development of New Technology and Perspectives for SWI Indicators
Approaches to SWI research include numerical modeling, analytical solutions, and GIS.
Recent modeling includes an uncertainty analysis and assesses the effect of alternative
SWI reduction measures. Using new observational data and statistical techniques, uncertainty
analysis was performed. Coulon et al. (2021) considered the uncertainties of an observation dataset and quantitatively reported
the standard deviation and 95 % confidence intervals obtained from shallow, deep open,
and pumping wells on Magdalen Island, Canada. To obtain an optimized extraction rate
for pumping, Rajabi and Ketabchi (2017) tested five new formulations for uncertainty-based simulation-optimization approaches,
which have the objective function of minimizing the sum of energy distances for various
reliability levels in the management plan. Roy and Datta (2020) attempted ensemble-based SWI prediction by combining artificial intelligence-based
prediction models and selecting five prediction models: adaptive neuro-fuzzy inference
system (ANFIS), Gaussian process regression (GPR), multivariate adaptive regression
spline (MARS), support vector regression (SVR), and probabilistic linear regression.
The prediction performance was compared to that of the dataset obtained from the five
monitoring locations using the following statistical metrics: rRMSE, MAPRE, R, NS,
IOA, and KGE. To reduce uncertainty, new observation data or a combination of new
and existing data were used in the model. For example, uncertainties can be reduced
by applying observation data using new electrical signals such as airborne electromagnetic
(AEM) (Meyer et al., 2019). The AEM resistivity data in the cross-sectional model was mapped and visually compared
to the simulated distribution of TDS. This demonstrated a further application for
quantitative comparison with conventional indicators. Goebel et al. (2019) combined offshore AEM and onshore electrical resistivity tomography (ERT) and discovered
that this approach was successful in regions previously considered inaccessible by
traditional monitoring methods. Hermans and Paepen (2020) combined inland and ocean ERT to improve the reliability of observations.
Studies related to SGD and oceanic oscillations have considered the effects of tidal
(Veerapaga et al., 2019) and seasonal changes in confined systems (Qu et al., 2020). Qu et al. (2020) noted that most studies have investigated the effects of oceanic oscillations on
unconfined aquifer systems and applied numerical approaches to confined systems using
several indicators. To compare the groundwater discharge from the system, seasonally
varying fresh submarine groundwater discharge (QFSGD), recirculated saline groundwater
discharge (QRSGD), and fluxes flowing out of the aquifer across the seaward boundary
(QSGD) were averaged over a seasonal cycle. Comparing freshwater storage capacities
showed that an idealized circular system could store much more freshwater than an
equivalent strip island. Tang et al. (2020) estimated the temporally varying cumulative volume of the channel (CVC) under different
tidal levels from the coastal aquifer caused by surface and groundwater interactions
through the river, and the change in the seawater-occupied channel volume, as a prediction
and adaptation plan for SWI.
Compared to experimental results, Memari et al. investigated a circular, two-dimensional
axisymmetric model for experiments on circular island systems (Memari et al., 2020). Veerapaga et al. (2019) developed new indicators for the validation of simulation results using (a) water
level, (b) salinity interface gradient (SIG), which is defined as the slope of a 10
% isoline, (c) salinity intrusion length for a salinity of 10 (SIL10), and (d) salinity
intrusion length for a salinity of 5 (SIL5). Table 4 summarizes representative SWI studies that have recently developed SWI indicators.
