# Oil spill risk analysis for the NEOM shoreline

H.V.R. Mittal  Computer, Electrical and Mathematical Sciences and Engineering
Division, King Abdullah University of Science and Technology, (KAUST), Thuwal
23955-6900, Saudi Arabia  Department of Mathematics, Indian Institute of
Technology Ropar, Rupnagar, Punjab 140001, India  Mohamad Abed El Rahman
Hammoud  Physical Science and Engineering Division, King Abdullah University
of Science and Technology, (KAUST), Thuwal 23955-6900, Saudi Arabia  Ana K.
Carrasco  Computer, Electrical and Mathematical Sciences and Engineering
Division, King Abdullah University of Science and Technology, (KAUST), Thuwal
23955-6900, Saudi Arabia  Ibrahim Hoteit  Physical Science and Engineering
Division, King Abdullah University of Science and Technology, (KAUST), Thuwal
23955-6900, Saudi Arabia  Omar M. Knio  Computer, Electrical and Mathematical
Sciences and Engineering Division, King Abdullah University of Science and
Technology, (KAUST), Thuwal 23955-6900, Saudi Arabia

###### Abstract

A risk analysis is conducted considering an array of release sources located
around the NEOM shoreline. The sources are selected close to the coast and in
neighboring regions of high marine traffic. The evolution of oil spills
released by these sources is simulated using the MOHID model, driven by
validated, high-resolution met-ocean fields of the Red Sea. For each source,
simulations are conducted over a 4-week period, starting from first, tenth and
twentieth days of each month, covering five consecutive years. A total of 48
simulations are thus conducted for each source location, adequately reflecting
the variability of met-ocean conditions in the region. The risk associated
with each source is described in terms of amount of oil beached, and by the
elapsed time required for the spilled oil to reach the NEOM coast, extending
from the Gulf of Aqaba in the North to Duba in the South. To further
characterize the impact of individual sources, a finer analysis is performed
by segmenting the NEOM shoreline, based on important coastal development and
installation sites. For each subregion, source and release event considered, a
histogram of the amount of volume beached is generated, also classifying
individual events in terms of the corresponding arrival times. In addition,
for each subregion considered, an inverse analysis is conducted to identify
regions of dependence of the cumulative risk, estimated using the collection
of all sources and events considered. The transport of oil around the NEOM
shorelines is promoted by chaotic circulations and northwest winds in summer,
and a dominant cyclonic eddy in winter. Hence, spills originating from release
sources located close to the NEOM shorelines are characterized by large
monthly variations in arrival times, ranging from less than a week to more
than two weeks. Similarly, large variations in the volume fraction of beached
oil, ranging from less then 50% to more than 80% are reported. The results of
this study provide key information regarding the location of dominant oil
spill risk sources, the severity of the potential release events, as well as
the time frames within which mitigation actions may need to deployed.

###### keywords:

The Red Sea, NEOM, Oil Spill, Risk Analysis, MOHID

## Introduction

NEOM is a smart city being developed in the Tabuk province[1], Kingdom of
Saudi Arabia. It is situated in the north western part of the Kingdom with
miles of Red Sea coastlines. At its northernmost point, it is just 50
kilometers from the Jordanian port of Aqaba. NEOM development plans include
establishing modern manufacturing facilities, industrial research and
development, in addition to a hydrogen plant, a desalination plant and an
international airport (see Fig. 1). Tourism facilities are also being
developed along its coastal environment hosting a diverse marine wildlife and
coral reserves[1].

With an estimated 6.2 million barrels per day of crude oil and refined
petroleum products transported through its main shipping lanes in 2018[2], the
Red Sea is one of the most active waterways in the world[3]. This poses a risk
of accidental oil spills that may contribute to marine pollution, disrupting
desalination operations, and consequently causing severe economic losses and
irreversible damages to the environment [4, 5, 3, 6]. Therefore a
comprehensive analysis of risk from accidental oil spill releases on coastal
Red Sea regions is of paramount importance, particularly to minimize potential
impact to both the environment and industrial activities, and to plan
emergency response and mitigation efforts in case of an accident.

