[orcid=0009-0005-6202-9081] [1]

Conceptualization of this study, Methodology, Writing - Original draft
preparation

[orcid=0000-0001-7629-8702]

[orcid=0000-0002-6340-4710]

[orcid=0000-0002-6065-8192]

[orcid=0000-0002-5781-5476]

1]organization=University of Dubai, city=Dubai, country=United Arab Emirates

2]organization=University of Milano-Bicocca, city=Milan, country=Italy

[cor1]Corresponding author

# Exploring Key Aspects of Sea Level Rise and their Implications: An Overview

Leena Elneel<EMAIL_ADDRESS>M. Sami Zitouni<EMAIL_ADDRESS>Husamuldin
Mukhtar<EMAIL_ADDRESS>Paolo Galli<EMAIL_ADDRESS>Hussain Al-Ahmad
<EMAIL_ADDRESS>[  [

###### Abstract

Sea Level Rise (SLR) is one of the most pressing challenges of climate change
and has drawn noticeable research interest over the past few decades. Factors
induced by global climate change such as temperature increase, have resulted
in both direct and indirect changes in sea levels at different spatial scales.
A number of climatic and non-climatic events drive the change in sea level and
impose risk on coastal and low-lying areas. Nevertheless, changes in sea level
are not uniformly distributed globally due to a number of regional factors
such as wave actions, storm surge frequencies, and tectonic land movement. The
high exposure to those factors increases the vulnerability of subjected areas
to SLR impacts. The impacts of events induced by climate change and SLR are
reflected in biophysical, socioeconomic, and environmental aspects. Different
indicator-based and model-based approaches are used to assess coastal areas’
vulnerabilities, response to impacts, and implementation of adaptation and
mitigation measures. Various studies were made to project future SLR impacts
and evaluate implemented protection and adaptation approaches to help
policymakers plan effective adaptation and mitigation measures and reduce
damage. This paper provides an overview of SLR and its key elements
encompassing contributing factors, impacts, and mitigation and adaptation
measures with a case study focus on the Arabian Gulf.

###### keywords:

sea level rise climate change coastal vulnerability inundation contributing
factors impacts

##  1 Introduction

Climate change is one of the most pressing challenges of the century. The
increase of $CO_{2}$ emissions and Greenhouse Gases (GHG) caused a rise in
temperature, which led to a range of far-reaching consequences for economies,
societies, and ecosystems around the world. One of the most significant
impacts of climate change is Sea Level Rise (SLR) Gregory (1988); Clark et al.
(2016). Over the past century, Global Mean Sea Level (GMSL) has been
increasing at accelerating rates and is estimated to reach up to 98 cm by the
end of the century Passeri et al. (2015). Intergovernmental Panel on Climate
Change (IPCC) projected future values of global SLR to range from 0.61 - 1.10
m by the end of the century Subraelu et al. (2022); Oppenheimer et al. (2019)
and up to 3 m by 2300 Oppenheimer et al. (2019) depending on the rate of GHG
emissions. SLR is primarily caused by two factors: the thermal expansion of
seawater, and the melting of ice sheets and glaciers Clark et al. (2016).
Other climatic and non-climatic events such as cyclone-driven events and
earthquakes also contribute to changes in Regional/Relative Mean Sea Level
(RMSL) Oppenheimer et al. (2019).

The impacts of sea level rise are already persistent in many coastal areas
globally. Coastal areas are of significant economic and environmental
importance, and increasing human activities are placing additional stress on
them Noor and Abdul Maulud (2022). Low-lying coastal areas, such as Small
Island Developing States (SIDS), are particularly vulnerable to inundation,
storm surges, and floods Oppenheimer et al. (2019); IPCC (2022). Areas exposed
to tidal events also experience impacts of SLR Hall et al. (2016). In some
areas, saltwater intrusion into groundwater and freshwater aquifers threatens
freshwater supplies and agricultural crop production Wright et al. (2020);
Oppenheimer et al. (2019). Furthermore, SLR can cause physical changes to the
coastal areas such as coastal erosion and flooding leading to loss of land and
infrastructure IPCC (2022); Bosello and De Cian (2014). To address challenges
caused by climate change and SLR and reduce their impacts, a comprehensive and
coordinated response is needed. This includes reducing GHG emissions to
mitigate the anticipated damage of future SLR scenarios and associated extreme
weather events Sweet et al. (2022), as well as investing in adaptation
measures to help vulnerable communities cope with the impacts that are already
occurring Matos et al. (2022) including hard or soft engineering solutions,
ecosystem-based structures, and strategy plans Dedekorkut-Howes et al. (2020).
The use of visualization tools that represent the relationship between impacts
and contributing factors to SLR can bridge the gap in understanding the
relation between different components of SLR, raising awareness, and helping
decision-makers form proper mitigation and adaptation measures Noor and Abdul
Maulud (2022).

This work presents an overview of SLR and its key elements as found in the
literature. Those elements were identified based on our review of IPCC reports
IPCC (2014, 2022). It also highlights the climatic and non-climatic drivers of
SLR and its consequences. The studies in this work cover all spatial scales
(global, regional, national, and local) and emphasize the distinction between
global and regional contributing factors to SLR. As we found a complex
interconnection relation among SLR impacts, we have grouped them into 3
categories: physical/biophysical, socioeconomic, and
environmental/ecological/chemical impacts. Furthermore, due to the lack of a
standardized way of categorizing different analysis approaches in the
literature and the vast variations among research studies, we have identified
two main categories for coastal analysis and assessment approaches, namely:
coastal impact modeling approaches and coastal vulnerability assessment
approaches. Figure 1 shows the main aspects related to SLR that were covered
in this paper. The reviewed papers were collected from different databases and
publications repositories including IEEE Xplore, Science Direct, JSTOR,
Springer, Elsevier, MDPI, Wiley Online Library, Francis and Taylor, and
Frontier for the years between 2015 and 2022 (with the exception of papers
that discuss general concepts, historical information, and highly relevant
info) using the following keywords and their combination: "sea level rise",
_"coastal vulnerability assessment"_ , _"factors of sea-level rise"_ ,
_"climate change impacts on sea level rise"_ , _"visualizing sea level
changes"_ , _"sea level rise impacts"_ , _"adaptation"_ , _"modeling sea-level
rise"_. A screening process of the search results was conducted and over 100
references were selected for the review process with emphasis on studies that
include highly vulnerable coastal areas/communities such as Southeast Asia and
Mediterranean Basin countries. Papers with sole focus on SLR impacts on
ecosystems such as mangroves were excluded from the review process.
Nevertheless, they were briefly mentioned in sections 3.3 and 5.1. Similarly,
papers that focus solely on flooding were excluded to differentiate flooding
caused by SLR and climatic events in coastal regions from flooding in inland
areas and riversides. Figure 2 shows the number of papers that were reviewed
in each category over the years.

Figure 1: Classification of SLR aspects as identified and discussed in this
paper Figure 2: Number of reviewed papers of each category over the years

##  2 Contributing Factors

SLR is a significant indicator of climate change as the increasing global
temperature contributes to the increase in GMSL. Thermal expansion of ocean
water and glacier and ice melts were identified as the main contributors to
global SLR Etkins and Epstein (1982). However, changes in sea levels are
neither linear nor uniform Cazenave and Le Cozannet (2014); Sweet et al.
(2022); Clark et al. (2016). The propagation of changes in GMSL depends on the
bathymetry of seas and oceans, which can take anywhere from less than a decade
to hundreds of years to reach deep ocean regions Clark et al. (2016).
Uncertainties associated with changes in sea levels should be accounted for in
SLR analysis and projections, as it was found that earlier IPCC reports did
not give them sufficient emphasis. Associated uncertainties and the variation
of different contributing factors can lead to changes in RMSL different from
GMSL Slangen et al. (2017); Jevrejeva et al. (2019); Cazenave and Le Cozannet
(2014). This section highlights the contributing factors of various spatial
scales and the associated uncertainties.

###  2.1 Global Factors

Global changes in sea level can be a result of both non-climatic (geological)
and climatic changes with the latter having more influence on SLR that is
noticeable Gregory (1988). In 1983, Hoffman identified five climatic change
factors that contribute to changes in sea level and used them to build 90
different scenarios. Hoffman factors include 1) Carbon dioxide emission, 2)
Fraction airborne, which considers the movement and chemical actions of carbon
between atmosphere, biosphere, and hydrosphere, 3) Concentration of other GHG
specifically methane, nitrous oxide, and chlorofluorocarbons, 4) Climate
sensitivity that measures the change in temperature due to gases emission, 5)
Thermal expansion of ocean water that measures the transport of heat into
deeper ocean water, 6) Snow and ice contribution Gregory (1988). The
contributing factors identified by IPCC Assessment Reports (AR) were narrowed
to 1) Thermal expansion of ocean water, and 2) Ice and glacier melting.
Nevertheless, Nicholls et al. (2011) and IPCC (2022) considered uncertainty
associated with the factors contributing to GMSL in their projections, such as
uncertainty in the future estimation of GHG. In addition to the factors listed
by IPCC, Cazenave and Le Cozannet (2014) have also identified additional
factors that contribute to GMSL, including 1) Land-based ice melting (that
will eventually end up in the sea), 2) Changes in land water storage due to
natural and human activities, such as building dams and extracting water for
agricultural activities. Those activities can result in both increase or
decrease in SLR rates. Although there were no historical observational data to
identify the contribution of land water storage, Cazenave and Le Cozannet
(2014) stated that it can cause a change of up to 2 mm/y during ENSO events
Trenberth (1997). Furthermore, uncertainties associated with global
contributing factors were considered for SLR projection. Those uncertainties
include deep ocean warming (although it has a relatively small contribution to
SLR compared to upper ocean warming), and dynamical instabilities in melting
glaciers and ice sheets that are below sea water level, which causes an
accelerated increase in SLR due to thermal expansion of ocean water. Overall,
it was found that considering uncertainties of thermal expansion and melting
ice sheet will cause a deviation by 10% and 15%, respectively, to GMSL by the
year 2100, making glaciers and ice sheets the main contributors to GMSL
Cazenave and Le Cozannet (2014). Figure 3 shows the Cazenave and Le Cozannet
(2014) interpretation of the contributing factors to GMSL in IPCC AR5. The
percentage of contributions of ice melt and thermal expansion to GMSL are
45.5% and 34%, respectively, where glaciers’ melts form the highest share of
land ice melting contribution. The estimated uncertainties associated with
land ice melt and thermal expansion factors are 15% and 10%. The uncertainty
is determined by how much the individual time series deviates from the mean.
This chart also considers the contribution of the net impact of anthropogenic
land water storage to GMSL and the small contribution of deep ocean water to
thermal expansion. The uncertainty associated with the residual 20% and the
other contributors to GMSL were not mentioned Cazenave and Le Cozannet (2014).
The contribution of different factors to GMSl is continuously analyzed and
corrected via sea level budget analysis where observations are compared with
estimated values of a sum of different factors Dieng et al. (2017). Equation 1
is an example of considering different contributing factors where
$GMSL_{Steric}$ refers to changes in GMSL due to thermal expansion and
salinity and $GMSL_{Barystatic}$ refers to changes in GMSL due to water
exchange between land and ocean such as ice melts and land water storage.
Updated versions of the sea level budget can include sea level change in deep
ocean due to thermal expansion Wang et al. (2022) and atmospheric water vapor
Dieng et al. (2017).

$GMSL_{Sterodynamic}=GMSL_{Steric}+GMSL_{Barystatic}$ (1)

Figure 3: Factors contributing to GMSL

###  2.2 Regional Factors

RMSL diverges from GMSL at 80% of coastal areas globally Carson et al. (2016).
The divergence between GMSL and RMSL is attributed to some adjustments in
global contributing factors. Those adjustments include contributing factors
that affect SLR at the regional scales and beyond such as vertical land
movement, melting land-based ice, and dynamic ocean movements Sweet et al.
(2017). The IPCC AR6 has considered the contribution of other factors and
uncertainties that affect both GMSL and RMSL. This report lists climate
drivers that impact coastal systems such as $CO_{2}$ concentration, Sea
Surface Temperature (SST), wave actions, and rainfall run-offs IPCC (2022).
Overall, regional SLR is a sum of a set of climatic and non-climatic factors
alongside GMSL Palanisamy et al. (2014). Figure 4 illustrates the variations
between RMSL and GMSL where positive values show high variation rates as can
be observed near Greenland and the majority of global coastal zones
(especially Southeast Asia, East Africa, and Northern Europe). However, this
figure excluded Semi-Closed Seas such as the Mediterranean Sea in which
numerous studies indicated that they will be highly impacted.

Figure 4: Variation between the average RMSL and GMSL. Positive values
indicate higher deviation from the GMSL Nicholls et al. (2011)

####  2.2.1 Land Movement

Land movements (up/down) due to non-climatic factors, such as tectonic
movements and gravitational effects were found to influence regional SLR Hall
et al. (2016); Meyssignac and Cazenave (2012). Tectonic movement along with
the Glacial-hydro-Isostatic Adjustments will lead to an increase in RMSL
levels beyond the global rates Antonioli et al. (2017). Examples of the
effects of land movements can be observed in Finland (uplifting tectonic
movements) and Japan (post-earthquakes effect), which have caused variations
in RMSL rates compared to GMSL Nicholls et al. (2011). Moreover, ground
motions influence tide gauge measurements and SLR rate estimations Meyssignac
and Cazenave (2012). Tide gauge data are used to measure sea level with
respect to the ground and can be used in monitoring land movements and
regional SLR Meyssignac and Cazenave (2012). Historical evidence shows that
non-climatic factors have contributed to changes in RMSL and induced other
extreme weather events associated with SLR. Historical evidence in the city of
Venice in Italy showed that vertical land movement caused by human activities
could significantly contribute to the RMSL rise, further worsening the hazard
posed by SLR and extreme weather events caused by climate change Zanchettin et
al. (2021). In Greece, massive destructive tsunami waves that impacted the
coastal areas occurred post-earthquake on Crete island Giannakidis et al.
(2014). It is also predicted that coastal areas in the Arabian Gulf will be
affected by post-earthquake tsunami waves. In case a significant earthquake
occurs in Markan Subduction Zone (MSZ), tsunami waves will pass through the
strait of Hormuz and cause an increase in sea levels by 1 - 2 m Hereher
(2020).

