Topographical Analysis of the 2013 Typhoon Haiyan Storm Surge Flooding by Combining the JMA Storm Surge Model and the FLO-2D Flood Inundation Model

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Article Topographical Analysis of the 2013 Typhoon Haiyan Storm Surge Flooding by Combining the JMA Storm Surge Model and the FLO-2D Flood Inundation Model

Lea Dasallas 1 and Seungsoo Lee 2,*

1 Department of Agricultural & Rural Eng., Chungnam National University, Daejeon 34134, Korea; [email protected] 2 Climate Prediction Team, APEC Climate Center, Busan 48058, Korea * Correspondence: [email protected]; Tel.: +82-51-745-3948

Received: 26 November 2018; Accepted: 8 January 2019; Published: 15 January 2019

Abstract: The floods associated with the effects of an incoming tropical cyclone have an immense effect in the Philippines, especially with respect to agriculture, industry, livelihood, and public safety. Knowledge of how such storm surge flooding can affect the community is therefore of great importance. In this study, the mechanisms behind Typhoon Haiyan’s anomalous storm surge flooding in 2013, which resulted in more than 6300 casualties and 2.86 billion USD worth of damage in the Philippines, were investigated. The Japan Meteorological Agency (JMA) storm surge model and the FLO-2D flood model were used to simulate Typhoon Haiyan’s storm surge height and the extent of inundation, respectively. The storm surge input data were obtained from JMA typhoon data, and the digital terrain models used were gathered from the airborne interferometric synthetic aperture radar data. The model’s accuracy was also validated using field validation data of the extent of the observed storm surge in affected coastal areas. Topographical analysis of the inundated regions showed the effects of coastal shape, elevation, and position relative to the typhoon’s approach angle on storm surge flow depth and velocity. Storm surge maximum velocity appears to increase as the fluid flows to an increasingly elevated area. Observing fluid velocity in a coastal area with uniform storm surge discharge from all directions also showed that flow velocity tends to increase at the center. Greater flood depths were experienced in areas with lower coastal elevation and not directly located at the coast, compared to higher elevation coastal areas. Greater extents of storm surge flooding are expected in coastal areas that have a concave shape, as fluid is more likely to be dispersed when hitting a convex coast. Extents are likewise observed to be greater in coastal regions that are located perpendicular to the direction of the typhoon. The research also validated the option of using a combination of typhoon and flood models to simulate the inundation flooding caused by extreme weather events.

Keywords: storm surge model; ﬂood model; extreme weather events; topographical characteristics; inundation extent; inundation depth

1. Introduction The natural hazards generated by a tropical cyclone impose signiﬁcant threats, especially to coastal communities. These hazards, such as rainfall-induced ﬂooding and landslides, and the storm surges generated by the strong winds, have been known to result in substantial loss of life and damage to properties in the Philippines. One particular cyclone, Typhoon Haiyan in 2013, affected 16 million people and resulted in about 6300 deaths, making it the deadliest typhoon to hit the country in recent

