Journal of
Marine Science 
and Engineering
Article
Coastal Flooding Caused by Extreme Coastal Water Level at the
World Heritage Historic Keta City (Ghana, West Africa)
Emmanuel K. Brempong 1,2,3,* , Rafael Almar 2 , Donatus Bapentire Angnuureng 1 ,
Precious Agbeko Dzorgbe Mattah 1, Philip-Neri Jayson-Quashigah 4, Kwesi Twum Antwi-Agyakwa 1,2
and Blessing Charuka 1,2
1 Africa Centre of Excellence in Coastal Resilience (ACECoR), University of Cape Coast, Cape Coast 00223, Ghana;
donatus.angnuureng@ucc.edu.gh (D.B.A.); pmattah@ucc.edu.gh (P.A.D.M.);
kwesi.antwi-agyakwa@stu.ucc.edu.gh (K.T.A.-A.); blessing.charuka@stu.ucc.edu.gh (B.C.)
2 Laboratoire d’Etudes en Géophysique et Océanographie Spatiales (LEGOS),
Université de Toulouse/CNRS/CNES/IRD, 31400 Toulouse, France; rafael.almar@ird.fr
3 Department of Fisheries and Aquatic Sciences, School of Biological Sciences, University of Cape Coast,
Cape Coast 00233, Ghana
4 Institute for Environment and Sanitation Studies, University of Ghana, Legon Box LG 209, Ghana;
pnjquashigah@ug.edu.gh
* Correspondence: emmanuel.brempong@stu.ucc.edu.gh
Abstract: Like low-lying sandy coasts around the world, the Ghanaian coast is experiencing increas-
ingly frequent coastal flooding due to climate change, putting important socioeconomic infrastructure
and people at risk. Our study assesses the major factors contributing to extreme coastal water
levels (ECWLs) from 1994 to 2015. ECWLs are categorized into low, moderate, and severe levels
corresponding to the 30th, 60th, and 98th percentiles, respectively. Using these three levels over
the Pleiades satellite-derived digital elevation model topography, potential flood extent zones are
Citation: Brempong, E.K.; Almar, R.; mapped. ECWLs have the potential to flood more than 40% of the study area, including socioeconom-
Angnuureng, D.B.; Mattah, P.A.D.; ically important sites such as tourist beach resorts, Cape St. Paul lighthouse, and Fort Prinzenstein.
Jayson-Quashigah, P.-N.; In this study, all coastal flooding events recorded by the municipality of Keta fall within the 98th
Antwi-Agyakwa, K.T.; Charuka, B. percentile category. Our results show a gradual increase in the frequency of flooding over the years.
Coastal Flooding Caused by Extreme Flooding events are caused by a compound effect of the tide, sea level anomaly, waves, and atmo-
Coastal Water Level at the World spheric conditions. Finally, while wave run-up is the major contributor to coastal flooding, the tide is
Heritage Historic Keta City (Ghana, the one varying most, which facilitates a simple early warning system based on waves and tide but
West Africa). J. Mar. Sci. Eng. 2023, 11, adds uncertainty and complicates long-term predictability.
1144. https://doi.org/10.3390/
jmse11061144
Keywords: Gulf of Guinea; wave run-up; sea level rise; coastal flooding
Academic Editors: Yuji Sakuno,
Mitsuhiro Toratani, Hiroto Higa and
João Miguel Dias
1. Introduction
Received: 25 April 2023
Revised: 23 May 2023 Globally, coastal flooding affects coastal areas due to sea level rise (SLR). Coastal areas
Accepted: 25 May 2023 serve as homes for about 2.4 billion people (40% of the world’s population) [1], as well as
Published: 30 May 2023 provide high economic value to coastal countries [2]. Over the 21st century, projections
have shown that SLR would increase the rate of coastal flooding globally [3]. This would
possibly displace most people living in low-lying coastal zones and impact socioeconomic
and ecological systems of great importance, particularly in Africa [4,5]. Generally, the main
Copyright: © 2023 by the authors. cause of coastal flooding is high water levels due to several factors. Coastal flooding is
Licensee MDPI, Basel, Switzerland. instigated by an amalgamation of numerous factors from the ocean and atmosphere, such as
This article is an open access article mean sea level changes, tides, storm surges, waves, river discharge, and rainfall [6]. When
distributed under the terms and two or more of these factors occur simultaneously, the severity of flooding can worsen,
conditions of the Creative Commons
leading to an amplified risk of coastal flooding. Examples of compound flooding events
Attribution (CC BY) license (https://
include river discharge and surges [7], rainfall and surges [8] on the coast of the United
creativecommons.org/licenses/by/
States, and rainfall, surge, and waves [9,10]. If these events are statistically dependent,
4.0/).
J. Mar. Sci. Eng. 2023, 11, 1144. https://doi.org/10.3390/jmse11061144 https://www.mdpi.com/journal/jmse
J. Mar. Sci. Eng. 2023, 11, 1144 2 of 18
meaning they share a mutual driving force, the likelihood of them occurring together is
higher than expected when considering each variable separately. This increased likelihood
of compound flooding events can lead to a higher chance of coastal flooding [6,11].
All these together would eventually lead to overtopping and inundating coastal
defenses in low-lying areas, which potentially cause damage to life and property. Coastal
flooding continues to pose a huge threat to coastal inhabitants. It is known to be one
of the most dangerous and costly of natural disasters [12,13]. Therefore, it is essential
to create awareness of the need to plan, mitigate, and consider alternatives to reduce
the effects of coastal flooding. Understanding the occurrence of extreme coastal water
levels (ECWLs) [11,14] can help support decision making in coastal zone management.
This would help identify regions affected by a strong increase in flood risk and prioritize
mitigation and adaptation efforts [15]. During ECWLs, overtopping is the main cause of
coastal flooding, with much water surpassing the maximum coastal elevation (e.g., dunes,
dykes, cliffs) [16]. Despite this, when overtopping occurs, all areas with low elevation
do not submerge though this phenomenon mainly drives localized coastal flooding and
damage to infrastructures.
Furthermore, overtopping due to ECWLs is even more catastrophic when there is a fail-
ure or absence of coastal protection, such as groins and revetments [17]. According to [18],
the principal components of ECWLs are sea level anomaly (SLA), dynamic atmospheric
conditions (DACs), tide (T), and wave run-up (R). On the Jamestown Beach in Accra, [19]
noted that shoreline change responds in decreasing order to sea level variations (86%),
waves (9%), and tidal cycles (5%) on daily bases. Wind-induced setup has limited effect
on the shoreline, while the observed most important component of SLA at that coast was
the influence from the inverse barometer. SLA is due to the steric effect, ocean circulation,
and transfer of mass from the continents (ice sheets, glaciers, land water) to the ocean,
DACs due to atmospheric pressure and winds, astronomical tide (T), and wave effects here
referred to collectively as run-up (R), which includes a time-averaged component (setup)
and an oscillatory component (swash) [18]. Quantifying these local contributions during
flooding events is key, as their relative contribution varies spatiotemporally. One key
factor to consider when assessing flooding and overtopping is a suitable and higher DEM
resolution. A higher-resolution DEM tends to preserve the topographical terrain features
thus determining where floods are most likely to cause problems for people and property
and estimating how water interacts with the environment depending on high-quality DEM
data. The precision and spatial resolution of the DEM affect the accuracy of forecasts of
flood depths [20]. Numerous publications have extensively examined the justification for
utilizing coarse DEM resolutions. They came to a conclusion that, despite their lack of
precision, their model simulations offer a suitable trade-off between readily accessible (free)
data, minimal computational requirements, and an appropriate depiction of hydrological
processes and catchment responses. However, even while increased spatial resolution and
accuracy frequently lead to better outcomes, it is rarely available since these are commercial
and not freely available. Recently, the high-resolution DEMs from sources such as Pleiades
stereo imagery have played a significant role in coastal morphology changes, making them
a great tool to explore and use [21,22].
The eastern coast of Ghana is known to be the most vulnerable coast in Ghana. It
experiences coastal flooding not less than twice every year [23]. In addition, the frequency
and intensity of coastal flooding and erosion have increased. Over the past decade, the
Volta Delta has significantly experienced coastal flooding events and duration [23–25].
Some studies have attributed this to intensive rainfall, oceanographic conditions (waves,
sea level rise, and tides), and human activities (watershed management) [23].
On the other hand, the construction of the Akosombo dam on the Volta River has
been identified as a major cause of erosion and flooding problems in the Volta Delta
region [26–28]. In particular, the Keta District has been affected by coastal erosion and
flooding. For this reason, one of Ghana’s largest sea defenses was constructed from 2000 to
J. Mar. Sci. Eng. 2023, 11, 1144 3 of 18
2004 to control coastal erosion and flooding. Therefore, it is very important to understand
hydrodynamic factors dominating coastal flooding in Keta and Volta Delta at large.
Our study uses Pleiades stereo images to develop the digital elevation model (DEM)
and wave, tide, and sea level anomaly data from satellite and globally freely available
reanalysis. The main aim of this study is to assess the predominant factors of coastal
flooding in the Keta municipality. To achieve this, ECWLs were first categorized into three
main percentiles (30th, 60th, and 98th); secondly, the spatial extent of flooding at each
percentile was assessed; and finally, the dominant and most varying factors were assessed
in the 98th percentile scenario. The importance of this study is that it explores the most
dominant factors and thus provides reasons to consider these factors in implementing
coastal flooding decisions.
2. Materials and Methods
2.1. Study Area
Keta City is located in a large wetland-protected area of 1200 km2. Keta lies on the
eastern coast of Ghana and is located precisely at the extreme east of the Volta Estuary.
This study used Pleiades satellite imagery (Figure 1c) covering major towns such as Woe,
Tegbi, and Keta. The largest lagoon in Ghana, the Keta Lagoon complex, is located in this
municipality. The Keta Lagoon facilitates water transportation to surrounding communities
and has the potential for large-scale commercial aquaculture. Generally, the municipality
serves as a breeding ground for many sea turtles and a temporary passage point for
migratory birds. The town is separated by a narrow sand strip separating the Keta Lagoon
from the sea. Keta is in the Keta Basin, and its geology largely includes mud, wabbly sand,
and gravel [29]. Studies by [30] indicate the presence of numerous canyons (valleys) in
the deep waters, which also signify active erosion. In the past, rampant coastal flood and
erosion cases were recorded, precisely in 1907 when the first coastal erosion occurred, then
in 1924, 1949, 1986, 1996, and 1997 [31]. This has led to the construction of sea defense
structures along the Keta municipality stretch.
The climate conditions are dry equatorial, with a mean rainfall below 1000 mm in
May and July and sometimes late August and October. Generally, wind conditions are
weak, with speeds less than 2.6 m/s [32,33]. Wave conditions are generally less than 3 m
and a maximum period of 19.68 s in the direction south and southwest with an average
period of 10.91 s. The tide condition is micro-tidal and estimated to be 1 m on average.
The beaches are generally sandy with a median grain size of 0.6 mm, and most inhabitants
survive by fishing and farming [34]. The inhabitants of Keta District are involved in various
economic activities. These include agriculture, fisheries, salt harvesting, sand mining, and
tourism. The agricultural and fishing sectors are more dominant than the other sectors.
Fort Prizenstein and Woe Lighthouse are two major tourist sites in the Keta District. The
Keta District is very important to the Volta Delta; therefore, it is imperative to assess and
understand the factors that influence coastal flooding in this area and for the Volta Delta as
a whole.
2.2. Datasets
This section describes the data used for this study. This study uses satellite imagery
and hydrodynamic data for the Keta area.
2.2.1. Pleiades Satellite Imagery and Acquisition
Satellite data provide key missing information for coastal management [35,36], includ-
ing ocean drivers, land use, and vulnerability assessment [18,33].
It turns out that in West Africa, there are several gaps in coastal management. In
addition, there is limited use of high-resolution satellites, thereby preventing any efficient
mitigation measure on a broad scale.
The Pleiades satellites 1A and 1B were launched in 2011 and 2012, respectively, at an
altitude of 694 km and can obtain a burst of up to 12 images during a single pass [37]. The
J. Mar. Sci. Eng. 2023, 11, 1144 4 of 18
Pleiades imagery comes in single or (tri)stereo images with panchromatic and multi-spectral
images of respective ground pixel resolutions of 0.5 m and 2 m. Thus, the sensors of the
Pleiades satellites work in the near-infrared and visible spectrum. However, the Pleiades
imagery was first established in line with the French–Italian Optical and Radar Federated
Earth Observation program (ORFEO). In recent years, other partners in Europe and other
J. Mar. Sci. Eng. 2023, 11, x FOR PEER REVcIoEuWn tries have used it in their studies. The major features of the Pleiades collection are 4 of 18 
 explained in detail in the user guide of Pleiades imagery [38]. For this study, tri-stereo
imagery was acquired from the Centre National D’etudes Spatiales (CNES).
 