####  2.2.2 Dynamic Ocean Movement (Ocean Circulations)

SLR rates are affected by the intensity, height, and frequency of wave actions
in the coastal and tidally influenced areas Chini et al. (2010); Hang (2022).
Whereas linear SLR scenarios and extreme events can also influence wave
actions in coastal areas Chini et al. (2010). Short-term impacts of tide-
driven and storm-driven events also exhibit prominent seasonal patterns that
are used to assess the changes in regional sea levels Hall et al. (2016). Wave
intensity can be calculated via Dyer function of wave power that uses
components such as wave height, gravity, and density Seenipandi et al. (2021).
Wave actions can be modeled using historical data Kantamaneni et al. (2019)
and numerical simulators such as SWAN Mullick et al. (2019); Ruzic et al.
(2021); Chini et al. (2010); Anfuso et al. (2021), TOMAWAC Chini et al.
(2010), XBeach Hang (2022), Method of Splitting Tsunami (MOST) Giannakidis et
al. (2014) or forecasting models Chini et al. (2010); Anfuso et al. (2021).
Google Earth engines and satellite images were used to monitor the dynamics of
seawater via an open-source Python-based tool PyGEE-SWToolbox Owusu et al.
(2022).

####  2.2.3 Ice Melting

The land-based ice melting continues to contribute to RMSL in two forms: 1)
The transfer of freshwater into the ocean alters the density structure of the
ocean and the wave actions which results in changes in RMSL over time 2) The
ocean basin deformation as an elastic response to the water mass movement from
land to the ocean Meyssignac and Cazenave (2012). Increasing evidence suggests
that the recent negative mass balance of ice sheets is caused by the
accelerated flow of outlet glaciers along certain margins of Greenland and
West Antarctica and the increasing rates of discharge by icebergs into the
surrounding oceans Meyssignac and Cazenave (2012). The mass loss in Greenland
and Antarctica is subject to Surface Mass Balance (SMB). Under the influence
of dynamic discharges and ocean warming, a huge mass loss of ice sheets will
occur Slangen et al. (2017). Once a certain threshold is exceeded, the
interplay between decreasing surface elevation and escalating surface melting
can trigger further ice loss, potentially resulting in the total loss of the
Greenland ice sheet Slangen et al. (2017). Although there are uncertainties
associated with future ice melting levels, they should be considered in SLR
projection to increase the results’ reliability. For instance, the
uncertainties associated with ice melting were included in a study by
Antonioli et al. (2017) to project SLR scenarios in the Italian peninsula that
have shown higher rates of projected RMSL than scenarios that do not consider
them.

####  2.2.4 Other Factors

In addition to the previous regional factors that were listed by Hall et al.
(2016), other factors that play a role in changing RMSL were found. Woodworth
et al. (2019) identified time and distance dimension as a source of deviation
between RMSL and GMSL as changes will rely on both of those dimensions to
propagate globally. Since events contributing to SLR at both global and local
scales are interconnected, changes can propagate through different water
levels to end up reflecting in coastal processes. Bosello and De Cian (2014)
listed other climatic and non-climatic components that cover environmental,
physical, and socioeconomic aspects of SLR and its impacts, including CO2
emissions, SST, characteristics of extreme weather events and rainfall, land
movement and subsidence due to natural and human activities, flooding, wetland
loss, impeded drainage, and saltwater intrusion. The salinity of the nearby
sea also alters SLR Cazenave and Le Cozannet (2014). Moreover, some studies
showed that there is a linear relationship between estuarine hydrodynamics and
SLR Wu et al. (2021). Ocean dynamics can potentially affect the salinity of
seawater Sweet et al. (2022). Adding salinity as a factor in future SLR
projections will give more accurate and realistic estimates of SLR impacts
than what is estimated by scenarios that don’t include such uncertainties
Antonioli et al. (2017). Salinity is attributed as a contributing factor
alongside thermal expansion contribution in sea level budget calculations
despite the fact that the changes caused by this factor become evident over
longer time periods Dieng et al. (2017).
SST, associated with the increasing global air temperature and thermal
expansion of the oceans, increases SLR rates. For example, Hereher (2020)
changes of SST in the Arabian Gulf as an associated factor to climate change
which caused thermal expansion of seawater. The study used various data to
create spatial distribution over 16 years and all seasons of the region. The
results have identified three thermal zones and confirmed that the Gulf region
is the warmest sea in the world (triple the global average rates). The
increase of SST in what is known as the Indian Ocean Dipole was also found to
increase SLR rates Palanisamy et al. (2014). The warming effects of ENSO (El
Nino oscillations) events were also found alongside other climatic components
to affect SLR in southeast Asia Faridatunnisa and Heliani (2018); Zou et al.
(2021).

Another uncertainty that should be considered in projecting SLR is rainfall
rates as they have a direct influence on flooding Antonioli et al. (2017). SLR
can also be impacted by the type of event causing it, whether it is short-term
or long-term Passeri et al. (2015). For example, extreme weather events will
have an impact on the wave height and intensity which can result in a short-
term SLR in the form of flooding Subraelu et al. (2022). Wave actions extents
cause erosion on coasts, cliffs, and marine Ruzic et al. (2021). Cites that
are subject to be impacted by SLR and extreme weather events respond
differently to those forcing factors based on a number of variations Hall et
al. (2016). A study on the contributing factors to SLR in mangrove areas found
that they are highly exposed to climatic components and listed factors like
SLR, increased wave, wind, increased air and water temperatures, increased
rainfall activities, and freshwater availability that affect salinity
processes, as the main causes that influence inundation periods and accretion
rates Ellison (2015). Other factors that determine the degree of impact on a
mangrove area in correspondence to those climatic components were used in
vulnerability assessment studies (see section 3.3). Generally, the increasing
regional SLR trends will increase the vulnerability of the exposed areas
Ellison (2015).

Other non-climatic components can also contribute to SLR rates and their
impacts. The elastic response to mass loss due to land-based ice melting and
water exchange between land and sea influence SLR and its impact Meyssignac
and Cazenave (2012); Cazenave and Le Cozannet (2014). Moreover, human
activities have contributed to physical shoreline changes and their
susceptibility to SLR El-Asmar and Taha (2022); Wang et al. (2019) in which
including building dams and extracting water for agricultural purposes also
play a role in changes in SLR Cazenave and Le Cozannet (2014). In Thailand,
groundwater increased SLR Nicholls et al. (2011). The increasing human
activities in the oil industry have increased land subsidence that will lead
to an increase in seawater levels in the low-lying coastal areas Hereher
(2020). Although non-climatic attributes were not a focal point in SLR
studies, they have a considerable influence on SLR and future projections
Meyssignac and Cazenave (2012).

##  3 Impacts

###  3.1 Physical Impacts

The physical impacts of SLR are highly visible in coastal areas where they
affect physical communities directly. The physical changes to coastal areas
due to SLR and climatic events occur in both short- and long- terms, where
they can be irreversible in the absence of adaptation measures (see section 5
for more details). Short-term physical impacts, including flooding and
inundation usually occur due to extreme weather events and can cause severe
damage to the impacted areas. Long-term physical impacts include coastal
erosion, wetland loss, saltwater intrusion into groundwater and raising water
tables, and impeded drainage Nicholls et al. (2011).

Under extreme GHG emission scenarios, coastal areas are at risk of high
erosion rates. In Krk island in the Mediterranean Sea, the increasing levels
of waves’ height and energy due to climatic components have caused both beach
and cliff erosions Ruzic et al. (2021). A higher deviation of RMSL over GMSL
at Victoria Beach in Spain caused a drastic increase in erosion rates leading
to an additional need for beach maintenance activities that are costly
Aguilera-Vidal et al. (2022). Erosion caused by increased and intense waves’
activities has also impacted the coastal areas at Beaufort Sea in the Arctic
Ocean Aryal et al. (2021), UAE Subraelu et al. (2022); Aldogom et al. (2020),
Mostaganem shoreline in Algeria Bengoufa et al. (2021), Hawaiian Islands Onat
et al. (2018), India Sudha Rani et al. (2015), Italy and shorelines in the
Mediterranean sea Sarkar et al. (2022), and Estonia Kont et al. (2003). In
addition to wave activities, erosion can also be caused by sediments transport
from rivers and onshore overwash activities Aguilera-Vidal et al. (2022).
Erosion rates can be predicted using Brunn Rule Aguilera-Vidal et al. (2022);
Passeri et al. (2015) which is used to predict geographical changes of sandy
coastlines under mean SLR situation. According to Brunn Rule, under SLR effect
the coast should naturally retreat in an upward horizontal direction, such
that the eroded sediment from the upper beach is deposited in the nearshore
area and the resulting elevation in the bottom of the nearshore area is
equivalent to SLR. Furthermore, physical impacts of SLR include wetland loss
Aguilera-Vidal et al. (2022); Passeri et al. (2015). Although coastal wetland
forms a natural protection structure for coastal areas against inundation and
flooding, the increasing rates of climate-induced activities, such as SLR and
wind-driven waves, makes them subject to coastal erosion and inundation which
threaten the ecosystem environment they reserve Smith et al. (2021); Saleh et
al. (2021).

Another long-term physical impact of SLR is altering coastal watersheds and
groundwater discharge which results in saltwater intrusion into groundwater
Passeri et al. (2015). This phenomenon is also known as water salinization.
Saltwater intrusion into groundwater has been reported in Wadi Ham in UAE, due
to misuse of water in agricultural activities Sowe et al. (2019). The
intrusion of saltwater into groundwater was also reported in coastal areas
along Gulf of Oman and Arabian Gulf Abdouli et al. (2019), SIDS Pernetta
(1992), Nile Delta in Egypt Susnik et al. (2015), and is expected to increase
in West African countries in the future Parthasarathy and Deka (2021). It is
worth noting that groundwater extraction approaches and other factors also
contribute to the salinization phenomena. Salinity was also found to increase
with the associated changes of estuarine hydrodynamic under SLR scenarios in
Eastern China Wu et al. (2021).

Flooding caused by SLR can lead to a rapid increase in water salinization
Oppenheimer et al. (2019). Additionally, short-term physical impacts of SLR
can be observed in the form of changing tidal hydrodynamics as observed in
increasing intensity and height of waves and inundation Passeri et al. (2015).
SLR and extreme weather events also lead to temporary flooding and permanent
flooding in the form of inundation Gesch (2018). SLR and other regional
factors increase the susceptibility of coastal flooding at coastal areas in
the Mediterranean Sea Antonioli et al. (2017); Sarkar et al. (2022); Ruzic et
al. (2021); Abou-Mahmoud (2021); Zanchettin et al. (2021); Carbognin et al.
(2010); Aguilera-Vidal et al. (2022), U.S. coasts Sweet et al. (2022); Winters
et al. (2020), UAE Arthur and Garland (2016), Bangladesh Mullick et al.
(2019), India Kantamaneni et al. (2019), Malaysia Maulud and Rafar (2015).

Physical shoreline changes can be measured using different remote sensing data
and geophysical location systems Aldogom et al. (2020); Barboza et al. (2021);
Subraelu et al. (2022); Dwarakish and Nithyapriya (2016); Maulud and Rafar
(2015). Moreover, soft computing techniques can be used on remote sensing data
in shoreline mapping to estimate Land Use (LU) and Land Cover (LC) which are
terms used to describe the use of land for human activities and the physical
characteristics of the land surface. Dwarakish and Nithyapriya Dwarakish and
Nithyapriya (2016) conducted a comparison study between conventional and soft
computing methods of inundation mapping using remote sensing data and
concluded that Support Vector Machine (SVM) Vishwanathan and Murty (2002)
algorithms show promising applications due to their adaptability and learning
capacity with limited data. Data that was used to analyze shoreline changes
are geomorphology, LU/LC, Elevation data, and tide gauges.

Figure 5: Areas Impacted by SLR and the affected percentage of the population
Clark et al. (2016)

###  3.2 Socioeconomic Impacts

The impacts of climate change-induced SLR can vary according to the nature of
the impacted areas. Low-lying lands will be highly impacted by SLR with an
increasing sociological and economic risk due to the high density of
population in those areas Fitz Gerald et al. (2008). In SIDS, a group of
countries that consists of small islands with similar topographical nature,
climate change, and SLR will result in changes in biodiversity and coastal
resources which will be reflected in economic losses and negative population
growth. For example, SLR in the Kingdom of Bahrain will result in a major hit
on socioeconomic due to the lack of capacity and space to accommodate the
changes Saleh et al. (2021). In many developed countries, the LU of the
coastal areas was restructured for economic purposes to the extent of building
artificial lands, such as Palm Islands in Dubai, however, these developed
coastal areas are considered more vulnerable to SLR due to their socioeconomic
importance. For instance, erosion caused by SLR affected properties and street
infrastructure along the coast in some emirates in UAE Subraelu et al. (2022).
The increasing economic pressure on coastal areas makes changes in seawater
levels and extreme weather events caused by climate change with significant
impact on different sectors, such as transportation or even leisure
activities, namely scuba diving and commercial trade Ku et al. (2021). An
approximate of 100 airports globally, that were built in low-lying lands, will
be impacted by SLR under a global temperature increase scenario of
$2^{\circ}C$ degrees. This impact will highly affect airports located in
southeast Asia that are highly susceptible to climatic SLR. Aside from
transportation, airports are viable for many sectors such as healthcare and
food security which means that the impacts of SLR on airports might lead to
economic paralysis Yesudian and Dawson (2021). In the middle east, 24 coastal
ports will be impacted under climate changes induced SLR and extreme weather
event scenarios Salimi and Al-Ghamdi (2020). SLR will also impact the
military’s marine operations and equipment installations Chadwick et al.
(2011). Therefore, loss of land due to the physical impacts of SLR will cause
migration of coastal habitations IPCC (2022). In Indonesia, it was estimated
that there will be a huge impact of SLR on the economy as an increase of 100
cm of seawater will cause a land loss of 34 km2. This loss of land will impact
coastal building and infrastructure, and the high population density living
near the coastal areas, which will result in huge national socioeconomic loss
Marfai and King (2008). In Mombasa - Kenya, SLR caused direct and indirect
impacts on coastal tourism activities and Geographic Information System (GIS)
based analysis showed high exposure of population and economic assets to
climate variability causing a loss of billions of dollars and negative growth
and distribution of population Kebede et al. (2010). Coastal areas in Some of
the Gulf Cooperation Council (GCC) countries have changed in the past few
years due to the construction of artificial lands for economic purposes
Hereher (2020). SLR will highly affect those man-built lands leading to huge
economic losses. Figure 5 illustrates the affected areas by SLR and the
percentage of the population that are at risk.