Table 4. Recent Indicators Used for the Identification and Prediction of SWI
Relevant studies
|
Model description
|
Issues of the study (stressors)
|
Output indicators
|
Coulon et al. (2021)
|
Field-scale analytical model for Magdalen Islands, Canada
|
Freshwater volume, interface elevation, parameter estimation, uncertainty analysis
|
The standard deviation and 95 % confidence intervals
|
Memari et al. (2020)
|
Radial experimental system
|
Comparison between circular and strip island systems
|
1) Salt wedge toe
2) Volume of freshwater lens
|
Roy and Datta (2020)
|
Hypothetical heterogeneous coastal aquifer system
|
Application of artificial intelligence method, weighted average ensemble
|
Statistical metrics: rRMSE, MAPRE, R, NS, IOA, and KGE
|
Tang et al. (2020)
|
Field-scale model for Pearl River Estuary, China
|
River discharge, mitigation of coastal channel
|
Temporally varied cumulative volume of the channel (CVC)
|
Veerapaga et al. (2019)
|
Field-scale model for Chikugo River estuary, Japan
|
Tidal effect
|
1) Water level
2) The salinity interface gradient (SIG), defined as the slope of 10 % isoline
3) Salinity intrusion length for salinity of 10 (SIL10)
4) Salinity intrusion length for salinity of 5 (SIL5)
|
3. Application of SWI Indicators in Recent Korean Studies
Early SWI studies of regional aquifers in Korea focused on Jeju Island, Korea’s largest
island, which lies south of the mainland. Groundwater development occurs actively
in the watersheds in the eastern and northern regions of Jeju Island (Won et al., 2006). Youn et al. (2003) and Kim et al. (2009) confirmed the presence of a saltwater-freshwater interface in the Handong-ri Watershed
of eastern Jeju Island. Kim et al. (2009) calculated the hydraulic characteristics of a coastal aquifer by applying a tidal
response technique and concluded that the range of tidal effects on eastern Jeju Island
is 3-5 km off the coast. Recently, Shin and Hwang (2020) analyzed the anisotropy of coastal aquifers using borehole data from the eastern
part of Jeju Island. The acquisition borehole data included electric conductivity,
temperature, and temperature data from the thermal line sensor. Kim et al. (2016) developed a method for predicting the SWI interface using artificial neural networks
(ANNs) by observing the seawater-freshwater interface on Jeju Island. Data on the
seawater-freshwater interface was collected by installing an interface egg measuring
device in the observation well, and past data was collected to predict the location
of the seawater-freshwater interface. Groundwater level, tide, and sea level interface
data were used to build an ANN model. Two ANN models were constructed and employed
to predict the groundwater level and freshwater-saltwater interface. Studies related
to SWI observations have been incorporated into the investigation of SGD on Jeju Island.
Lee et al. (2016a; 2016b; 2016c) performed chemical and microbial community analyses in groundwater, considering tidal
and coastal runoff in the southeastern part of Jeju Island, and used a thermal infrared
imager mounted on an unmanned aerial vehicle (UAV) to inspect the SGD Kang et al. (2019) quantitatively evaluated coastal runoff using a thermal infrared sensor mounted on
an UAV at two different tidal conditions.
A recent trend in SWI research in Korea is actively employing numerical modeling in
inland coastal areas and predicting and evaluating future SWI on reclaimed land (Chang et al., 2020; El-Kadi et al., 2014). El-Kadi et al. (2014) assessed the water resource sustainability of Jeju Island under various climate change
scenarios. They used pumping-sustainability yield (P-SY) indicators to represent the
changes in spring flow under various recharge and pumping scenarios. To reflect the
seasonal characteristics in Korea, the monthly differences in the index have been
indicated using GALDIT. Various methods have been attempted, such as analyzing observational
data centered on Jeju Island, predicting future situations, and evaluating vulnerabilities
through numerical modeling. Chang et al. (2019) applied GALDIT to Jeju Island, South Korea. GALDIT was comparatively analyzed using
calculations from observations in 2010 and 2015 on Jeju Island. This is the first
study in which the SWI vulnerability of aquifers across Jeju Island was studied using
GALDIT, a coastal groundwater assessment method. To analyze the risk of SWI on Jeju
Island using long-term observations, parameters such as groundwater reserves across
Jeju Island, hydraulic conductivity of aquifers, groundwater levels above sea level,
distance from the coastline, effects of existing SWI, and aquifer thickness were analyzed.
In addition, Chang et al. (2020) applied future scenarios to the northeastern part of Jeju Island, Korea. Three future
scenarios exist in which the amount of pumped water increases, and the scenarios are
divided into average, wet, and dry according to the applied climate. The simulations
were conducted for a period of 10 years. This was the first attempt to incorporate
the spatial distribution of freshwater levels and salinity obtained from numerical
simulations as GALDIT input parameters.
The number of SWIs in inland Korea has increased in recent years. Jung et al. (2021) applied an SLR scenario to reclaimed lands in South Korea. Using the RCP scenario,
the model SLR was subjected to sea level rise of 4.5, 8.5, and 2050, respectively.
The SWI on the reclaimed land showed that when SLR exceeded the fresh groundwater
level, it proceeded rapidly inland. Lee et al. (2015) evaluated the effects of SWIs on underground structures in a few areas of Yeosu along
the southern coast. The seawater mixing ratio was used to evaluate the effect of the
SWI on the underground structure and the effect of the SWI on the chloride concentration
in the seepage. Kim et al. (2018) analyzed the effect of the SWI around an artificial waterway in Incheon and Gimpo,
Korea. Electrical conductivity was measured and analyzed by installing a multi-depth
monitoring sensor in the observation well around the waterway and by SEAWAT modeling.