Figure 1. A map of the study area showing (A) an image of Ghana with all districts and the Volta
FiguDree l1ta. sAha mdeadpin oyfe tlhloew s; t(uBd) ayn aimreaag eshofotwheinVgol t(aAD) ealtna simhoawgine goafl lGthheadniast rwicittshw aitlhl tdhieswtraictetsr  baonddie sthe Volta 
Deltain sthhaedVeodlt ainD eyletall;oawnd; ((BC)) aann imimagaegeo foKf etthaeD Vistorlitcat  wDiethltaP lesihaodwesinmgu latil-ls pthecet rdalisstartiecltlsit ewimithag tehe water 
bodiseusp ienri mthpeo sVedolsthao wDienlgtath; eamndaj o(rCto) wans iamndagtoeu roisf tKsietetsai nDtihsetrKicetta wDiitshtr icPtloefiathdeeVs omltauDlteil-tsap, Gechtarnaal satellite 
imag(Ge usluf pofeGriuminpeoas, eWde ssthAofwrician)g.  the major towns and tourist sites in the Keta District of the Volta 
Delta, Ghana (Gulf of Guinea, West Africa). 
At Keta, such burst and tri-stereo images (Figure 2) were acquired on 20 October 2020
2.2. aDnadtacsoentsst ituted the data used in this study. The time difference between the individual
images is set for each acquisition to dT = 6 s. This gives a base-to-height (B-H) ratio
foTrhtihse stewcotiodnat adesestcsroibfeBs/ tHhe= d0.a1t2a. uGseende rfaollry ,thfoirs flsatut darye.a sT,haislo swtuBd/yH uvsaelsu esaptreolvliitdee simagery 
and bheyttderohdeiygnhat maciccu rdaactyao ffotrh tehsete Kreeotsac oapryea[3. 9]. The Pleiades images were acquired at an
approximately equal tidal elevation of +0.8 m. These images were finally downloaded
2.2.1f.r oPmleCiaednetrse SNaatteiollnitael  DIm’etaugdeersyS paantdia lAesc(qCuNisEiSt)iowne bsite. Unfortunately, these data are not
freely available but were acquired with the help of LEGOS.
Satellite data provide key missing information for coastal management [35,36], in-
cluding ocean drivers, land use, and vulnerability assessment [18,33]. 
It turns out that in West Africa, there are several gaps in coastal management. In ad-
dition, there is limited use of high-resolution satellites, thereby preventing any efficient 
mitigation measure on a broad scale. 
The Pleiades satellites 1A and 1B were launched in 2011 and 2012, respectively, at an 
altitude of 694 km and can obtain a burst of up to 12 images during a single pass [37]. The 
Pleiades imagery comes in single or (tri)stereo images with panchromatic and multi-spec-
tral images of respective ground pixel resolutions of 0.5 m and 2 m. Thus, the sensors of 
the Pleiades satellites work in the near-infrared and visible spectrum. However, the Plei-
ades imagery was first established in line with the French–Italian Optical and Radar Fed-
erated Earth Observation program (ORFEO). In recent years, other partners in Europe and 
other countries have used it in their studies. The major features of the Pleiades collection 
are explained in detail in the user guide of Pleiades imagery [38]. For this study, tri-stereo 
imagery was acquired from the Centre National D’etudes Spatiales (CNES). 
At Keta, such burst and tri-stereo images (Figure 2) were acquired on 20 October 2020 
and constituted the data used in this study. The time difference between the individual 
images is set for each acquisition to dT = 6 s. This gives a base-to-height (B-H) ratio for the 
two data sets of B/H = 0.12. Generally, for flat areas, a low B/H value provides better height 
 
J. Mar. Sci. Eng. 2023, 11, x FOR PEER REVIEW 5 of 18 
 
accuracy of the stereoscopy [39]. The Pleiades images were acquired at an approximately 
equal tidal elevation of +0.8 m. These images were finally downloaded from Centre Na-
J. Mar. Sci. Eng. 2023, 11, 1144 tional D’etudes Spatiales (CNES) website. Unfortunately, these data are not freely 5aovfa1i8la-
ble but were acquired with the help of LEGOS. 
 
FFiigguurree 22.. AAnn iimaaggee ooff tthheet trrii--sstteerreeooi mimaaggeess( P(PHHRR1A1A) )a acqcquuirierdedo non2 020O Octcotboebre2r0 22002f0o frotrh tehKe eKtaetsat ustduydy 
aarreeaa ((mmaaddeeu ussiinnggG GooooggleleE Eaartrhth).). 
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Too ccoomppuutteeE ECCWWLLss, ,i titi sisr erqequuirierdedt otou sueseh yhdyrdordoydnyanmamici,cm, meteetoeroorloolgoigcaicl,ala,n adndti dtiadlal 
ppaarraameetteerrss [[1111]].. FFoorr tthhiiss ssttuuddyy, ,t htheep paararammeetetersrsu usesdedi nicnlculudde;es; esaeale lveevleal naonmomalayly(S (LSAL)A, ), 
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rreeffeerreenncceedd ttoot thheeW WGGSS8844d daatutumma anndde xextrtarcatcetdeda taat ag rgirdidp opionitnot folfa ltaittuitduedoef o6f. 064.07437636
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gglloobbaall ttidideeF FEESS( fi(nfiinteitee leemlemenetnsto lsuotliuotnio) n20) 1240m14o mdeold[4e0l ][.4T0h].e sTehdeaseta dwaetare wperroed upcreoddubycetdh eby 
Lthaeb oLraabtoorryatoofrGy eoofp GhyesoipcahlyasnicdaOl acneadn OogcreaapnhoigcrSappahtiiacl SSptuadtiiaels SotfuTdoiuelso oufs eT(oLuEloGuOsSe) .(LAEVGISOOS). 
(AAVrcIhSOiv in(Ag,rcVhailvidinatgio, nVaalniddaItnitoenr parnetda tIinotneorpf rSeattaetliloitne Oofc eSaantoegllritaep hOicc)eadnatoag,rwapithhitch) edsautpap, owritth 
othf eth seuCpepntrewinds comorpt oo
Nf tahtieo Cnael dnent (DAntCr
’Ee tNudaetisoSnpaal tdia’Eletsu(dCeNs ESpS), distributes the atmosphers) generated by the MOaGtia2lDesm (CoNdeElSf)r,o dmisLtrEibGuOteSs 
itchper aetsmsuore andof the Cosllpehcteeric 
Lporecassliusraeti aonndS awteinllditse sco(CmLpSo)nSepnatc (eDOAcCeasn) oggernaeprhatyedD ibvyis tihoen M[41O]G. M2Dor me iondfoelr mfroatmio nLEcGanObSe of 
ftohuen Cdoalltehctttep L:/o/cwalwisawt.iaovni sSoa.ateltlilmiteest r(yC.fLr/S)( aScpcaecsese Odcoenan2o0gMraaprchhy 2D02iv2i)s.ion [41]. More infor-
matiAonlt icmane tbreic f-oduernidv eadt hSttLpA:,//iwncwluwd.ianvgisgol.oabltaiml meterayn.frl/e(vaeccl ersisseed(G oMn 2S0L RM),arwchas 2e0x2t2r)a. cted
from AAVltiImSOet’srinc-edaererisvtegdr idSeLdAd, aintaclpuodiinntgo fgtlhoebaall tmimeeatnry ldevaetal priosien t(G[4M2]S. LR), was extracted 
fromA AllVwISaOv’es rnuena-ruespt dgraitdaewd edraetao bptoaiinnte dof ftrhoem alEtiRmAe-tirnyt edraimta preoainnat l[y4s2i]s. (global climate
and wAella wthaevred rautna-auvpa dilaatbal ewferroem ob1t9a7in9eodn fwroamrd E) RatAa-in0.t5e◦ri×m r0e.5a◦narelyssoilsu (tgiolonb. aTl chleimseadtea taand 
weeraethperor dduactead avbayiltahbelEe ufrroompe a1n97C9e onntewrafrodr)M ate dai 0u.m5°- R× a0n.5g°e rWeseoaltuhteior nF.o Trehceasseti dngat(aE wCMerWe pFr)o-
mduocdeedl, bwyh tihceh Euusreodpweaanv eCdenattaera fto6r- hMoeudriluymre-sRoaluntgieo nW. eTahtehecra lFcourleactiaosntionfgw (EaCveMrWunF-)u mp o(Rd)el, 
wahsicphe rufsoerdm wedavues idnagtath aet d6i-shsoipuartliyv reebsoealuchtioenq√.u Tahtieo ncaplcrouplaotsioedn boyf w[4a3v]e( sreuen[-1u1p]) (.R) was per-formed using the dissipative beach equation √proposed by [43] (see [11]). R = 0.043 HsLo (1)R = 0.043 HsLo (1)
Hs is the offshore significant wave height, and Lo is the wavelength. All the parameters
mentiHosn eisd tahbeo ovffesahroerree ssaigmnpifilecdanhto wuralyvef rhoemig1h9t9, 4antod 2L0o1 5is. tAhlel hwyadvreoldeynngathm. iAc ldl atthaeu psaerdaimne-
ttheirss smtuednytiwonereedf raebeolyvea vaariela rbelseaamndpleexdt rhaoctuerdlyfo frrothme K19e9ta4 atroe a20w1i5th. iAnlilt shcyodnrfoodrmyninamg gicr idsa.ta 
 