Several studies assessed the risk of oil spill accidents for different regions
around the world. These encompassed the Mediterranean sea[7, 8, 9, 10, 11],
the southern Adriatic and the northern Ionian (SANI) sea[12], Canadian
waters[13], Caribbean sea[14], Sicily coasts [15] and Bay of Bengal[16]. A few
studies have investigated the risk of oil spills on specific regions of the
Red Sea, namely pertinent to the Egyptian coastlines [17], the Bashayer
shorelines [18] and the Saudi Arabian-Yemeni coastlines [6]. Periáñez[19]
presented a Lagrangian model for the whole Red Sea. Mittal et al.[3] provided
a broad assessment of oil spill hazards for the whole Red Sea, stemming from
its main shipping lane along the longitudinal axis. Pertinent to the risk
analysis of oil spills for the NEOM shoreline, a study is still lacking, where
existing studies in the literature that focus on NEOM encompass atmospheric
conditions and air quality assessment[20], geological assessment [21, 22] and
wind energy assessment [23] only.

This study is part of an effort aimed at developing a fundamental
understanding of the risk associated by possible oil release sources on the
NEOM coastline, and consequently establishing a knowledge base that can assist
in the design of efficient strategies safeguard its coastal environment from
accidental oil spills. Specifically, a hazard analysis is conducted
considering an array of 37 potential release sources located around the NEOM
coastline in regions of high marine traffic (see Fig. 1). The risk associated
with each source is described by the amount of oil beached following the
initial release, and by the elapsed time required for the spilled oil to reach
the NEOM coast. The evolution of the oil spill is simulated using the MOHID
oil spill model [24, 25, 26, 27]. The model enables realistic, three-
dimensional simulations of oil trajectories, accounting for weathering
phenomena such as evaporation, dispersion, sedimentation, dissolution, and
emulsification. Extensively-validated, high-resolution met-ocean fields[28] of
the Red Sea are used to drive the oil spill model. For each release source,
simulations are conducted over a 28-day period, starting from the first, tenth
and twentieth days of each month, covering five consecutive years ranging from
2013 to 2017. A total of 180 simulations are thus conducted for each source,
adequately reflecting the variability of met-ocean conditions in the region.
In addition to characterizing the impact of individual sources, the simulation
results are analyzed by segmenting the NEOM shoreline, extending from the Gulf
of Aqaba in the North to Duba in the South, based on important coastal
developments and installations. For each subregion, an inverse analysis is
finally conducted to identify regions of dependence of the cumulative risk
estimated using the collection of sources considered.

## Methods and Data

### Red Sea Met-Ocean Reanalysis

Met-ocean data are extracted from an extensively-validated reanalysis of the
circulation in the Red Sea [28]. The simulated fields have been shown to
suitably describe the general oceanic and atmospheric circulations of the Red
Sea at the highest available resolution [28, 29, 30, 31]. The zonal and
meridional winds were fetched from a 5 km regional atmospheric reanalysis
generated using the Weather Research Forecasting (WRF) model assimilating all
available regional observations [30, 31]. WRF initial and boundary conditions
were acquired from the European Centre for Medium-Range Weather Forecasts
(ECMWF) reanalysis Interim data [32] (ERA-I). The wave conditions [33] in the
Red Sea were reconstructed using the WAVEWATCH III (WWIII) model forced with
the aforementioned high-resolution WRF reanalysis winds [34] on a uniform grid
of 1 km resolution.

