Journal of Marine Science and Engineering

Article The Contribution of Forerunner to Storm Surges along the Coast

Tam Thi Trinh 1,2,*, Charitha Pattiaratchi 2 and Toan Bui 2,3

1 Vietnam National Centre for Hydro-Meteorological Forecasting, No. 8 Phao Dai Lang Street, Lang Thuong Commune, Dong Da District, 11512, Vietnam 2 Oceans Graduate School and the UWA Oceans Institute, The University of Western Australia, 35 Stirling Highway, Perth WA 6009, Australia; [email protected] (C.P.); [email protected] (T.B.) 3 Faculty of Marine Sciences, Hanoi University of Natural Resource and Environment, No. 41A Phu Dien Road, Phu Dien Commune, North-Tu Liem District 11916, Hanoi, Vietnam * Correspondence: [email protected]; Tel.: +84-988-132-520

 Received: 3 May 2020; Accepted: 7 July 2020; Published: 10 July 2020 

Abstract: Vietnam, located in the tropical region of the northwest Pacific Ocean, is frequently impacted by tropical storms. Occurrence of extreme water level events associated with tropical storms are often unpredicted and put coastal infrastructure and safety of coastal populations at risk. Hence, an improved understanding of the nature of storm surges and their components along the Vietnam coast is required. For example, a higher than expected extreme during Kalmegi (2014) highlighted the lack of understanding on the characteristics of storm surges in Vietnam. Physical processes that influence the non-tidal water level associated with tropical storms can persist for up to 14 days, beginning 3–4 days prior to storm landfall and cease up to 10 days after the landfall of the typhoon. This includes the forerunner, ‘direct’ storm surge, and coastally trapped waves. This study used a continuous record of six sea level time series collected over a 5-year period (2013–2017) from along the Vietnam coast and to examine the contribution of the forerunner to non-tidal water level. The forerunner is defined as the gradual increase in mean water level, 2–3 days prior to typhoon landfall and generated by shore parallel winds and currents that result in a mean higher water level at the coast. Results indicated that a forerunner was generated by almost all , at least at one station, with a range between 20 and 50 cm. The forerunner contributed up to 50% of the water level change due to the storm. Combination of forerunner and onshore winds generated storm surges that were much higher (to 70 cm). It was also found that the characteristics of the typhoon (e.g., path, speed, severity and size) significantly influenced the generation of the forerunner. It is recommended that the forerunner that is not currently well defined in predictive models should be included in storm surge forecasts.

Keywords: storm surge; forerunner; mean sea level; typhoons; Vietnam coast

1. Introduction Potential impacts of extreme water level events on global coastlines are increasing as populations grow and mean sea levels rise. To better prepare for the future, coastal engineers and managers require accurate estimates of extreme water levels. Occurrence of extreme water levels along low-lying, highly populated and/or developed coastlines can lead to considerable loss of life and billions of dollars of damage to coastal infrastructure [1,2]. For countries located along tropical and sub-tropical regions, the most extreme events are generated through tropical storm systems referred to as tropical , hurricanes, and typhoons, depending on the region (here we adopt the term ‘typhoon’, the relevant

J. Mar. Sci. Eng. 2020, 8, 508; doi:10.3390/jmse8070508 www.mdpi.com/journal/jmse J. Mar. Sci. Eng. 2020, 8, 508 2 of 24 term for the study region). These storm systems are among the most energetic forcing agents for the coastal ocean and are associated with extreme winds and rainfall that result in severe damages to property and lives not only in coastal regions but also in the hinterland. Extreme high-water levels and surface gravity waves are also associated with typhoons, resulting in additional damage to coastlines through erosion and inundation [3,4]. In recent years, many typhoons have had severe impacts on coastal regions and through climate change it is expected that the severity and frequency of these events will increase. Recent notable storms include: (known in the as Super Typhoon Yolanda); in Vietnam; Tropical Cyclones Kyarr and Gonu in the Arabian Sea; Tropical Cyclones Hudhud in Bay of Bengal; Tropical Enawo in Madagascar; Hurricanes Sandy, Katrina and Harvey in the US; and Hurricane Irma in the Caribbean. Recently, under the impact of global warming and climate change, concerns about such storm surges and other extreme weather events have raised the awareness in scientists, conservationists, and governments [1,2,4–6]. Vietnam is one of the most vulnerable countries impacted from the extreme sea levels associated with storm surges due to a high proportion of low-lying coastal areas and being in a typhoon basin. Risks associated with storm surges were highlighted in public and scientific communities in Vietnam because of the devastating storm surge caused by Typhoon Haiyan (2013) in the Philippines, a country is also located in the same typhoon region as Vietnam. Very few studies addressing storm surges in Vietnam have been conducted, especially in terms of engineering and planning perspectives. A recent study by Thai et al. [7] examined the interaction of tide, surge, and waves on storm surges by using the SuWAT model. Others mainly focused on the general character and social impacts of storm surges in specific parts, for example, the vulnerability, experience or risk management in the southern part or Red River delta. This indicates a lack of understanding of storm surges particularly physical processes that contribute to the mechanisms of storm surge generation in Vietnam. The use of numerical models in predicting storm surges to provide early warnings and potential risks for coastal communities has become more popular in operational forecasting due to many advantages like their ability, power, and accuracy. Some storm surge models are well-developed such as the Sea, Lake, and Overland Surges from Hurricanes model (SLOSH); the Mike21 and Mike Flood models; the Delft3D coastal surge model; and the Meteorological Agency (JMA) storm surge model [8]. However, these models have not considered all the physical processes that contribute to generating the non-tidal sea level (defined as the residual component of the water level subsequent to removal of the tidal component) 2–3 days prior to storm landfall or forerunner. This can be the reason why forecasters in the of America (USA) were not able to predict a high surge along the western Louisiana and the northern Texas coasts before Hurricane Ike (2008) made landfall [9]. Similarly, forecasters in Vietnam did not provide any flood warnings regarding an abnormal increase in sea level for coastal regions due to (in 2014) when it made landfall in the north of Vietnam [10]. This indicates a major shortcoming of existing storm surge models in Vietnam and an urgent need to improve them for better future forecasting. Forerunner is defined as an increase in the mean water level up to several days prior to storm landfall. When the typhoon is far offshore (>500 km), the wind direction usually has a shore parallel component that generates alongshore currents bounded by the shoreline. This leads to an approximate geostrophic balance between the Coriolis force generated by the alongshore current and the across shelf pressure gradient (Figure1). The increase in water level at the coast is defined as Ekman set-up and expressed as [9,11–13]: Z f U ζ = dx (1) EK g where f is the Coriolis parameter (= 2Ωsinφ; Ω is angular velocity of the Earth and φ is the latitude), U is the alongshore ocean current speed, g is the gravitational acceleration, and x-axis aligned in the cross-shore direction. Thus, the factors that control timing and magnitude of forerunner are the Coriolis parameter; alongshore-current speed U and the cross-shore width of the current; the offshore limit of the integration is in Equation (1). Usually, at the beginning of the forerunner, the typhoon is located J. Mar. Sci. Eng. 2020, 8, 508 3 of 24 J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 3 of 25 usuallytyphoon far is (500–1000 located usually km) from far land,(500–1000 and thus,km) from the atmospheric land, and thus, pressure the atmosp effect (invertedheric pressure barometric effect e(invertedffect) does barometric not have a effect) significant does contribution. not have a signif Dueicant to the contribution. scale of the storm Due to system, the scale the windof the action storm onsystem, the continental the wind shelfaction is on present the continental a few days shelf prior is to present typhoon a few landfall, days resultingprior to typhoon in an increase landfall, in theresulting mean waterin an increase level, adding in the to mean the total water water level, level adding at the to peakthe total of the water storm level surge. at the Under peak strong of the 1 alongshorestorm surge. currents Under (~1strong ms− alongshore), the sea level currents gradients (~1 ms can−1), be the ~1 sea cm level per kmgradients of cross-shelf can be ~1 distance cm per that km canof cross-shelf be significant distance for storm that systemscan be thatsignificant have diameters for storm ~100 systems km orthat greater have [14diameters]. This is ~100 illustrated km or throughgreater the[14]. example This is illustrated of Hurricane through Ike (2008), the exampl where ite causedof Hurricane a large Ike and (2008), unpredicted where increaseit caused in a water large levelsand unpredicted along western increase Louisiana in water and northernlevels along Texas western coasts (USA)Louisiana with and water northern levels reachingTexas coasts 3 m above(USA) meanwith water sea level, levels 12–24 reaching h prior 3 tom hurricaneabove mean landfall sea level, [9]. 12–24 h prior to hurricane landfall [9].