###  3.3 Environmental, Ecological, and Chemical Impacts

SLR and extreme climatic events will have a high impact on environmental
ecosystems. The impacts on ecosystems can be perceived as both positive and
negative. Generally, some ecosystems have the ability to adapt naturally to
increases in SLR up to a certain rate. However, variations between the rates
of SLR and the ecosystem’s adaptability to cope with those changes will lead
to a negative impact Passeri et al. (2015). Marine habitats are subject to
extinction due to the effect of rising SST and thermal expansion of the oceans
IPCC (2022); Hereher (2020). The increase in sea temperature due to climatic
events causes coral bleaching which damages marine habitats and the
biodiversity of the coral species Saleh et al. (2021); Lincoln et al. (2021).
Changes in climatic components associated with regional SLR, such as El-Nino
oscillation, led to coral bleaching and decreasing in mangrove-covered areas
in the Arabian Gulf Hereher (2020); Lincoln et al. (2021). Additionally, the
increase in salinity, increase in temperature, and the decrease in oxygen
levels in the Arabian Gulf have impacted the biodiversity of fish species. On
the contrary, the increase in the surface and deep ocean temperature in The
Arabian Gulf and Gulf of Oman caused Jellyfish outbreaks Lincoln et al.
(2021). In California, similar variations between positive and negative
impacts on the number of fish species were noticed in correlation with the
increasing SST Scavo and Singh (2016). Marine ecosystems in the South China
Sea are experiencing both negative and positive impacts with the regional
variation of SLR Zuo et al. (2021). Coastal wetland areas are expected to
decrease by 22% globally before the end of the century, which will directly
impact the ecosystem and the natural habitats they reserve Saleh et al.
(2021). Chemical changes in carbon dynamics due to water intrusion will lead
to metal toxicity of the soil, as well as affect the quality and volume of the
agricultural crops Oppenheimer et al. (2019). In Portugal, ecologists are
concerned with water pollution in case coastal erosion in the city of Maceda
paved the way for seawater to reach the nearby landfill Matos et al. (2022).
Additionally, water pollution is a potential aftermath scenario to flooding
events where drinking water intersects with sewage water, causing serious
health problems to human life Wright et al. (2020). Pollution in the sea and
ocean water can also occur as a result of SLR’s and extreme weather events’
impact on coastal power infrastructure, such as power stations. Flooding can
also impact drinking water supplies and create a habitat for disease-carrying
insects like mosquitoes Mohanty (2021). However, due to the lack of available
data about pollution rates pre- and post- extreme weather events, such as
storm surges and flooding events, there are not enough studies that model
pollution impacts with future SLR scenarios Lincoln et al. (2021).

##  4 Coastal Assessment

###  4.1 Coastal Impact Modelling

The inundation model or bathtub model Mcleod et al. (2010) is a popular and
simple model that is used to assess the coastal impacts of SLR and climate-
driven events at different spatial scales (i.e.: Global, regional, national,
and local). The inundation model is a quantitative model that uses topographic
maps to indicate and predict impacted areas by calculating adjacent cells that
are under a certain elevation value. Although this model is widely used in
research, it is reliant on the accuracy of elevation data and might give false
inundation results with low resolution or inaccurate data with high
uncertainties. Gesch (2018) has utilized the inundation model to assess SLR
and flooding in coastal areas and highlighted the importance of considering
vertical uncertainties inherited in the used digital elevation data. Error
correction and transformation methods can also be applied to digital elevation
data to increase the reliability and confidence of the analysis results. For
example, Aster Global Digital Elevation Map (GDEM), a public source of
elevation data has conducted a validation assessment of their data and
enhanced the resolution using different horizontal shifts, and elevation error
estimation and correction of Band3B error Tachikawa et al. (2015).

Variety of researches relied on modeling SLR via inundation. Arthur and
Garland (2016) used Light Detection and Ranging (LiDAR) Weitkamp (2005) remote
sensing data to build an inundation model to project different scenarios of
SLR in the eastern emirates of UAE. Every 8 pixels in the LiDAR images were
grouped to determine the adjacent pixels that should be inundated for each SLR
scenario. Wu et al. (2021) used an integrated modeling system combining
hydrodynamic and hydrological models, to study the impact of SLR and river
discharge on estuarine hydrodynamics and ecosystems. The 3D model utilized
various tidal and climatic factors with different SLR scenarios. The results
suggested that salinization is impacted by the river discharge process more
than SLR. However, SLR will have more impact on estuarine hydrodynamics that
will eventually alter freshwater transportation into the sea. Al-Abdouli et
al. (2019) used the Hydrological model and watershed modeling system HEC-
GeoHMS to simulate watersheds from land to sea, which occur due to climatic
events, such as rainfall and post-storm floods, as well as other human
activities, including aquifers usage. The used data in the simulation included
public elevation data, LU/LC from governmental data, and simulated rainfall
data measured based on different public repositories. SimCLIM Warrick et al.
(2005) is an integrated modeling system that is used to assess coastal
sensitivity to climate change and variability using biophysical and
socioeconomic parameters. This commercialized tool also includes SLR scenario
generator that is used to measure the sensitivity of coastal areas to climatic
events. This tool is applicable to all spatial scales and various
computational requirements depending on the data and resolution Mcleod et al.
(2010). Nicholls et al. (2011) used SimCLIM modeling to project future changes
in specific locations. The tool was set to utilize the normalized patterns of
Atmosphere-Ocean General Circulation Models (AOGCMs) Fox-Kemper et al. (2019)
in a multi-mode by combining GMSL, regional, and local factors Nicholls et al.
(2011). Other ocean modeling tools were used to analyze the impact of SLR in
coastal areas, such as HYCOM Chassignet et al. (2009) ocean modeling that was
developed for the area of the Gulf of Mexico Wright et al. (2020). Another
example of an integrated modeling system that uses biophysical and
socioeconomic components is Dynamic Interactive Vulnerability Assessment
(DIVA) Nicholls et al. (2011), which produces quantitative results on the
coastal vulnerabilities on spatial scales ranging from regional to local. This
model utilizes a number of regional factors that contribute to SLR to measure
regional SLR changes. Examples of the adaptation and impact options integrated
within DIVA model include flooding, population density, erosion, estimated
land for nourishment, wetland loss, and salinization Bosello and De Cian
(2014). Pycroft et al. (2016) used biophysical and socioeconomic data from
DIVA model with regional variables to estimate the global economic loss and
coastal damage under different SLR scenarios. This study estimated a global
Gross Domestic Product (GDP) loss of 0.5% and a 2% decrease in human well-
being under high SLR scenarios. The drawback of this model was the elimination
of some of the climate-induced factors that contribute to SLR, such as storm
surges effects, and only considering a limited range of adaptation measures
Nicholls et al. (2011). Other models that were used to study the ecological
and environmental impacts of SLR and climatic events were the Ecological
Landscape Spatial Simulation Models and Sea Level Affecting Marshes Model
(SLAMM) Mcleod et al. (2010).

Figure 6: Projection of GMSL (in meters) under different RCP scenarios with
associated uncertainties (shading) IPCC (2014)

Climate change models make use of future estimations of GHG emissions, as well
as ice sheets and glaciers melting to project future SLR based on how sea
levels respond to changes in those factors Koroglu et al. (2019). Future
projections can be based on IPCC’s Representative Concentration Pathway (RCP)
emission scenarios that estimate the influence of GHG emissions on future
global mean and temperature levels. Future projections under RCP2.6., RCP4.5.,
RCP6.0., and RCP8.5. will increase the global mean temperature by 1, 1.8, 2.2,
3.7 Celsius degrees, and GMSL by 0.4, 0.47, 0.48, 0.63 meters, respectively,
by the year 2100 IPCC (2014). Figure 6 shows the projection of GMSL under
different RCP scenarios according to IPCC AR5 IPCC (2014). Antonioli et al.
(2017) used IPCC’s RCP emission scenarios, local vertical land movement, and
glacio-hydro-isostatic movement to project future flooding scenarios in the
Italian peninsula using topographic maps, elevation data, bathymetry data, and
Interferometric Synthetic Aperture Radar (InSAR) satellite images. The results
obtained from this study showed that coastal areas are highly vulnerable and
subject to marine flooding by the year 2100. Vertical land movement data can
be extracted from tide gauge data Zanchettin et al. (2021). Wang et al. (2019)
modeled different scenarios to study the impact of future SLR on the low-lying
land and protection structure in the coastline of the Tianjin-Hebei district
in China. Three scenarios were modeled according to values obtained from the
literature (low, medium, and high) for different years (2030, 2050, and 2100),
taking into consideration land subsidence and flooding that can result from
extreme storm surge events (occurrence rate of once in 50 years, 100 years,
200 years, and 500 years) which were modeled via Regional Ocean Modelling
System (ROMS). Matos et al. (2022) used SLR projection as a part of a
systematic planning strategy to evaluate three different adaptation measures
that range from hard-engineered protection structures to planning Land Use
(LU) and maintenance activities in coastal areas, namely beach nourishment,
under extreme scenarios (IPCC RCP8.5.). The results of this projection were
showcased to help in decision-making. Jevrejeva et al. (2019) surveyed the
increasing use of probabilistic models in projecting future SLR scenarios as
they consider the uncertainties associated with the contributing factors to
regional and local sea level changes, such as mesoscale ocean processes.
However, probabilistic projections also have uncertainties due to their
dependence on emission scenarios, which are also uncertain. Passeri et al.
(2015) mentioned that SLR future LU/LC projections were used to evaluate the
impacts of storm surges on coastal areas. They also mentioned that statistical
and probabilistic Bayesian Networks were used with SLR projections to study
the long-term coastal impacts of SLR. Although Hall et al. (2016) did not
consider geomorphological and biological impacts of SLR and the exposure of
near-shore structures to SLR in their projection scenarios, they admitted the
influence of uncertainties associated with climatic and non-climatic events,
as they believed that scenarios are built to determine the influence of
different factors and not project uncertainties. A number of regional factors
were used in SLR projection and visualization due to their huge influence on
SLR. With the inclusion of regional components in projection, scenarios can
amplify the impacts of global factors, which will be reflected in an increase
in the projected SLR levels Slangen et al. (2017); Nicholls et al. (2011).
Slangen et al. (2017) suggested including factors concerned with the Atlantic
Ocean alongside the regional factors to provide comprehensive projections
about SLR in the Mediterranean Sea. Figure 7 illustrates a standard framework
developed by an IPCC working group Nicholls et al. (2011) that can be applied
in building SLR scenarios using a set of contributing factors as identified by
IPCC.

Figure 7: Methodology for developing SLR scenarios for impact, mitigation and
adaptation assessments Nicholls et al. (2011)

Some studies incorporated AI techniques into SLR projection and simulation.
Luccioni et al. (2021) used AI-based visualization of flooding in the province
of Quebec-Canada to raise awareness about climate change. Due to the lack of
accurate and high-quality flooding data, a simulated environment was created
to obtain data in addition to other real data to be used in training a custom
Generative Adversarial Networks (GAN). GAN models use a generator to create
data based on real data parameters and then use a discriminator to distinguish
between the generated data and the real data Pan et al. (2019). The flooded
areas were covered using a painter model to give a realistic flooding scenario
Luccioni et al. (2021). Giannakidis et al. (2014) re-modeled an extreme SLR
scenario that occurred in the Mediterranean Sea 350 AD to visualize how it
would impact the current Heraklion port in Crete, Greece. Spatial data that
contains topographic, bathymetry and LU data were fed alongside modeled SLR
and tsunami waves scenarios into an animation suite to build a 3D
visualization tool with scientific output. Others have used historical data to
calculate SLR over the years to analyze the trends causing SLR for future
projects. Faridatunnisa and Heliani (2018) have used Matlab to pre-process and
calculate SLR and Mean Sea Level (MSL) trends using linear regression models
applied on historical tide gauge data collected from tide stations’ sensors in
Indonesia Faridatunnisa and Heliani (2018). Zou et al. (2021) performed error
correction on historical tide gauge data of Hong Kong coasts and used multiple
regression models to calculate changes in sea levels. Hassan et al. (2021)
conducted a comparative study on the use of historical GMSL data for
forecasting using different machine and deep learning algorithms, where the
results showed that deep learning algorithms such as Dense Neural Network
(DNN) and WaveNet Convolutional Neural Networks provided more reliable results
than linear regression.

###  4.2 Coastal Vulnerability Assessment

Part of studying the impacts of SLR on coastal areas is to assess to which
extent they are vulnerable to SLR. The term "vulnerability" is used to define
the susceptibility level of coastal areas when being exposed to certain
climate hazards Nguyen et al. (2016). This term also encompasses concepts such
as risk, resilience, and adaptability Ku et al. (2021). Different analytical
approaches can be used to assess the degree of vulnerability by incorporating
elements of different aspects, such as physical, socioeconomic, and
ecological. Furthermore, vulnerability assessments are spatially scale-
dependent, therefore the spatial scale of the study area (regional, national,
or local) should be taken into consideration. Vulnerability can be defined as
"The degree to which a system, sub-system, or component is likely to
experience harm due to exposure to a hazard, either a perturbation or stress"
Nguyen et al. (2016), with it having three components: exposure, sensitivity,
and adaptive capacity Nguyen et al. (2016); Ku et al. (2021). The exposure
(e.g.: population density and LU) and sensitivity (e.g.: inundation depth)
components are used to measure the impact of climate hazards while the
adaptive capacity component (e.g.: disaster management and response) is used
to identify the ability of socioeconomic factors (e.g.: humans,
infrastructure, habitats, …, etc.) to adapt to changes following climate
hazards Ku et al. (2021). Nguyen et al. (2016) have identified indicator-based
approaches as the main coastal vulnerability assessment tools in events
related to climate change. Index-based vulnerability assessment methods are a
sub-category of indicator-based methods that focus on simplifying the complex
interaction among different parameters of various aspects and representing
them in a simpler and more understandable format, to help decision-makers plan
proper responses. Another sub-category of indicator-based methods is variable-
based methods, which focus on studying a set of independent variables related
to coastal issues. Many researchers relied on using index-based approaches to
assess coastal vulnerabilities. Index-based approaches include Coastal
Susceptibility/sensitivity Index (CSI), Coastal Vulnerability Index (CVI),
Coastal Risk Index (CRI), and socioeconomic Vulnerability Index (SVI) Sarkar
et al. (2022). CVI is the most popular index-type approach in research that is
used to study and assess the coastal vulnerabilities of specific study areas
Pantusa et al. (2018). A standardized and quantitative way of assessing CVI
was developed by Gornitz Gornitz (1991). His approach uses geometric average
(eq. 2) of different biophysical parameters, including geomorphology,
shoreline change rate (i.e. erosion and accretion), coastal slope, regional
SLR, mean significant wave height, and mean tidal range Kantamaneni et al.
(2019).