The MIT general circulation model (MITgcm [35]) was implemented to simulate
the 3D ocean currents on a grid with 1-km resolution in horizontal planes and
$50$ vertical layers. The model was forced using the aforementioned high-
resolution WRF reanalysis fields and the Copernicus Marine Service Environment
Monitoring Service (CMEMS) global ocean reanalysis fields [36] across the
open-boundary in the Gulf of Aden at a 6 hourly and 24 hourly temporal
frequency, respectively. The resulting MITgcm outputs for the Red Sea have
been extensively employed to analyze the general and over-turning circulations
[37, 38], internal/baroclinic tides [39], mesoscale eddies characteristics
[40], deep-water formation events [41], temperature and salinity budgets [42]
as well as the chlorophyll variability [43]. We refer readers to [28] for a
more detailed description of the met-ocean conditions.

### Northern Red Sea Circulation

Mesoscale eddies [44, 45] play a dominant role in pollutant transport in the
northern Red Sea region. A typical cyclonic eddy dominates the circulation
during the winter season, and is characterized by a rotational velocity that
are generally larger than that of the background flow [3]. These eddies tend
to become more energetic during winter months following the development of
intense baroclinic instabilities [46, 44], and they represent the dominant
structures except for some strong semi-permanent wind-driven gyres that occur
in summer [47].

The high mountain ranges on both sides of the Red Sea forces the wind to blow
along its axis [48]. During summer seasons, from April till October, a
northwest (NW) wind blows along the whole length of The Red sea, with speeds
close to 10 ms-1, and frequently exceeding 15 ms-1 [34]. During winter, the
same northerly wind dominates over the northern part of the basin. The
narrower valleys along the eastern coasts of the Red Sea also creates westward
blowing jets in the northern part and generally lasts for 2-3 days with a
maximum speed up to 15 ms-1. The wave variability in the Red Sea is naturally
associated with the dominant regional wind regimes [48]. Despite the moderate
winds, the prolonged duration and long fetch along the whole basin may
generate waves as high as 3.5 m. During the summer months, the northwesterly
winds prevailing over the whole Red Sea generate mean wave heights of 1 m-1.5
m in the north [48, 49], throughout the year.

### Oil Spill Model

The MOHID oil spill model was adopted to simulate the instantaneous release of
oil and its evolution from fixed sources in the northern Red Sea. It relies on
a Lagrangian formalism that considers oil as a collection of Lagrangian
particles and associates to each particle oil properties and a location [50,
51]. The Lagrangian particles are transported using the met-ocean conditions,
and their properties are updated by solving empirical models describing
physio-chemical transformations of oil. Typically, these weathering processes
result in changes in oil’s physical properties and also impact the oil slick’s
geometry. In the present study, dissolution and sedimentation processes were
not considered, thus eliminating their effect on the oil mass balance.
However, evaporation, dispersion and emulsification were accounted for.
Specifically, evaporation processes are described by the algorithms of Stiver
and Mackay [52], whereas dispersion and emulsification processes are
represented using the algorithms by Mackay et al. [53]. Finally, the influence
of surface winds on the motion and deformation of the oil slick was
incorporated using a wind coefficient of $3\%$ [54].

### Experimental Setup

As briefly discussed below, the present study adapts the setup presented in
[3, 55] to the region surrounding NEOM. The computational domain covers the
northern Red Sea region, extending across the longitudes $32^{\circ}$ to
$37^{\circ}$ and latitudes $25^{\circ}$ to $30^{\circ}$ and up to a depth of
approximately 2746 m . The domain is discretized using a computational mesh
that is uniform in horizontal planes and non-uniform in the vertical
direction. It uses 500 equally-spaced nodes along the longitudinal axis, 500
equally-spaced nodes along the latitudinal axis, and 50 layers in the vertical
direction. The horizontal grid resolution is approximately 1 km.

From the met-ocean fields outlined above, the 3D ocean currents, surface
winds, wave height and wave period from the years 2013 till 2017 were
extracted and used an inputs to drive MOHID. The Lagrangian particle transport
model and weathering processes were solved using time steps of size 3600 s and
60 s, respectively.