A 10-year predictive model was used to simulate the effect of the SWI due to the inflow
of seawater into the waterway around the waterway. The analysis was performed on the
data collected using a multi-depth monitoring sensor, and the salinity trend according
to the effect of the SWI in the vicinity of the waterway was estimated according to
the criteria. Kim and Yang (2018) analyzed the prediction of SWI by 2099 and the effect of applying alternatives to
Taean, one of the western coasts of Korea. On the West Coast, areas vulnerable to
SWI were identified based on groundwater level, seawater level, groundwater usage,
and reclamation trends. The RCP scenario was simulated until 2099. Kim et al. (2021) applied GALDIT to nine administrative districts in the northern region of the western
coast of South Korea. To represent seasonal changes in SWI vulnerability, the GALDIT
index was divided into dynamic and static indices. For a sensible consideration of
the monthly changes, the GALDIT score criterion, divided by the existing quartiles,
was changed to a decile and evaluated. The representative Korean SWI studies are summarized
in Table 5.
Table 5. Description of Indicators Used to Identify and Predict SWI in Korean Regional Aquifers
Relevant studies
|
Model
|
Issues of the study (stressors)
|
Output indicators
|
El-Kadi et al. (2014)
|
Field-scale numerical model for Jeju Island, Korea
|
Climate change based on the IPCC SRES scenario
|
Pumping-sustainability yeid (P-SY)
|
Chang et al. (2020)
|
Field-scale numerical model for Jeju Island, Korea
|
Hypothetical future recharge and pumping scenarios
|
Salt wedge toe
|
Jung et al. (2021)
|
West coast reclaimed land, Korea
|
SLR scenario under IPCC RCP 4.5 and 8.5 scenarios
|
1) SWI area and rate
2) Soil salinization area and rate
3) Spatial average groundwater level
|
Kim and Yang (2018)
|
West Coast aquifer of inland Korea
|
SLR and recharge scenario under IPCC RCP 4.5 and 8.5 scenarios.
Future pumping scenario using linear regression
|
1) SWI area and rate
2) Reduction of SWI area and rate using countermeasures
|
4. Discussion and Conclusion
The sustainability of coastal groundwater resources is threatened by changing environmental
stresses. Accurate indicators are required to evaluate aquifer conditions and increase
the reliability of interpretations. We discuss how studies indicate SWIs that can
identify the occurrence of conceptualized and regional-scale SWIs. Early coastal groundwater
simulations were limited to laboratory experiments or conceptualized SWI models; however,
an increasing number of field modeling studies have been conducted globally with various
and combined approaches to represent the severity or vulnerability of SWI in detail.
Thus, significant discoveries and enhanced knowledge exchange have occurred among
researchers of different nationalities. This review shows that analytical SWI indicators
provide insight into the characterization and understanding of SWI. By using sophisticated
coding, the SWI indicators produced by numerical simulation can include complex boundary
conditions as well as site-specific heterogeneous and anisotropic parameters. Recent
conceptual and regional studies have developed new indicators and updated conventional
SWI metrics. For SWI studies in Korea, the quantity and quality of research have increased
recently; however, the development of SWI analysis using new indicators is still insufficient.
Research on SWI identification and prediction involves analyzing future situations
by simulating future environmental changes. Analysis of the SWI according to future
scenarios and optimization of alternatives will require a highly reliable model. The
high volume of data and many input parameters have resulted in high levels of uncertainty,
so improving the reliability of the observation data is necessary to accurately predict
the SWI. In addition to method optimization, alternative probabilistic approaches
are required. To increase the reliability of the SWI model and reduce uncertainty,
high-quality data from various locations is required for aquifers and coastal areas.
Recent technological developments have improved data quality, and densely obtained
data is collected using observation techniques such as AEM. The application of management
strategies to prevent SWI damage requires location optimization, operating time for
climatic characteristics, and water resource requirements.
Regarding the modeling of SWI in coastal aquifers, further attempts are being made
to reflect the spatial and temporal heterogeneity of groundwater recharge and SLR
scenarios to reflect recently updated climate change scenarios. More sophisticated
scenario-based estimations are expected to be produced using a combination of detailed
future scenarios and various field data-mining technologies.
In Korea's coastal groundwater research, the GIS-based index & ranking method represented
by GALDIT and the numerical approach represented by SEAWAT are increasing. The vulnerability
index is suitable to be used as a policy tool when quickly diagnosing the current
status of the aquifer. Numerical modeling, such as SEAWAT, have the advantage of enabling
mid- to long-term projections. In the future researches, integrated vulnerability
diagnosis and numerical prediction for a single study site is expected to become a
widely accepted SWI approach.
Finally, effective methods for transforming field data into conceptualized SWI model
inputs collected from testbeds should also be devised. In numerical modeling, compared
to calibration using groundwater head, calibration and verification methods using
SWI indicators are currently applied selectively due to the difficulty of low suitability.
Understanding the rapid changes in salinity that occur near the mixing zone and saltwedge
and developing a suitable calibration process are expected to be important factors
in improving the reliability of the SWI numerical modeling.