$CVI=\frac{\sqrt{x_{1}\times x_{2}\times x_{3}\times...\times x_{n}}}{n}$ (2)

_where:_
_$x$ represents a parameter_
_$n$ refers to the total number of parameters used in the CVI assessment_

Gornitz’s CVI assessment approach was widely utilized and adopted by others
Kantamaneni et al. (2019). Thieler and Hammer-Klose (defined the standard U.S.
Geological Survey (USGS) CVI assessment) Thieler and Hammar-Klose (1999),
Lopez Royo et al. (2016) used similar physical parameters to assess both
positive and negative coastal responses to climate change driven events
Koroglu et al. (2019). Coastal assessment methods are applicable to regional,
national, and local spatial scales, and can provide a detailed assessment of
different segments of the shoreline.

Index-based approaches that depends on Gornitz’s method used a variety of
physical parameters to assess coastal vulnerability with a variation of the
selected variables, according to the nature of the study area. On a national
level, Norezan et al. (2021) stated that The Department of Irrigation and
Drainage in Malaysia used a CVI method in what is called the National Coastal
Vulnerability Index Study (NCVI), to assess the vulnerability of coastal areas
to SLR and help policymakers in adopting proper adaptation measures.
Nevertheless, a CVI study was conducted on Tok Jembal beach using Thelier and
Hammar-Klose ranking scheme but with 3 physical variables and climatic-related
components. The set of used parameters included geomorphology, coastal slope,
regional SLR, temperature, wind speed, and wave’s type. Other CVI based works
included variables from other non-climatic parameters, such as environmental
and socioeconomic Mullick et al. (2019); Sudha Rani et al. (2015). Mclaughlin
and Cooper (2010) (eq.3) believed in the importance of including socioeconomic
factors in CVI assessment, thus they developed a CVI technique that
incorporates parameters related to climatic components, socioeconomic, and
physical parameters.

$\displaystyle CVI=\frac{CC\ sub\\!-index+CF\ sub\\!-index+SE\
sub\\!-index}{3}$ (3)

_Where:_
_CC: Coastal Characteristic sub-index associated with physical/biophysical
parameters_
_CF: Coastal Forcing sub-index associated with climatic components_
_SE: socioeconomic sub-index_

Ellison (2015) used Cooper and McLaughlin (1998) guidelines to conduct a
vulnerability of ecosystem habitats, such as mangrove and seagrass beds in
some countries in Africa and the Pacific Islands. The assessment involved 19
different parameters that fall under three categories: exposure, morality, and
adaptive capacity. The study found that the regional factors, namely the
continuous tectonic uplift movement in the study area of Tanzania led it to
have higher resilience to climate change and SLR compared to the other areas
involved in the study. Vulnerability assessments were also used to evaluate
economic risk and market shock via Economic Vulnerability Assessment (EVA)
model Saleh et al. (2021) that was developed by UN and Fedri using different
socioeconomic attributes that fall under either exposure (size,
specialization, location) or shocks (trade and natural shocks) Feindouno and
Goujon (2016). Coastal Sensitivity Index (CSI) was also used in a number of
research works to include ecosystem and oceanographic parameters, in addition
to the physical parameters used in CVI assessments Al-Awadhi et al. (2020).

Generally, the selection of the type and number of indexes will be reflected
in the assessment results, and the selected index set can vary according to
the specification of the studied areas. Anfuso et al. (2021) listed some of
the criteria that should be considered in choosing an index-based assessment
approach, such as the availability of the data associated with the selected
index and its robustness to be influenced by other indexes. Further, the
number of selected indexes should be sufficient to obtain reliable assessment
results Anfuso et al. (2021). Table LABEL:tab:CVI_comparison shows different
types of parameters that were used in research.

Table 1: Comparison of Coastal Vulnerability Assessment (CVI) techniques in literature |  |  |  |
---|---|---|---|---
Paper | Year |  Region | Spatial Scale |  Approach
Thieler and Hammar-Klose (1999) | 1999 |  US Atlantic coast | National |  CVI (Geomorphology, Shoreline change-rate, Coastal-slope, regional SLR, Mean significant wave-height, Mean tidal-range)
Mclaughlin and Cooper (2010) | 2010 |  Northern Ireland | Local |  CVI coastal characteristics (resilience and susceptibility)+ coastal forcing + socioeconomic factors
Bagdanaviciute et al. (2015) | 2015 |  Lithuania in the south-eastern Baltic Sea | Local |  CVI are combined with DS(the outcome analytical hierarchical process (AHP))
Lopez Royo et al. (2016) | 2016 |  peninsular coastline of Spain | National |  CVI (Geomorphology, Shoreline change-rate, Coastal slope, regional SLR, Mean significant wave height, Mean tidal range)
Pantusa et al. (2018) | 2018 |  Italy | Local |  CVI with 10 parameters (1)Geologic (Geomorphology, Coastal slope, Shoreline Erosion/accretion, Emerged beach width, Dune width), 2) Physical process(regional SLR, Mean significant wave height, Mean tide range), 3) Vegetation(Width of vegetation behind the beach, Posidonia oceanica))
Onat et al. (2018) | 2018 |  Hawaiian Islands | Local |  CVI and InVEST model to calculate Exposure Index (EI). parameters: bathymetry, shoreline geomorphology, regional SLR, wind and wave actions, LU/LC, population
Kantamaneni et al. (2019) | 2019 |  Andhra Pradesh (CAP) region in India | Local |  CVI (Geomorphology, Shoreline change-rate, Coastal-slope, regional SLR, Mean significant wave-height, Mean tidal-range)
Mullick et al. (2019) | 2019 |  Bangladesh | National |  CVI method of Mclaughlin and Cooper(2010) Mclaughlin and Cooper (2010) that consists of three sub-indices: 1) coastal characteristics vulnerability sub-index; 2) coastal forcing vulnerability sub-index 3) socioeconomic vulnerability sub-index
Hereher et al. (2020) | 2020 |  Sultanate of Oman | National |  CVI (Coastal geomorphology Coastal slope, Coastal elevation, Tidal range, Bathymetry)
Norezan et al. (2021) | 2021 |  Malaysia’s east coast, Terengganu State beaches | Local |  CVI of coastal vulnerabilities using Hammar-Klose and Thieler CVI rankings
Seenipandi et al. (2021) | 2021 |  South India | Regional |  CVI with 10 parameters: geomorphology, shoreline erosion/accretion rate, coastal slope, regional SLR, mean significant wave height, mean tide range, storm wave run-up, regional elevation, LU/LC change, mean wave height
Ku et al. (2021) | 2021 |  South Korea | National |  CVI of 3 main components: Exposure(population density and age group distribution, coastal industrial facilities, GRDP), Sensitivity (inundated depth and impacted areas), Adaptive capacity (humans, emergency response and disaster management, relief fund, public officials)
Oloyede et al. (2022) | 2022 |  Nigeria | National |  CVI using physical and socioeconomic parameters (geomorphology, coastal slope, bathymetry, wave height, mean tidal range, shoreline change rate, regional SLR, population, cultural heritage, LU/LC, and road network)
Ennouali et al. (2023) | 2023 |  Northern area of the estuary of Sebou’ in Morocco | Local |  CVI with machine learning algorithms (geomorphology, elevation, slope, shoreline change, natural habitat, SLR, maximum wave height, and tidal range)
Table 1: (Continued) Comparison of Coastal Vulnerability Assessment (CVI)
techniques in literature

##  5 Mitigation and Adaptation Measures (MAM)

This section discusses Mitigation and Adaptation Measures (MAM) that can be
implemented in response to SLR induced by climate change. Overall, those
responses can be categorized into mitigation measures as a proactive response
to SLR, and adaptation measures that act as both proactive and reactive
responses. Those measures include man-made/natural structures and non-
structural measures that involve policies and sets of best practices to
protect coastal areas and communities. Utilizing both structural and non-
structural measures will increase the effectiveness of protection and reduce
losses Dedekorkut-Howes et al. (2020).

###  5.1 Structural Adaptation Measures

Structural mitigation measures are widely implemented across the globe.
Various research works were conducted to study both their long- and short-
terms effectiveness in protecting coastal areas against SLR Dedekorkut-Howes
et al. (2020). Examples of structural mitigation measures:

  * •

Seawall: a protection structure used to protect the coastal landform from SLR
impacts Subraelu et al. (2022).

  * •

Breakwater: it is used to protect coastal zone areas and beach material
against strong wave actions. It also enables extending the beach area for
different human/economic activities. Breakwaters are built either tangible or
parallel to the beach according to the beach’s nature, to provide maximum
protection. The elevation of the breakwater should be determined according to
the height of the local maximum tidal wave Subraelu et al. (2022).

  * •

Seagrass beds: eco-system-based protection measure of high ecological value
that adds means of support to other structural mitigation measures against SLR
impacts Dedekorkut-Howes et al. (2020); Ellison (2015); Schernewski et al.
(2022).

  * •

Dike: structures built along the coast to protect against high wave actions
and flooding Dedekorkut-Howes et al. (2020)

  * •

Dunes: Piles of sand and/or other materials that are formed due to either
natural causes such as wind actions or built constructions (filled with
artificial material) to reduce the impact of wave actions and coastal erosion
Winters et al. (2020).

  * •

Beach Nourishment: a structural measure that is implemented by filling up the
impacted areas with artificial material to protect the coastal areas from SLR
impacts. Beach nourishment is costly and may cause a negative impact on the
environment, therefore it is usually implemented after conducting assessments
studies and strategical evaluation of the affected areas Dedekorkut-Howes et
al. (2020).

El-Asmar and Taha (2022) studied the impact of structural infrastructure at
Damietta Promontory - Egypt in protecting coastal areas against SLR using
satellite images and historical data on changes in the shoreline. It was found
that seawalls and detached breakwater bodies have effectively protected the
coastline against SLR. However, it was found that anthropogenic interventions
were causing changes to shorelines far greater than SLR. Further, the
importance of the maintenance of those structural bodies to maintain the
current elevation was also highlighted. Additionally, some recommendations
about extending the length of the breaker were provided. Abou-Mahmoud (2021)
also mentioned some of the other existing adaptation measures that are being
used to protect against high waves and SLR impacts in the northeastern coast
of Egypt, specifically the city of Alexandria. This includes the Muhammed Ali
Seawall and the international coastal road that was constructed with a high
elevation ($3$m) by the government. The impact of the type of material used to
construct the structural protection measures was also discussed in the
literature as it determines their efficiency in stopping SLR. Winters et al.
(2020) used Unmanned Armed Vehicles (UAV) Fahlstrom and Gleason (2012) to
study the prior and post outcomes of constructing a dune at Cardiff State
Beach - California to protect against flooding and extreme water events. The
constructed dune consisted of different materials, including vegetation, sand,
cobble, and rip-rap. Although this dune was affected by a storm during the
construction process, the post-construction imagery results obtained from UAV
during an observation period of 9 months, showed that it had, overall,
effectively reduced flooding impacts. In China, a seawall was constructed to
protect the coastline of Tianjin-Hebei District against SLR and extreme
weather events that occur once every 100 years; however, Wang et al. (2019)
suggested that the current elevation of this seawall will not be able to
survive against such an extreme event by 2030, based on a presented study of
future projections of SLR. This study simulated different SLR scenarios
including the existence and absence of extreme weather events of different
intensities using the Regional Ocean Modelling System (ROMS) and other data
related to that area. Seawalls were built in the coastal areas of Mombasa in
Kenya by the tourism sectors to protect against the SLR, extreme weather event
impacts, and coastal erosion, in addition to maintaining tourism activities
Kebede et al. (2010). Subraelu et al. (2022) studied the effectiveness of some
of the implemented structural adaptation measures at some of UAE’s vulnerable
coastal areas to prevent erosion that results from SLR and wave activities.
Detached breakwaters and offshore barriers were found to be better at
protecting the coastal areas against coastal erosion compared to seawalls, as
they impacted the beach and were degraded over time due to tidal wave actions.

Sediment movement due to Wave actions during storm surges in the Croatian Krk
island in the Adriatic Sea led to the formation of beach winter profile that
acted as a natural protection structure for the coastal areas against erosion.
It was found that this naturally formed protection structure acted better in
protecting against coastal erosion than the existing landslide protection wall
which was constructed with low quality and is susceptible to collapse under
the impact of continuous intense wave actions Ruzic et al. (2021). Existing
wetlands and lakes near the coastal zone in Alexandria-Egypt were considered
as a defense line to hold seawater from propagating further into the land in
case of the occurrence of an extreme weather event Abou-Mahmoud (2021).
Schernewski et al. (2022) studied the impact of using different scenarios by
combining adaptation measures with natural ecosystems to protect coastal
areas. This study relied on using data-based approaches from literature and
qualitative-based approaches carried out via field studies in the area
Mecklenburg-Germany in the southeast coast of the Baltic Sea by groups of
experts. This research found that combining structural adaptation measures
with submerged vegetation will result in both positive economic and
environmental impact by adding an appealing look and value to the coastal
areas, which could attract both tourists and environmental scientists.