J. Mar. Sci. Eng. 2023, 11, x FOR PEER REVIEW 6 of 18 
 
used in this study were freely available and extracted for the Keta area within its conform-
ing grids. 
J. Mar. Sci. Eng. 2023, 11, 1144 6 of 18
2.3. Workflow and Methodology 
This section describes all methods used in this study; this includes DEMs derived 
2f.r3o.mW othrkefl oPwleiaanddeMs, ectohomdpoluogtiyng extreme coastal water levels (ECWLs), and flood extent 
mapTphinisgs. ection describes all methods used in this study; this includes DEMs derived from
the Pleiades, computing extreme coastal water levels (ECWLs), and flood extent mapping.
2.3.1. Pleiades-Derived Topography 
2.3.1. Pleiades-Derived Topography
Digital elevation models (DEMs) were generated from the Pleiades stereo imagery 
usinDg tighiet aNl AelSeAva AtiMonEmS Sotdeerelso (PDipEeMlisn)ew (AerSeP)g seonfetrwaaterde tfor oombtathine tPhlee tioapdoesgrsateprheioc vimaraiagteiroyn 
uosfi nthget hbeeaNcAh.S TAhAe MASEPS SsotefrtweoaPreip uesliense t(hAe StPri)-sstoefrtewoagrreatmomobettariyn mtheethtoopdo, gwrahpehreic DvEarMiast iaorne 
odfetrhiveebde farcohm.  Tthhee sAenSsPosr oleftvwela. rFeour stehse tthrie-sttreir-setoe rmeoetghroadmomloegtyry apmpertohaocdh, owf hAeSrPe, DthErMees paarne-
dcehrriovmedatfirco immatghees saerne stoarkelenv aesl .inFpourt t(h0.e5 tmri -rsetseoreluotimone)t h[4o4d]o. Tlohgeysea aprper tohaecnh coofrrAelSaPt,edth preaeir 
pbayn pchariro,m aantdic ai mfinagale scoarrreetlaaktieonn abseitnwpeuetn( 0th.5e mtwroe scoolurrteiolant)ed[4 4p]a.iTrsh gesiveeasr ethteh eonutcpourrte DlaEteMd. 
pTahirisb my petahiro,da nudseasfi tnhael rcaotriorenlaatl iopnolbyentowmeeianl tchoeetffiwcoiecnotr r(eRlPatCed), pwahirischg iavcecsotmhepaonuitepsu tthDeE iMm-.
Tahgiesrmy eatnhdo dfuurstheserth perroavtiidonesa lap roellyantioomnsiahlipco beeffitwcieeennt (tRhPe Csa),tewllhitiech imacacgoem apnadn giersotuhne imageryand further provides a relationship between the satellite image and ground coodrd cionoartdesi-.
TnhaeteDs.E TMhew DaEsMth ewnaso rtthheonr eocrttihfioerdecbtyifireedf ebryen rceifnegretnhceinmg ttohepmla ntoim pelatrnicimcoetorridc icnoaotreds.inTahteiss. 
wTahsisp werafso rpmerefdorumseindg uAsiMngE AS MSteErSe oStPeripeoel Pinipeesloinftew saorfetw. Iatrhea. sIta hfaesa tau freeatthuaret  othrtaht oo-rrtehcoti-fireecs-
atisfieenss ao rs-elenvseolr-slaetveellli tseatiemllaitgee iumsaingge iutssignego imtse gtreyombyettrhye bayc ctohme pacacnoiemdpRaPniCedfi lRePaCn dfi,lien atnhde, 
pinro tchees sp,rcorceeastse,s careDatEesM a aDt EaMse at tr eas soeltu rteiosnolu(steioena l(ssoee[ 2a1ls,2o2 []2).1,T22h]i)s. TDhEiMs DiEs Mau itso mauattoimcaalltyi-
ocratlhlyo roerctthifioeredc,taififteedr, wafhteicrh wthheicsha ttehleli tseatiemllaitgee ismaatgthese aset nthsoe rsaernesoprr oajreec tperdojoenctteodt hoenDtoE tMhe 
sDoEtMha stot htheaot uthtpe uotutipmuatg imesaagrees afurell yfuollryt hoortrheocrtieficetidfieadn dancdo crorercretecdtedu suisnigngg grorouunnddc coonnttrrooll 
ppooininttss[ 3[388].].T ThheeP Pleleiaiaddesesp panacnhcrhormomataictiicm imagaegse(s0 .(50.m5 mre sroelsuotliuotni)onp)r opdruocdeudcead2 am 2r meso rleustoiolun-
DtiEonM D. TEMhe. Tvahrei ovuasriosutesp ssteipnsv oinlvveodlveind Pinle Pialedieasdeism iamgaegryeryp rporcoecsessinsignga raeres usummmmaarirzizeedd iinn 
FFiigguurree3 3.. 
 