### Risk Quantification

The risks of individual oil spill sources are quantified in terms the arrival
times of oil particles, and the volume fractions of oil beached on the NEOM
shorelines. The arrival times represent the minimum traveling time of oil
particles from each release source to the NEOM shorelines. For each source,
the volume fractions reflect the ratio of oil volume beached to the volume
initially released. The arrival times are divided into four classes, namely
$<7$ days, 7-14 days, 14-30 days, and $>30$ days (as surrogate for no arrival
during the simulation period). Similarly, the volume beached are divided into
four classes, namely $>50\%$ of the initial release, $25-50\%$, $<25\%$, and
$0\%$ (when no oil is beached). The results are illustrated using pie charts
that depict the frequencies of the classes considered. When generated for
individual months of the year, the charts represent the outcome of fifteen
experiments, as three simulations per month are performed for the five
consecutive years investigated.

A finer analysis is also conducted where, instead of considering the entire
NEOM coastline, smaller segments (approximately 25-km wide) are considered
around specific sites, namely The Line, Duba, Sharma, Gayal and Magna. For
each site, a histogram of the volume fraction is generated showing, for each
source and release event considered, the amount of volume beached classified
(using colors) in terms of the corresponding arrival time class. The
histograms provide key information regarding the severity of the potential
release event, and the time frame within which mitigation actions need to be
deployed to minimize the impact on coastal areas.

Finally, an aggregate probability of volume beached along a given shoreline
($p_{i}$) is computed as:

$p_{i}=\frac{\sum_{j=1}^{15}\mathcal{V}_{i,j}}{\sum_{k=1}^{37}\sum_{j=1}^{15}\mathcal{V}_{k,j}},$
(1)

where, $\mathcal{V}_{i,j}$ is the fraction of volume beached from release
location $i$ for event $j$, such that the event $j$ is an enumeration on the
release times. The aggregate probability of volume beached measures the
contribution of a given release source with respect to all the release
sources. This metric allows contrasting sources by ranking release source
based on their likely impact on the NEOM shoreline.

## Results and Discussion

### Risk analysis for the NEOM shoreline

Figure 2 and Supplementary Figures S1–S2 illustrate pie charts representing
the impact of fifteen release events occurring during the months from January
to December. The pie charts depict, for each release source, the travel time
needed by the oil particles to reach the NEOM shoreline as well as the volume
fraction of oil beached at the end of the simulation period. Figure 3 and
Supplementary Figure S3 depict the region of the NEOM shoreline affected by
beached oil particles, 7, 14 and 21 days following the release. Particles
originating from all release sources are used to generate these contours, thus
illustrating the aggregate risk. Release events originating during the months
of January, June, and October are used for this purpose.

Figure 2 and Supplementary Figure S1 indicate that spills originating from
sources $S_{35}-S_{37}$, which are located in the narrow Gulf of Aqaba and
thus close to the shorelines, are characterized by short arrival times. Within
one week from the onset of the spill, entire segments of NEOM shoreline
adjoining the Gulf of Aqaba are generally impacted; this occurs for all
scenarios except for a few releases occurring during the summer months. In the
summer months, the prevailing southwards currents in the Gulf of Aqaba tend to
push the oil slicks towards the Tiran and Sanfir islands. Therefore, some
segments of shorelines, located north of Magna city, may be shielded. Within
one week from the time of the spill, over 50% of the volume of oil released by
sources ($S_{35}-S_{37}$) may generally beach on the NEOM shore. This occurs
over the whole year except for the month of June. In June, the volume fraction
of oil released from source $S_{35}$ that beaches on the NEOM shore is less
than $25\%$ by the end of the first week, but may rise to around $50\%$ by the
end of the third week following the onset of the spill.