###  5.2 Non-structural Adaptation Measures

The negative impact of climate change and SLR on islands can be more severe in
comparison to coastal states IPCC (2022); Saleh et al. (2021). For example,
SIDS, will be highly impacted by environmental changes and SLR Saleh et al.
(2021). As per IPCC, economically disadvantaged SIDS countries are highly
vulnerable to climate change and SLR impacts IPCC (2022); hence, which arises
the necessity of implementing proper and effective adaptation measures.
Conversely, financially well-off members of SIDS like the Kingdom of Bahrain
can adopt non-structural adaptation measures in the form of policies and
regulations on energy-related sectors to reduce CO2 and GHG emissions Saleh et
al. (2021). Due to challenges associated with SIDS, Oppenheimer et al. (2019)
suggested that those countries use Eco-based Adaptation (EbA) measures which
"provide a combination of protect and advance benefits based on the
sustainable management, conservation, and restoration of ecosystems"
Oppenheimer et al. (2019). EbA provides protection by deflecting waves and
reducing erosion, thus increasing land elevation via sediment built-ups, which
has proven its effectiveness in mitigating SLR impacts Oppenheimer et al.
(2019); Lincoln et al. (2021). Shuvo et al. (2019) made use of reinforcement
learning to build a model that helps policymakers evaluate the economic cost
of investing in adaptation infrastructure to SLR. The used model was based on
Marcov Decision Process (MDP) model. It provided a proactive measurement by
evaluating the cost of implementing a protection infrastructure versus the
economic loss as a consequence of SLR impact. The model also considered
different SLR scenarios according to NOAA model, the impact of extreme natural
events associated with SLR (such as storms and hurricanes), and residents’ and
corporations’ willingness to support the government’s decision to implement
the SLR adaptation measures. Fu (2020) conducted a national survey in the US
to measure how local communities perceive different SLR adaptation measures
adopted by their local authorities. Fu’s definition of adaptation measures was
used to create four categories, as follows: 1) Protection 2) Accommodation 3)
Managed Retreats 4) Planning. The survey results showed that adaptation
measures at coastal localities focus more on planning, which includes hazard
plans, coastal and emergency management plans, SLR vulnerability assessments,
raising public awareness and education, forming SLR taskforce, and conducting
vulnerability assessments. Nevertheless, planning might give a false sense of
safety, thus the public preferred having protection measures implemented as
part of adaptation measures to SLR. Matos et al. (2022) used a participatory
approach that involved different stakeholders to evaluate the impact of
implementing different adaptation measures in Portugal’s coasts to protect
them from erosion. Adaptation measures were classified into adaption,
nourishment, management, relocation, protection, and restoration. The overall
benefit-to-cost ratio was in favor of scenarios that involved protection
structures and beach nourishment when considering environmental, economic, and
social impacts. Da Lio and Tosi (2019) gave some adaptation suggestions to be
used alongside the existing structural protection measure in Po River Delta -
Italy to reduce the impact of SLR, such as farmers switching crops that will
yield higher economic returns. Noor and Abdul Maulud (2022) evaluated the
adaptation measure in Southeast Asian countries, like Malaysia, and identified
the existing flaws, including the unavailability of documentation, the absence
of a centralized assessment and strategies platform, the gap between
assessment and application, and the emphasis on the importance of
socioeconomic component in assessments, which is reflected negatively on the
produced set of policies and strategies.

Table 2: Comparison of different Mitigation and Adaptation Measures (MAM) in literature MAM Category |  Type (# papers) |  Papers |  Examples
---|---|---|---
Structural |  Hard Structures (6) |  Wang et al. (2019); Ruzic et al. (2021); Subraelu et al. (2022); Matos et al. (2022); Abou-Mahmoud (2021); El-Asmar and Taha (2022); Da Lio and Tosi (2019) |  Breakwaters, seawalls, dikes
Soft Structures (2) |  Winters et al. (2020); Ruzic et al. (2021) |  Dunes, beach nourishment
Eco-Based Structures (4) |  Schernewski et al. (2022); Oppenheimer et al. (2019); Abou-Mahmoud (2021); Lincoln et al. (2021) |  Seagrass bed, wetlands & reef conversion and restoration
Non-Structural |  Policies and Regulations (6) |  Saleh et al. (2021); Oppenheimer et al. (2019); Shuvo et al. (2019); Program (2019b); Fu (2020); Da Lio and Tosi (2019) |  Relocation , hazard mapping, public awareness

Generally, hard-engineered structures are preferred as protection measures to
reduce the impact of SLR, wave actions, and extreme weather events; however,
they might result in negative socioeconomic and ecological impacts. The built-
in protection structures might limit the usage of the coastal areas, which are
usually utilized for habitation and touristic activities. This can be even
more challenging in cities that are highly dependent on coastal activities and
lack the necessary land capacity for relocation, such as SIDS countries
Winters et al. (2020). Additionally, some hard structures may reduce the
attractiveness of the beach and impact fisheries activities, leading to
negative economic impact Subraelu et al. (2022). A combination of eco-based
systems and other structural protection measures can be effective in
protecting against SLR physical impacts while maintaining the socioeconomic
and ecological value of the coastal areas; however, we find that such
scenarios might not be feasible in all regions, due to many local factors like
environmental conditions Oppenheimer et al. (2019), which play a role in
determining the effectiveness of implementing such measures. Some ecosystems,
that protect the shoreline against change,s have the ability to adapt to
changes in their environment and can cope with SLR up to a certain extent
Lincoln et al. (2021). However, we still question their ability to continue
adapting to the recent SLR trends and provide the same level of protection to
shorelines against changes. Although stakeholders are aware of the importance
of using protection structures in mitigating coastal changes induced by
climate change and SLR, they still prefer soft adaptation measures, such as
beach nourishment, as they are easier, simpler, and cost-efficient to
implement Matos et al. (2022). Overall, the trade-off between economic cost
loss, such as the value of land and properties, and implementation costs
drives the decision to implement proper adaptation measures to reduce the risk
caused by climatic-driven events Bosello and De Cian (2014). At the local
scale, communities are also playing a role in protecting their areas by taking
proactive measures and supporting local authorities Oppenheimer et al. (2019);
Fu (2020). Table 2 compares different mitigation and adaptation measures
covered in literature over the past 5 years. furthermore,

##  6 Sea Level Changes in the Arabian Gulf

The Arabian Gulf is a semi-enclosed sea located in Western Asia, extending
from the Gulf of Oman and the Indian Ocean through the Straight of Hormuz. Its
bathymetry ranges from 5 meters for shallow zones to 94 meters for the deepest
zones. It is surrounded by eight countries: United Arab Emirates (UAE), Qatar,
Kingdom of Saudia Arabia (KSA), Kingdom of Bahrain, Sultanate of Oman, Kuwait,
Iran, and Iraq. The majority of those countries have prosperous economies
driven by their substantial oil reserves Hereher (2020). The coastal
population and activities in these countries have also witnessed substantial
growth, leading to an increasing demand for coastal urbanization Subraelu et
al. (2022). The increasing SST in the Arabian Gulf due to a number of climatic
and non-climatic drivers resulted in significant impacts in the region
Alosairi et al. (2020). In addition to its environmental impact including
coral bleaching, extension of fish species, and salinity changes, the
increasing evaporation rate, driven by the increase in sea and air
temperatures, resulted in the physical and chemical processes that altered the
intensity and frequency of climatic events occurrence. Trend analysis showed
that there are homogeneous changes in SST throughout the Arabian Gulf with
visible seasonal patterns in summer and autumn seasons, specifically in the
inner northwestern zone of the Gulf that varies from the outer zone near the
Hormuz strait Moradi (2020). An unobserved extreme SST of 37.8o C was recorded
near Kuwait’s coasts in 2020 Lincoln et al. (2021). The difference in water
levels between the Arabian Gulf and the Gulf of Oman, driven by high
evaporation rates, which results in a movement of a water mass of 416 km3/y
through the Strait of Hormuz into the Arabian Gulf Hereher (2020); Bruciaferri
et al. (2022). Difference in water pressure also plays a significant role in
inward and outward water movement through the Strait of Hormuz Al-Subhi and
Abdulla (2021). Additionally, the growing population and demand for water
supply, coupled with limited freshwater resources, have led to an increase in
the number and operations of seawater desalination plants along the coastal
areas of the Arabian countries in the Gulf. This has resulted in an increasing
salinity of seawater in beach areas and contributed to rising steric sea
levels Al-Maamary et al. (2017). Furthermore, wind actions and extreme events
caused by Shamal wind during the winter season can influence local tidal wave
patterns, potentially resulting in a sea-level rise of a few meters Chow and
Sun (2022) with evaporation being higher in the winter season compared to the
summer despite the high temperature during this season Al-Subhi and Abdulla
(2021). In summer, Kaus wind, in addition to wind draft influenced by
variations of temperature between land and water, causes complex variability
in short-term sea level changes and drives surface pollutants towards the
coastal areas. The freshwater budget of the Arabian Gulf is driven by
evaporation, precipitation, and freshwater discharge, particularly in the
northwestern region Al-Subhi and Abdulla (2021). The flow of water through the
Strait of Hormuz occurs in two layers: the upper layer consists of an inward
flow from the Gulf of Oman and the Indian Ocean toward the Arabian Gulf,
including seasonal surface variations. The second layer (bottom layer)
consists of an outward flow toward the Gulf of Oman and includes saltier water
Campos et al. (2020). non-eustatic sea level rise can be triggered by the
occurrence of sudden events such as earthquakes generated from the north and
east zones or tsunamis generated by extreme events in the Arabian Sea and the
Indian Ocean Al-Awadhi et al. (2020). The timeline of changes in sea levels in
the Arabian Gulf can be traced back to the late Quaternary when the Gulf
experienced a reflooding, reaching its peak of more than 1 meter above current
sea levels during the mid-Holocene. Sea levels gradually decreased to reach
their present levels by late-Holocene Lokier et al. (2015).

The majority of the northeastern shorelines of the Arabian Gulf near the
Hormuz Strait are protected by the Hajar and Zagros mountains, while the
shorelines within the Gulf basin contain various landforms, including sandy
beaches, mudflats, mangroves, seagrass beds, and sabkhas Vaughan et al.
(2019). The geomorphology of these areas serves as a natural barrier against
sea level rise (SLR) in some regions, while in other areas it makes them
vulnerable to variations in sea levels. In low-lying areas, offshore barriers,
including both natural and man-made structures, have been employed to reduce
wave velocity and intensity Subraelu et al. (2022). Satellite images data and
modeling tools enabled the monitoring of changes and impacts of sea levels in
the Arabian Gulf. An example of this is the GULF18-4.0 Model which simulates
various oceanographic features with a high resolution of 1.8 km and optimizes
the vertical grid for important physical processes, including SST, waves, and
tides Bruciaferri et al. (2022). A combination of hydrodynamics and spectral
wave models was customized to incorporate regional factors such as the
wind/wave effects induced by the Shamal wind, the characteristics of the inner
water channel regions, and the submerged areas of the Arabian Gulf, with a
focus on studying the southeastern region Chow and Sun (2022). Satellite
altimetry were used to study changes in sea level SST Hereher (2020); Alosairi
et al. (2020); Al-Subhi and Abdulla (2021)

##  7 Discussion and Conclusion

There is no doubt that climate change is a major force that is leading to
overall negative changes worldwide. The rise in temperature increases the
melting rates of ice sheets and glaciers. It also causes a temperature rise of
ocean water that propagates both horizontally and vertically, which impacts
both the underwater environment and coastal areas. One of the perceived
changes to those global temperature changes is SLR. Further, climate change
leads to changes in other climatic events, such as the increase in the
occurrence and intensity of extreme weather events, like storm surges and
rainfall rates. Additionally, these climatic changes directly influence wave
and wind actions, leading to rapid changes in shorelines and increasing the
coastal areas’ exposure to inundation and flooding events. Nevertheless,
studies showed that non-climatic regional effects can have a noticeable
influence on SLR and climatic events Gregory (1988) (e.g.: tsunamis are
triggered by an earthquake Ruzic et al. (2021); Lincoln et al. (2021)). Those
components include vertical land movement, land-based ice melting, water
exchange between land and sea, and elastic response to mass loss of land ice
melting. Although the percentage of contribution of the different climatic and
non-climatic components to SLR can vary, they should be considered in future
projections and scenario-building topology to obtain realistic results.

It is worth mentioning that uncertainty associated with those components
should be considered for future projections. Even though the earlier IPCC
reports did not consider them in future projections, recent studies
incorporated them in estimating future SLR change and its related components.
Generally, the majority of studies use IPCC RCP emission scenarios in
estimating future GMSL and customize them to calculate regional SLR trends
according to the specifications of the studied areas. Other studies used
scenarios developed by NOAA and other research groups (e.g.: NCA) or relied on
historical regional data to measure the regional SLR. Studies have also found
a deviation between RMSL and GMSL rates when incorporating regional factors
and uncertainties. This is to be explained by the fact that SLR are not
uniform globally and they depend on the geographical location. Furthermore,
the propagation of changes in GMSL are timescale-dependent. For example, the
contribution of Greenland’s ice melting to SLR will be observed in North
America before other regions. The relation between the geographical location
and propagation of changes is perhaps what led Cazenave and Le Cozannet (2014)
to identify some of the regional factors, according to findings of studies, as
global contributing factors. Other climatic Regional factors, such as ENSO
Meyssignac and Cazenave (2012) can play a significant role in SLR both
globally and regionally. Nevertheless, we believe that SLR projections and
scenarios should incorporate both regional factors and uncertainties
associated with global factors that contribute to GMSL to provide a
comprehensive analysis and reliable estimations of the impacts of SLR. Those
uncertainties and deviations in estimated results should be presented clearly
by researchers without any conflict of interest to avoid the consequences of
false results. Therefore, researchers should present their analysis outcomes
to policymakers as a proactive measure in mitigating events driven by climate
change. Additionally, visualization tools should be utilized to showcase the
impacts of climate change and SLR, towards appropriate planning and decision-
making, as well as awareness campaigning.

Data requirements vary according to the purpose of the study and the number
and type of parameters to be used in the analysis. Spatial data (Satellite
images and digital elevation data) were used in the majority of SLR studies as
they can be easily obtained from a number of public repositories, such as
AsterGDEM and USGS. Using other types of data adds more value to the
assessment SLR impacts. Examples of other data that can be used for
assessments are population, LU/LC, shoreline geomorphology, and climatic and
non-climatic factors. However, the research works on this topic suffer from
the lack of accurate and recent data needed for projection and visualization.
Simulation tools were used to cope with the lack of available data associated
with different contributing factors, while visualization tools were used to
interpret changes and impacts of climate-driven events. Several studies used
simulation tools to generate data related to contributing factors, such as
wave action and rainfall, to be used in impact assessment (see section 2).
Historical data were used to make future projections about GMSL and RMSL to
assess their impacts and propose adaptation measures. Those data were fed into
different models and tools for processing. machine learning algorithms such as
linear regression were also used in SLR forecasting studies. However, a recent
comparative study by Hassan et al. (2021) proved that deep learning provides
more reliable results in forecasting GMSL based on historical data. Although
the use of deep learning algorithms is plausible, neither machine learning nor
deep learning studies did not consider the uncertainties associated with the
contributing factors to SLR. SLR trends have accelerated in the twentieth
century Cazenave and Le Cozannet (2014), which raises the question of to which
extent the historical data is reliable in obtaining realistic results.