Water 2019, 11, 144; doi:10.3390/w11010144 www.mdpi.com/journal/water Water 2019, 11, 144 2 of 18 history [1]. The number of casualties was primarily associated with the anomalous storm surge flooding generated by the typhoon [2]. However, modeling storm surges is not as common and well known as flood and debris flow modeling. This is mainly due to the rarity of storm-surge-related hazards compared with the hazards associated with flooding events. In recent years, however, several storm surge models have been developed by atmospheric and coastal researchers. These storm surge models include the Sea Lake and Overland Surges from Hurricanes model by the National Oceanic and Atmospheric Administration (NOAA) and National Hurricane Center [3,4], the Mike21 and Mike Flood models [5] developed by the DHI Water and Environment Corporation, the Delft3D coastal surge model [6] by Deltares, and the Japan Meteorological Agency (JMA) storm surge model [7] from the Japan Meteorological Agency. The 2013 Typhoon Haiyan storm surge has been evaluated by several researchers using different models, these topics include: ensemble forecasting [8,9], boulder and sediment transport [10,11], storm surge simulations [2,12–14], local amplifications [15], and spatial variation of damages [16]. Most of these research efforts focused on simulation of the storm surge of the event, but very few of those presented the inland flooding generated by the storm surge. [2] presented a simulated storm surge inundation and field validation results using the same models as in the present study. However, the paper showed only one affected area (Tacloban City) and focused more on the social aspects of the event rather than the scientific factors behind. To determine the factors leading to the extreme flooding caused by the Haiyan storm surge, it is important to analyze the meteorological and geographical conditions present during the event that caused the aggravation of storm surge flooding. In other words, the topographical characteristics of the areas that made them more susceptible to inundation compared with the other areas that experienced the same typhoon intensity must be considered. The objective of this paper is to analyze the geographical characteristics of the most affected areas during the typhoon storm surge event. The motivation in doing so is the lack of related literature discussing the vulnerability of coastal areas to storm surges as a result of their topographical characteristics. These analyses include the effects of coastal shape, elevation, the position relative to the typhoon’s angle of approach to the storm surge depth, extent, and flow velocity. However, the meteorological conditions that instigated such intensity, which in turn caused the anomalous storm surge, are not covered in this research, but will be discussed in greater detail in our future research. This research utilized the JMA storm surge model for storm surge simulation. The JMA model uses a two-dimensional shallow water equation and is used to model typhoon-induced storm surges that form in the northwestern Pacific basin. While there are a number of storm surge models that can simulate the same effect with much finer resolution, the JMA model was chosen for this research due to its higher advantage in terms of computational speed. The JMA model’s capability of simulating reliable storm surge timeseries with less computational time is ideal for simulating larger domains, while not losing the objective of the research. The inland storm surge flooding, however, which the model lacks, was supplemented using the FLO2D flood model, a flood routing model that simulates overland flow over complex topography. In this study, field validation data are also provided to show the accuracy of the models used. The structure of the paper is arranged as follows: Section2 describes the data and the data sources used, as well as the governing equations and procedures behind the storm surge and flood models. Section3 shows the results of the inundation model, comparison with field observations, and the analysis of the storm surge depth and velocity in response to the difference in topography. Finally, Section4 provides a summary and final conclusion for the research.

2. Data and Methods To simulate the storm surge ﬂooding generated by Typhoon Haiyan 2013, the research utilized the tropical cyclone best track data [17] obtained from the JMA typhoon archive. Water 2019, 11, 144 3 of 18

The study domain elevation and topographical characteristics were represented using the airborne interferometric synthetic aperture radar-derived digital terrain model (DTM) with a 5 m spatial resolution. The bathymetric data used for the domain were the 2 min Global Gridded Elevation data (ETOPO2) gathered from the NOAA. To simulate the Haiyan-induced storm surge height and the extent of inundation for the most affected coastal areas, the JMA storm surge model and the FLO-2D ﬂood routing model were used together.

2.1. Storm Surge Model (JMA) To simulate Typhoon Haiyan’s storm surge height, the JMA storm surge model, a two-dimensional numerical model that uses a vertically integrated momentum equation and a continuity equation [7], was employed. This model computes storm surge height caused by wind and inverted barometric effect, and its inputs are the typhoon track and bathymetric data. The storm surge model’s governing equations are as follows:

∂U ∂(η − η ) τ τ − f V = −g(D + η) 0 + sx − bx (1) ∂t ∂x ρ ρ

∂V ∂(η − η ) τsy τby − f U = −g(D + η) 0 + − (2) ∂t ∂y ρ ρ ∂η ∂U ∂V + + = 0 (3) ∂t ∂x ∂y where U and V are mass ﬂuxes in the x and y directions, deﬁned as

η Z U ≡ udz (4) −D

η Z V ≡ vdz (5) −D where u and v are the x and y direction velocities, z is the vertical length, D is the water depth below mean sea level, η is the surface elevation, η0 is the water column height corresponding to the inverse barometer effect, f is the Coriolis parameter, g is the acceleration due to gravity, ρ is water density, τsx and τsy are the x and y components of wind stress on the sea surface, and τbx τby are the x and y components of bottom friction stress. Wind stresses are expressed as

τ(sx,sy) = cdρaW(uwvw) (6)

p 2 2 where cd is the drag coefﬁcient, ρa is the air density, W ≡ uw + vw is the wind speed, and (uw, vw) is the wind velocity. The drag coefﬁcient is set using the results of [18] and [19]: ( (0.63 + 0.066W) × 10−3 if (W < 25 m/s) c = (7) d (2.28 + 0.033(W − 25)) × 10−3 if (W ≥ 25 m/s)