The arrival times of oil particles originating from most of the sources in
$S_{4}-S_{8}$ are less than one week during the whole year except during the
months of June (except $S_{7}$), September and October. The volume fractions
of oil beached originating from sources $S_{4}-S_{8}$ are less than $25\%$ by
the end of the first week, but may rise to greater than $50\%$ within two
weeks after the onset of the spill, during Jan-May, July and August. These
volume fractions are seen to exceed 50% by the end of the first week of the
onset during the months of November and December. This transport of spilled
oil towards the NEOM shorelines is promoted by a cyclonic eddy that dominates
the circulation in the Northern Red Sea region during the winter seasons.[3]

For the majority of release sources $S_{19}-S_{29}$, located in the open
waters and close to the Egyptian coast, the arrival times fall in the interval
of two to three weeks from the onset, for the months of November-March and
July. By the end of third week after the onset of the spill, the volume
fractions of oil originating from these sources remain below $25\%$. During
the remaining months, only a few of the sources $S_{19}-S_{29}$ could impact
the NEOM shorelines. Furthermore, the volume fraction of oil beached is less
than $25\%$, with relatively longer arrival times of around four weeks or no
beaching in some scenarios.

For sources $S_{32}-S_{34}$, which are located in the Gulf of Suez, a
measurable impact on the NEOM shoreline is only observed during the months of
January-May and July. Beaching of oil originating from $S_{32}$ is recorded
after week one during February, within one-two weeks in March and in May, two-
three weeks in January and July. Oil released from $S_{33}$ impacts the NEOM
shorelines within two-four weeks in May and from January-March. For $S_{33}$,
the arrival times fall within two-three weeks in January and three-four weeks
in April and May. The volume fraction of oil released by sources
$S_{32}-S_{34}$ and beached on the NEOM shore remains less than 25% by the end
of the fourth week, following the onset of the spill.

Figure 3 shows that the NEOM shoreline extending from The Line in the north to
Duba in the South is impacted in its entirety during January to May, but
during June to December some segments are not significantly impacted.
Specifically, by the end of the third week after the onset of the spill,
beaching on the shoreline between Sharma and The Line is not predicted during
June and from September to October. Additionally, beaching on the shoreline
between The Port of NEOM and The Line is not observed from May to September.
The energetic meso- and submeso-scale circulations and northwesterly winds in
the northern Red Sea region tend to split the oil slicks into different
fragments.[3] These fragments are then transported in the opposite directions,
towards both the Egyptian and Saudi Arabian shorelines, thereby sparing some
segments between The Line and Sharma from beached oil during the months from
June-December.

Figure 4 and Supplementary Figure 4 isolate the contributions of release
sources $S_{4}-S_{8}$ which lie inside the NEOM boundary and are closest to
its coastline. For these sources, beaching on the shorelines adjoining the
Gulf of Aqaba is not observed in June and from August to October. For the
remaining months, a measurable impact is observed on the shorelines adjoining
the Gulf of Aqaba, from oil particles originating from $S_{4}$ (January-March
and May), $S_{5}-S_{6}$ (February), $S_{7}$ (February-May, July and October-
December) and $S_{8}$ (February, March and May). A substantial impact on the
NEOM shoreline extending from The Line to Sharma is observed from the oil
particles originating from $S_{4}$ (November), $S_{5}$ (October-November) and
$S_{7}$ (October and December). Additionally, beaching of oil on the segment
extending from the airport to Duba is not observed for $S_{8}$ during
(January-May, November and December) and for $S_{5}$ during (January-August).
Overall, the results indicate that individual sources near the coastal may
have severe impacts away from their location, as measured by the volume
fraction of oil beached, and their impact may strongly depend on the seasonal
variations of meto-cean conditions.

### Risk analysis for specific sites

The risk associated with the individual release sources is now analyzed for
specific sites along the Neom coast, namely The Line, Duba, Sharma, Gayal and
Magna. Figures 5, 7 and Supplementary Figures S5–S9 plot the histograms of
volume fractions for each source and release event considered, showing the
amount of volume beached and the corresponding arrival time class (classified
using colors), during the months from January to December. Figures 6, 8 and
Supplementary Figures S10–S12 depict the (inverse) risk probabilities for each
of the specific sites considered. These probabilities characterize the region
of dependence of the spill risk, as estimated using Eq. 1.