The impacts of SLR and its associated climatic events on physical,
socioeconomic, and environmental/ecological/chemical aspects are correlated
and perceived negatively except for the ecological aspects, in which some
ecosystems were impacted positively with the increase of SLR and SST. The
level of SLR impact relies on a number of characteristics of the studied area
such as the geographical location and geomorphology of the coast which
determines the type of regional factors that influence SLR and the coast’s
vulnerability to it. Socioeconomic components also help in determining the
level of impact and can be a driving force toward implementing MAM policies.
According to IPCC (2022), Southeast Asian countries are classified as highly
vulnerable to SLR, as they suffer from increasing frequency and intensity of
extreme weather events and have vulnerable communities. We found that the
majority of the papers reviewed in this study were about the Mediterranean
Basin and Southeast Asian countries which match with the distribution of
vulnerable areas and communities to SLR as shown in figure 5. Nevertheless, it
is worth mentioning that SLR is not the only source of physical changes on
shoreline as anthropogenic interventions impacts can exceed those caused by
SLR and climatic events.

Index-based approaches were used widely in assessing coastal vulnerability to
SLR impacts while coastal modeling approaches were used for assessing SLR
impacts. Each assessment approach used a different set of contributing factors
and other parameters to assess coastal areas based on the spatial scale,
available data, objective of the assessment, and processing requirements. The
results of those assessments are used to suggest the proper MAM strategies to
be implemented. It was noticed that the studies conducted on the Mediterranean
Basin countries and the U.S. area rely more on adaption structures, in
comparison to those of Southeast Asian countries, where adaptation policies
are mainly seen as their MAM strategy. Nevertheless, we believe that both MAM
strategies should be implemented to reduce potential damage. An example of
long-term planning of MAM strategies was found in the UAE Program (2019a),
where several protection structures were built at the vulnerable segments of
coasts and national monitoring platforms are used to monitor and forecast
climate hazards. Additionally, adaptation plans and policies were placed as
part of the national development plan toward achieving sustainability. Some
final remarks to be considered for future researches:

  * •

Uncertainties associated with global contributing factors to be properly
Incorporated into GMSL and RMSL projections and updated versions of sea level
budget to be used (as per data availability)

  * •

GMSL is not uniformly distributed globally; hence time and location dimensions
should be considered when estimating individual contributing factors for SLR
projections.

  * •

Geomorphological changes on coastal zones due to anthropogenic interventions
to be isolated from SLR impacts during coastal assessment studies as changes
caused by prior can surpass the latter leading to false results and
interpretation of the current status.

  * •

The lack of available data is limiting studies on SLR impacts on ecosystems
and chemical aspects (e.g.: groundwater contamination, seawater pollution
after flooding and natural disasters).

  * •

There is a limit to the extent of considering historical data reliable in
forecasting future SLR, where significant changes of trends in climatic events
and GMSL are evident (noting that historical data are not sufficient on their
own in forecasting future SLR and other factors and their associated
uncertainties must be considered )