Figure 1. (a) Schematic of the mechanics of forerunner formation in the northern hemisphere. When the Figure 1. (a) Schematic of the mechanics of forerunner formation in the northern hemisphere. When typhoon is far from the coast, shore parallel winds and alongshore currents drive an onshore Ekman the typhoon is far from the coast, shore parallel winds and alongshore currents drive an onshore flow; and (b) a cross-shelf schematic of the Ekman set-up, ζEK. Ekman flow; and (b) a cross-shelf schematic of the Ekman set-up, . The main aim of this paper is to examine, through analysis of tide gauge records, the contribution The main aim of this paper is to examine, through analysis of tide gauge records, the of forerunner to the non-tidal water level along the Vietnam coast. The results will contribute to an contribution of forerunner to the non-tidal water level along the Vietnam coast. The results will understanding of processes and the improvement of storm surge forecasting in Vietnam and other contribute to an understanding of processes and the improvement of storm surge forecasting in locations that are impacted by tropical storms (TCs). Vietnam and other locations that are impacted by tropical storms (TCs). This paper is arranged as follows: the study region (bathymetry, historical storm characteristics, This paper is arranged as follows: the study region (bathymetry, historical storm characteristics, and tidal regime) is described in Section2; the methodology is presented in Section3. Results and and tidal regime) is described in Section 2; the methodology is presented in Section 3. Results and discussion are given in Sections4 and5, respectively; and conclusions in Section6. discussion are given in Sections 4 and 5, respectively; and conclusions in Section 6. 2. Study Region 2. Study Region Vietnam is an S-shaped strip of land, stretching from 23◦230N to 8◦270N latitude in Southeast Asia borderingVietnam the Northwestis an S-shaped Pacific strip Ocean of land, (Figure stretching2). It is from in one 23°23 of the′N mostto 8°27 active′N latitude Typhoon in basinsSoutheast in Asia bordering the Northwest Pacific Ocean (Figure 2). It is in one of the most active Typhoon basins the world (~30% of the world0s annual tropical storms occur in this region). On average, 11 typhoons impactin the theworld East (~30% Sea of of Vietnam the world (also′s calledannual the tropical South Chinastorms Sea) occur annually, in this with region). ~6 typhoons On average, directly 11 atyphoonsffecting the impact Vietnam the East coast Sea [15 of,16 Vietnam]. Under (also climate called change, the South the numberChina Sea) and annually, intensity with of typhoons ~6 typhoons are expecteddirectly toaffecting increase the [1, 15Vietnam]. With 3260coast km [15,16]. of coastline Under and climate a high change, population the density,number Vietnam and intensity is one ofof thetyphoons most vulnerable are expected regions to increase to be impacted [1,15]. byWith typhoons 3260 km and of associatedcoastline and storm a high surges. population During Typhoon density, LindaVietnam (in 1997),is one 788of the people most died, vulnerable 1142 were regions injured, to be 2541 impacted people by were typhoons missing, and and associated 2789 boats storm and shipssurges. were During sunk [Typhoon16]. Deadly Linda Typhoon (in 1997), Doksuri 788 (2017) people was died, the most1142 destructivewere injured, typhoon 2541 topeople impact were the regionmissing, and and caused 2789 widespread boats and ships localized were flooding sunk [1 with6]. Deadly a severe Typhoon storm surge Doksuri (>2 m)(2017) [17]. was the most destructive typhoon to impact the region and caused widespread localized flooding with a severe storm surge (>2 m) [17].

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FigureFigure 2. 2. LocationLocation of of the the study study region region showing showing the the locations locations of of sea sea level level measuring measuring stations. stations. BathymetryBathymetry is is in in meters. meters. 2.1. Bathymetry 2.1. Bathymetry There are contrasting topographic and bathymetric features between the northern and southern There are contrasting topographic and bathymetric features between the northern and southern regions of the Vietnam coast. The ‘S’ shape coastline is bounded by two shallow Gulfs: regions of the Vietnam coast. The ‘S’ shape coastline is bounded by two shallow Gulfs: Gulf of Tonkin in the north and Gulf of in the south. The central region consists of a relatively narrow in the north and Gulf of Thailand in the south. The central region consists of a relatively narrow continental shelf. There are two main delta regions: Red River delta in the north and the Mekong continental shelf. There are two main delta regions: Red River delta in the north and the Mekong delta in south. These regions consist of low coastal topography (from 1 to 10 m above mean sea level), delta in south. These regions consist of low coastal topography (from 1 to 10 m above mean sea level), are associated with mega cities (e.g., population of Ho Chi Minh > 5 million) and are vulnerable are associated with mega cities (e.g., population of Ho Chi Minh > 5 million) and are vulnerable coastal regions to storm surge impacts. The Vietnam coast can be separated into three sections based coastal regions to storm surge impacts. The Vietnam coast can be separated into three sections based on the topographic characteristics of the continental shelf: (1) Northern section from 17 N to 22 N, on the topographic characteristics of the continental shelf: (1) Northern section from 17°N◦ to 22°N,◦ including the Gulf of Tonkin, which is relatively shallow with 50 m depth contour located far away including the Gulf of Tonkin, which is relatively shallow with 50 m depth contour located far away from the coastline (Figure2) and which is semi-enclosed with many estuaries, islands, and lowland from the coastline (Figure 2) and which is semi-enclosed with many estuaries, islands, and lowland areas; (2) Central section is from 11 N to 17 N with many mountains along the coast with narrow areas; (2) Central section is from 11°N◦ to 17°N◦ with many mountains along the coast with narrow coastal plains, and where the continental shelf is very narrow and steep with the 50 m depth contour coastal plains, and where the continental shelf is very narrow and steep with the 50 m depth contour located close to the coastline (Figure2); and (3) Southern section is a large tidal-flat with mild slopes located close to the coastline (Figure 2); and (3) Southern section is a large tidal-flat with mild slopes and shallow depths, and which is the downstream area of the Mekong River with many estuaries to and shallow depths, and which is the downstream area of the Mekong River with many estuaries to the East Sea. the East Sea.

2.2. Storm Characteristics Typhoons impact the East Sea mainly between June and November (northern summer) with ~11 typhoons impacting the coast annually. However, their influence on each of the three sections of coast is different: The Northern coast experiences the highest number (~58%), followed by the Central coast

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2.2. Storm Characteristics Typhoons impact the East Sea mainly between June and November (northern summer) with ~11 typhoons impacting the coast annually. However, their influence on each of the three sections of coast is different: The Northern coast experiences the highest number (~58%), followed by the Central coast (~37%) and the Southern coasts (~5%) [16]. Therefore, the northern and central sections are the most vulnerable impacts from typhoons and storm surges. The maximum storm surge height (3.6 m) was recorded in Ha Tinh province in Typhoon Dan (1989) along the northern section [15]. According to the results of the study of Takagi et al. [18], the potential maximum of the storm surge for the southern sections is ~1 m, which has a close relationship with potential disaster risks from TCs and storm surges for this coastal region. The frequency of typhoons and super-typhoons have increased in recent years by 28% from 1951 to 2014 for typhoons over level 12, and 47% for typhoons over level 10 [15].

2.3. Tidal Regime The tidal regime along the coast includes both diurnal and semi-diurnal tides with transitions between the two regimes (Table1). This is due to the tidal waves originating from both the Pacific and Indian Oceans propagating through narrow straits influenced by the complex topography of the East Sea [19]. The semi-diurnal tides from the Pacific Ocean entering the East Sea become predominantly diurnal with significant increasing amplitudes. Tidal amplitudes at the Hon Dau in the Northern Gulf of Tonkin are the largest with tidal amplitudes over 4 m (Figure2). Along the Vietnam coast, the tidal regime is diverse with different amplitudes that can be divided into four types: dominantly diurnal, mixed diurnal, semi-diurnal, and mixed semi-diurnal (Table1).

Table 1. Tidal characteristics along the coast of Vietnam [19].

Coastal Amplitude of Tidal Constituents Tidal Character Mean Tidal Location Section (Form Factor) Range (m) M2 (cm) S2 (cm) K1 (cm) O1 (cm) Mixed, semi-diurnal Chinese coast Hong Kong 38 15 36 29 2.0–2.5 (1.2) Northern Hon Dau 6 3 85 25 Diurnal tides (12.2) 3–4 Hon Ngu Diurnal tides (4.1) Coast 30 9 103 58 1.2–2.5 Son Tra 17 6 20 13 Mixed, semi-diurnal 0.5 Central Coast Quy Nhon (1.4) Mixed diurnal 18 6 34 27 1.2–2.0 regime (2.5) Southern Mixed semi-diurnal Vung Tau 79 30 61 46 3–4 Coast (1.0)

3. Methodology

3.1. Data Water level data, at hourly intervals, from 1 January 2013 to 31 December 2017, were obtained from the Hydro-meteorological and Environmental Station Network Center (the Hymenet, Vietnam) for five tide gauge stations along the Vietnam coast (Figure2). These data were augmented by the record at Hong Kong from the research quality dataset available through the NOAA National Centers for Environmental Information joint archive for sea level [20]. Although the Hong Kong station does not belong to the Vietnam coast, it was included because forerunner or storm surges at this location could propagate to the south, influencing sea levels along the northern coast of Vietnam. The locations of the tide gauges used in the analysis are (see Figure2 and Table2): Hong Kong, Hon Dau, Hon Ngu, Son Tra, Quy Nhon, and Vung Tau, spanning a distance ~2330 km. J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 6 of 25

Table 2. Tide gauge information.

J. Mar.Station Sci. Eng. Name2020, 8 ,* 508 Latitude Longitude Country 6 of 24

Hong Kong 22.30 114.20 Hong Kong Table 2. Tide gauge information. Hon Dau 20.66 106.80 Vietnam Station Name * Latitude Longitude Country Hon Ngu Hong Kong 18.80 22.30 114.20 105.76 Hong Kong Vietnam Hon Dau 20.66 106.80 Vietnam Son Tra Hon Ngu 16.10 18.80 105.76 108.21 Vietnam Vietnam Son Tra 16.10 108.21 Vietnam Quy NhonQuy Nhon 13.77 13.77 109.25 109.25 Vietnam Vietnam Vung Tau 10.34 107.07 Vietnam Vung Tau 10.34 107.07 Vietnam * the sampling interval at all locations was 1 h. * the sampling interval at all locations was 1 h. Information on historic typhoon events were extracted from the Japan Meteorological Agency (JMA),Information which provided on historic details typhoon on time events histories were of extracted storm parameters from the such Japan as Meteorological longitude and latitude Agency of (JMA),central which pressure, provided storm details intensity, on maximumtime histories sustained of storm wind, parameters wind direction, such as and longitude radius forand maximum latitude ofwinds. central The pressure,u and v stormcomponents intensity, of windmaximum at 10 msu werestained obtained wind, fromwind ERA5 direction, observations and radius ECMWF for maximumreanalysis winds. (ERA5) The at hourly u and intervals.v components These of databases wind at 10 were m usedwere toobtained identify from the characteristics ERA5 observations of each ECMWFtyphoon reanalysis that included (ERA5) its path, at intensity,hourly intervals. size, and windThese field. databases were used to identify the characteristicsOver the of study each periodtyphoon (2013 that andincluded 2017), its ~25 path, typhoons intensity, from size, a and total wind 59 typhoons field. in the region propagatedOver the into study the period East Sea (2013 of Vietnam, and 2017), and ~25 directly typhoons affected from the a Vietnamtotal 59 typhoons coast with in strong the region winds, propagatedheavy rains, into large the waves,East Sea and of Vietnam, high storm and surges. directly This affected study the focused Vietnam only coast on with typhoons strong that winds, were heavycategorized rains, large as tropical waves, storms and high (TS) andstorm higher surges. in severityThis study according focused to only Categorization on typhoons of that TCs were in the categorizedwestern North as tropical Pacific withstorms the (TS) max and wind higher speed in from severity 34 kt according and higher to (i.e., Categorization severe tropical of TCs storm in (STS)the westernand typhoon North (TY))Pacific and with made the landfallmax wind either speed along from the 34 Vietnam kt and coasthigher and (i.e., along severe the southerntropical storm (STS)coast, and which typhoon also a(TY))ffected and the made water landfall levels alongeither thealong Vietnam the Vietnam coast through coast and direct along storm the surgesouthern and Chinaforerunner. coast, which Based also on preliminaryaffected the analysiswater levels and along typhoon the Vietnam information, coast 14 th tropicalrough direct storms, storm including surge andsevere forerunner. tropical storms, Based andon 11preliminary typhoons wereanalysis chosen and for typhoon analysis (Figureinformation,3a). 14 tropical storms, including severe tropical storms, and 11 typhoons were chosen for analysis (Figure 3a).