### The Line

Figure 5 and Supplementary Figure S5 plot histograms of the volume fractions
beached at the shorelines of The Line, where predictions from all the release
sources and events are classified in terms of the corresponding arrival times.
The histograms present a uni-modal distribution of the volume fractions with
tails varying from approximately 10% to 80%. The spills originating primarily
from sources $S_{35}-S_{37}$ are characterized by the highest severity (low
arrival times and high volume fractions) amongst other sources. During the
months of April and from September-December, the volume fraction of oil
released from source $S_{35}$ and beached around The Line may rise to 85% by
the end of the first week. The volume fraction of oil released from source
$S_{36}$ and beached around The Line is greater than 60% over the whole year
except during April, June and October (greater than 90%). The volume fraction
of oil released from source $S_{37}$ is greater than 50% throughout the year,
except during the months of June and August (around 20%), by the end of first
week. The prevailing northwards currents[3] towards The Gulf of Aqaba tend to
quickly push oil released from $S_{8}$ towards The Line; in March, the volume
fraction may rise to more than 90%. However, the volume fractions remain less
than 50% for the whole year except for March, June and September. The segments
around The Line may be weakly affected by oil originating from $S_{8}$ in June
and September. Additional events having early arrival times are associated
with $S_{18}$ and $S_{24}$, which are located close to the northern tip of The
Red Sea between The Gulf of Aqaba and The Gulf of Suez (near Sharm El-Sheikh).
Here, the transport of spilled oil towards The Line is promoted by the
prevailing coastal currents, which dominate the circulation during the months
from December to May. The arrival times fall within one-two weeks during these
months. Specifically, the arrival time is less than one week during December,
February, and April for $S_{18}$, and during January and April for $S_{24}$.
Events with short arrival time (less than one week) are also associated with
$S_{5}$ (in March and October) with volume fractions of around 40%. However,
very few sources among $S_{9}-S_{34}$ are characterized by moderate arrival
times (two to three weeks), and generally have low severity in terms of amount
of beached oil (volume fractions less than 10%).

Figure 6 depicts the seasonal distribution of risk probabilities, estimated
using Eq. 1 for oil beached around The Line. Sources $S_{35}-S_{37}$, located
in The Gulf of Aqaba, are responsible for the largest risk. The risk
associated with $S_{36}$ is the highest amongst $S_{35}-S_{37}$ in spring,
summer and autumn seasons, whereas the risk associated with $S_{35}$ is
highest in winter. The risk associated with the remaining sources is
appreciably smaller than that observed for $S_{35}-S_{37}$. In addition, the
associated probabilities are very small, except possibly for sources
$S_{5}$–$S_{8}$ for which appreciable values may occur. Overall, the results
of Figures 5 and 6 indicate that for The Line, the risk is primarily dominated
by sources located in the Gulf of Aqaba, followed to a lower extent by sources
located close to its shoreline.

Spills originating from sources located in the Gulf of Aqaba generally lead to
severe events, with a large fraction of the oil released beaching within a
short period (< 7 days) from the time of the release. Consistent with the
histograms in Figure 5, sources located in the Red Sea and close to the NEOM
shoreline may result in severe impact on The Line, but these events have low
probability of occurrence, leading to small risk values reported in Figure 6.

### Duba

Figure 7 and Supplementary Figure S6 plot histograms of the volume fractions
beached at the shorelines of Duba. In contrast to those corresponding to The
Line, the results indicate that the shoreline surrounding Duba is vulnerable
to sources located in the entire region facing its coast. This is reflected by
the fact that multiple events with severe impacts are observed for sources
$S_{4}-S_{21}$, which are located in the open waters facing the NEOM coast. As
expected, sources $S_{4}-S_{8}$, which lie closest to the NEOM coastline are
characterized by higher impacts and shorter arrival times than $S_{9}-S_{21}$.
Overall, sources $S_{4}-S_{21}$ lead to events of various severity, and the
histogram accordingly exhibits a large scatter over the corresponding segment.
The Duba region appears to be less susceptible to sources lying in the Gulf of
Suez, which are far away from the Duba region, and in the Gulf of Aqaba,
except for $S_{35}$ located at the tip of the Gulf which may result with low
probability in a large fraction of oil beached near Duba.