FFiigguurree 33. . AA ssuummmmaarryy ooff tthhee mmeetthhooddoollooggyy ffoorr NNAASSAA’’ss AAmmeess SStteerreeoo PPiippeelliinnee ssooffttwwaarree ((AASSPP)) ffoorr 
ssttrruuccttuurreef rformomm motoiotniopnr opcreodcuedreuwreo rwkoflrokwfloimwp ilmempelenmteednitnedth iins  stthuids y.stTuhdiys .a pTphriso aacphpdreoraicvhe sdDeEriMvess 
aDnEdMorst hanopdh oorttohsofprhoomtossa fterollmite siamtealglieter yi.magery. 
PPlleeiiaaddeess IImaaggeerryy PPrree--PPrroocceesssiningg 
TThhiiss iis the preliminary sttage ooff PPleleiaiaddeess ddaatata pprorocecsesisning.g .In Itnhitsh isstasgtea,g de,atdaa atarea sre-
slelcetcetde danadn dorogragnainziezde dbyb ymmatacthcihnign gsismimilailra rfefaetauturerse.s T. Theh eppaiarisr soof fPPlelieaidadese simimagaegse scaclallelded 1 
1anandd 2 a2nadn 2d a2nda n3d ar3e saerleecsteeldec atned panrodcepsrsoecde.s Aseftde.r tAhfiste, rthteh siste, rtehoe imsteargeeo piamirasg aerep saeilresctaerde, 
saenledc tbeadn,da nodneb iasn sdeloecnteedis fsreolmec teeadchfr iommageea cahndim aaligeneadn dusailni gn tehde uaffisi nge-tehpeipaoffilanre a-elgpoipriothlamr  
aolfg oArSitPh m[45o]f. IAmSaPg[e4s5 a].reIm pareg-easliagrneepdr.e T-ahliisg nperodc.eTshs iiss pcraorcrieesds iosucta brryi eideonuttifbyyinigd etinet-ipfyoingts 
tbi e-tpwoeiennts thbemtw, ewenhitche amre,  wthheinc husaerde toh eanlteursneadtet othael tsernnsaotre stphoet steon gsuoarrsapnotet et othgaut aprainrste oef 
tchoant jpuagiartseo efpciopnojulagra ltieneps iaproel abroltihn ecsoal-rleinbeoatrh acnodl- lainaealorgaonuds atnoa olongeo oufs tthoeo inmeaogfet haxeeism [a4g6e]. 
aDxuese [t4o6 t]h. eD inuceotmo pthaetibinilciotym opf atthieb iAliStyP osfoftthweaAreS Pwistohf tmwualrtei-bwainthd mimualgtie-brya,n tdhiism staugdeyry o, pthteisd 
sftourd ay poapntcehdrfoomr atipca inmchagroem (baatnicdi m1)a agse i(t boaffnedrs1 a)  awsiidteo frfaenrgsea owf irdaedriancgee doaftraa adniadn scueidtaabtlae 
and suitable graphic and visual contrast, which aids in distinguishing image features from
their respective shadows.
Corresponding of Features
 In the ASP, features are matched with each other after the images are prepared for
feature matching (Figure 3). The search window algorithms (SWs) in ASP approximates
J. Mar. Sci. Eng. 2023, 11, 1144 7 of 18
the superficial motion of each scene point by linking the search windows of each local
window with each pixel in the stereo pair [47]. For each stereo pair, a relative disparity
map is produced with a search window algorithm [45].
Reconstruction of Topography from Pleiades (3D Reconstruction)
After data are processed and corresponding features are identified, ASP uses the stereo
triangulation algorithm method (Figure 3) to merge features to obtain a 3D location (x, y, z)
of the nearby intersection between lines that link the sensor orbital location to all matched
pixels in both the left and right images. This approach combines all the information on
altitudes, the model of each sensor, and the disparity map. The result of the process is a
raster format consisting of four bands containing triangulated coordinates for x, y, and
z, as well as a triangulation error metric stored in the fourth band. This metric is useful
in evaluating the quality of the sensor model, ephemeris/attitude data, and the disparity
matches [41]. Next, the output is transformed into a 3D point cloud, which is geocoded
using the WGS84 UTM 31N coordinate system and ellipsoid heights. The post-processing
stage of the ASP involves determining the orthometric heights of the 3D point cloud
using ground control points (GCPs) with known orthometric heights to calculate a height
correction factor for the 3D point cloud [48]. Ground control points (GCPs) in the form
of concrete pillars buried at ground level and marked for identification purposes were
used for the study. These GCPs were obtained from [49] for the Keta area. The GCPs
obtained were in the form of X, Y, and Zs of ground pillars. They were connected to the
Ghana meter grid system to establish and coordinate the concrete pillars as ground control
points (GCPs). This process followed the standards set by the Ghana Survey Department,
ensuring consistency and conformity. The static differential GPS (D-GPS) method was
employed to observe each control point, with an average observation time of not more than
30 min. These observations were referenced to the established Ghana national coordinated
pillars [49]. Finally, the 3D point cloud is utilized to generate DEMs and orthophotos. The
final DEM and orthophotos are exported and saved for further analysis using the ASP
stereo-RPC algorithms.
2.3.2. The Altimeter Corrected Elevations Version 2 (ACE2)
To assess the quality of DEMs for coastal flooding studies, DEM was downloaded from
the website of the Data Center in NASA’s Earth Observing System Data and Information
System (EOSDIS). ACE2 is the second iteration of the global digital elevation model that the
Shuttle Radar Topography Mission produced using satellite radar altimetry (SRTM). Version
1 data have been upgraded with the release of the Altimeter Corrected Elevations Version 2
(ACE2) generated by combining satellite radar altimetry with SRTM. Global Observations
to Benefit the Environment (GLOBE), the original altimeter corrected elevations (ACEs)
digital elevation model (DEM), and additional matrices produced by reprocessing European
remote sensing (ERS-1) images are among the data sources used in the adjustment. The
three arc seconds data (90 m) were downloaded with the same datum for this study to
compare with Pleiades-derived DEM [50].
2.3.3. Computing Extreme Coastal Water Level (ECWL)
Using the hourly datasets described earlier, ECWLs were calculated hourly from 1994
to 2015. This approach followed the [7] formula.
ECWL = SLA + DAC + T + R (2)
Three major percentiles were used to show how severe ECWLs go; the 30th, 60th,
and 98th percentiles were obtained and further confirmed with in situ flood occurrence
dates from 2000 to 2015 from the National Disaster Management Organization of the
Keta District. In Keta, extreme coastal water levels (ECWLs) are determined using an
approach that combines several parameters such as sea level anomaly (SLA), storm surge
height caused by atmospheric pressure and winds (DACs), astronomical tide level (T),
J. Mar. Sci. Eng. 2023, 11, 1144 8 of 18
and height of wave breaking (R). The worst scenario of water levels is defined as the
top 2% or 98th percentile to determine the physical impact of ECWLs in the area. The
cumulative annual occurrence of the time spent over this threshold is computed over
the study period. Topographic data are set to a geoidal coordinate system to assess the
potential for overtopping and flooding. ECWLs are converted to geodetic data using the
vertical datum value of [51] to superimpose with the topographic data. Finally, MATLAB
software’s linear regression is used to analyze the trend of ECWLs in the Keta area based
on annualized data.
2.3.4. Flood Extent Mapping
Potential flood extent zones were mapped using the bathtub inundation model. This
approach assumes that areas would be flooded with elevations lower than the ECWL for
all percentiles. Flooded areas are mapped in the geographic information system (GIS)
environment using a simple calculation (Equation (3)). All elevations in each cell of the
Pleiades DEM are equated against a predicted ECWL, and all cells that fall below the ECWL
are considered flooded. Since only data on the elevation are needed for its application, the
approach allows for estimates without detailed hydrological data [52].
Using the raster calculator tool in ArcGIS 10.4, ECWLs for the different percentiles
were computed to assess areas that fall below ECWLs along Keta City. All ECWLs for the
three percentiles were considered for a bathtub analysis. This approach is frequently used
for this type of analysis [53–56]. The ESRI’s ArcGIS 10.4 and MATLAB software was used
for statistical calculation and map development.
Elevation projection by ECWL(p) = (DEM ≤ ECWL (p)) (3)
where (p) are percentiles (30th, 60th, or 98th percentile).
3. Results
3.1. Percentile of Coastal Flood Occurrences
All ECWLs were ranked 30th, 60th, and 98th percentiles, corresponding to 0.54 m,
J. Mar. Sci. Eng. 2023, 11, x FOR PEER0 .R9E7VmIE,Wa nd 1.62 m, respectively. For example, from Figure 4, flood events obtained f9r oofm 18 
 the National Disaster Management Organization (NADMO) in the Keta District occurred
around the 98th percentile (1.62 m).
 
FFiigguurree4 4.. AAp peerrcceennttiilleed diissttrriibbuuttiioonn pplloott ooffe exxttrreemmee ccooaassttaall wwaatteerr lleevveellss ffrroomm 11999944 ttoo2 2001155s shhoowwiinngg 
tthhee 3300tthh,, 6600tthh,,a anndd9 988tthhp peerrcceennttiilleew witithho occccuurrrreenncceesso offc cooaassttaallfl flooooddiinnggo obbttaaiinneedd ffrroommN NAADDMMOO iinn 
bblluueed doottss.. 
3.2. Time Spent on 98th Percentile 
Generally, for the 98th percentile, the maximum time spent on coastal flooding is 200 
h per year. However, there is an increase in time spent from 1994 to 2015. Figure 5 shows 
a gradual increase in hours spent every year. 
 
Figure 5. Time spent by an occurrence at the 98th percentile in hours per year from 1994 to 2015, 
showing blue dots of maximum annual ECWL data and red trend line from 1994 to 2015. 
3.3. In-Situ Flood Occurrence Data 
All data obtained from NADMO show that coastal flooding usually occurs from June 
to September (Table 1). July represents the most dominant month of coastal flooding be-
tween 2002 and 2015. While June is the least month of flood occurrence. 
 
J. Mar. Sci. Eng. 2023, 11, x FOR PEER REVIEW 9 of 18 
 
 
Figure 4. A percentile distribution plot of extreme coastal water levels from 1994 to 2015 showing 
J. Mar. Sci. Eng. 2023, 11, 1144 the 30th, 60th, and 98th percentile with occurrences of coastal flooding obtained from NADMO9 oinf  18
blue dots. 
33..22.. TTiimmee SSppeenntt oonn 9988tthh PPeerrcceennttiillee 
GGeenneerraallllyy,, ffoorr tthhee 9988tthh ppeerrcceennttiillee, ,tthhee mmaaxximimuumm titmimee spspenent tono ncocaosatsatla fll ofloodoidnign gis i2s0200 0
hh ppeerr yyeeaarr.. HHoowweevveerr,, tthheerree iiss aann iinnccrreeaassee iinn ttiimmee ssppeenntt ffrroomm 11999944 ttoo 22001155.. FFiigguurree 55 sshhoowwss a
ag rgardaudaulailn icnrceraesaesein inh ohuoursrss pspenent te veveeryryy yeeaar.r. 
 
Figure 5. Tiimee ssppeenntt bbyy aann oocccuurrerenncece aat tththe e989t8hth pperecrecnetnilteil eini nhohuorusr ps epre yreyaera frrofrmom 1919949 t4o t2o021051, 5,
sshowiing bllue dottss off maaxxiimuum aannnnuuaal lEECCWWLL ddaatata aanndd reredd trterenndd lilnine efrforomm 191949 4tot o2021051.5 .
33..33.. IInn--SSiittuu FFlloooodd OOccccuurrrreennccee DDaattaa 
AAllll ddaattaa oobbttaaiinneedd ffrroom NNAADDMMOO sshhooww ththaat tcocoasatsatla fll flooododinign gusuusaulalyll yococcucrusr fsrofrmom JuJnuen e
ttoo SSeepptteembbeerr (T(Taabblele 1)1.) J.ulJyu lryeprreepsreenstesn tthset hmeosmt odsotmdionmanitn manotnmtho onft hcooafstcaol aflsotaoldiflnogo bdein- g
tbwetewene e2n00220 0a2nda n2d01250.1 W5.hWileh JiluenJeu inse thise tlheeaslte amsot nmtho notfh floofofldo oocdcuorcrceunrcree.n ce.
Table 1. The occurrence of coastal flooding was obtained from NADMO from 2002 to 2015. Generally,
all data obtained from NADMO range between 2002 and 2015. There are no data available from 1994
to 2002. All data sets were collected by observing flooding and reporting by the NADMO office in
 Keta, Ghana.
Year of Occurrence Month of Occurrence
2002 August
2003 September
2004 July
2005 July
2006 September
2007 July
2008 August
2009 September
2010 August
2011 July
2012 June
2013 July
2014 August
2015 September
J. Mar. Sci. Eng. 2023, 11, x FOR PEER REVIEW 10 of 18 
 
Table 1. The occurrence of coastal flooding was obtained from NADMO from 2002 to 2015. Gener-
ally, all data obtained from NADMO range between 2002 and 2015. There are no data available from 
1994 to 2002. All data sets were collected by observing flooding and reporting by the NADMO office 
in Keta, Ghana. 
Year of Occurrence Month of Occurrence 
2002 August 
2003 September  
2004 July 
2005 July  
2006 September  
2007 July 
2008 August 
2009 September  
2010 August 
2011 July 
2012 June 
2013 July 
J. Mar. Sci. Eng. 2023, 11, 1144 2014 August 10 of 18
2015 September 
33..44.. FFlloooodd EExxtteenntt MMaappppiinngg ccoonncceerrnniinngg PPeerrcceennttiilleess 
FFiigguurree 66 sshhoowwss tthheee exxtteenntta annddp perecrecnentatgaegeo fopf optoetnetniatlilayllflyo floodoeddeadr eaarseafosr faolrl aplel rpceenrctielnes-.
tTilhees.h Tighhee hsitgpheerscte pnetargceenotfagthee oafr tehaefl aoroeda efldowodaesdr ewcoarsd reedcoirndtehde i9n8 tthhep 9e8rcthen ptielrec.eTnhtielefi. gTuhree 
fishgouwres sthhoawt 4s3 t%haotf 4t3h%e  aorfe athwe oaurelda pwootuenldti aplolyteflnotioadll.yT flhoeo3d0.t hThaen d306t0ht hanpde r6c0etnht ipleesrschenotwileeds 
sthhoatw1e9d% thaantd 192%9% anodf  t2h9e%a roefa twheo uarldeap wotoeunltdia plloytebnetiflaolloyd beed ,flroeosdpeedct,i vreeslpy.ecHtiovweleyv. eHr,otwhe-
emvoers,t tsheev emreosflto soedvienrge flisooobdsienrgv eisd oibnstehreve9d8 tihn ptherec 9e8ntthil epescrecennatriiloe. scenario. 
 