Figure 3. Cont.

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Figure 3. Tracks of typhoons that made landfall on the Vietnam coast from 2013 to 2017: (a) all tracks; Figure(b) typhoons 3. Tracks making of typhoons landfall that on made the Northern landfall coast;on the (c Vietnam) typhoons coast making from landfall2013 to on2017: the (a Central) all tracks; coast; (b()d )typhoons typhoons making making landfall landfall on on the the Northern South coast. coast; Key (c) to typhoons the numbers making referring landfall to on typhoons: the Central 1–TS coast;Bebinca (d) typhoons (June-13), making 2–STS Rumbia landfall (June-13), on the South 3–STS coast. Jebi (August-13),Key to the numbers 4–TS Mangkhut referring (August-13), to typhoons: 5–TY 1– TSUtor Bebinca (August-13), (June-13), 6–TY 2–STS Wutip Rumbia (September-13), (June-13), 3–STS 7–TY Jebi Nari (August-13), (October-13), 4–TS 8–TY Mangkhut Haiyan (November-13), (August-13), 5–TY9–TS PodulUtor (August-13), (November-13), 6–TY 10–TY Wutip Rammasun (September (July-14),-13), 11–TY 7–TY Kalmaegi Nari (October-13), (September-14), 8–TY 12–TS Haiyan Sinlaku (November-13),(November-14), 9–TS 13–TS Podul Kujira (June-15),(November-13), 14–TS Vamco10–TY (September-15),Rammasun (July-14), 15–TY Mujigae 11–TY (October-15),Kalmaegi (September-14),16–STS Mirinae 12–TS (July-16), Sinlaku 17–TS (November-14), Dianmu (August-16), 13–TS Kujira 18–TS (June-15), Rai 14–TS (September-16), Vamco (September-15), 19–TY Sarika 15–TY(October-16), Mujigae 20–STS (October-15), Talas (July-17), 16–STS 21–TSMirinae Sonca (July-16), (July-17), 17–TS 22–TY Dianmu Doksuri (August-16), (September-17), 18–TS 23–TY Rai (September-16),Damrey (November-17), 19–TY Sarika 24–TS (October-16), Kirogi (November-17), 20–STS Talas 25–TY (July-17), Tembin 21–TS (December-17). Sonca (July-17), Abbreviations: 22–TY DoksuriTS: Tropical (September-17), Storm, STS: 23–TY Severe Damrey Tropical (November-17), Storm, TY: Typhoon. 24–TS Kirogi (November-17), 25–TY Tembin (December-17). Abbreviations: TS: Tropical Storm, STS: Severe Tropical Storm, TY: Typhoon. 3.2. Data Analysis 3.2. DataTime Analysis series data at hourly intervals, obtained from the six different stations (Figure2) between JanuaryTime 2013 series and data December at hourly 2017, intervals, were allobtained very high from quality the six with different no missing stations data (Figure along 2) the between Vietnam Januarycoast. All 2013 the and time December series were 2017, subjected were all to very different high analysesquality with to extract no missing information data along on the the direct Vietnam storm coast.surge All and the forerunner. time series were subjected to different analyses to extract information on the direct storm surge and forerunner. 3.2.1. Tidal Harmonic and Fourier Analysis 3.2.1. TidalThe storm Harmonic surge and is defined Fourier as Analysis the non-tidal component of the water level. Thus, the initial step of theThe analysis storm was surge to remove is defined the as tidal the component non-tidal compon from theent observed of the water time series.level. Thus, This wasthe initial undertaken step ofthrough the analysis standard was harmonic to remove analysis the thattidal used compon a leastent squares from analysisthe observed technique time to extractseries. informationThis was undertakenon the amplitude through and standard phase ofharmonic the major analysis tidal constituents that used a in least the timesquares series analysis [21,22 ].technique The T_TIDE to extractpackage information developed on and the implemented amplitude and for MATLABphase of bythe Pawlowicz,major tidal etconstituents al. [23] was in used the for time extracting series [21,22].the tidal The constituents. T_TIDE package A total developed of 35 tidal and constituents implemented were for used MATLAB in the by analysis. Pawlowicz, T_TIDE et al. output [23] was also usedincludes for extracting the residual the time tidal series constituents. (the storm A surgetotal of component) 35 tidal constituents as the difference were used between in the the analysis. observed T_TIDEwater level output and also the includes predicted the tide. residual time series (the storm surge component) as the difference betweenAlthough the observed classical water harmonic level and analysis the predicted has been tide. developed and widely accepted to analyze and predictAlthough tides, itclassical still has harmonic some shortcomings. analysis has Tidalbeen edevelopedffects in shallow and widely coastal accepted regions to may analyze be aff ectedand predictby a variety tides, it of still nonlinear has some effects shortcomings. when tides Tidal on the effects deep in ocean shallow propagate coastal through regions shallowermay be affected coastal bywaters a variety [23]. of These nonlinear effects effects may include when interactiontides on the between deep ocean tides andpropagate varying through topography, shallower a combination coastal watersof tide [23]. and wave,These andeffects seasonal may changesinclude inintera salinityction and between flow near tides estuaries and varying [24,25]. topography, Of relevance a to combinationthis study is of the tide role and of tidewave,/surge and interaction.seasonal changes With strong in salinity forces, and such flow as near that experiencedestuaries [24,25]. during Of a relevancetyphoon, to the this timing study of low is /thehigh role water of cantide/surge shift in relationinteraction. to the With predicted strong timing forces, of lowsuch/high as water.that experiencedThus, when during the diff erencea typhoon, between the thetiming observed of low/hi watergh levels water and can predicted shift in re tidelation is calculated to the predicted to obtain timing of low/high water. Thus, when the difference between the observed water levels and predicted tide is calculated to obtain the residual time series, a component of the tidal energy is contained in

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J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 8 of 25 the residual time series, a component of the tidal energy is contained in the residual time series [26]. Asthe an residual example, time the series residual [26]. time As an series example, for Typhoon the residual Kalmaegi time series indicated for Typhoon a diurnal Kalmaegi signal at Hon indicated Dau asa diurnal shown bysignal the blueat Hon line Dau in Figure as shown4. by the blue line in Figure 4.

FigureFigure 4. 4. CombinationCombination betweenbetween tidaltidal analysisanalysis andand low-passlow-pass filterfilter atat HonHon DauDau stationstation inin TyphoonTyphoon KalmaegiKalmaegi (September (September 2014). 2014).

TidalTidal time time series series were were subjected subjected to to Fourier Fourier analysis analysis to to identify identify the the dominant dominant frequencies frequencies in in the the records.records. Here, Here, the the power power spectrum spectrum was was constructed constructed using using the hourlythe hourly data overdata theover 5-year the 5-year period period using theusing Welch the methodWelch method as implemented as implemented in MATLAB. in MATLAB. 3.2.2. Low-Pass Filtering 3.2.2. Low-Pass Filtering A low-pass filter was used to minimize the issues associated with harmonic analysis, particularly A low-pass filter was used to minimize the issues associated with harmonic analysis, particularly the tide/surge interaction. The low-pass filtering has the effect of removing higher frequency signals the tide/surge interaction. The low-pass filtering has the effect of removing higher frequency signals including tides in the observed sea levels [27]. In this study, the 72-h filter proposed by Pugh [13] including tides in the observed sea levels [27]. In this study, the 72-h filter proposed by Pugh [13] specifically for the analysis of hourly tidal records was used and has the characteristic of removing specifically for the analysis of hourly tidal records was used and has the characteristic of removing periods less than 36 h [6,13]. According to Pugh [13], the filter is “a good compromise between periods less than 36 h [6,13]. According to Pugh [13], the filter is “a good compromise between minimizing the loss of data at the beginning and end of each record and maximizing the sharpness minimizing the loss of data at the beginning and end of each record and maximizing the sharpness of the cut-off at frequencies just below the diurnal tidal band”. After filtering, the low-pass water of the cut-off at frequencies just below the diurnal tidal band”. After filtering, the low-pass water level time series of 16 days extent was used for each storm that included 6 days before and 10 days level time series of 16 days extent was used for each storm that included 6 days before and 10 days after landfall (Figure4). To identify the signals associated with the storm on sea level, the low-pass after landfall (Figure 4). To identify the signals associated with the storm on sea level, the low-pass water level was compared at all stations. The purpose of this task was to identify and eliminate water level was compared at all stations. The purpose of this task was to identify and eliminate the the local influence of synoptic systems on sea level observations at a single station [6]. In addition, local influence of synoptic systems on sea level observations at a single station [6]. In addition, the the propagation of a water level signal can be examined by using the trough and crest points for a propagation of a water level signal can be examined by using the trough and crest points for a signal. signal. The trough point precedes a larger water level peak, which is a marker of a positive surge The trough point precedes a larger water level peak, which is a marker of a positive surge formed formed due to TC at the nearest tide station [6]. due to TC at the nearest tide station [6]. 3.2.3. Influence of Local Features and Storm Parameters 3.2.3. Influence of Local Features and Storm Parameters To assess the influence of local features on the forerunner, the selected storms were divided To assess the influence of local features on the forerunner, the selected storms were divided based on their landfall locations along the three coastal sections described in Section 2.1: northern based on their landfall locations along the three coastal sections described in Section 2.1: northern (including the southern coast of China, Figure3b), central (Figure3c) and southern (Figure3d) sections. (including the southern coast of China, Figure 3b), central (Figure 3c) and southern (Figure 3d) Similar storm paths were considered to examine the role of local features based on the difference in sections. Similar storm paths were considered to examine the role of local features based on the water levels due to typhoons [7]. difference in water levels due to typhoons [7]. Storm parameters were also considered to provide the intensity of typhoons on the generation and propagation of forerunner as well as to extract forerunner surge from the water level time series. According to Liu & Irish [11], storm track parameters influence the generation of forerunner surge,