Figure 8 illustrates the seasonal distribution of the aggregate probability of
volume beached corresponding to oil spills that affect the Duba shoreline. As
opposed to the Line’s shoreline, which is primarily affected by the release
sources in The Gulf of Aqaba, sources $S_{4}-S_{12}$ and $S_{14}-S_{15}$ are
characterized by the highest aggregate probabilities of volume beached at the
Duba shoreline, throughout the year. The aggregate probability of $S_{4}$ is
the highest in autumn season. Few of the sources located in The Gulf of Aqaba
are characterized by insignificant probabilities (< 0.01) in the spring
($S_{36}$) and autumn ($S_{36}-S_{37}$) seasons. The majority of the sources
($S_{26}-S_{34}$) located farther from the Saudi coastline and closer to
Egyptian coast or in the Gulf of Suez are characterized by the lowest
probabilities throughout the year.

### Magna, Sharma and Gayal

For Magna, Sharma and Gayal, histograms of the volume fractions of oil beached
and of risk distributions are shown in Supplementary Figures S7–S8 (Magna),
S9–S10 (Sharma) and S11–S12 (Gayal). For the sake of brevity, the main takeway
findings are provided in this section.

The plots for Magna indicate similarities to those obtained for The Line,
where Magna’s shoreline is seen to be predominantly at risk from the release
sources in the Gulf of Aqaba. These sources tend to be associated with the
highest impact, with short arrival times and large volumes of oil beached.
Furthermore, the results corresponding to Sharma and Gayal exhibit key
similarities with those obtained for Duba. Specifically, the Sharma and Gayal
shorelines are primarily vulnerable to the release sources nearest to the
Saudi coast, with decreasing risk from the release sources located far from
the Saudi coastline. The Gayal shoreline is generally protected from oil
spills, which may be attributed to the nearby islands and the shape of its
bay. In contrast, Sharma’s coastline is more exposed to oil spills, where more
moderate and high severity events are reported from the release sources lying
in the first two rows facing the NEOM shoreline.

## Conclusion

We conducted a risk assessment associated with accidental oil spills from
fixed sources on the NEOM shoreline, focusing in particular on key sites and
installations. For each potential release site, oil spill simulations were
conducted over a 28-day period, starting from the first, tenth and twentieth
days of each month, over five consecutive years ranging from 2013 to 2017. The
simulations were carried out using the MOHID’s oil spill model, driven with
validated high-resolution met-ocean fields of the Red Sea. The risk associated
with each release event was characterized by the minimum travel time for an
oil particle to reach the coast, and by the percentage of the total volume of
oil released that was beached on the NEOM coast at the end of the simulation
period.

The results indicate that spills originating in the Gulf of Aqaba are
characterized by short arrival times and high volume fractions, making them
the most hazardous to the NEOM shoreline. This occurs throughout the year
except for the summer months, when the prevailing southwards currents in the
Gulf of Aqaba tend to push the oil slicks towards the Tiran and Sanfir
islands, which does not minimize their potential impact because these islands
are key sites for tourism. Release sources located in the open water closest
to the Saudi Arabian shoreline are generally associated with short arrival
times, except during the months of September and October. These release
sources impact NEOM’s islands and the region connecting Sharma to Duba
throughout the year. On the other hand, these release sources have weak impact
on the NEOM shoreline lying in the Gulf of Aqaba, between June and December.
Release sources located in the Gulf of Suez have a slight impact on the NEOM
shoreline during the months of January, Februrary and March. Finally, spills
originating from release sources located in the open waters close to the
Egyptian coast are characterised by moderate arrival times and low volume
fractions, throughout the year.