We solved these equations by numerical integration, using the explicit ﬁnite difference method. For more details on the JMA storm surge model, refer to [20]. Typhoon track data were composed of meteorological descriptions of Typhoon Haiyan, such as its formation and dissipation dates, eye center location (latitude and longitude), maximum sustained wind speed in kts, and central pressure in hPa. Using these input data, which are essential for running the JMA storm surge model, we ran a storm surge simulation using three domains, as shown in Figure1: Water 2019, 11, 144 4 of 20

(0.63 0.066W ) 103 if ( W  25 m/s) c  d  3 (7) (2.28 0.033(W  25)) 10 if ( W  25 m/s)

We solved these equations by numerical integration, using the explicit finite difference method. For more details on the JMA storm surge model, refer to [20]. Typhoon track data were composed of meteorological descriptions of Typhoon Haiyan, such as its formation and dissipation dates, eye center location (latitude and longitude), maximum sustained wind speed in kts, and central pressure in hPa. Using these input data, which are essential for running the JMA storm surge model, we ran a storm surge simulation using three domains, as shown in Figure 1: (1) 114° E–135° E, 5° N–25° N; (2) 118° E–128° E, 8° N–24° N; and (3) 115° E–160° E, 5° N– 25° N. Domain 1 covers the storm surge rise and fall over the Philippine Area of Responsibility (regionWater 2019 in, 11 the, 144 northwestern pacific where the Philippines’ national meteorological agency monitors4 of 18 significant weather disturbances), domain 2 identifies the regions that recorded the highest sea level rise during Typhoon Haiyan, and domain 3 shows Typhoon Haiyan’s life span from cyclogenesis to ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ dissipation.(1) 114 E–135 E, 5 N–25 N; (2) 118 E–128 E, 8 N–24 N; and (3) 115 E–160 E, 5 N–25 N. DomainAt the 1 coverssea–land the interface, storm surge the normal rise and component fall over the of Philippinevelocity is zero. Area At of open Responsibility boundaries, (region the sea in surfacethe northwestern elevation is pacific given where by the the inverted Philippines’ barometer national effect; meteorological a gravity wave agency radiation monitors condition significant was usedweather in this disturbances), study. Tides domain can be 2simulated identifies but the are regions excluded, that recorded since the the major highest object sea of level this simulation rise during isTyphoon for surge Haiyan, heights and domain 3 shows Typhoon Haiyan’s life span from cyclogenesis to dissipation.

(2) (1) Storm heightStorm surge (cm)

(3)

FigureFigure 1. DomainsDomains used used for for simulating simulating the the storm storm surge surge height height using using JMA (Japan Meteorological Agency)Agency) storm surge surge model.

OurAt the simulations sea–land interface,produced the storm normal surge component height time of velocity series for is zero. each Atdomain. open boundaries, We modified the the sea stormsurface surge elevation height is to given incorporate by the astronomical inverted barometer tides computed effect; a gravity using WxTide, wave radiation a Windows-based condition tide was andused current in this study.prediction Tides program. can be simulated We determined but are excluded,the coastal since provinces the major targeted object for of flood this simulation simulation is fromfor surge the results heights. of our JMA storm surge simulation. The storm tide (storm surge depth + astronomical tide) Ourtime simulations series from produced the JMA storm model surge was height used timeas the series input for file each for domain. our storm We modifiedsurge inundation the storm simulation.surge height to incorporate astronomical tides computed using WxTide, a Windows-based tide and currentThe prediction JMA storm program. surge model We determinedsimulation identified the coastal the provinces provinces targeted of Iloilo, for Leyte, flood Palawan, simulation Samar, from andthe resultsEastern of Samar our JMA as stormhaving surge experienced simulation. the The highest storm storm tide (storm surge surgeduring depth Typhoon + astronomical Haiyan. tide)The locationtime series of from the provinces the JMA model can be was viewed used as in the the input coastal file for inundation our storm model surge inundation results. The simulation. greatest The JMA storm surge model simulation identified the provinces of Iloilo, Leyte, Palawan, Samar, and Eastern Samar as having experienced the highest storm surge during Typhoon Haiyan.

The location of the provinces can be viewed in the coastal inundation model results. The greatest estimated storm surge height reached about 3.9 m in Batad, Iloilo (Figure2). We then used the catchments of these provinces for our ﬂood inundation simulation. Water 2019, 11, 144 5 of 20

Water 2019, 11, 144 5 of 18 estimated storm surge height reached about 3.9 m in Batad, Iloilo (Figure 2). We then used the catchments of these provinces for our flood inundation simulation.