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J. Mar.Storm Sci. Eng. parameters 2020, 8, x FOR were PEER alsoREVIEW considered to provide the intensity of typhoons on the generation 9 of 25 and propagation of forerunner as well as to extract forerunner surge from the water level time series. especiallyAccording a torapid Liu and & Irish large [11 forerunner.], storm track They parameters reported that influence sea level the pressure, generation radius, of forerunner and speed surge,were theespecially most significant a rapid and parameters large forerunner. because slow-moving, They reported large, that intense sea level storms pressure, can generate radius, and and sustain speed strongwere the alongshore most significant currents. parameters Intense typhoons because slow-moving,may create higher large, forerunner intense storms and canstorm generate surges. and In contrast,sustain strong this may alongshore not be accurate currents. for Intense all the typhoons cases of higher may create storm higher surges forerunner due to the and dependence storm surges. on manyIn contrast, other thisfactors, may for not example, be accurate angle for of all approach the cases of of the higher typhoo stormn, radius surges of due maximum to the dependence winds and theon manyslope otherof the factors, continental for example, shelf may angle also of have approach an influence of the typhoon, [28]. Some radius historical of maximum typhoons winds in Vietnamand the slopeindicated of the that continental not every severe shelf may typhoon also havegenerated an influence a high storm [28]. surge. Some historicalFor example, typhoons typhoon in DamreyVietnam (2017), indicated the that strongest not every storm severe in the typhoon last 16 generatedyears that aimpacted high storm the surge. central For part example, of the Vietnam typhoon coast,Damrey had (2017), no record the strongest on high storm storm surges in the lastduring 16 years the typhoon. that impacted Storm thesize central is a significant part of the factor Vietnam that contributescoast, had noto recordthe generation on high stormof forerunner surges during[9,11] through the typhoon. the effects Storm of size Ekman is a significantsetup (Figure factor 1; Equationthat contributes (1)). Storm to the size generation can indicate of forerunnerthe spatial [re9,gion11] through influenced the by eff ectsthe typhoon. of Ekman Therefore, setup (Figure wind1; fieldEquation will be (1)). used Storm to determine size can indicate wind direction the spatial and region the size influenced of the storm. by the Moreover, typhoon. with Therefore, tide gauge wind onfield the will right be side used of to the determine storm in windthe northern direction hemisphere, and the size the of difference the storm. between Moreover, the withforerunner tide gauge and directon the storm right sidesurge of due the stormto onshore in the wind northern was hemisphere,difficult to distinguish. the difference Therefore, between it the was forerunner necessary and to usedirect the storm storm surge size dueto assess to onshore the spatial wind wasextent diffi ofcult the to storm distinguish. as well Therefore, as wind field it was to necessary make a better to use estimate.the storm The size speed to assess of storm the spatial was also extent considered of the storm during as wellthe period as wind 4–6 field days to before make ait bettermade estimate.landfall. TheThe storm speed speed of storm was was estimated also considered using longitude during theand period latitude 4–6 information days before on it the made location landfall. of the The central storm pressurespeed was at estimatedspecific times. using longitude and latitude information on the location of the central pressure at specific times. 4. Results 4. Results 4.1. Sea Level Time Series 4.1. Sea Level Time Series Time series of sea level at the two stations located at the two extremes, in the north and south, Hon DauTime (HD) series and of Vung sea level Tau at (VT), the tworecorded stations the locatedhighest atwater the twolevel extremes, range of ~4.0 in the m northwhilst and the range south, atHon Son Dau Tra (HD) (ST) andwas Vungthe smallest Tau (VT), with recorded a range the 2.0–2.5 highest m water(Figure level 5). rangeThus, ofthe ~4.0 tidal m range whilst decreases the range fromat Son north Tra (ST) to the was central the smallest section with and a then range increases 2.0–2.5 m to (Figure the south.5). Thus, The theincrease tidal rangein tidal decreases range in from the northnorth and to the south central may section be related and thento the increases presence to of the relatively south. Theshallow increase water in in tidal these range regions in the (Figure north 2).and Also, south Hon may Dau be has related mainly to the diurnal presence tides of whilst relatively Vung shallow Tau experiences water in these semi-diurnal regions (Figure tides and2). Also,thus thereHon Dauis also has a transition mainly diurnal in the tidestidal whilstcharacter Vung from Tau north experiences to south semi-diurnal (Table 1). tides and thus there is also a transition in the tidal character from north to south (Table1).

FigureFigure 5. 5. ObservedObserved water water level level time time series series at at all all stations stations.. Note Note that that each each time time is is displaced displaced by by 300 300 cm. cm.

Low-pass filtered sea level time series, constructed using the 72-h filter [13], indicated two main features (Figure 6): (1) the seasonal cycle with higher mean water levels towards the end of the year during winter (November/December) and lower water levels during the summer (July/August); and, (2) isolated peaks in water level relating to storm surges generated from typhoon forcing. Some other

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Low-pass filtered sea level time series, constructed using the 72-h filter [13], indicated two main features (Figure6): (1) the seasonal cycle with higher mean water levels towards the end of the year duringJ. Mar. Sci. winter Eng. 2020 (November, 8, x FOR PEER/December) REVIEW and lower water levels during the summer (July/August); 10 of and,25 (2) isolated peaks in water level relating to storm surges generated from typhoon forcing. Some peaks may reflect an increase in water level due to localised changes in atmospheric conditions and/or other peaks may reflect an increase in water level due to localised changes in atmospheric conditions tropical depression. The northern stations (Hong Kong, Hon Dau, Hon Ngu and Son Tra) indicated and/or tropical depression. The northern stations (Hong Kong, Hon Dau, Hon Ngu and Son Tra) a higher number of surges compared to the two southern stations (Quy Nhon and Vung Tau), indicated a higher number of surges compared to the two southern stations (Quy Nhon and Vung indicating a higher number of storm impacts on the northern and central coasts compared to the Tau), indicating a higher number of storm impacts on the northern and central coasts compared to southern coast. In addition, there were a higher number of surges between June and December the southern coast. In addition, there were a higher number of surges between June and December (Figure 6). In fact, the strongest typhoons recorded during the study period occurred during the (Figuresecond6 ).half In of fact, the the year. strongest These include, typhoons Typhoon recorded Utor during (August the 2013)—number study period occurred5, Typhoon during Wutip the second(September half of2013)—number the year. These 6, include, Typhoon (October Utor (August 2013)—number 2013)—number 7, Typhoon 5, Typhoon Haiyan Wutip (September(November 2013)—number 2013)—number 6, 8, Typhoon Typhoon Nari Kalmaegi (October (September 2013)—number 2014)—number 7, Typhoon 11, Haiyan and (NovemberTyphoon 2013)—numberDoksuri (September 8, Typhoon 2017)—number Kalmaegi (September22. In combination 2014)—number with higher 11, andmean Typhoon seasonal Doksuri water (Septemberlevel, the 2017)—numberstorm surges occurring 22. In combination later in the withyear resulted higher mean in higher seasonal total waterwater level,levels. the There storm was surges also an occurring inter- laterannual in the variability: year resulted during in higher 2013 and total 2017 water there levels. were There more was storm also surges an inter-annual compared variability: to other years during 2013(Figure and 6). 2017 there were more storm surges compared to other years (Figure6).

FigureFigure 6. 6.Time Time seriesseries of low frequency frequency water water levels levels from from 2013 2013 to to2017. 2017. Note Note that that each each time time series series is isdisplaced displaced by by 100 100 cm. cm. Key Key to to numbers numbers referring referring to to typhoons: typhoons: 1–TS 1–TS Bebinca Bebinca (June-13), (June-13), 5–TY 5–TY Utor Utor (August-13),(August-13), 6–TY 6–TY Wutip Wutip (September-13), (September-13), 7–TY 7–TY Nari Nari (October-13), (October-13), 8–TY 8–TY Haiyan Haiyan (November-13),(November-13), 11–TY11– KalmaegiTY Kalmaegi (September-14), (September-14), 13–TS 13–TS Kujira Kujira (June-15), (June-15), 14–TS 14–TS VamcoVamco (September-15), 16–STS 16–STS Mirinae Mirinae (July-16),(July-16), 18–TS 18–TS Rai Rai (September-16), (September-16), 19–TY19–TY Sarika Sarika (O (October-16),ctober-16), 20–STS 20–STS Talas Talas (July-17), (July-17), 22–TY 22–TY Doksuri Doksuri (September-17).(September-17). Abbreviations:Abbreviations: L: L: Low Low pressure, pressure, TD TD:: Tropical Tropical Depression, Depression, TS: TS: Tropical Tropical Storm, Storm, STS: STS: SevereSevere Tropical Tropical Storm, Storm, TY: TY: Typhoon, Typhoon, MS: MS:Monsoon Monsoon SeasonSeason (Northeast wind). wind).

ResultsResults of of Fourier Fourier spectralspectral analysisanalysis to the obse observedrved time time series series indicated indicated the the highest highest spectral spectral energyenergy at at the the diurnal diurnal and and semi-diurnalsemi-diurnal frequencies (Figure (Figure 7).7). The The seasonal seasonal cycle cycle was was reflected reflected with with peakspeaks at at 180 180 days days and and 11 year.year. TheThe dominationdomination of the the diurnal diurnal tides tides at at Hon Hon Dau Dau station station was was reflected reflected withwith the the diurnal diurnal peak peak largerlarger thanthan thethe semi-diurnal peak; peak; whilst whilst at at Vung Vung Tau, Tau, the the semi-diurnal semi-diurnal peak peak waswas dominant. dominant. AtAt otherother stations,stations, the characteristics of of tidal tidal regime regime indicated indicated a amix mix of ofdiurnal diurnal and and semi-diurnalsemi-diurnal frequencies frequencies (Figure (Figure7 ).7). There There werewere alsoalso peaks at intra-tidal frequencies frequencies (<12 ( <12 h), h), reflecting reflecting thethe shallow shallow water water tides tides at at8, 8, 66 andand 44 h.h. The The 6 6 h h peak peak was was highest highest at at Hong Hong Kong Kong whilst whilst there there appeared appeared toto be be a a broad broad peak peak at at SonSon TraTra stationstation betweenbetween 2 2 h h and and 3 3 h h that that may may be be related related to to continental continental shelf shelf seichesseiches [29 [29].].