Fiigure 6. Fllood exttentt mappiing ffor each scenariio of ECWLs where:: ((A)) 30tth percenttiille of ECWLs 
wiitth 19% off tthe area beiing pottenttiialllly flooded,, ((B)) 60tth percenttiille off ECWLss wiitth 29% off tthe area 
bbeeiinngg ppootteennttiiaallllyy flflooooddeedd,, ((CC)) 9988tthh ppeerrcceennttiillee ooff tthhee aarreeaa bbeeiinngg ppootteennttiiaallllyy flflooooddeedd.. 
3.5. Comparison of Pleiades DEM and ACE2 DEM
A simple comparison between the two DEMs was assessed to show the ability of
Pleiades DEMs to capture beach evolution and coastal flooding. Figure 7A and B show
 the location of the transect (red line) from which elevation profiles were extracted for both
Pleiades and ACE2 DEMs. Figure 7C exhibits profile variations extracted for a distance of
about 600 m, and this further shows a maximum elevation of 11 m high for Pleiades DEM
and 2 m high for ACE2 DEM. This shows a very suitable representation and variation in
the beach’s topography. Figure 7D is a box plot showing the variation and distribution of
elevation along the transect. While Pleiades DEM shows a suitable elevation distribution
and variation, ACE2 DEM barely shows any variation distribution in elevation within the
same transect.
3.6. Hydrodynamic Contributors of Coastal Flooding
The major factors contributing to coastal flooding in the 98th percentile was evaluated
from 1994 to 2015. Figure 8 shows the distribution of dominant factors in the ECWL.
Waves have the highest contribution of 1.2 m compared to all other factors. Tide is the
second highest contributor with 0.6 m, followed by SLA and DACs. Figure 8 shows that
wave run-up dominates 98% of ECWLs to coastal flooding, followed by the tides, SLA,
and DACs.
J. Mar. Sci. Eng. 2023, 11, x FOR PEER REVIEW 11 of 18 
 
3.5. Comparison of Pleiades DEM and ACE2 DEM 
A simple comparison between the two DEMs was assessed to show the ability of 
Pleiades DEMs to capture beach evolution and coastal flooding. Figure 7A and B show the 
location of the transect (red line) from which elevation profiles were extracted for both 
Pleiades and ACE2 DEMs. Figure 7C exhibits profile variations extracted for a distance of 
about 600 m, and this further shows a maximum elevation of 11 m high for Pleiades DEM 
and 2 m high for ACE2 DEM. This shows a very suitable representation and variation in 
the beach’s topography. Figure 7D is a box plot showing the variation and distribution of 
elevation along the transect. While Pleiades DEM shows a suitable elevation distribution 
J. Mar. Sci. Eng. 2023, 11, 1144 and variation, ACE2 DEM barely shows any variation distribution in elev1a1toifo1n8  within the 
same transect. 
 
FFiigguurree 77.. ((A)) Ann oorrththopohpohtootoof othf ethPel ePialdeieasdimesa igmeraygfeorryt hfoerK tehtae mKuetnaic mipaulnitiyciwpiatlhitay rweditlhin ae .red line. (B) 
T(Bh)eT zhoeozmooemde-idn- inorotrhtohopphhoottoo ooff tthhee sseelelcetcetdedtr atnrasencsteacnt danadp uar pplue rbpolxe sbhoowx isnhgotwheinbgea tchhea rbeeaa. ch area. (C) 
E(C) Elevation variation for Pleiades and ACE2 with a purple box showing the beach area of theJ. Mar. Sci. Eng. 2023, 11, x FOR PEER RleEvVaItEioWn  variation for Pleiades and ACE2 with a purple box showing the beach area of the tra1n2 soefc 1t8.  
 (tDra)n Ase cbt.o(xD p) lAotb ooxf eplleotvoaftieolenv aatlioonnga ltohneg ttrhaentsraencst escht oshwosw tshteh eddisisttrriibbuuttiioonno of fP Plelieaidaedseasn danAdC AE2CE2 imagery 
eilmeavgaetrioyne lvevaalutioens.v alues.
3.6. Hydrodynamic Contributors of Coastal Flooding 
The major factors contributing to coastal flooding in the 98th percentile was evalu-
ated from 1994 to 2015. Figure 8 shows the distribution of dominant factors in the ECWL. 
Waves have the highest contribution of 1.2 m compared to all other factors. Tide is the 
second highest contributor with 0.6 m, followed by SLA and DACs. Figure 8 shows that 
wave run-up dominates 98% of ECWLs to coastal flooding, followed by the tides, SLA, 
and DACs. 
 
FFiigguurree8 8. .H Hydyrdordoydnyamnaicmciocn ctroibnutrtoibrsuttoorcso atsot acloflaosotdailn flgoaot d98in%g,  sahto 9w8i%ng, tshheocwonintrgib uthtieo ncoonf atrllibution of all 
 ppaarraammeetetersrsin inE CEWCLWsLats 9a8t% 9o8f%E CoWf ELCs.WThLese. xTthreem eexlterfetmsheo wlesfta sshtaocwk bs aar pstlaoct kof baallrp palroamt oeft earsll, parameters, 
wwiitthhw waavveeR R, t,i dtied, eSL, SAL, Aan,d aDndA CDsAshCosw sihngowthiendgo tmhien adnocme hiniearnarcceh yh,ireersaprecchtiyve, lrye. sAplel cptliovtseliyn.b Alulel plots in blue 
sshhoowwv vaaryryinigngle vleevlseolsf  tohfe stehepsaera pmaertaemrs eintetrhse isna mtheeh siearmarech hy.ierarchy. 
3.7. Variability of ECWL Parameters 
Figure 9 shows the variability of parameters in 98% of ECWLs. A mean deviation plot 
was plotted to assess the variability between each parameter. From Figure 9, tide shows 
the highest variability among all the parameters, with about 0.75 m of variation. Though 
wave dominates from Figure 8, tides vary greatly during coastal flooding of 98% of 
ECWLs. On the other hand, parameters such as SLA and DACs exhibit very low variabil-
ity between −0.05 and 0.05 m. 
 
Figure 9. Variation in contributors of ECWLs from 1994 to 2015 using the yearly mean deviation of 
all parameters for 98% of ECWLs from 1994 to 2015. 
 
J. Mar. Sci. Eng. 2023, 11, x FOR PEER REVIEW 12 of 18 
 
 
Figure 8. Hydrodynamic contributors to coastal flooding at 98%, showing the contribution of all 
parameters in ECWLs at 98% of ECWLs. The extreme left shows a stack bar plot of all parameters, 
J. Mar. Sci. Eng. 2023, 11, 1144 with wave R, tide, SLA, and DACs showing the dominance hierarchy, respectively. All plots i1n2 bolfu1e8 
show varying levels of these parameters in the same hierarchy. 
3..7.. Vaarriiaabbiilliittyy ooff EECWLL Paarraameetteerrss 
Fiigure 9 shows the variiability of parameters in 98% of ECWLs. A meeaan deeviiattiion pllott 
was plotttted to  aasssseesssst thheev vaariraiabbiliiltiytyb ebtewtweenene aecahchp aprarmametert.eFr.r oFmromFi gFuigreu9re,  t9id, etisdheo swhsowthse 
thhieg heigsthveastr ivaabriliiatbyilaimtyo anmgoanllgt haell ptahrea pmaertaemrse, tweritsh, wabitohu at b0o.7u5t m0.7o5f vma roiaf tvioanri.aTtihoonu. gTh owuagvhe 
wdoamvei ndaotems ifnroatmesF figroumre 8F,igtiudrees v8,a rtyidgerse avtalyryd ugrienagtlyco adsutrailnflgo ocodainstgaol ffl9o8o%dionfgE CofW 9L8s%.  Oonf 
EthCeWoLthse. rOhna tnhde, optahrearm heatnedrs, psuarcahmaestSeLrsA suacnhd aDs ASLCAs  eaxnhdi bDitAvCesr yexlohwibivt avreirayb illoitwy vbaertwiabeeiln-
i−ty0 b.0e5twaneden0 .−005.0m5 .and 0.05 m. 
 
Fiigure 9.. Varriiattiion iin conttrriibuttorrss off ECWLss ffrrom 1994 tto 2015 usiing tthe yearlly mean deviiattiion off 
aallll paarraameetteerrss fforr 9988% off ECWLss ffrrom 11999944 tto 22001155.. 
4. Discussion
 Generally, low-lying regions experience coastal flooding. The findings of this study
indicate that even with the smallest level of extreme coastal water levels (ECWLs), coastal
flooding occurs. This phenomenon is common in low-lying areas such as deltas. Coastal
flooding due to ECWLs can result from a single factor or a compound effect from several
factors. On average, the Volta Delta experiences frequent flooding events (Figure 10) [32].
Most of these events have been attributed to “tidal waves”. However, as global warming
increases extreme sea levels in the coming decades, coastal flooding is expected to become
more frequent [57]. To understand which parameter contributes the most to extreme coastal
events, all ECWLs were aggregated into severity ranks where the 98th percentile is the
most severe, the 60th percentile is severe, and the 30th percentile is less severe. This study
shows that all coastal flooding events in Keta District are in the 98th percentile of coastal
flooding. This further indicates that coastal flooding in the Keta area was reported as a
severe event from 2002 to 2015. ECWLs can result from a single ocean factor such as high
tide, wave, or SLA. However, combining all these factors makes the scenario worse and
more extreme. Coastal flooding at a given location occurs at varying temporal and spatial
scales. This study shows that over the years, there has generally been an increase in the
time spent by ECWLs. From Figure 5, the time spent by ECWLs reaches values as high as
200 hrs/year. This is consistent with the general sentiment of coastal flooding in the Volta
Delta of Ghana over the past decade. In addition, global warming is still on the rise and
thus accounts for the increase in flooding events and time spent per year during the most
extreme events.
J. Mar. Sci. Eng. 2023, 11, x FOR PEER REVIEW 13 of 18 
 