## References

  * Abdouli et al. (2019) Abdouli, K.A., Hussein, K., Ghebreyesus, D., Sharif, H.O., 2019.  Coastal runoff in the united arab emirates—the hazard and opportunity.  Sustainability 11, 5406.  doi:https://doi.org/10.3390/su11195406.
  * Abou-Mahmoud (2021) Abou-Mahmoud, M.M.E., 2021.  Assessing coastal susceptibility to sea-level rise in alexandria, egypt.  The Egyptian Journal of Aquatic Research 47, 133–141.  doi:https://doi.org/10.1016/j.ejar.2021.04.002.
  * Aguilera-Vidal et al. (2022) Aguilera-Vidal, M., Munoz-Perez, J.J., Contreras, A., Contreras, F., Lopez-Garcia, P., Jigena, B., 2022.  Increase in the erosion rate due to the impact of climate change on sea level rise: Victoria beach, a case study.  Journal of Marine Science and Engineering 10, 1912.  doi:https://doi.org/10.3390/jmse10121912.
  * Al-Awadhi et al. (2020) Al-Awadhi, T., Mansour, S., Hereher, M., 2020.  Assessment of coastal sensitivity to non-eustatic sea level rise: a case study on muscat coast—sultanate of oman.  Arabian Journal of Geosciences 13, 1–11.  doi:https://doi.org/10.1007/s12517-020-05321-x.
  * Al-Maamary et al. (2017) Al-Maamary, H.M., Kazem, H.A., Chaichan, M.T., 2017.  Climate change: the game changer in the gulf cooperation council region.  Renewable and Sustainable Energy Reviews 76, 555–576.  doi:https://doi.org/10.1016/j.rser.2017.03.048.
  * Al-Subhi and Abdulla (2021) Al-Subhi, A.M., Abdulla, C.P., 2021.  Sea-level variability in the arabian gulf in comparison with global oceans.  Remote Sensing 13.  doi:10.3390/rs13224524.
  * Aldogom et al. (2020) Aldogom, D., Albesher, S., Al Mansoori, S., Nazzal, T., 2020.  Assessing coastal land dynamics along uae shoreline using gis and remote sensing techniques, in: IOP Conference Series: Earth and Environmental Science, IOP Publishing. p. 012031.  doi:https://doi.org/10.1088/1755-1315/540/1/012031.
  * Alosairi et al. (2020) Alosairi, Y., Alsulaiman, N., Rashed, A., Al-Houti, D., 2020.  World record extreme sea surface temperatures in the northwestern arabian/persian gulf verified by in situ measurements.  Marine Pollution Bulletin 161, 111766.
  * Anfuso et al. (2021) Anfuso, G., Postacchini, M., Di Luccio, D., Benassai, G., 2021.  Coastal sensitivity/vulnerability characterization and adaptation strategies: A review.  Journal of Marine Science and Engineering 9, 72.  doi:https://doi.org/10.3390/jmse9010072.
  * Antonioli et al. (2017) Antonioli, F., Anzidei, M., Amorosi, A., Presti, V.L., Mastronuzzi, G., Deiana, G., De Falco, G., Fontana, A., Fontolan, G., Lisco, S., 2017.  Sea-level rise and potential drowning of the italian coastal plains: Flooding risk scenarios for 2100.  Quaternary Science Reviews 158, 29–43.  doi:https://doi.org/10.1016/j.quascirev.2016.12.021.
  * Arthur and Garland (2016) Arthur, R.M., Garland, G., 2016.  of inundation due to sea-level rise: Al hamra development, ras al khaimah, uae. a pilot project.  Miscellanea Geographica 20, 25–31.  doi:https://doi.org/10.1515/mgrsd-2016-0012.
  * Aryal et al. (2021) Aryal, B., Escarzaga, S.M., Vargas Zesati, S.A., Velez-Reyes, M., Fuentes, O., Tweedie, C., 2021.  Semi-automated semantic segmentation of arctic shorelines using very high-resolution airborne imagery, spectral indices and weakly supervised machine learning approaches.  Remote Sensing 13, 4572.  doi:https://doi.org/10.3390/rs13224572.
  * Bagdanaviciute et al. (2015) Bagdanaviciute, I., Kelpaaite, L., Soomere, T., 2015.  Multi-criteria evaluation approach to coastal vulnerability index development in micro-tidal low-lying areas.  Ocean and Coastal Management 104, 124–135.  doi:https://doi.org/10.1016/j.ocecoaman.2014.12.011.
  * Barboza et al. (2021) Barboza, E.G., Dillenburg, S.R., do Nascimento Ritter, M., Angulo, R.J., da Silva, A.B., da Camara Rosa, M.L.C., Caron, F., de Souza, M.C., 2021.  Holocene sea-level changes in southern brazil based on high-resolution radar stratigraphy.  Geosciences 11, 326.  doi:https://doi.org/10.3390/geosciences11080326.
  * Bengoufa et al. (2021) Bengoufa, S., Niculescu, S., Mihoubi, M.K., Belkessa, R., Rami, A., Rabehi, W., Abbad, K., 2021.  Machine learning and shoreline monitoring using optical satellite images: case study of the mostaganem shoreline, algeria.  Journal of applied remote sensing 15, 026509–026509.  doi:https://doi.org/10.1117/1.JRS.15.026509.
  * Bosello and De Cian (2014) Bosello, F., De Cian, E., 2014.  Climate change, sea level rise, and coastal disasters. a review of modeling practices.  Energy Economics 46, 593–605.  doi:https://doi.org/10.1016/j.eneco.2013.09.002.
  * Bruciaferri et al. (2022) Bruciaferri, D., Tonani, M., Ascione, I., Al Senafi, F., O’Dea, E., Hewitt, H.T., Saulter, A., 2022.  Gulf18, a high-resolution nemo-based tidal ocean model of the arabian/persian gulf.  Geoscientific Model Development Discussions , 1–38doi:https://doi.org/10.5194/gmd-15-8705-2022.
  * Campos et al. (2020) Campos, E.J., Gordon, A.L., Kjerfve, B., Vieira, F., Cavalcante, G., 2020.  Freshwater budget in the persian (arabian) gulf and exchanges at the strait of hormuz.  Plos one 15, e0233090.
  * Carbognin et al. (2010) Carbognin, L., Teatini, P., Tomasin, A., Tosi, L., 2010.  Global change and relative sea level rise at venice: what impact in term of flooding.  Climate Dynamics 35, 1039–1047.  doi:https://doi.org/10.1007/s00382-009-0617-5.
  * Carson et al. (2016) Carson, M., Kohl, A., Stammer, D., A. Slangen, A., Katsman, C., W. van de Wal, R., Church, J., White, N., 2016.  Coastal sea level changes, observed and projected during the 20th and 21st century.  Climatic Change 134, 269–281.  doi:https://doi.org/10.1007/s10584-015-1520-1.
  * Cazenave and Le Cozannet (2014) Cazenave, A., Le Cozannet, G., 2014.  Sea level rise and its coastal impacts.  Earth’s Future 2, 15–34.  doi:https://doi.org/10.1002/2013EF000188.
  * Chadwick et al. (2011) Chadwick, B., Flick, R., Helly, J., Nishikawa, T., Wang, P.F., O Reilly, W., Guza, R., Bromirski, P., Young, A., Crampton, W., 2011.  A framework for sea level rise vulnerability assessment for southwest us military installations, in: OCEANS’11 MTS/IEEE KONA, IEEE. pp. 1–10.  doi:https://doi.org/10.23919/oceans.2011.6107212.
  * Chassignet et al. (2009) Chassignet, E.P., Hurlburt, H.E., Metzger, E.J., Smedstad, O.M., Cummings, J.A., Halliwell, G.R., Bleck, R., Baraille, R., Wallcraft, A.J., Lozano, C., 2009.  Us godae: global ocean prediction with the hybrid coordinate ocean model (hycom).  Oceanography 22, 64–75.  URL: https://www.jstor.org/stable/24860960.
  * Chini et al. (2010) Chini, N., Stansby, P., Leake, J., Wolf, J., Roberts-Jones, J., Lowe, J., 2010.  The impact of sea level rise and climate change on inshore wave climate: A case study for east anglia (uk).  Coastal Engineering 57, 973–984.  doi:https://doi.org/10.1016/j.coastaleng.2010.05.009.
  * Chow and Sun (2022) Chow, A.C., Sun, J., 2022.  Combining sea level rise inundation impacts, tidal flooding and extreme wind events along the abu dhabi coastline.  Hydrology 9, 143.
  * Clark et al. (2016) Clark, P.U., Shakun, J.D., Marcott, S.A., Mix, A.C., Eby, M., Kulp, S., Levermann, A., Milne, G.A., Pfister, P.L., Santer, B.D., 2016.  Consequences of twenty-first-century policy for multi-millennial climate and sea-level change.  Nature climate change 6, 360–369.  doi:https://doi.org/10.1038/nclimate2923.
  * Cooper and McLaughlin (1998) Cooper, J., McLaughlin, S., 1998.  Contemporary multidisciplinary approaches to coastal classification and environmental risk analysis.  Journal of Coastal Research , 512–524URL: https://www.jstor.org/stable/4298806.
  * Da Lio and Tosi (2019) Da Lio, C., Tosi, L., 2019.  Vulnerability to relative sea-level rise in the po river delta (italy).  Estuarine, Coastal and Shelf Science 228, 106379.  doi:https://doi.org/10.1016/j.ecss.2019.106379.
  * Dedekorkut-Howes et al. (2020) Dedekorkut-Howes, A., Torabi, E., Howes, M., 2020.  When the tide gets high: A review of adaptive responses to sea level rise and coastal flooding.  Journal of Environmental Planning and Management 63, 2102–2143.  doi:https://doi.org/10.1080/09640568.2019.1708709.
  * Dieng et al. (2017) Dieng, H.B., Cazenave, A., Meyssignac, B., Ablain, M., 2017.  New estimate of the current rate of sea level rise from a sea level budget approach.  Geophysical Research Letters 44, 3744–3751.  doi:https://doi.org/10.1002/2017GL073308.
  * Dwarakish and Nithyapriya (2016) Dwarakish, G., Nithyapriya, B., 2016.  Application of soft computing techniques in coastal study -a review.  Journal of Ocean Engineering and Science 1, 247–255.  doi:https://doi.org/10.1016/j.joes.2016.06.004.
  * El-Asmar and Taha (2022) El-Asmar, H.M., Taha, M.M., 2022.  Monitoring coastal changes and assessing protection structures at the damietta promontory, nile delta, egypt, to secure sustainability in the context of climate changes.  Sustainability 14, 15415.  doi:https://doi.org/10.3390/su142215415.
  * Ellison (2015) Ellison, J.C., 2015.  Vulnerability assessment of mangroves to climate change and sea-level rise impacts.  Wetlands Ecology and Management 23, 115–137.  doi:https://doi.org/10.1007/s11273-014-9397-8.
  * Ennouali et al. (2023) Ennouali, Z., Fannassi, Y., Lahssini, G., Benmohammadi, A., Masria, A., 2023.  Mapping coastal vulnerability using machine learning algorithms: A case study at north coastline of sebou estuary, morocco.  Regional Studies in Marine Science 60, 102829.  doi:https://doi.org/10.1016/j.rsma.2023.102829.
  * Etkins and Epstein (1982) Etkins, R., Epstein, E.S., 1982.  The rise of global mean sea level as an indication of climate change.  Science 215, 287–289.  doi:https://doi.org/10.1126/science.215.4530.287.
  * Fahlstrom and Gleason (2012) Fahlstrom, P.G., Gleason, T.J., 2012.  Introduction to UAV systems.  4 ed., John Wiley and Sons, NJ, USA.  doi:https://doi.org/10.1002/9781118396780.
  * Faridatunnisa and Heliani (2018) Faridatunnisa, M., Heliani, L.S., 2018.  Study of sea level rise using tide gauge data year 1996 to 2015 at semarang and prigi stations, in: 2018 4th International Conference on Science and Technology (ICST), IEEE. pp. 1–4.  doi:https://doi.org/10.1109/ICSTC.2018.8528668.
  * Feindouno and Goujon (2016) Feindouno, S., Goujon, M., 2016.  The retrospective economic vulnerability index, 2015 update.  URL: https://ferdi.fr/en/publications/the-retrospective-economic-vulnerability-index-2015-update. ferdi Working Paper.
  * Fitz Gerald et al. (2008) Fitz Gerald, D.M., Fenster, M.S., Argow, B.A., Buynevich, I.V., 2008.  Coastal impacts due to sea-level rise.  Annual Review of Earth and Planetary Sciences 36, 601–647.  doi:https://doi.org/10.1146/annurev.earth.35.031306.140139.
  * Fox-Kemper et al. (2019) Fox-Kemper, B., Adcroft, A., Boning, C., Chassignet, E., Curchitser, E., Danabasoglu, G., Eden, C., England, M.M., Gerdes, R., Greatbatch, R.J., Griffies, S.M., Hallberg, R.W., Hanert, E., Heimbach, P., Hewitt, H.T., Hill, C.N., Komuro, Y., Legg, S., Le Sommer, J., Masina, S., Marsland, S.J., Penny, S.G., Qiao, F., Ringler, T.D., Treguier, A.M., Tsujino, H., Uotila, P., Yeager, S.G., 2019.  Challenges and prospects in ocean circulation models.  Frontiers in Marine Science 6.  doi:https://doi.org/10.3389/fmars.2019.00065.
  * Fu (2020) Fu, X., 2020.  Measuring local sea-level rise adaptation and adaptive capacity: A national survey in the united states.  Cities 102, 102717.  doi:https://doi.org/10.1016/j.cities.2020.102717.
  * Gesch (2018) Gesch, D.B., 2018.  Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure.  Frontiers in Earth Science 6, 230.  doi:https://doi.org/10.3389/feart.2018.00230.
  * Giannakidis et al. (2014) Giannakidis, A., Giakoumidakis, G., Mania, K., 2014.  3d photorealistic scientific visualization of tsunami waves and sea level rise, in: 2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings, IEEE. pp. 167–172.  doi:https://doi.org/10.1109/IST.2014.6958467.
  * Gornitz (1991) Gornitz, V., 1991.  Global coastal hazards from future sea level rise.  Palaeogeography, Palaeoclimatology, Palaeoecology 89, 379–398.  doi:https://doi.org/10.1016/0031-0182(91)90173-O.
  * Gregory (1988) Gregory, K.J., 1988.  Review of greenhouse effect and sea level rise; a challenge for this generation.  The Geographical Journal 154, 118–119.  URL: http://www.jstor.org/stable/633502, doi:https://doi.org/10.2307/633502.
  * Hall et al. (2016) Hall, J.A., Gill, S., Obeysekera, J., Sweet, W., Knuuti, K., Marburger, J., 2016.  Regional sea level scenarios for coastal risk management: Managing the uncertainty of future sea level change and extreme water levels for Department of Defense coastal sites worldwide.  Technical Report. U.S. Department of Defense, Strategic Environmental Research and Development Program.  URL: https://apps.dtic.mil/sti/citations/AD1013613.
  * Hang (2022) Hang, Y., 2022.  Numerical study of coastal erosion based on sea level rise, in: International Conference on Hydraulic and Civil Engineering: Deep Space Intelligent Development and Utilization Forum (ICHCE), pp. 495–498.  doi:https://doi.org/10.1109/ICHCE57331.2022.10042607.
  * Hassan et al. (2021) Hassan, K.M.A., Haque, M.A., Ahmed, S., 2021.  Comparative study of forecasting global mean sea level rising using machine learning, in: 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), IEEE. pp. 1–4.  doi:https://doi.org/10.1109/ICECIT54077.2021.9641339.
  * Hereher et al. (2020) Hereher, M., Al-Awadhi, T., Al-Hatrushi, S., Charabi, Y., Mansour, S., Al-Nasiri, N., Sherief, Y., El-Kenawy, A., 2020.  Assessment of the coastal vulnerability to sea level rise: Sultanate of oman.  Environmental Earth Sciences 79, 1–12.  doi:https://doi.org/10.1007/s12517-020-05321-x.
  * Hereher (2020) Hereher, M.E., 2020.  Assessment of climate change impacts on sea surface temperatures and sea level rise—the arabian gulf.  Climate 8, 50.  doi:https://doi.org/10.3390/cli8040050.
  * IPCC (2014) IPCC, 2014.  Climate Change 2014: Synthesis Report.  IPCC, Geneva, Switzerland.  doi:https://doi.org/10.1017/9781009325844. aR5.
  * IPCC (2022) IPCC, 2022.  Climate Change 2022: Impacts, Adaptation, and Vulnerability.  IPCC, Geneva, Switzerland.  doi:https://doi.org/10.1017/9781009325844. aR6.
  * Jevrejeva et al. (2019) Jevrejeva, S., Frederikse, T., Kopp, R., Le Cozannet, G., Jackson, L., Van De Wal, R., 2019.  Probabilistic sea level projections at the coast by 2100.  Surveys in geophysics 40, 1673–1696.  doi:https://doi.org/10.1007/s10712-019-09550-y.
  * Kantamaneni et al. (2019) Kantamaneni, K., Sudha Rani, N., Rice, L., Sur, K., Thayaparan, M., Kulatunga, U., Rege, R., Yenneti, K., Campos, L.C., 2019.  A systematic review of coastal vulnerability assessment studies along andhra pradesh, india: A critical evaluation of data gathering, risk levels and mitigation strategies.  Water 11, 393.  doi:https://doi.org/10.3390/w11020393.
  * Kebede et al. (2010) Kebede, A.S., Nicholls, R.J., Hanson, S., Mokrech, M., 2010.  Impacts of Climate Change and Sea-Level Rise: A Preliminary Case Study of Mombasa, Kenya.  Journal of Coastal Research 28, 8–19.  doi:https://doi.org/10.2112/JCOASTRES-D-10-00069.1.
  * Kont et al. (2003) Kont, A., Jaagus, J., Aunap, R., 2003.  Climate change scenarios and the effect of sea-level rise for estonia.  Global and Planetary Change 36, 1–15.  doi:https://doi.org/10.1016/S0921-8181(02)00149-2.
  * Koroglu et al. (2019) Koroglu, A., Ranasinghe, R., Jimenez, J.A., Dastgheib, A., 2019.  Comparison of coastal vulnerability index applications for barcelona province.  Ocean and Coastal Management 178, 104799.  doi:https://doi.org/10.1016/j.ocecoaman.2019.05.001.
  * Ku et al. (2021) Ku, H., Kim, T., Song, Y.i., 2021.  Coastal vulnerability assessment of sea-level rise associated with typhoon-induced surges in south korea.  Ocean and Coastal Management 213, 105884.  doi:https://doi.org/10.1016/j.ocecoaman.2021.105884.
  * Lincoln et al. (2021) Lincoln, S., Buckley, P., Howes, E.L., Maltby, K.M., Pinnegar, J.K., Ali, T.S., Alosairi, Y., Al-Ragum, A., Baglee, A., Balmes, C.O., Hamadou, R.B., Burt, J.A., Claereboudt, M., Glavan, J., Mamiit, R.J., Naser, H.A., Sedighi, O., Shokri, M.R., Shuhaibar, B., Wabnitz, C.C.C., Le Quesne, W.J.F., 2021.  A regional review of marine and coastal impacts of climate change on the ropme sea area.  Sustainability 13.  doi:https://doi.org/10.3390/su132413810.
  * Lokier et al. (2015) Lokier, S.W., Bateman, M.D., Larkin, N.R., Rye, P., Stewart, J.R., 2015.  Late quaternary sea-level changes of the persian gulf.  Quaternary Research 84, 69–81.  doi:10.1016/j.yqres.2015.04.007.
  * Lopez Royo et al. (2016) Lopez Royo, M., Ranasinghe, R., Jimenez, J.A., 2016.  A rapid, low-cost approach to coastal vulnerability assessment at a national level.  Journal of Coastal Research 32, 932–945.  doi:https://doi.org/10.2112/JCOASTRES-D-14-00217.1.