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Figure 7. Spectral analysis of water levels at six stat stationsions from 2013 to 2017. Key: HK: HK: Hong Hong Kong; Kong; HD: Hon Dau; HN: Hon Ngu; ST: SonSon Tra,Tra, QN:QN: QuyQuy Nhon;Nhon; VT:VT: VungVung Tau. Tau.

4.2. Forerunner 4.2.1. Forerunner Generated by Alongshore Wind 4.2.1. Forerunner Generated by Alongshore Wind The forerunner is generated through the combination of alongshore winds and currents that The forerunner is generated through the combination of alongshore winds and currents that induces an Ekman set-up at the coast (Figure1). In general, for all typhoons, a gradual increase in water induces an Ekman set-up at the coast (Figure 1). In general, for all typhoons, a gradual increase in level at the coast occurred several days prior to landfall of the typhoon (i.e., when the typhoon was far water level at the coast occurred several days prior to landfall of the typhoon (i.e., when the typhoon away from the coast). As an example, during (September 2013), the forerunner may be was far away from the coast). As an example, during Typhoon Wutip (September 2013), the clearly identified in the time series (Figure8). On 25 September 2013, a tropical depression started to forerunner may be clearly identified in the time series (Figure 8). On 25 September 2013, a tropical intensify off the west coast of the Philippines. The system tracked towards the west and strengthened depression started to intensify off the west coast of the Philippines. The system tracked towards the to a tropical storm that was named Wutip on 27 September. Tropical storm Wutip strengthened further west and strengthened to a tropical storm that was named Wutip on 27 September. Tropical storm to a severe tropical storm as it moved westwards on 28 September, and rapidly became a typhoon Wutip strengthened further to a severe tropical storm as it moved westwards on 28 September, and and made landfall at 16:00 local time (09Z UTC) on 30 September to the north of Son Tra (Figure9b). rapidly became a typhoon and made landfall at 16:00 local time (09Z UTC) on 30 September to the The time-series indicated that the mean sea level started to increase around 19:00 local time (12Z UTC) north of Son Tra (Figure 9b). The time-series indicated that the mean sea level started to increase on 25 September when the storm was located close to the Philippines and continued to increase linearly around 19:00 local time (12Z UTC) on 25 September when the storm was located close to the until 6:00 local time on 29 September 2013 (23Z UTC 28 September), followed by a more rapid increase Philippines and continued to increase linearly until 6:00 local time on 29 September 2013 (23Z UTC in the water level (Figure8). The initial increase in the water level was ~50 cm and this is the forerunner 28 September), followed by a more rapid increase in the water level (Figure 8). The initial increase in (Figure8). All the coastal stations, except Hong Kong, indicated an increase in mean sea level over the water level was ~50 cm and this is the forerunner (Figure 8). All the coastal stations, except Hong this period (Figure9a). The wind field associated with a typhoon in the northern hemisphere rotates Kong, indicated an increase in mean sea level over this period (Figure 9a). The wind field associated anti-clockwise around the and as the typhoon approaches the Vietnam coast there is onshore with a typhoon in the northern hemisphere rotates anti-clockwise around the eye and as the typhoon (offshore) winds to the north (south) of the landfall. This allows for an increase (decrease) in water approaches the Vietnam coast there is onshore (offshore) winds to the north (south) of the landfall. level to regions to the north (south) of the typhoon landfall due to onshore (offshore) winds. In the case This allows for an increase (decrease) in water level to regions to the north (south) of the typhoon of Typhoon Wutip, stations Hon Dau and Hon Ngu recorded an increase in water level (positive storm landfall due to onshore (offshore) winds. In the case of Typhoon Wutip, stations Hon Dau and Hon surge, +0.75 m) as expected (Figure9a). However, station Son Tra located to the south of the landfall Ngu recorded an increase in water level (positive storm surge, +0.75 m) as expected (Figure 9a). also recorded a positive storm surge (Figures8 and9a) and this is the forerunner. During Severe However, station Son Tra located to the south of the landfall also recorded a positive storm surge Tropical Storm Talas (in 2017), a similar situation occurred with the forerunner contributing to an (Figures 8 and 9a) and this is the forerunner. During Severe Tropical Storm Talas (in 2017), a similar situation occurred with the forerunner contributing to an increase in water level at all stations before

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J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 12 of 25 increase in water level at all stations before landfall (Figure 10). Tracks for both these typhoons were verylandfall similar (Figure (Figure 10).9b Tracks or Figure for both10b). these The tracktyphoons for Typhoon were very Utor similar (in 2013)(Figure was 9b located or Figure further 10b). toThe thetrack north for such Typhoon that Hong Utor (in Kong 2013) recorded was located a positive furthe stormr to the surge north due such to onshore that Hong winds. Kong In recorded contrast, a stationspositive located storm insurge Vietnam due recordedto onshore the winds. forerunner, In contrast, prior to stations landfall located (Figure in 11 Vietnama), and after recorded landfall the thereforerunner, was a rapid prior decrease to landfall in the (Figure water 11a), level and as theafter off landfallshore winds there transported was a rapid water decrease away in fromthe water the coastlevel (Figure as the 11offshorea). winds transported water away from the coast (Figure 11a).

FigureFigure 8. 8.Time Time series series of of observed, observed residual, residual and and low-pass low-pass filtered filtered water water levels levels during during Typhoon Typhoon Wutip Wutip (24(24 September September to to 9 October9 October 2013) 2013) at at Son Son Tra Tra tide tide gauge. gauge.

AnalysisAnalysis of of the the low low passed passed filtered filtered time seriestime series from 2013 from to 2013 2017 atto each2017 of at the each six stationsof the six indicated stations thatindicated most of that the selectedmost of stormsthe selected indicated storms the indica presenceted ofthe a presence forerunner of (Figure a forerunner 12a). The (Figure mean 12a). height The ofmean the forerunner height of atthe all forerunner typhoons duringat all typhoons 5 years was during 0.20 m.5 years The highest was 0.20 forerunner m. The highest magnitudes forerunner were recordedmagnitudes at Hon were Dau recorded and Son at Tra Hon with Dau 40–50 and Son cm (FigureTra with 12 40–50a) with cm Hon (Figure Ngu, 12a) Quy with Nhon Hon and Ngu, Hong Quy KongNhon recording and Hong maximum Kong recording forerunners maximum between forerunners 20 and 30 cm.between 20 and 30 cm. 4.2.2. Forerunner in Combination with Onshore Wind 4.2.2. ForerunnerAn increase in of Combination water level prior with to Onshore landfall Wind was caused not only by the forerunner (generated by alongshoreAn increase wind), of water but levelthere prior were to landfallinstances was where caused onshore not only wind by the (wind forerunner set-up) (generated was also by a alongshorecontributing wind), factor. but When there werea typhoon instances is far where from onshorethe coast, wind its wind (wind field set-up) is alongshore, was also a generating contributing the factor.forerunner When to a typhoon increase isthe far water from thelevel coast, at the its statio windn fieldclosest is alongshore,to its location. generating When the the typhoon forerunner moves to increasecloser to the the water coast, level the distance at the station between closest typhoon to its ce location.ntre and When tide station the typhoon becomes moves shorter. closer Stations to the to coast,the north the distance of the typhoon between track typhoon will centreexperience and tidean onshore station becomescomponent shorter. of wind Stations that will to the increase north ofthe thewater typhoon level trackat the will coast. experience In this ansituation, onshore it componentwas difficult of windto separate, that will using increase the observations, the water level the atcontribution the coast. In of this alongshore situation, and it was onshore difficult wind to components. separate, using Therefore, the observations, a category the that contribution combined both of alongshoremechanisms and was onshore created. wind Station components. Son Tra in Therefore, typhoon aNari category (October that 2013) combined is an example. both mechanisms The mean wassea created.level began Station increasing Son Tra around in typhoon 19:00 Narilocal (Octobertime from 2013) (12Z is UTC) an example. on 10 October The mean 2013 seawhen level the begantyphoon increasing was approaching around 19:00 the local Philippines, time from and (12Z then UTC) it continued on 10 October to increase 2013 when significantly the typhoon until was 17Z approachingUTC 14 October the Philippines, with the highest and then forerunner it continued magnit to increaseude peak significantly of about 74 untilcm (Figure 17Z UTC 13a). 14 The October wind withvector the maps highest indicated forerunner that magnitudewind direction peak near of about Son Tra 74 cm island (Figure station 13a). was The always wind parallel vector maps to the indicatedshore, even that when wind the direction typhoon near was Son close Tra islandto this stationstation was(Figure always 13c–f). parallel In addition, to the shore, Son Tra even is whenlocated theto typhoonthe right wasof the close typhoon to this track, station because (Figure onshore 13c–f). wind In addition, will transport Son Tra wate is locatedr onshore to the (Figure right of13g,h). the typhoonThis combined track, because with onshorestorm intensity wind will created transport an extremely water onshore high (Figure surge 13thatg,h). included This combined both the withforerunner storm intensity and direct created storm an surge. extremely high surge that included both the forerunner and direct storm surge.

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FigureFigure 9. (a )9. Time (a) Time series series of low-passof low-pass filtered filtered water water levelslevels during Typhoon Typhoon Wutip Wutip (24 (24 September September to 9 to 9 OctoberOctober 2013) 2013) at all at stations;all stations; (b )(b the) the track track of of Typhoon Typhoon Wutip;Wutip; and and ( (cc––hh)—wind)—wind fields fields at 12Z at 12Z UTC UTC from from 25 September25 September to 30 to September 30 September 2013. 2013. Key: Key: HK: HK: HongHong Kong; HD: HD: Hon Hon Dau; Dau; HN: HN: Hon Hon Ngu; Ngu; ST: Son ST: SonTra, Tra, QN: QuyQN: Quy Nhon; Nhon; VT: VT: Vung Vung Tau. Tau.