4. Discussion 
Generally, low-lying regions experience coastal flooding. The findings of this study 
indicate that even with the smallest level of extreme coastal water levels (ECWLs), coastal 
flooding occurs. This phenomenon is common in low-lying areas such as deltas. Coastal 
flooding due to ECWLs can result from a single factor or a compound effect from several 
factors. On average, the Volta Delta experiences frequent flooding events (Figure 10) [32]. 
Most of these events have been attributed to “tidal waves”. However, as global warming 
increases extreme sea levels in the coming decades, coastal flooding is expected to become 
more frequent [57]. To understand which parameter contributes the most to extreme 
coastal events, all ECWLs were aggregated into severity ranks where the 98th percentile 
is the most severe, the 60th percentile is severe, and the 30th percentile is less severe. This 
study shows that all coastal flooding events in Keta District are in the 98th percentile of 
coastal flooding. This further indicates that coastal flooding in the Keta area was reported 
as a severe event from 2002 to 2015. ECWLs can result from a single ocean factor such as 
high tide, wave, or SLA. However, combining all these factors makes the scenario worse 
and more extreme. Coastal flooding at a given location occurs at varying temporal and 
spatial scales. This study shows that over the years, there has generally been an increase 
in the time spent by ECWLs. From Figure 5, the time spent by ECWLs reaches values as 
high as 200 hrs/year. This is consistent with the general sentiment of coastal flooding in 
the Volta Delta of Ghana over the past decade. In addition, global warming is still on the 
J. Mar. Sci. Eng. 2023, 11, 1144 rise and thus accounts for the increase in flooding events and time spent per year d1u3roifn1g8 
the most extreme events. 
 