4.5

4.0 General MacArthur Marabut 3.5 Tacloban Batad 3.0 Carles El Nido 2.5

2.0

1.5

1.0

0.5 Storm Surge Height (m) 0.0

-0.5

-1.0 03:00 06:00 12:00 15:00 18:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 09:00 21:00 11/07 00:00 11/07 00:00 11/08 00:00 11/09 Time (UTC) Figure 2. Most affected coastal areas ( upper panel ) and their respective simulated storm surge time series results ( lower panel ).

2.2. Coastal Inundation Inundation Model Model One of of the the JMA JMA storm storm surge surge model’s model’s limitations limitations is that is it does that not it does simulate not storm simulate surge storm inundation. surge inundation.The extent of The storm extent surge of storm flooding surge predictions flooding predictions is crucial for is pre-emptivecrucial for pre-emptive evacuation evacuation and long-term and long-termurban planning. urban Stormplanning. surge Storm inundation surge mapsinundation were employedmaps were in thisemployed study usingin this the study FLO2D using model, the FLO2Da flood model, routing a model flood routing that simulates model that channel, simulates overland, channel, and overland, street flow and over street complex flow over topography. complex topography.We chose the We FLO2D chose model the FLO2D to conduct model our toflood conduct simulation our flood because simulation of its because compatibility of its compatibility with the JMA withstorm the surge JMA model. storm Thesurge FLO2D model. model’s The FLO2D limitations, model’s such limitations, as the absence such of as wave the absence coefficients of wave and coefficientsreceding parameters, and receding were parameters, not deemed were significant not deemed enough significant to drastically enough affect to thisdrastically study’s affect objective this study’sand results. objective FLO2D’s and results. governing FLO2D’s equations governing are as follows:equations are as follows: ∂h ∂hV h+  hV = r (8) t x  re (8) ∂t∂  x e ∂h V ∂V 1 ∂V S = S − − − − (9) f 0 ∂x g ∂x g ∂t

Water 2019, 11, 144 6 of 20

hV  V1  V SS   (9) f 0 xg  xgt  whereWater t2019 represents, 11, 144 time; h is the flow depth; hV is the depth-averaged velocity in one of eight6 of 18 flow directions; x , re is the excess rainfall intensity; S f is the friction slope component based on

Manning’swhere t represents equation; time;S0 is h theis the bed flow slope depth; pressurehV is thegradient; depth-averaged and g is velocity the acceleration in one of eightdue to flow gravity.directions; This x equation, re is the excess represents rainfall a intensity; one-dimensional,S f is the friction depth-averaged slope component channel based flow. on For Manning’s the floodplain,equation; althoughS0 is the bedFLO-2D slope is pressure a multi-direction gradient; and flowg is model, the acceleration FLO-2D’s due equations to gravity. of motion This equation are appliedrepresents by computing a one-dimensional, average flow depth-averaged velocity across channel a grid flow.element For boundary the floodplain, one direction although at FLO-2D a time is a [21].multi-direction flow model, FLO-2D’s equations of motion are applied by computing average flow velocityThe catchments across a grid to simulate element for boundary flooding one were direction chosen atusing a time the [previous21]. storm surge simulation; we selectedThe catchmentsthe top five to provinces simulate forwith flooding the highest were simulated chosen using storm the surge. previous We storm applied surge boundary simulation; gridswe to selected the province the top shapefiles five provinces to create with a catchment. the highest Data simulated with a grid storm resolution surge. of We 20 applied m × 20 m boundary were usedgrids through to the the province provided shapefiles DTM data. to create The a flood catchment. simulation Data runs with smoothly a grid resolution if the storm of 20 surge’s m × 20 m estimatedwere used peak through discharge, the providedQ, divided DTM data.by the The grid flood element simulation surface runs area, smoothly A, does if the not storm exceed surge’s 3 3 −1 m sestimated. After considering peak discharge, the simulationQpeak, divided time, we by set the the grid grid element size to surface20 m. Figure area, 3A showsgrid, does a sample not exceed of inundation3 m3 s−1 results,. After consideringsuch as the themaximum simulation water time, surface we set depth the grid and size maximum to 20 m. flow Figure depth,3 shows as shown a sample in theof inundationFLO-2D model. results, such as the maximum water surface depth and maximum flow depth, as shown in the FLO-2D model.

kilometer

Figure 3. Maximum water surface depth and maximum ﬂow depth in m as shown in the FLO2D model. Figure 3. Maximum water surface depth and maximum flow depth in m as shown in the FLO2D model.We used the calculated hydro-curve produced using the JMA storm surge model as input data for the FLO2D model, and then conducted inundation modeling. We used the calculated hydro-curve produced using the JMA storm surge model as input data for 3.the Results FLO2D and model, Discussion and then conducted inundation modeling.