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Figure 10. (a) Time series of observed, residual and low-pass filtered water levels during Severe Figure 10. (a) Time series of observed, residual and low-pass filtered water levels during Severe Tropical Storm Talas (11–27 July 2017) at all stations; (b) the track of Severe Tropical Storm Talas; and Tropical Storm Talas (11–27 July 2017) at all stations; (b) the track of Severe Tropical Storm Talas; and (c–f)—wind(c–f)—wind field field at 12Z at 12Z UTC UTC from from 13 13 to to 16 16 July July 2013. 2013. Key:Key: HK: HK: Hong Hong Kong; Kong; HD: HD: Hon Hon Dau; Dau; HN: HN: Hon Hon Ngu;Ngu; ST: Son ST: Tra,Son Tra, QN: QN: Quy Quy Nhon; Nhon; VT: VT: Vung Vung Tau. Tau. The The wind wind speedspeed colour scale scale is is given given on on Figure Figure 9. 9.

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Figure 11. (a) Time series of observed, residual and low-pass filtered water levels during Figure 11. (a) Time series of observed, residual and low-pass filtered water levels during Typhoon (9–23 August 2013) at all stations; (b) the track of Typhoon Utor; and (c–h)—wind fields at 12Z UTC Utor (9–23 August 2013) at all stations; (b) the track of Typhoon Utor; and (c–h)—wind fields at 12Z from 11 to 16 August 2013. Key: HK: Hong Kong; HD: Hon Dau; HN: Hon Ngu; ST: Son Tra, QN: Quy UTC from 11 to 16 August 2013. Key: HK: Hong Kong; HD: Hon Dau; HN: Hon Ngu; ST: Son Tra, Nhon; VT: Vung Tau. The wind speed colour scale is given on Figure9. QN: Quy Nhon; VT: Vung Tau. The wind speed colour scale is given on Figure 9.

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Figure 12. Magnitude of typhoons induced: (a) forerunner surge; and (b) combination of forerunner and onshore wind.

FigureResults 12. of Magnitude the magnitude of typhoons surge induced: generated (a) by forerunner the combined surge; actionand (b)of combination the forerunner of forerunner and onshore windand indicated, onshore as wind. expected, higher surges than the forerunner alone (Figure 12). The highest magnitude was at Son Tra, 74 cm during Typhoon Nari (October 2013); followed by Hong Kong, 68 cm during Typhoon Kalmaegi (September 2014); and, Hon Ngu, 63 cm during Typhoons Wutip (September 2013) and Nari (Figure 12b). Under other storms, the magnitudes of the combined surge were 20–40 cm. It was also noticeable that Vung Tau observed a combined surge with onshore wind during (December 2017) with a magnitude of 13 cm. Consequently, the mean sea level during this Typhoon increased compared to similar storms in previous years. This event was significant at the regional level because it occurred later in the year when the mean sea level was higher and experienced higher spring tides, which usually cause flooding to the Southern coast of Vietnam. Examination of the storm tracks, which generated the combined surge at Hong Kong, revealed that they all have very similar travel paths (Figure 14). These tracks originated in the Pacific Ocean or in the Northeast of the East Sea, and then moved toward the Northwest (Figure 14a). The high combined surges at Hon Ngu were due to typhoons that made landfall along the Central part and

J. Mar. Sci. Eng. 2020, 8, 508 17 of 24 included Typhoons Wutip (September 2013), Nari (October 2013) and TS Vamco (September 2015) and J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 17 of 25 Rai (September 2016) (Figure 14b).

FigureFigure 13. (13.a) Time(a) Time series series of observed, of observed, residual residual and and low-pass low-pass filtered filtered water water levels levels during during Typhoon Typhoon Nari (8–24Nari October (8–24 2013)October at 2013) all stations; at all stations; (b) the (b track) the track of Typhoon of Typhoon Nari Nari and and (c– h(c)—wind–h)—wind fields fields at at 12Z 12Z UTC fromUTC 10 to from 15 October 10 to 15 2013.October Key: 2013. HK: Key: Hong HK: Kong; Hong HD:Kong; Hon HD: Dau; Hon HN: Dau; Hon HN: Ngu;Hon Ngu; ST: Son ST: Tra, Son QN:Tra, Quy Nhon;QN: VT: Quy Vung Nhon; Tau. VT: The Vung wind Tau. speed The wind colour speed scale colour is given scale in is Figuregiven in9. Figure 9.

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Results of the magnitude surge generated by the combined action of the forerunner and onshore wind indicated, as expected, higher surges than the forerunner alone (Figure 12). The highest magnitude was at Son Tra, 74 cm during Typhoon Nari (October 2013); followed by Hong Kong, 68 cm during Typhoon Kalmaegi (September 2014); and, Hon Ngu, 63 cm during Typhoons Wutip (September 2013) and Nari (Figure 12b). Under other storms, the magnitudes of the combined surge were 20–40 cm. It was also noticeable that Vung Tau observed a combined surge with onshore wind during Typhoon Tembin (December 2017) with a magnitude of 13 cm. Consequently, the mean sea level during this Typhoon increased compared to similar storms in previous years. This event was significant at the regional level because it occurred later in the year when the mean sea level was higher and experienced higher spring tides, which usually cause flooding to the Southern coast of Vietnam. Examination of the storm tracks, which generated the combined surge at Hong Kong, revealed that they all have very similar travel paths (Figure 14). These tracks originated in the Pacific Ocean or in the Northeast of the East Sea, and then moved toward the Northwest (Figure 14a). The high combined surges at Hon Ngu were due to typhoons that made landfall along the Central part and included Typhoons Wutip (September 2013), Nari (October 2013) and TS Vamco (September 2015) J. Mar. Sci. Eng. 2020, 8, 508 18 of 24 and Rai (September 2016) (Figure 14b).

Figure 14. Similar storm paths when observing a high surge due to a combination between forerunner and onshore wind at: (a) Hong Kong; and (b) Hon Ngu and Son Tra. Numbers refer to typhoons listed Figurein Figure 14.3 Similar. storm paths when observing a high surge due to a combination between forerunner and onshore wind at: (a) Hong Kong; and (b) Hon Ngu and Son Tra. Numbers refer to typhoons listed 4.2.3.in FrequencyFigure 3. of Forerunner and the Combination Surge Analysis of the low pass filtered time series at each of the six stations indicated that at least one 4.2.2. Frequency of Forerunner and the Combination Surge station recorded a forerunner or a combined surge during all of the storms analysed, except Typhoon DamreyAnalysis and of TS the Kirogi. low pass Forerunner filtered time surges series were at observedeach of the at six all stations stations indicated along the that Vietnam at least coast one stationwith frequency recorded 20–25%a forerunner at Hon or a Dau, combined Hon Ngu, surge Son during Tra, andall of Quy the storms Nhon (Figureanalysed, 15 a).except There Typhoon was no Damreyforerunner and observed TS Kirogi. at Forerunner Vung Tau because surges were of a rareobserved occurrence at all stations of storms along (Figure the Vietnam15a). For coast combined with frequencysurge due 20–25% to forerunner at Hon and Dau, onshore Hon wind,Ngu, VungSon Tra, Tau and recorded Quy Nhon 6%, whilst (Figure there 15a). are noThere such was surges no forerunnerat Hon Dau observed and Quy atNhon, Vung Tau and because about 25% of a at rare Hon occurrence Ngu and of Son storms Tra (Figure (Figure 15 15a).b). At For Hong combined Kong, surgethe only due station to forerunner not on theand Vietnam onshore coast,wind, theVung forerunner Tau recorded surge 6%, occurred whilst ~12%, there are with no the such combined surges J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 19 of 25 atsurge Hon being Dau and the maximumQuy Nhon, at and 44% about (Figure 25% 15 ).at Hon Ngu and Son Tra (Figure 15b). At Hong Kong, the only station not on the Vietnam coast, the forerunner surge occurred ~12%, with the combined surge being the maximum at 44% (Figure 15).

Figure 15. Percentage of frequency of forerunner (a) and the combination of forerunner and onshore windFigure (b 15.). Percentage of frequency of forerunner (a) and the combination of forerunner and onshore wind (b). 5. Discussion 5. DiscussionStorm surge (or residual) is usually defined as the non-tidal component of water level when the predictedStorm surge astronomical (or residual) tide is isusua subtractedlly defined from as thethe observednon-tidal timecomp seriesonent [ 12of ].water However, level when there the are manypredicted processes astronomical with di tidefferent is subtracted time scales from that the contribute observed totime the series non-tidal [12]. However, component there that are include many seiches,processes tsunamis, with different coastally time trapped scales waves,that contribute and seasonal to the and non-tidal inter-annual component variability that include in sea level seiches, [29]. tsunamis, coastally trapped waves, and seasonal and inter-annual variability in sea level [29]. Extreme storm surges are usually associated with severe tropical storm systems (hurricanes, typhoons and tropical cyclones depending on the geographic region) where extreme winds and changes in contribute to the ‘direct’ storm surge that occurs within hours for the case when the storm makes landfall as in this study. However, the influence of the storm on the coastal sea levels begin 2–3 days prior to landfall due to the forerunner [9,11] and can last up to 10 days after landfall due to coastally trapped waves (e.g., continental shelf and edge waves, [6]). In this paper, we examined the characteristics of the forerunner along the Vietnam coast using a 5-year (2013–2017) record of hourly sea levels records. Over this period, 25 typhoons directly and indirectly affected the Vietnam coast. The observations indicated that the maximum total water level range decreased from north to the central section and then increased to the south. The maximum range was ~4.0 m at Hon Dau (northern section) and Vung Tau (southern section). There was also a transition from mainly diurnal tides in the north and semi-diurnal tides in the south.