Figure 10.. TTheh eimimagaegreyr yof obfeabceha crhesroerstos ratffs eacftfeedc tbeyd EbCyWELCsW; (LAs); a(nA a) earniala verieiawl  voife twoo fmtawjoor bmeajcohr 
rbeesaocrhtsr (eAsobrotrsig(Ainbeos rbigeiancehs rbeseoarcth arnedso Artgbanlodr LAogdbgloer) iLno Kdegtea) winitKhoetuat wfloitohdoiuntg;fl (oBo)d girnagd;u(aBl) flgoroadiunagl 
oflfo tohdei nAggobflothr eLAodggbelo arnLdo AdgbeoraingdinAesb boeraigcihn eressboerat cbheirnegso arfft becetiendg bayff eEcCteWd Lbsy; E(C)W anL sa;e(rCia) la vnieawer ioafl 
wviaevwe oofvweratvopepoivnegr tionp tphien gKientat haereKae tdauaer etoa dEuCeWtoLsE. C(PWhLosto.  (APh oobtotaAineodb tfarionmed hfttropms:/h/wttwpsw:/.a/gwbwlowr-.
laogdbgloer.cloodmg/eg.acollmer/yg_caallte/raye_ricaalt-/paheortioagl-rpahpoht/o g(araccpehs/se(da ccoens s2e1d Aonpr2i1l A20p2r3il);2 0p2h3o)t;op hcoretodictr eodf itBo af nBda nCd: 
BCr:eBmrepmonpgo nEgmEmmamnuaenlu).e l).
TThhee ssppaattiiaall eexxtteenntt ooff ccooaassttaall flflooooddiinngg bbyy EECCWLLss sshhoowwss tthhaatt moosstt ooff tthhee aarreeaa iiss ppootteenn--
ttiiaallllyy flflooooddeedd iinn tthhee 9988tthh ppeerrcceennttiillee ooff ccooaassttaall flflooooddiinngg.. IInntteerreessttiinnggllyy,, iinn tthhee 9988tthh ppeerrcceennttiillee 
ccaassee,, ppaarrttss ooff tthhee maaiinn rrooaadd aanndd hhoouusseess woouulldd ppootteennttiiaallllyy flfloooodd iinn tthhee moosstt eexxttrreemee ccaassee.. 
Thhiiss iiss bbeeccaauussee tthhee aarreeaa iiss ggeenneerraallllyy lloow--llyyiinngg,, aannd aannyy miinnuuttee iinnccrreeaassee iinn ECWLss woouulld 
rreessulltt iin ccoaassttaall floodiing.. Thee aannuaall numbeerr off hourrss sshowss aa possiittiivee ((ii..ee..,, iinccrreeaassiing)) 
trend (calculated using the hourly ECWL time series for all years) (Figure 5.). The highest
increase rates were observed in the 98th percentile and 2015. This is possible because
coastal areas generally have low ECWL variability (time series variance). Thus, even a
 small increase in regional sea level can greatly impact coastal flooding [58]. ECWLs at
the coast results from various, sometimes unrelated contributions, i.e., both natural and
anthropogenic [59]. These include tides, meteorological processes, wave conditions, and
sea level anomalies. Assessing the combination of these factors helps reduce the risk posed
by coastal flooding [60].
For this reason, many global studies have attributed the cause of coastal flooding to
sea level rise without considering the role of other factors along the coast [61–64]. This
factor is equally important, as knowing about it would reduce the magnitude and exposure
of physical damage. Appropriate planning and mitigation measures can be implemented
with proper forecasting of extreme coastal events and a better understanding of the major
contributors to coastal flooding at the local scale [65,66]. It is, however, worth knowing that,
from Figure 7, Pleiades DEM can capture enough beach variations, such as the back beach,
berm, and nearshore topography, compared to global DEM. This is relevant for coastal
flooding studies since beach topography is very important for coastal flood prediction and
how coastal structures impact and protect beaches.
Furthermore, in this study, wave run-up is the primary contributor to ECWLs for
the Keta area. This confirms studies indicating that the Volta Delta is primarily wave-
dominated, and thus all beaches are primarily formed and affected by wave activity [66,67].
In this study, the ECWLs at the 98th percentile have the wave run-up contributing as much
as 70% to them (see Figure 8). In general, in the swash zone, single waves propagate
beyond the slope of the beach and shoreline [68,69]. This area experiences significant
J. Mar. Sci. Eng. 2023, 11, 1144 14 of 18
erosion and wave overtopping during storms, as seen in Figure 10C. Water continuously
covers and uncovers this area (Figure 10B). In particular, during a strong storm surge,
waves affect the barrier beach or foredune more [70]. This also confirms the study by [18],
which indicates that coastal flooding occurrences are expected to increase significantly in
the coming decades.
For this reason, as the sea level rises, the study results show that considering wave
contributions to long-term changes in total water level at the coast would lead to more
accurate decadal forecasts and longer-term projections of total water level at the coast.
Figure 9 shows the variability of the contributors in the ECWL. The tide is more variable
on all time scales. Normally, the tide varies, including diurnal, biweekly with spring
and neap tides, and seasonal and interannual scales. This explains why the tide is the
most variable parameter among all parameters despite the predominance of waves in
Figure 8. However, it is impossible to predict the phase between the tide and the wave
run-up, which depend on independent factors. Overall, this increases the uncertainty
of both short-term deterministic forecasts [17], while the probabilistic approach is more
suitable for long-term prediction of overtopping and flooding [71]. Other factors, such
as SLA and DACs, contribute to ECWLs, but their effects are rarely seen in the ECWLs.
Using the Pleiades-derived DEM has proven to show how promising the Pleiades would
contribute to studies in the coastal environment compared to global DEMs. This would be
very helpful locally, particularly for coastal managers and engineers. Although operational
accuracy without a ground control point is not yet possible for coastal studies at this stage
(as stated by [22,35,72]), the limitations in accuracy encountered suggest that satellite-based
topography monitoring can be a significant advancement in overcoming long-standing
technological barriers in monitoring, thereby supporting local coastal engineering and
coastal monitoring. By offering local perspectives, this study offers valuable insights for
developing evidence-based measures to minimize damage and injury. As a result, using
ECWLs will attract considerable attention from diverse audiences in West Africa, including
researchers, practitioners, decision makers, and policymakers from various fields such
as catchment management, engineering, economics, disaster management, and science-
informed policy planning.
5. Conclusions
This study quantified coastal flooding from 1994 to 2015 in the historical city of Keta in
the Volta Delta, Ghana. High-resolution coastal topography derived from Pleiades satellites
and extreme coastal water levels (ECWLs), including wave contribution, were used to
map potential flood areas. During the most severe situation (98% percentile of ECWLs),
43% of the Keta area is potentially flooded, including roads and inhabitants’ houses. The
major hydrodynamic contributor to coastal flooding is wave run-up, which differs from
Senegal, where tide dominates. On the other hand, while wave run-up dominates, the
tide is more variable, indicating the key role of the phasing between components. The
time spent (hrs/year) with potential severe flooding from 1994 to 2015 is increasing due to
global mean sea level rise, as observed by satellite altimetry. It will most likely accelerate
over the twentieth century with more and more people being displaced. Our local findings
can help decrease exposure to damage and injury by developing pertinent science-based
protection and mitigation solutions.
Author Contributions: Conceptualization, E.K.B. and R.A.; methodology, R.A. and E.K.B.; validation,
E.K.B.; formal analysis, E.K.B.; investigation, data curation, E.K.B., R.A. and D.B.A.; writing—original
draft preparation; E.K.B.; writing—review and editing, E.K.B., R.A., D.B.A., P.-N.J.-Q., K.T.A.-A. and
B.C.; supervision, R.A., D.B.A. and P.A.D.M. All authors have read and agreed to the published
version of the manuscript.
J. Mar. Sci. Eng. 2023, 11, 1144 15 of 18
Funding: This research was funded by the Africa Centre of Excellence in Coastal Resilience (ACECoR),
University of Cape Coast, with support from the World Bank and the Government of Ghana, World
Bank ACE Grant Number is credit number 6389-G and Institute of Research Development coast
under control project (IRD/JEAI) and the ACE PARTNER project of IRD.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments: This paper is part of a Ph.D. thesis under the Africa Centre of Excellence in
Coastal Resilience (ACECoR), the University of Cape Coast, with the support of the World Bank and
the Government of Ghana.
Conflicts of Interest: The authors declare no conflict of interest.
References
1. Braun, A. Retrieval of digital elevation models from Sentinel-1 radar data—Open applications, techniques, and limitations. Open
Geosci. 2021, 13, 532–569. [CrossRef]
2. McGranahan, G.; Balk, D.; Anderson, B. The rising tide: Assessing the risks of climate change and human settlements in low
elevation coastal zones. Environ. Urban. 2007, 19, 17–37. [CrossRef]
3. Peter Sheng, Y.; Paramygin, V.A.; Yang, K.; Rivera-Nieves, A.A. A sensitivity study of rising compound coastal inundation over
large flood plains in a changing climate. Sci. Rep. 2022, 12, 3403. [CrossRef] [PubMed]
4. Dada, O.; Almar, R.; Morand, P.; Menard, F. Towards West African coastal social-ecosystems sustainability: Interdisciplinary
approaches. Ocean Coast. Manag. 2021, 211, 105746. [CrossRef]
5. Vousdoukas, M.I.; Clarke, J.; Ranasinghe, R.; Reimann, L.; Khalaf, N.; Duong, T.M.; Ouweneel, B.; Sabour, S.; Iles, C.E.; Trisos,
C.H.; et al. African heritage sites threatened as sea-level rise accelerates. Nat. Clim. Chang. 2022, 12, 256–262. [CrossRef]
6. Marcos, M.; Rohmer, J.; Vousdoukas, M.I.; Mentaschi, L.; Le Cozannet, G.; Amores, A. Increased Extreme Coastal Water Levels
Due to the Combined Action of Storm Surges and Wind Waves. Geophys. Res. Lett. 2019, 46, 4356–4364. [CrossRef]
7. Olbert, A.I.; Moradian, S.; Nash, S.; Comer, J.; Kazmierczak, B.; Falconer, R.A.; Hartnett, M. Combined statistical and hy-
drodynamic modelling of compound flooding in coastal areas—Methodology and application. J. Hydrol. 2023, 620, 129383.
[CrossRef]
8. Wahl, T.; Mudersbach, C.; Jensen, J. Assessing the hydrodynamic boundary conditions for risk analyses in coastal areas: A
multivariate statistical approach based on Copula functions. Nat. Hazards Earth Syst. Sci. 2012, 12, 495–510. [CrossRef]
9. Bilskie, M.V.; Hagen, S.C. Defining Flood Zone Transitions in Low-Gradient Coastal Regions. Geophys. Res. Lett. 2018, 45,
2761–2770. [CrossRef]
10. Paprotny, D.; Vousdoukas, M.I.; Morales-Nápoles, O.; Jonkman, S.N.; Feyen, L. Compound flood potential in Europe. Hydrol.
Earth Syst. Sci. Discuss. 2018, preprint. [CrossRef]
11. Almar, R.; Ranasinghe, R.; Bergsma, E.W.J.; Diaz, H.; Melet, A.; Papa, F.; Vousdoukas, M.; Athanasiou, P.; Dada, O.; Almeida, L.P.;
et al. A global analysis of extreme coastal water levels with implications for potential coastal overtopping. Nat. Commun. 2021,
12, 3775. [CrossRef]
12. Nerem, R.S.; Beckley, B.D.; Fasullo, J.T.; Hamlington, B.D.; Masters, D.; Mitchum, G.T. Climate-change–driven accelerated
sea-level rise detected in the altimeter era. Proc. Natl. Acad. Sci. USA 2018, 115, 2022–2025. [CrossRef] [PubMed]
13. Church, J.A.; Clark, P.U.; Cazenave, A.; Gregory, J.M.; Jevrejeva, S.; Levermann, A.; Merrifield, M.A.; Milne, G.A.; Nerem, R.S.;
Nunn, P.D.; et al. Sea-Level Rise by 2100. Science 2013, 342, 1445. [CrossRef] [PubMed]
14. Gregory, J.M.; Griffies, S.M.; Hughes, C.W.; Lowe, J.A.; Church, J.A.; Fukimori, I.; Gomez, N.; Kopp, R.E.; Landerer, F.; Cozannet,
G.L.; et al. Concepts and Terminology for Sea Level: Mean, Variability and Change, Both Local and Global. Surv. Geophys. 2019,
40, 1251–1289. [CrossRef]
15. De Moel, H.; Jongman, B.; Kreibich, H.; Merz, B.; Penning-Rowsell, E.; Ward, P.J. Flood risk assessments at different spatial scales.
Mitig. Adapt. Strat. Glob. Chang. 2015, 20, 865–890. [CrossRef]
16. Van der Meer, J.; Nieuwenhuis, J.-W.; Steendam, G.J.; Reneerkens, M.; Steetzel, H.; van Vledder, G. Wave Overtopping Measure-
ments at a Real Dike. Coast. Struct. 2019, 2019, 1107–1117. [CrossRef]
17. Bertin, X.; Li, K.; Roland, A.; Zhang, Y.J.; Breilh, J.F.; Chaumillon, E. A modeling-based analysis of the flooding associated with
Xynthia, central Bay of Biscay. Coast. Eng. 2014, 94, 80–89. [CrossRef]