3. Results3.1. Results and ofDiscussion Storm Surge Coastal Inundation Model We overlaid storm surge inundation results from the FLO-2D model on a satellite image for 3.1.validation. Results of Storm Validation Surge maps Coastal show Inundation an inundation Model color scale with ranges of 0.01–0.5 m (light yellow), 0.51–1.00 m (yellow), 1.01–1.50 m (red), 1.51–2.00 m (violet), and 2.01 m and above (blue). Validation points shown as blue circles observed flooding, and points represented as red circles experienced no inundation. Figure4 shows a comparison of observed inundation and simulated flooding for Tolosa, Leyte. Observed inundation and simulation results generally agreed. Flooding was observed (blue circles) in areas where the model had simulated storm surge inundation, and no flooding was observed (red Water 2019, 11, 144 7 of 18 circles) in areas with no simulated inundation. The same was true for the storm surge validation maps Waterof El 2019 Nido,, 11,Palawan 144 (Figure5), Carles, Iloilo (Figure6), and Marabut, Samar (Figure7). 8 of 20

Figure 4. Storm surge inundation and validation points of Tanauan and Tolosa, Leyte. Validation Figurepoints: 4. blue Storm circle surge (observed inundation ﬂooding) and and validation red circle points (no ﬂooding). of Tanauan and Tolosa, Leyte. Validation points: blue circle (observed flooding) and red circle (no flooding).

Water 2019, 11, 144 8 of 18 Water 2019, 11, 144 9 of 20

Figure 5. Storm surge inundation and validation points of El Nido, Palawan. Validation points: blue Figurecircle (observed 5. Storm surge ﬂooding) inundation and red and circle validation (no ﬂooding). points of El Nido, Palawan. Validation points: blue circle (observed flooding) and red circle (no flooding).

Water 2019, 11, 144 9 of 18 Water 2019, 11, 144 10 of 20

Figure 6. Storm surge inundation and validation points of Carles, Iloilo. Validation points: blue circle Figure(observed 6. Storm ﬂooding) surge and inundation red circle and (no validation ﬂooding). points of Carles, Iloilo. Validation points: blue circle (observed flooding) and red circle (no flooding).

Water 2019, 11, 144 10 of 18 Water 2019, 11, 144 11 of 20

Figure 7. Storm surge inundation and validation points of Marabut, Samar. Validation points: blue Figurecircle (observed 7. Storm flooding)surge inundation and red circleand validation (no flooding). points of Marabut, Samar. Validation points: blue circle (observed flooding) and red circle (no flooding). However, the same cannot be said about the storm surge validation map of Guiuan, Eastern Samar (FigureHowever,8). Flooding the same was observedcannot be at said several about validation the storm points surge but validation showed no map simulated of Guiuan, storm Eastern surge, Samarwith the (Figure flooded 8). areaFlooding located was 2000 observed km away at several from the validation coast (Figure points8). Thisbut showed is because no thesimulated flooding storm that surge, with the flooded area located 2000 km away from the coast (Figure 8). This is because the

Water 2019, 11, 144 11 of 18 Water 2019, 11, 144 12 of 20 floodinginterviewees that experienced interviewees may experienced have been caused may have by rainfall, been causedas Eastern by Samar rainfall, experienced as Eastern about Samar 120 experiencedmm of precipitation about 120 (Figure mm of8, precipitation lower left image) (Figure during 8, lower Typhoon left image) Haiyan. during Typhoon Haiyan.