5.1. Seasonal Sea Level Variation Features of the seasonal mean sea level time series, with higher mean water level in winter and lower mean water level in summer, is consistent with previous studies [30,31]. This variability has been attributed to monsoon winds: the southwest monsoon in summer and the northeast monsoon in winter. Offshore winds during the summer decrease the mean level whilst onshore winds during winter increase the water level with an annual range of 20 cm [30]. Seasonal changes were presented at all the stations (Figures 5 and 6) as well in the spectral analysis (Figure 7). The main feature of the seasonal sea level variability was that storms that occurred later in the year will have higher total water level and thus higher potential for extreme coastal flooding. For example, if we consider two storms: TS Kujira (June 2015) and TS Vamco (September 2015) created the same storm surge (~40 cm) but the latter storms had higher total water level due to the mean seasonal sea level being higher (Figure 6).

J. Mar. Sci. Eng. 2020, 8, 508 19 of 24

Extreme storm surges are usually associated with severe tropical storm systems (hurricanes, typhoons and tropical cyclones depending on the geographic region) where extreme winds and changes in atmospheric pressure contribute to the ‘direct’ storm surge that occurs within hours for the case when the storm makes landfall as in this study. However, the influence of the storm on the coastal sea levels begin 2–3 days prior to landfall due to the forerunner [9,11] and can last up to 10 days after landfall due to coastally trapped waves (e.g., continental shelf and edge waves, [6]). In this paper, we examined the characteristics of the forerunner along the Vietnam coast using a 5-year (2013–2017) record of hourly sea levels records. Over this period, 25 typhoons directly and indirectly affected the Vietnam coast. The observations indicated that the maximum total water level range decreased from north to the central section and then increased to the south. The maximum range was ~4.0 m at Hon Dau (northern section) and Vung Tau (southern section). There was also a transition from mainly diurnal tides in the north and semi-diurnal tides in the south.

5.1. Seasonal Sea Level Variation Features of the seasonal mean sea level time series, with higher mean water level in winter and lower mean water level in summer, is consistent with previous studies [30,31]. This variability has been attributed to monsoon winds: the southwest monsoon in summer and the northeast monsoon in winter. Offshore winds during the summer decrease the mean level whilst onshore winds during winter increase the water level with an annual range of 20 cm [30]. Seasonal changes were presented at all the stations (Figures5 and6) as well in the spectral analysis (Figure7). The main feature of the seasonal sea level variability was that storms that occurred later in the year will have higher total water level and thus higher potential for extreme coastal flooding. For example, if we consider two storms: TS Kujira (June 2015) and TS Vamco (September 2015) created the same storm surge (~40 cm) but the latter storms had higher total water level due to the mean seasonal sea level being higher (Figure6).

5.2. Role of Forerunner The results of the analysis of time series indicated that the forerunner was a common feature associated with storms that impact the Vietnam coast. The maximum height for the forerunner was 50 cm at Son Tra (in Typhoon Wutip), whilst for the case of the combined forerunner and onshore wind, the highest height was 74 cm, which was also at Son Tra (in Typhoon Nari) (Figure 12). The magnitudes of forerunner were similar to that observed in the Yellow Sea of 50 cm [12]. However, these values were not as high as those recorded during Hurricane Ike (2008) in the United States that reached 1.0 m [9]. The mean forerunner height was ~20 cm at all the stations for all typhoons. Thus, the forerunner formed a significant component (up to ~50%) of the total storm surge. As the forerunner occurs prior to storm landfall, it acts as a pre-conditioning of the water level before the generation of the main storm surge. Therefore, the contribution of forerunner is an important component that contributes to flooding and inundation of coastal areas of Vietnam. It was impossible to separate the contribution of forerunner in a storm surge that was formed by the combination of forerunner and onshore wind at stations to the right of the storm path. For stations on its left and near landfall location, the contribution of forerunner could be identified as water levels at these stations would be negative due to offshore wind. A good example is Typhoon Wutip that made landfall at Quang Binh province, belonging to the Northern part. Son Tra was on the left of storm path; Hon Dau and Hon Ngu were on the right. It was expected that a high storm surge would occur at Hon Ngu and Hon Dau, and a negative storm surge would occur at Son Tra due to offshore winds with a decrease in water level. However, there was a large increase in mean water levels, and it is clearly a forerunner. Therefore, in this case, because of the forerunner, a positive storm surge was present at Son Tra station (Figure9). Similarly, Quy Nhon is the station that was usually on the left of typhoons, but the mean water levels at this station increased during almost all typhoons. J. Mar. Sci. Eng. 2020, 8, 508 20 of 24

5.3. Influence of Local Features and Storm Characteristics The “S” shape of the Vietnam coastline is another interesting feature relating to the typhoon’s path and formation of forerunner. In the Northern section, Tonkin Gulf has crescent shape, so it is easy to generate a geostrophic current when there is a low-pressure area or a typhoon approaching the East of Island. This can be the reason why three stations, Hon Dau, Hon Ngu and Son Tra, usually observed the forerunner with the frequency of 20–25% (Figure 15). In addition, as most typhoons originating from the Pacific Ocean have made landfall on the Northern section, its northwest path easily generates a combination of forerunner and onshore wind that causes high sea levels at Son Tra station located at the mouth of Tonkin Gulf, and the coastal areas along central provinces. Another station located at the central part of Vietnam coastline is Quy Nhon, which also had a high frequency of forerunner with 21% (Figure 15). Its location is near the open sea (Central East Sea) where there are many favorable conditions for forming and maintaining the strength of storms, so it was easy to observe a forerunner at this station when a storm moved/formed in Central East Sea. The intensity of typhoons relates to the maximum sustained wind speed and central pressure change, which is believed to contribute significantly to the increase of water level during typhoons [3,6,15]. In general, a large radius and more intense storm creates higher storm surges, forerunner, and coastal inundation [32,33]. However, this assumption may not be correct for all cases particularly along the Vietnam coast. , for example, generated a low storm surge at Quy Nhon station and no forerunner surges at other stations, although it was one of the strongest typhoons when making landfall (centre pressure of 970 hPa and maximum sustained wind 1 speeds > 150 kmhr− ). On the other hand, the intensity and size are important for the generation of forerunner when the typhoon is far offshore. For example, Typhoons Utor or Nari (both in 2013), when the centre was near the Philippines, >1000 km away from the Vietnam coast, the mean sea level at some stations along the Northern section indicated an increase in water levels (Figures 11 and 13). Examining the wind fields at the start of these typhoons, the wind speed was not very strong whilst the wind direction was coast parallel. This indicated the significant influence of intense typhoons to the atmosphere and ocean at the regional scale. In addition, for the surge generated by a combination of forerunner and onshore wind, the intensity via wind speed pushed more water onshore (wind set-up), which increased the water level higher than the forerunner alone. The size of a typhoon is determined through the wind’s field with its radius, which is the distance between the center of a typhoon and its band of strongest wind (i.e., the largest radius of 30 kn winds), and is classified by the radius of the area in which the wind speed exceeds 15 m/s The typhoon size shows how large an area is influenced by the typhoon. According to Liu & Irish [11], when a typhoon is large, it generates a higher surge. The results of time series analysis of 25 storms indicated that typhoon’s size can be a first factor to predict the forerunner, even when the center of the typhoon is far from the coast and its intensity is weak. If the typhoon was intense, it was easy to observe a large forerunner on the coastal area. Severe Tropical Storm Talas (July 2017) was not very strong as Typhoons Wutip, Nari (in 2013), with its centre pressure of 985 hPa and maximum sustained wind speeds of 95 kmhr 1 (51 kt). However, its size (~340–390 km) was similar to Typhoon Wutip or Nari − and it generated a 10 cm high forerunner at Son Tra (Figure 10). The path of the typhoon was important for sea level variations at the Tonkin Gulf. Four different storms (Tropical Depression 2013; Typhoon Nari; TS Vamco 2015; and TS Rai 2016) had similar paths, making landfall to the south of Son Tra (Figure 16). For each storm, the sea level changes inside the Tonkin Gulf were very similar (Figure 16). This indicates that a typhoon making landfall around Son Tra will result in high storm surges along the whole coast to the north of Son Tra, due to a combination of forerunner and onshore winds. J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 22 of 25 J. Mar. Sci. Eng. 2020, 8, 508 21 of 24

FigureFigure 16. Low-pass waterwater level level at allat stationall station during during Tropical Tropical Depression Depression (September (September 2013) (a ),2013) Typhoon (a), TyphoonNari (October Nari (October 2013) (b), 2013) Tropical (b), Storm Tropical Vamco Storm (September Vamco (September 2013) (c), Tropical 2013) (c Storm), Tropical Rai (SeptemberStorm Rai (September2016) (d), and 2016) their (d), paths and (theire). Numbers paths (e). refer Numbers to typhoons refer to listed typhoons on Figure listed3. on Figure 3. 5.4. Implications

Sea level time series indicated that for each of the 25 tropical storms that were present in the record, a forerunner was present on at least one of the stations. The forerunner generated 2–3 days prior to storm impact, is due to a combination of shore parallel winds and currents that increase the mean

J. Mar. Sci. Eng. 2020, 8, 508 22 of 24 water level at the coast due to Ekman set-up [9,11]. The forerunner signal was manifested when the center of the storm was up to 1000 km away (e.g., the coast of the Philippines). The model domain for many numerical prediction models for storm surges usually does not cover such a large area, mainly due to the availability of computer resources. Hence, these models are unable to predict the forerunner with some degree of accuracy. The results of this study indicated that the forerunner is a significant contribution to the total water level. For the future, it is recommended that the forerunner should be included in storm surge forecasts for Vietnam.

6. Conclusions A 5-year, continuous time series of hourly sea levels at 5 stations along the Vietnam coast and Hong Kong was subjected to a variety of analysis techniques (tidal analysis, low-pass filtering and Fourier analysis) together with storm characteristics to examine the contribution of forerunner to sea level variations during the typhoon impact. The conclusions can be summarized as follows:

1. The forerunner contributed significantly to the increase of the mean sea level prior to landfall, with the largest magnitude being 50 cm and 74 cm; the latter being in combination with onshore wind. Almost all typhoons generated a forerunner at least at one station with the forerunner contributing up to 50% of the total storm surge. Stations on the right of the typhoon track were often observed to contain a forerunner combining with onshore wind due to the typhoon winds rotating anti-clockwise around the low pressure under the Coriolis Effect. 2. Similar typhoon paths often lead to similar signals of forerunners at certain stations. The size of the typhoon was more important than its intensity in the generation of the forerunner. 3. Seasonal variability in the mean level was such that storms occurring later in the year coincided with higher mean sea levels due to the monsoon and spring tides, and thus, potentially have a higher maximum total water level.