18. Melet, A.; Meyssignac, B.; Almar, R.; Le Cozannet, G. Under-estimated wave contribution to coastal sea-level rise. Nat. Clim.
Chang. 2018, 8, 234–239. [CrossRef]
19. Angnuureng, D.B.; Appeaning Addo, K.; Almar, R.; Dieng, H. Influence of sea level variability on a micro-tidal beach. Nat.
Hazards 2018, 93, 1611–1628. [CrossRef]
20. Vaze, J.; Teng, J.; Spencer, G. Impact of DEM accuracy and resolution on topographic indices. Environ. Model. Softw. 2010, 25,
1086–1098. [CrossRef]
J. Mar. Sci. Eng. 2023, 11, 1144 16 of 18
21. Almeida, L.; Almar, R.; Bergsma, E.; Berthier, E.; Baptista, P.; Garel, E.; Dada, O.; Alves, B. Deriving High Spatial-Resolution
Coastal Topography From Sub-meter Satellite Stereo Imagery. Remote Sens. 2019, 11, 590. [CrossRef]
22. Taveneau, A.; Almar, R.; Bergsma, E.W.J.; Sy, B.A.; Ndour, A.; Sadio, M.; Garlan, T. Observing and Predicting Coastal Erosion at
the Langue de Barbarie Sand Spit around Saint Louis (Senegal, West Africa) through Satellite-Derived Digital Elevation Model
and Shoreline. Remote Sens. 2021, 13, 2454. [CrossRef]
23. Appeaning Addo, K.; Jayson-Quashigah, P.-N.; Codjoe, S.N.A.; Martey, F. Drone as a tool for coastal flood monitoring in the Volta
Delta, Ghana. Geoenviron. Disasters 2018, 5, 17. [CrossRef]
24. Bokpe, S. Azizanya is drowning 2010. Available online: http://sethbnews09.blogspot.com/2010/08/helpazizanya-is-drowning-
friday-august.html (accessed on 23 April 2023).
25. Matteo, F. West Africa is being swallowed by the sea. October 2016. Available online: http://foreignpolicy.com/2016/10/21
/west-africa-is-being-swallowed-by-the-seaclimate-change-ghana-benin (accessed on 23 April 2023).
26. Anthony, E.J. Patterns of Sand Spit Development and Their Management Implications on Deltaic, Drift-Aligned Coasts: The
Cases of the Senegal and Volta River Delta Spits, West Africa. In Sand and Gravel Spits; Randazzo, G., Jackson, D.W.T., Cooper,
J.A.G., Eds.; Coastal Research Library; Springer International Publishing: Cham, Switzerland, 2015; Volume 12, pp. 21–36.
ISBN 978-3-319-13715-5.
27. Appeaning Addo, K.; Brempong, E.K.; Jayson-Quashigah, P.N. Assessment of the dynamics of the Volta river estuary shorelines
in Ghana. Geoenviron. Disasters 2020, 7, 19. [CrossRef]
28. Ly, C.K. The role of the Akosombo Dam on the Volta river in causing coastal erosion in central and eastern Ghana (West Africa).
Mar. Geol. 1980, 37, 323–332. [CrossRef]
29. Akpati, B.N. Geologic structure and evolution of the Keta basin, Ghana, West Africa. Geol. Soc. Am. Bull. 1978, 89, 124. [CrossRef]
30. Kabo-bah, A.; Diji, C.J. (Eds.) Sustainable Hydropower in West Africa: Planning, Operation, and Challenges; Academic Press, an
imprint of Elsevier: London, UK; San Diego, CA, USA, 2018; ISBN 978-0-12-813016-2.
31. Garr, E. Infrastructure policy reforms and rural poverty reduction in Ghana: The case of the Keta Sea Defence Project, University
of the Western Cape. 2010. Available online: https://core.ac.uk/download/pdf/58913725.pdf (accessed on 23 April 2023).
32. Addo, K.A.; Nicholls, R.J.; Codjoe, S.N.A.; Abu, M. A Biophysical and Socioeconomic Review of the Volta Delta, Ghana. J. Coast.
Res. 2018, 345, 1216–1226. [CrossRef]
33. Roest, L.W.M. The coastal system of the Volta delta, Ghana Opportunities and strategies for development. Available online:
https://pure.tudelft.nl/ws/files/37464456/Roest_2018_The_coastal_system_of_the_Volta_delta.pdf (accessed on 20 March 2023).
34. Duku, E.; Dzorgbe Mattah, P.A.; Angnuureng, D.B. Assessment of wetland ecosystem services and human wellbeing nexus in
sub-Saharan Africa: Empirical evidence from a socio-ecological landscape of Ghana. Environ. Sustain. Indic. 2022, 15, 100186.
[CrossRef]
35. Turner, I.L.; Harley, M.D.; Almar, R.; Bergsma, E.W.J. Satellite optical imagery in Coastal Engineering. Coast. Eng. 2021, 167, 103919.
[CrossRef]
36. Almar, R.; Bergsma, E.W.J.; Maisongrande, P.; de Almeida, L.P.M. Wave-derived coastal bathymetry from satellite video imagery:
A showcase with Pleiades persistent mode. Remote Sens. Environ. 2019, 231, 111263. [CrossRef]
37. Almar, R.; Stieglitz, T.; Addo, K.A.; Ba, K.; Ondoa, G.A.; Bergsma, E.W.J.; Bonou, F.; Dada, O.; Angnuureng, D.; Arino, O. Coastal
Zone Changes in West Africa: Challenges and Opportunities for Satellite Earth Observations. Surv. Geophys. 2023, 44, 249–275.
[CrossRef]
38. Gleyzes, M.A.; Perret, L.; Kubik, P. Pleiades system architecture and main performances. Int. Arch. Photogramm. Remote Sens. Spat.
Inf. Sci. 2012, XXXIX-B1, 537–542. [CrossRef]
39. Jacobsen, K.; Topan, H. Corrigendum to “DEM Generation with short base length pleiades triplet”. Int. Arch. Photogramm. Remote
Sens. Spat. Inf. Sci. 2015, XL-3/W2, 297. [CrossRef]
40. Carrere, L.; Lyard, F.; Cancet, M.; Guillot, A. FES 2014, a new tidal model on the global ocean with enhanced accuracy in shallow
seas and in the Arctic region. In Proceedings of the EGU General Assembly, Vienna, Austria, 12–17 April 2015.
41. Soudarin, L.; Rosmordu, V.; Guinle, T.; Nino, F.; Birol, F.; Mertz, F.; Schgounn, C.; Gasc, M. Aviso+: Altimetry satellite data and
products for ocean-oriented applications (and others). In Proceedings of the 20th EGU General Assembly, EGU2018, Vienna,
Austria, 4–13 April 2018; p. 1608.
42. Marti, F.; Cazenave, A.; Birol, F.; Passaro, M.; Léger, F.; Niño, F.; Almar, R.; Benveniste, J.; Legeais, J.F. Altimetry-based sea level
trends along the coasts of Western Africa. Adv. Space Res. 2021, 68, 504–522. [CrossRef]
43. Stockdon, H.F.; Holman, R.A.; Howd, P.A.; Sallenger, A.H. Empirical parameterization of setup, swash, and runup. Coast. Eng.
2006, 53, 573–588. [CrossRef]
44. Rupnik, E.; Pierrot-Deseilligny, M.; Delorme, A. 3D reconstruction from multi-view VHR-satellite images in MicMac. ISPRS J.
Photogramm. Remote Sens. 2018, 139, 201–211. [CrossRef]
45. Palaseanu-Lovejoy, M.; Bisson, M.; Spinetti, C.; Buongiorno, M.F.; Alexandrov, O.; Cecere, T. High-Resolution and Accurate
Topography Reconstruction of Mount Etna from Pleiades Satellite Data. Remote Sens. 2019, 11, 2983. [CrossRef]
46. Shean, D.E.; Alexandrov, O.; Moratto, Z.M.; Smith, B.E.; Joughin, I.R.; Porter, C.; Morin, P. An automated, open-source pipeline
for mass production of digital elevation models (DEMs) from very-high-resolution commercial stereo satellite imagery. ISPRS J.
Photogramm. Remote Sens. 2016, 116, 101–117. [CrossRef]
J. Mar. Sci. Eng. 2023, 11, 1144 17 of 18
47. Facciolo, G.; Franchis, C.D.; Meinhardt, E. MGM: A Significantly More Global Matching for Stereovision. In Proceedings of the
British Machine Vision Conference 2015, Swansea, UK, 7–10 September 2015; pp. 90.1–90.12.
48. Knuth, F.; Shean, D.; Bhushan, S.; Schwat, E.; Alexandrov, O.; McNeil, C.; Dehecq, A.; Florentine, C.; O’Neel, S. Historical
Structure from Motion (HSfM): Automated processing of historical aerial photographs for long-term topographic change analysis.
Remote Sens. Environ. 2023, 285, 113379. [CrossRef]
49. Jayson-Quashigah, P.-N.; Appeaning Addo, K.; Amisigo, B.; Wiafe, G. Assessment of short-term beach sediment change in the
Volta Delta coast in Ghana using data from Unmanned Aerial Vehicles (Drone). Ocean Coast. Manag. 2019, 182, 104952. [CrossRef]
50. Mesa-Mingorance, J.L.; Ariza-López, F.J. Accuracy Assessment of Digital Elevation Models (DEMs): A Critical Review of Practices
of the Past Three Decades. Remote Sens. 2020, 12, 2630. [CrossRef]
51. Collilieux, X.; Wöppelmann, G. Global sea-level rise and its relation to the terrestrial reference frame. J. Geod. 2011, 85, 9–22.
[CrossRef]
52. Yunus, A.; Avtar, R.; Kraines, S.; Yamamuro, M.; Lindberg, F.; Grimmond, C. Uncertainties in Tidally Adjusted Estimates of Sea
Level Rise Flooding (Bathtub Model) for the Greater London. Remote Sens. 2016, 8, 366. [CrossRef]
53. Leal-Alves, D.C.; Weschenfelder, J.; Albuquerque, M.d.G.; Espinoza, J.M. de A.; Ferreira-Cravo, M.; Almeida, L.P.M. de Digital
elevation model generation using UAV-SfM photogrammetry techniques to map sea-level rise scenarios at Cassino Beach, Brazil.
SN Appl. Sci. 2020, 2, 2181. [CrossRef]
54. Didier, D.; Baudry, J.; Bernatchez, P.; Dumont, D.; Sadegh, M.; Bismuth, E.; Bandet, M.; Dugas, S.; Sévigny, C. Multihazard
simulation for coastal flood mapping: Bathtub versus numerical modelling in an open estuary, Eastern Canada. J. Flood Risk
Manag. 2019, 12, e12505. [CrossRef]
55. Rizzo, A.; Vandelli, V.; Gauci, C.; Buhagiar, G.; Micallef, A.S.; Soldati, M. Potential Sea Level Rise Inundation in the Mediterranean:
From Susceptibility Assessment to Risk Scenarios for Policy Action. Water 2022, 14, 416. [CrossRef]
56. Snoussi, M.; Ouchani, T.; Niazi, S. Vulnerability assessment of the impact of sea-level rise and flooding on the Moroccan coast:
The case of the Mediterranean eastern zone. Estuar. Coast. Shelf Sci. 2008, 77, 206–213. [CrossRef]
57. Walsh, K.J.E.; McInnes, K.L.; McBride, J.L. Climate change impacts on tropical cyclones and extreme sea levels in the South
Pacific—A regional assessment. Glob. Planet. Chang. 2012, 80–81, 149–164. [CrossRef]
58. Taherkhani, M.; Vitousek, S.; Barnard, P.L.; Frazer, N.; Anderson, T.R.; Fletcher, C.H. Sea-level rise exponentially increases coastal
flood frequency. Sci. Rep. 2020, 10, 6466. [CrossRef]
59. Woodworth, P.L.; Melet, A.; Marcos, M.; Ray, R.D.; Wöppelmann, G.; Sasaki, Y.N.; Cirano, M.; Hibbert, A.; Huthnance, J.M.;
Monserrat, S.; et al. Forcing Factors Affecting Sea Level Changes at the Coast. Surv. Geophys. 2019, 40, 1351–1397. [CrossRef]
60. Imani, M.; Kuo, C.-Y.; Chen, P.-C.; Tseng, K.-H.; Kao, H.-C.; Lee, C.-M.; Lan, W.-H. Risk Assessment of Coastal Flooding under
Different Inundation Situations in Southwest of Taiwan (Tainan City). Water 2021, 13, 880. [CrossRef]
61. Amores, A.; Marcos, M.; Pedreros, R.; Le Cozannet, G.; Lecacheux, S.; Rohmer, J.; Hinkel, J.; Gussmann, G.; Van Der Pol, T.;
Shareef, A.; et al. Coastal Flooding in the Maldives Induced by Mean Sea-Level Rise and Wind-Waves: From Global to Local
Coastal Modelling. Front. Mar. Sci. 2021, 8, 665672. [CrossRef]
62. Mimura, N. Sea-level rise caused by climate change and its implications for society. Proc. Jpn. Acad. Ser. B 2013, 89, 281–301.
[CrossRef]
63. Nicholls, R.J. Analysis of global impacts of sea-level rise: A case study of flooding. Phys. Chem. Earth Parts A/B/C 2002, 27,
1455–1466. [CrossRef]
64. Nicholls, R.J.; Tol, R.S.J. Impacts and responses to sea-level rise: A global analysis of the SRES scenarios over the twenty-first
century. Phil. Trans. R. Soc. A. 2006, 364, 1073–1095. [CrossRef] [PubMed]
65. Ciavola, P.; Ferreira, O.; Haerens, P.; Van Koningsveld, M.; Armaroli, C. Storm impacts along European coastlines. Part 2: Lessons
learned from the MICORE project. Environ. Sci. Policy 2011, 14, 924–933. [CrossRef]
66. Anthony, E.J.; Orford, J.D. Between Wave- and Tide-Dominated Coasts: The Middle Ground Revisited. J. Coast. Res. 2002, 36,
8–15. [CrossRef]
67. Jayson-Quashigah, P.-N.; Appeaning Addo, K.; Wiafe, G.; Amisigo, B.A.; Brempong, E.K.; Kay, S.; Angnuureng, D.B. Wave
dynamics and shoreline evolution in deltas: A case study of sandy coasts in the Volta delta of Ghana. Interpretation 2021, 9,
SH99–SH113. [CrossRef]
68. Deng, B.; Zhang, W.; Yao, Y.; Jiang, C. A laboratory study of the effect of varying beach slopes on bore-driven swash hydrodynam-
ics. Front. Mar. Sci. 2022, 9, 956379. [CrossRef]
69. Chen, W.; Van Der Werf, J.J.; Hulscher, S.J.M.H. A review of practical models of sand transport in the swash zone. Earth-Sci. Rev.
2023, 238, 104355. [CrossRef]
70. Mehrtens, B.; Lojek, O.; Kosmalla, V.; Bölker, T.; Goseberg, N. Foredune growth and storm surge protection potential at the
Eiderstedt Peninsula, Germany. Front. Mar. Sci. 2023, 9, 1020351. [CrossRef]
J. Mar. Sci. Eng. 2023, 11, 1144 18 of 18
71. Vousdoukas, M.I.; Mentaschi, L.; Voukouvalas, E.; Verlaan, M.; Jevrejeva, S.; Jackson, L.P.; Feyen, L. Global probabilistic projections
of extreme sea levels show intensification of coastal flood hazard. Nat. Commun. 2018, 9, 2360. [CrossRef] [PubMed]
72. Cisse, C.O.T.; Brempong, E.K.; Taveneau, A.; Almar, R.; Sy, B.A.; Angnuureng, D.B. Extreme coastal water levels with potential
flooding risk at the low-lying Saint Louis historic city, Senegal (West Africa). Front. Mar. Sci. 2022, 9, 993644. [CrossRef]
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