  * Luccioni et al. (2021) Luccioni, A., Schmidt, V., Vardanyan, V., Bengio, Y., 2021.  Using artificial intelligence to visualize the impacts of climate change.  IEEE Computer Graphics and Applications 41, 8–14.  doi:https://doi.org/10.1109/MCG.2020.3025425.
  * Marfai and King (2008) Marfai, M.A., King, L., 2008.  Potential vulnerability implications of coastal inundation due to sea level rise for the coastal zone of semarang city, indonesia.  Environmental geology 54, 1235–1245.  doi:https://doi.org/10.1007/s00254-007-0906-4.
  * Matos et al. (2022) Matos, F.A., Alves, F., Coelho, C., Lima, M., Vizinho, A., 2022.  Participatory approach to build up a municipal strategy for coastal erosion mitigation and adaptation to climate change.  Journal of Marine Science and Engineering 10, 1718.  doi:https://doi.org/10.3390/jmse10111718.
  * Maulud and Rafar (2015) Maulud, K.N.A., Rafar, R.M., 2015.  Determination the impact of sea level rise to shoreline changes using gis, in: 2015 International Conference on Space Science and Communication (IconSpace), IEEE. pp. 352–357.  doi:https://doi.org/10.1109/IconSpace.2015.7283798.
  * Mclaughlin and Cooper (2010) Mclaughlin, S., Cooper, J.A.G., 2010.  A multi-scale coastal vulnerability index: A tool for coastal managers?  Environmental Hazards 9, 233–248.  doi:https://doi.org/10.3763/ehaz.2010.0052.
  * Mcleod et al. (2010) Mcleod, E., Poulter, B., Hinkel, J., Reyes, E., Salm, R., 2010.  Sea-level rise impact models and environmental conservation: A review of models and their applications.  Ocean and Coastal Management , 507–517doi:https://doi.org/10.1016/j.ocecoaman.2010.06.009.
  * Meyssignac and Cazenave (2012) Meyssignac, B., Cazenave, A., 2012.  Sea level: a review of present-day and recent-past changes and variability.  Journal of Geodynamics 58, 96–109.  doi:https://doi.org/10.1016/j.jog.2012.03.005.
  * Mohanty (2021) Mohanty, A., 2021.  Impacts of climate change on human health and agriculture in recent years, in: 2021 IEEE Region 10 Symposium (TENSYMP), IEEE. pp. 1–4.  doi:https://doi.org/10.1109/TENSYMP52854.2021.9550876.
  * Moradi (2020) Moradi, M., 2020.  Trend analysis and variations of sea surface temperature and chlorophyll-a in the persian gulf.  Marine Pollution Bulletin 156, 111267.
  * Mullick et al. (2019) Mullick, M.R.A., Tanim, A., Islam, S.S., 2019.  Coastal vulnerability analysis of bangladesh coast using fuzzy logic based geospatial techniques.  Ocean and Coastal Management 174, 154–169.  doi:https://doi.org/10.1016/j.ocecoaman.2019.03.010.
  * Nguyen et al. (2016) Nguyen, T.T., Bonetti, J., Rogers, K., Woodroffe, C.D., 2016.  Indicator-based assessment of climate-change impacts on coasts: A review of concepts, methodological approaches and vulnerability indices.  Ocean and Coastal Management 123, 18–43.  doi:https://doi.org/10.1016/j.ocecoaman.2015.11.022.
  * Nicholls et al. (2011) Nicholls, R.J., Hanson, S.E., Lowe, J.A., Warrick, R.A., Lu, X., Long, A.J., Carter, T., 2011.  Constructing Sea-Level Scenarios for Impact and Adaptation Assessment of Coastal Areas: A Guidance Document.  Technical Report. Supporting Material, Intergovernmental Panel on Climate Change Task Group on Data and Scenario Support for Impact and Climate Analysis (TGICA).  URL: https://research.fit.edu/media/site-specific/researchfitedu/coast-climate-adaptation-library/africa/morocco-algeria-tunisia/Nicholls.--2011.--Sea-Level-Scenarios-for-Impact--Adaptation-for-Coastal-Areas.pdf.
  * Noor and Abdul Maulud (2022) Noor, N.M., Abdul Maulud, K.N., 2022.  Coastal vulnerability: A brief review on integrated assessment in southeast asia.  Journal of Marine Science and Engineering 10, 595.  doi:https://doi.org/10.3390/jmse10050595.
  * Norezan et al. (2021) Norezan, N.N.M., Ma’arof, I., Sulaiman, S.A., 2021.  Offshore vulnerability and sustainability assessment affected by sea breakwater construction using geomatics engineering technology, in: 2021 IEEE 12th Control and System Graduate Research Colloquium, IEEE. pp. 104–109.  doi:https://doi.org/10.1109/ICSGRC53186.2021.9515239.
  * Oloyede et al. (2022) Oloyede, M.O., Williams, A.B., Ode, G.O., Benson, N.U., 2022.  Coastal vulnerability assessment: A case study of the nigerian coastline.  Sustainability 14.  doi:https://doi.org/10.3390/su14042097.
  * Onat et al. (2018) Onat, Y., Marchant, M., Francis, O.P., Kim, K., 2018.  Coastal exposure of the hawaiian islands using gis-based index modeling.  Ocean and Coastal Management 163, 113–129.  doi:https://doi.org/10.1016/j.ocecoaman.2018.06.003.
  * Oppenheimer et al. (2019) Oppenheimer, M., Glavovic, B., Hinkel, J., van de Wal, R., Magnan, A.K., Abd-Elgawad, A., Cai, R., Cifuentes Jara, M., Deconto, R.M., Ghosh, T., 2019.  IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. Cambridge University Press, Cambridge, UK and New York, NY, USA. chapter Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities.  pp. 321–445.  doi:https://doi.org/10.1017/9781009157964.006.
  * Owusu et al. (2022) Owusu, C., Snigdha, N.J., Martin, M.T., Kalyanapu, A.J., 2022.  Pygee-swtoolbox: A python jupyter notebook toolbox for interactive surface water mapping and analysis using google earth engine.  Sustainability 14, 2557.  doi:https://doi.org/10.3390/su14052557.
  * Palanisamy et al. (2014) Palanisamy, H., Cazenave, A., Meyssignac, B., Soudarin, L., Wöppelmann, G., Becker, M., 2014.  Regional sea level variability, total relative sea level rise and its impacts on islands and coastal zones of indian ocean over the last sixty years.  Global and Planetary Change 116, 54–67.  doi:https://doi.org/10.1016/j.gloplacha.2014.02.001.
  * Pan et al. (2019) Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F., Zheng, Y., 2019.  Recent progress on generative adversarial networks (gans): A survey.  IEEE access 7, 36322–36333.  doi:https://doi.org/10.1109/ACCESS.2019.2905015.
  * Pantusa et al. (2018) Pantusa, D., D’Alessandro, F., Riefolo, L., Principato, F., Tomasicchio, G.R., 2018.  Application of a coastal vulnerability index. a case study along the apulian coastline, italy.  Water 10.  doi:https://doi.org/10.3390/w10091218.
  * Parthasarathy and Deka (2021) Parthasarathy, K., Deka, P.C., 2021.  Remote sensing and gis application in assessment of coastal vulnerability and shoreline changes: a review.  ISH Journal of Hydraulic Engineering 27, 588–600.  doi:https://doi.org/10.1080/09715010.2019.1603086.
  * Passeri et al. (2015) Passeri, D.L., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015.  The dynamic effects of sea level rise on low-gradient coastal landscapes: A review.  Earth’s Future 3, 159–181.  doi:https://doi.org/10.1002/2015EF000298.
  * Pernetta (1992) Pernetta, J.C., 1992.  Impacts of climate change and sea-level rise on small island states: national and international responses.  Global Environmental Change 2, 19–31.  doi:https://doi.org/10.1016/0959-3780(92)90033-4.
  * Program (2019a) Program, N.C.C.A., 2019a.  Adaptation of the UAEs Infrastructure to Climate Change.  Technical Report. Ministry of Climate Change and Environment.  URL: https://www.moccae.gov.ae/.
  * Program (2019b) Program, N.C.C.A., 2019b.  UAE Climate Risk Assessment and Adaptation Measures in Key Sectors: Health, Energy, Infrastructure and Environment.  Technical Report. Ministry of Climate Change and Environment.  URL: https://www.moccae.gov.ae/.
  * Pycroft et al. (2016) Pycroft, J., Abrell, J., Ciscar, J.C., 2016.  The global impacts of extreme sea-level rise: a comprehensive economic assessment.  Environmental and Resource Economics 64, 225–253.  doi:https://doi.org/10.1007/s10640-014-9866-9.
  * Ruzic et al. (2021) Ruzic, I., Benac, C., Jovancevic, S.D., Radisic, M., 2021.  The application of uav for the analysis of geological hazard in krk island, croatia, mediterranean sea.  Remote Sensing 13, 1790.  doi:https://doi.org/10.3390/rs13091790.
  * Saleh et al. (2021) Saleh, A.S., Altaei, S., Alkhaldi, F.K., 2021.  Climate change implications on small island developing states (sids): a socioeconomic perspective from the kingdom of bahrain, in: 2021 Third International Sustainability and Resilience Conference: Climate Change, IEEE. pp. 39–44.  doi:https://doi.org/10.1109/IEEECONF53624.2021.9668116.
  * Salimi and Al-Ghamdi (2020) Salimi, M., Al-Ghamdi, S.G., 2020.  Climate change impacts on critical urban infrastructure and urban resiliency strategies for the middle east.  Sustainable Cities and Society 54, 101948.  doi:https://doi.org/10.1016/j.scs.2019.101948.
  * Sarkar et al. (2022) Sarkar, N., Rizzo, A., Vandelli, V., Soldati, M., 2022.  A literature review of climate-related coastal risks in the mediterranean, a climate change hotspot.  Sustainability 14, 15994.  doi:https://doi.org/10.3390/su142315994.
  * Scavo and Singh (2016) Scavo, A.N., Singh, R.P., 2016.  Effect of climate change on california fish species, in: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE. pp. 6070–6073.  doi:https://doi.org/10.1109/IGARSS.2016.7730586.
  * Schernewski et al. (2022) Schernewski, G., Voeckler, L.N., Lambrecht, L., Robbe, E., Schumacher, J., 2022.  Building with nature—ecosystem service assessment of coastal-protection scenarios.  Sustainability 14, 15737.  doi:https://doi.org/10.3390/su142315737.
  * Seenipandi et al. (2021) Seenipandi, K., Ramachandran, K., Chandrasekar, N., 2021.  Modeling of coastal vulnerability to sea-level rise and shoreline erosion using modified cvi model, in: Remote Sensing of Ocean and Coastal Environments, pp. 315–340.  doi:https://doi.org/10.1016/B978-0-12-819604-5.00018-4.
  * Shuvo et al. (2019) Shuvo, S.S., Yilmaz, Y., Bush, A., Hafen, M., 2019.  Scenario planning for sea level rise via reinforcement learning, in: 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), IEEE. pp. 1–5.  doi:https://doi.org/10.1109/GlobalSIP45357.2019.8969548.
  * Slangen et al. (2017) Slangen, A., Adloff, F., Jevrejeva, S., Leclercq, P.W., Marzeion, B., Wada, Y., Winkelmann, R., 2017.  A review of recent updates of sea-level projections at global and regional scales.  Integrative Study of the Mean Sea level and Its Components , 395–416doi:https://doi.org/10.1007/978-3-319-56490-6_17.
  * Smith et al. (2021) Smith, K.E., Terrano, J.F., Pitchford, J.L., Archer, M.J., 2021.  Coastal wetland shoreline change monitoring: A comparison of shorelines from high-resolution worldview satellite imagery, aerial imagery, and field surveys.  Remote Sensing 13, 3030.  doi:https://doi.org/10.3390/rs13153030.
  * Sowe et al. (2019) Sowe, M.A., Sadhasivam, S., Mostafa Mohamed, M., Mohsen, S., 2019.  Modeling the mitigation of seawater intrusion by pumping of brackish water from the coastal aquifer of wadi ham, uae.  Sustainable Water Resources Management 5, 1435–1451.  doi:https://doi.org/10.1007/s40899-018-0271-3.
  * Subraelu et al. (2022) Subraelu, P., Ebraheem, A.A., Sherif, M., Sefelnasr, A., Yagoub, M., Rao, K.N., 2022.  Land in water: The study of land reclamation and artificial islands formation in the uae coastal zone: A remote sensing and gis perspective.  Land 11, 2024.  doi:https://doi.org/10.3390/land11112024.
  * Sudha Rani et al. (2015) Sudha Rani, N., Satyanarayana, A., Bhaskaran, P.K., 2015.  Coastal vulnerability assessment studies over india: a review.  Natural Hazards 77, 405–428.  doi:https://doi.org/10.1007/s11069-015-1597-x.
  * Susnik et al. (2015) Susnik, J., Vamvakeridou-Lyroudia, L.S., Baumert, N., Kloos, J., Renaud, F.G., La Jeunesse, I., Mabrouk, B., Savic, D.A., Kapelan, Z., Ludwig, R., 2015.  Interdisciplinary assessment of sea-level rise and climate change impacts on the lower nile delta, egypt.  Science of the Total Environment 503, 279–288.  doi:https://doi.org/10.1016/j.scitotenv.2014.06.111.
  * Sweet et al. (2022) Sweet, W.V., Hamlington, B.D., Kopp, R.E., Weaver, C.P., Barnard, P.L., Bekaert, D., Brooks, W., Craghan, M., Dusek, G., Frederikse, T., Garner, G., Genz, A., Krasting, J., Larour, E., Marcy, D., Marra, J.J.and Obeysekera, J., Osler, M., Pendleton, M., Roman, D., Schmied, L., Veatch, W., White, K., Zuzak, C., 2022.  Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines.  Technical Report NOAA Technical Report NOS 01. National Oceanic and Atmospheric Administration, National Ocean Service. Silver Spring, MD.  URL: https://oceanservice.noaa.gov/hazards/sealevelrise/noaa-nos-techrpt01-global-regional-SLR-scenarios-US.pdf.
  * Sweet et al. (2017) Sweet, W.V., Kopp, R.E., Weaver, C.P., Obeysekera, J., Horton, R.M., Thieler, E.R., Zervas, C., 2017.  Global and regional sea level rise scenarios for the united states.  URL: https://ntrs.nasa.gov/citations/20180001857.
  * Tachikawa et al. (2015) Tachikawa, T., Kaku, M., Iwasaki, A., 2015.  Aster gdem version 3 validation report.  Japan Space System.
  * Thieler and Hammar-Klose (1999) Thieler, E.R., Hammar-Klose, E.S., 1999.  National assessment of coastal vulnerability to sea-level rise: preliminary results for the US Atlantic coast.  Technical Report. US Geological Survey.  doi:https://doi.org/10.3133/ofr99593.
  * Trenberth (1997) Trenberth, K.E., 1997.  The definition of el nino.  Bulletin of the American Meteorological Society 78, 2771–2778.  doi:https://doi.org/10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2.
  * Vaughan et al. (2019) Vaughan, G.O., Al-Mansoori, N., Burt, J.A., 2019.  The arabian gulf, in: World seas: An environmental evaluation. Elsevier, pp. 1–23.
  * Vishwanathan and Murty (2002) Vishwanathan, S., Murty, M.N., 2002.  Ssvm: a simple svm algorithm, in: Proceedings of the 2002 International Joint Conference on Neural Networks, IEEE. pp. 2393–2398.  doi:https://doi.org/10.1109/IJCNN.2002.1007516.
  * Wang et al. (2019) Wang, F., Li, J., Shi, P., Shang, Z., Li, Y., Wang, H., 2019.  The impact of sea-level rise on the coast of tianjin-hebei, china.  China Geology 2, 26–39.  doi:https://doi.org/10.31035/cg2018061.
  * Wang et al. (2022) Wang, F., Shen, Y., Chen, Q., Geng, J., 2022.  Revisiting sea-level budget by considering all potential impact factors for global mean sea-level change estimation.  Scientific Reports 12, 10251.  doi:https://doi.org/10.1038/s41598-022-14173-2.
  * Warrick et al. (2005) Warrick, R.A., Ye, W., Kouwenhoven, P., Hay, J., Cheatham, C., 2005.  New developments of the simclim model for simulating adaptation to risks arising from climate variability and change, in: International Congress on Modelling and Simulation, Modelling and Simulation Society of Australia and New Zealand. pp. 170–176.  URL: https://www.mssanz.org.au/modsim05/papers/warrick.pdf.
  * Weitkamp (2005) Weitkamp, C., 2005.  Introduction to Lidar. volume 102.  Springer Science & Business, New York, NY.  doi:https://doi.org/10.1007/0-387-25101-4_1.
  * Winters et al. (2020) Winters, M.A., Leslie, B., Sloane, E.B., Gallien, T.W., 2020.  Observations and preliminary vulnerability assessment of a hybrid dune-based living shoreline.  Journal of Marine Science and Engineering 8.  doi:https://doi.org/10.3390/jmse8110920.
  * Woodworth et al. (2019) Woodworth, P.L., Melet, A., Marcos, M., Ray, R.D., Woppelmann, G., Sasaki, Y.N., Cirano, M., Hibbert, A., Huthnance, J.M., Monserrat, S., 2019.  Forcing factors affecting sea level changes at the coast.  Surveys in Geophysics 40, 1351–1397.  doi:https://doi.org/10.1007/s10712-019-09531-1.
  * Wright et al. (2020) Wright, L.D., Caruson, K., D Elia, C., Draayer, J., Nichols, R., Weiss, R., Zarillo, G., 2020.  Assessing and planning for the impacts of storms, flooding and sea level rise on vulnerable gulf of mexico coastal communities: A white paper, in: Global Oceans 2020: Singapore–US Gulf Coast, IEEE. pp. 1–6.  doi:https://doi.org/10.1109/IEEECONF38699.2020.9389265.
  * Wu et al. (2021) Wu, W., Yang, Z., Zhang, X., Zhou, Y., Tian, B., Tang, Q., 2021.  Integrated modeling analysis of estuarine responses to extreme hydrological events and sea-level rise.  Estuarine, Coastal and Shelf Science 261, 107555.  doi:https://doi.org/10.1016/j.ecss.2021.107555.
  * Yesudian and Dawson (2021) Yesudian, A.N., Dawson, R.J., 2021.  Global analysis of sea level rise risk to airports.  Climate Risk Management 31, 100266.  doi:https://doi.org/10.1016/j.crm.2020.100266.
  * Zanchettin et al. (2021) Zanchettin, D., Bruni, S., Raicich, F., Lionello, P., Adloff, F., Androsov, A., Antonioli, F., Artale, V., Carminati, E., Ferrarin, C., 2021.  Sea-level rise in venice: historic and future trends.  Natural Hazards and Earth System Sciences 21, 2643–2678.  doi:https://doi.org/10.5194/nhess-21-2643-2021.
  * Zou et al. (2021) Zou, F., Tenzer, R., Fok, H.S., Meng, G., Zhao, Q., 2021.  The sea-level changes in hong kong from tide-gauge records and remote sensing observations over the last seven decades.  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, 6777–6791.  doi:10.1109/JSTARS.2021.3087263.
  * Zuo et al. (2021) Zuo, X., Su, F., Yu, K., Wang, Y., Wang, Q., Wu, H., 2021.  Spatially modeling the synergistic impacts of global warming and sea-level rise on coral reefs in the south china sea.  Remote Sensing 13, 2626.  doi:https://doi.org/10.3390/rs13132626.