Author Contributions: This study was done as a part of master’s thesis by T.T.T. All data analysis and validation were done by T.T.T under the supervision of C.P., and the support of T.B. Conceptualization, T.T.T., C.P.; methodology, T.T.T., C.P.; software, C.P., T.B.; data analysis, T.T.T.; resources, C.P.; a part of data source, T.B.; writing—original draft preparation, T.T.T., C.P.; writing—review and editing, T.T.T., C.P.; visualization, T.T.T., C.P. All authors have read and agreed to the published version of the manuscript. Funding: The research is funded by master’s dissertation at the University of Western Australia, and DFAT who sponsored Australia Awards Scholarships for the master’s course. Acknowledgments: The observed hourly sea level data used in this research were sourced from the Hydro-meteorological and Environmental Station Network Center (the Hymenet) thanks to the support of Toan Bui. Data at Hong Kong tide gauge is available at the NOAA National Centres for Environmental Information as the Joint Archive for Sea Level at https://uhslc.soest.hawaii.edu/. The typhon information is available at the Japan Meteorological Agency (JMA) (https://www.jma.go.jp/en/typh/), the National Oceanic and Atmospheric Administration (NOAA) (https://www.coast.noaa.gov/hurricanes/). The Digital Typhoon database is available at http://agora.ex.nii.ac.jp/digital-typhoon/. The wind field was extracted from https://cds.climate.copernicus.eu/. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Lin, N.; Emanuel, K.; Oppenheimer, M.; Vanmarcke, E. Physically based assessment of hurricane surge threat under climate change. Nat. Clim. Chang. 2012, 2, 462–467. [CrossRef] 2. Haigh, I.D.; MacPherson, L.R.; Mason, M.; Wijeratne, E.M.S.; Pattiaratchi, C.; Crompton, R.P.; George, S. Estimating present day extreme water level exceedance probabilities around the coastline of Australia: -induced storm surges. Clim. Dyn. 2013, 42, 139–157. [CrossRef] 3. Vecchi, G.A.; Soden, B.J. Effect of remote sea surface temperature change on tropical cyclone potential intensity. Nature 2007, 450, 1066–1070. [CrossRef][PubMed] 4. Le, T.A.; Takagi, H.; Heidarzadeh, M.; Takata, Y.; Takahashi, A. Field Surveys and Numerical Simulation of the 2018 Typhoon Jebi: Impact of High Waves and Storm Surge in Semi-enclosed Osaka Bay, Japan. Pure Appl. Geophys. 2019, 176, 4139–4160. [CrossRef] J. Mar. Sci. Eng. 2020, 8, 508 23 of 24

5. Heidarzadeh, M.; Teeuw, R.; Day, S.; Solana, C. Storm wave run ups and sea level variations for the September 2017 Hurricane Maria along the coast of Dominica, eastern Caribbean Sea: Evidence from field surveys and sea level data analysis. Coast. Eng. J. 2018, 60, 371–384. [CrossRef] 6. Eliot, M.; Pattiaratchi, C. Remote forcing of water levels by tropical cyclones in southwest Australia. Cont. Shelf Res. 2010, 30, 1549–1561. [CrossRef] 7. Thai, T.H.; Thuy, N.B.; Dang, V.H.; Kim, S.; Hole, L.R. Impact of the interaction of surge, wave and tide on a storm surge on the north coast of Vietnam. Procedia IUTAM 2017, 25, 82–91. [CrossRef] 8. Dasallas, L.; Lee, S. Topographical Analysis of the 2013 Typhoon Haiyan Storm Surge Flooding by Combining the JMA Storm Surge Model and the FLO-2D Flood Inundation Model. Water 2019, 11, 144. [CrossRef] 9. Kennedy, A.; Gravois, U.; Zachry, B.C.; Westerink, J.J.; Hope, M.E.; Dietrich, J.C.; Powell, M.; Cox, A.T.; Luettich, R.A.; Dean, R.G. Origin of the Hurricane Ike forerunner surge. Geophys. Res. Lett. 2011, 38. [CrossRef] 10. Thuy, N.B.; Cuong, H.D.; Tien, D.D.; Chien, D.D.; Kim, S.Y. Assessment of changes in sea level caused by Typhoon No. 3 (Kalmaegi) in 2014 and forecast problems. Sci. Tech. Meteorol. J. 2014, 647, 16–20. (In Vietnamese) 11. Liu, Y.; Irish, J.L. Characterization and prediction of tropical cyclone forerunner surge. Coast. Eng. 2019, 147, 34–42. [CrossRef] 12. Suh, S.-W.; Lee, H.-Y. Forerunner storm surge under macro-tidal environmental conditions in shallow coastal zones of the Yellow Sea. Cont. Shelf Res. 2018, 169, 1–16. [CrossRef] 13. Pugh, D.T. Tides, Surges and Mean Sea Level—A Handbook for Engineers and Scientists; John Wiley & Sons: Chichester, UK, 1987; p. 472. 14. Janowitz, G.S.; Pietrafesa, L.J. Subtidal frequency fluctuations in coastal sea level in the mid and south Atlantic Bights: A prognostic for coastal flooding. J. Coast. Res. 1996, 12, 79–89. 15. Thuy, N.B.; Kim, S.; Chien, D.D.; Dang, V.H.; Cuong, H.D.; Wettre, C.; Hole, L.R. Assessment of Storm Surge along the Coast of Central Vietnam. J. Coast. Res. 2016, 33, 518–530. [CrossRef] 16. Thuy, V.T.T. Storm Surge Modeling for Vietnam’s Coast. Master’s Thesis, IHE Delft: Delft, The Nederlands, 2003; p. 140. 17. Express.co.uk. Vietnam Lashed by Typhoon Doksuri—Flooding, Loss of Power and Houses Destroyed. Available online: https://www.express.co.uk/news/world/854559/Vietnam-Typhoon-Doksuri-2017-path- weather-flood-evacuation (accessed on 16 March 2019). 18. Takagi, H.; Thao, N.D.; Esteban, M. Tropical Cyclones and Storm Surges in Southern Vietnam. In Coastal Disasters and Climate Change in Vietnam; Thao, N.D., Takagi, H., Esteban, M., Eds.; Elsevier: Amsterdam, The Netherlands, 2014; pp. 3–16. 19. Fang, G.; Kwok, Y.K.; Yu, K.; Zhu, Y. Numerical simulation of principal tidal constituents in the , Gulf of Tonkin and Gulf of Thailand. Cont. Shelf Res. 1999, 19, 845–869. [CrossRef] 20. Caldwell, P.; Merrifield, M.; Thompson, P. Sea level measured by tide gauges from global oceans as part of the Joint Archive for Sea Level (JASL) from 1846-01-01 to 2015-07-31. Natl. Oceanogr. Data Center 2015, 10, V5V40S7W. 21. Pugh, D.; Woodworth, P. Sea-Level Science: Understanding Tides, Surges, Tsunamis and Mean Sea-Level Changes; Cambridge University Press: Cambridge, UK, 2014. 22. Jin, G.; Pan, H.; Zhang, Q.; Lv, X.; Zhao, W.; Gao, Y. Determination of Harmonic Parameters with Temporal Variations: An Enhanced Harmonic Analysis Algorithm and Application to Internal Tidal Currents in the South China Sea. J. Atmos. Ocean. Technol. 2018, 35, 1375–1398. [CrossRef] 23. Pawlowicz, R.; Beardsley, B.; Lentz, S. Classical tidal harmonic analysis including error estimates in MATLAB using T_TIDE. Comput. Geosci. 2002, 28, 929–937. [CrossRef] 24. Moftakhari, H.R.; Jay, D.A.; Talke, S.A.; Kukulka, T.; Bromirski, P.D. A novel approach to flow estimation in tidal rivers. Water Resour. Res. 2013, 49, 4817–4832. [CrossRef] 25. Consoli, S.; Recupero, D.R.; Zavarella, V. A survey on tidal analysis and forecasting methods for Tsunami detection. arXiv 2014, arXiv:1403.0135. 26. Wijeratne, E.; Pattiaratchi, C.; Eliot, M.; Haigh, I.D. Tidal characteristics in Bass Strait, south-east Australia. Estuar. Coast. Shelf Sci. 2012, 114, 156–165. [CrossRef] 27. Thompson, R.O.R.Y. Low-Pass Filters to Suppress Inertial and Tidal Frequencies. J. Phys. Oceanogr. 1983, 13, 1077–1083. [CrossRef] J. Mar. Sci. Eng. 2020, 8, 508 24 of 24

28. Sebastian, M.; Behera, M.R.; Murty, P. Storm surge hydrodynamics at a concave coast due to varying approach angles of cyclone. Ocean Eng. 2019, 191, 106437. [CrossRef] 29. Pattiaratchi, C. Coastal tide gauge observations: Dynamic processes present in the Fremantle record. In Operational Oceanography in the 21st Century; Springer: Berlin/Heidelberg, , 2011; pp. 185–202. 30. Amiruddin, A.M.; Haigh, I.D.; Tsimplis, M.; Calafat, F.M.; Dangendorf, S. The seasonal cycle and variability of sea level in the South China Sea. J. Geophys. Res. Oceans 2015, 120, 5490–5513. [CrossRef] 31. Ho, C.-R.; Zheng, Q.; Soong, Y.S.; Kuo, N.-J.; Hu, J. Seasonal variability of sea surface height in the South China Sea observed with TOPEX/Poseidon altimeter data. J. Geophys. Res. Space Phys. 2000, 105, 13981–13990. [CrossRef] 32. Orton, P.M.; Talke, S.A.; Jay, D.A.; Yin, L.; Blumberg, A.F.; Georgas, N.; Zhao, H.; Roberts, H.J.; MacManus, K. Channel Shallowing as Mitigation of Coastal Flooding. J. Mar. Sci. Eng. 2015, 3, 654–673. [CrossRef] 33. Atkinson, J.; Smith, J.M.; Bender, C. Sea-Level Rise Effects on Storm Surge and Nearshore Waves on the Texas Coast: Influence of Landscape and Storm Characteristics. J. Waterw. Port Coast. Ocean Eng. 2013, 139, 98–117. [CrossRef]

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