The shorelines of Magna and The Line are subject to a similar response to the
oil spill scenarios considered, where both were vulnerable to the release
sources located in the Gulf of Aqaba. Moreover, release events south of Tiran
and near Sanafir islands may have a significant impact on The Line’s shore,
particularly during winter and more so in spring. Duba, Sharma and Gayal’s
shorelines exhibit similar behavior in response to accidental oil spills from
the sources considered. Specifically, release sources lying closest to the
Saudi Arabian shoreline have the biggest impact on the shorelines of these
sites. The releases are characterized by short arrival times and large
fractions of volume beached. The adjacent release sources also exhibit a
considerable impact, that is weaker during the Autumn months. These release
events are typically associated with medium severity arrival times and
fractions of volume beached. Finally, Duba, Sharma and Gayal’s shorelines
appear to be at low risk from accidental oil spill scenarios originating from
release sources near the African shoreline during the summer and autumn
seasons.

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## Acknowledgements

The work was funded by the Office of Sponsored Research (OSR) at King Abdullah
University of Science and Technology (KAUST) under the Virtual Red Sea
Initiative (Award No. REP/1/3268-01-01), OSR-Competitive Research Grant (Award
No. OSR-CRG2018-3711), and the Saudi ARAMCO-KAUST Marine Environmental
Research Center (SAKMERC). The simulations were performed on the KAUST
supercomputing facility, SHAHEEN.

## Author contributions statement

H.V.R.M. and M. A. E. H. conceptualization, methodology, software, data
curation, formal analysis, writing original draft, A. K. C. conceptualization,
methodology; I.H. and O.K. conceptualization, funding acquisition, resources,
writing-review and editing.

## Competing interests

The authors declare that they have no known competing financial interests or
personal relationships that could have appeared to influence the work reported
in this paper.

## Data Availability

The datasets used and/or analyzed during the current study are available from
the corresponding author upon reasonable request.

Figure 1: (Left) General map of the northern Red Sea region illustrating key
installations along the NEOM coastlines as well as the source locations chosen
for spill simulations as well as the bathymetry of the northern Red Sea
colored by depth in meters, and (Right) Contours exhibiting the marine traffic
density, as the number of ships per 23 $km^{2}$ averaged over a year.[56]

Figure 2: Pie charts centered at each release source, representing the
corresponding travel time of oil particles to the Neom shoreline (top row) and
the volume fractions of beached oil particles (bottow row).

Figure 3: Regions of the Neom shoreline affected by beaching, for 7, 14 and 21
days after the onset of the spill. Particles originating from all release
sources are used to generate the contours. Plots are generated for release
events occurring during the January, June and October months, as indicated.

Figure 4: Regions of the Neom shoreline affected by beaching at the end of the
28-day simulation period. The contributions of selected sources are isolated
using different color scheme for the individual sources, as indicated. Plots
are generated for release events occurring during the January, June and
October months, as identified by the titles.

Figure 5: Histograms of the volume fractions beached at the shorelines of The
Line. Predictions from all release sources and events are classified (using
colors) in terms of the corresponding arrival times. Plots are generated for
release events occurring during the January, April, July and October months,
as indicated.

Figure 6: Risk probabilities for shorelines of The Line. The probabilities,
estimated using Eq. 1, characterize the region of dependence of the overall
risk. Plots are generated for release events occurring during the four
seasons, as indicated.

Figure 7: Histograms of the volume fractions beached at the shorelines of
Duba. Predictions from all release sources and events are classified (using
colors) in terms of the corresponding arrival times. Plots are generated for
release events occurring during the January, April, July and October months,
as indicated.

Figure 8: Risk probabilities for shorelines of Duba. The probabilities,
estimated using Eq. 1, characterize the region of dependence of the overall
risk. Plots are generated for release events occurring during the four
seasons, as indicated.