FigureFigure 8. Storm surgesurge inundation inundation and and validation validation points points of Guiuan,of Guiuan, Eastern Eastern Samar. Samar. The upperThe upper left figure left figureshows shows the transect the transect elevation elevation for a cross-sectional for a cross-sectional transect (blue transect arrow) (blue in thearrow) storm in surge the storm inundation surge inundationmap (right map image). (right The image). lower The left imagelower left shows image accumulated shows accumulated rainfall during rainfall the during event the in mm/day, event in mm/day,the area circled the area in circled purple in is thepurple area is shown the area on shown the right on panel.the right panel. 3.2. Model Results Analysis 3.2. Model Results Analysis After establishing the reliability of the models that were used, it was necessary to determine why After establishing the reliability of the models that were used, it was necessary to determine why some coastal areas experiences worse storm surge compared to other areas. Based on the research some coastal areas experiences worse storm surge compared to other areas. Based on the research by by [22], Iloilo, Samar, Leyte, Palawan, and Eastern Samar provinces were among the top 25 Philippine [22], Iloilo, Samar, Leyte, Palawan, and Eastern Samar provinces were among the top 25 Philippine coastal provinces most vulnerable to storm surge. A hypothetical track of a typhoon with Haiyan’s coastal provinces most vulnerable to storm surge. A hypothetical track of a typhoon with Haiyan’s wind strength would have a storm surge height as high as 7.45 m. This demonstrates that these wind strength would have a storm surge height as high as 7.45 m. This demonstrates that these provinces might possess geographical characteristics that make them more susceptible to damaging provinces might possess geographical characteristics that make them more susceptible to damaging storm surges. storm surges. 3.2.1. Effect of Elevation on Storm Surge Flow Depth 3.2.1. Effect of Elevation on Storm Surge Flow Depth Varying storm surge flooding behavior can be seen in different elevation topographies. Figures9 Varying storm surge flooding behavior can be seen in different elevation topographies. Figures and 10 show cross-sectional and x height storm surge flooding in some of the simulated catchments. 9 and 10 show cross-sectional and x height storm surge flooding in some of the simulated catchments. Figure9 shows the effect of elevation on storm surge height, with lower elevation areas having greater Figure 9 shows the effect of elevation on storm surge height, with lower elevation areas having storm surge height, as seen in the A to A0 and B to B0 transects. However, Figure 10 shows that coastal greater storm surge height, as seen in the A to A′ and B to B′ transects. However, Figure 10 shows areas are not automatically vulnerable to storm surges if the area’s elevation exceeds the maximum that coastal areas are not automatically vulnerable to storm surges if the area’s elevation exceeds the storm surge height. maximum storm surge height.

Water 2019, 11, 144 12 of 18 Water 2019, 11, 144 13 of 20

B’ B

B’ ⟵ B A’ A

A’ ⟵ A

FigureFigure 9. Storm 9. Storm surge surge inundation inundation (left (left panel)panel) and and cross-sectional cross-sectional x height x height elevation elevation (red line (red in lineright in right panel); storm surge ﬂooding (black line) in terms of elevation for transects A to A0 and B to B0 (blue Waterpanel); 2019, 11 storm, 144 surge flooding (black line) in terms of elevation for transects A to A′ and B to B′ (blue 14 of 20 solidsolid lines lines in the in the left left panel,) panel,) showing showing effecteffect of elevation elevation on on storm storm surge surge height. height.

A → A’ B’ A’ B A

B → B’

FigureFigure 10. Storm 10. Storm surge surge inundation inundation (right (right panel) panel) and andcross-sectional cross-sectional x height x height elevation elevation with with no noﬂooding (red lineflooding in left (red panel) line forin left transects panel) Afor to transects A0 and A B to BA′0 (blueand B solidto B′ lines(blue insolid right lines panel), in right showing panel), effect showing effect of elevation in storm surge height. of elevation in storm surge height. 3.2.2. Effect of Elevation on Storm Surge Velocity Determining flood velocity is also important for early warning efforts, because even low-level fast flowing floods are likely to cause significant damage. In addition to flow depth, the FLO-2D model can also map flow velocity, inundation duration, time to maximum depth, impact force, static pressure, specific energy, and hazard level. Figure 11 shows a noticeable flow velocity increase that can be seen in the circled area. The circled area’s elevation is shown in the boxed graph. The elevation graph shows that maximum flow velocity increased in the raised section in the middle, which can be explained by fluid behavior in that flow velocity is greater in areas that would cause fluids to compress (i.e., a corresponding velocity increase in smaller areas with a constant fluid volume).

Water 2019, 11, 144 13 of 18

3.2.2. Effect of Elevation on Storm Surge Velocity Determining flood velocity is also important for early warning efforts, because even low-level fast flowing floods are likely to cause significant damage. In addition to flow depth, the FLO-2D model can also map flow velocity, inundation duration, time to maximum depth, impact force, static pressure, specific energy, and hazard level. Figure 11 shows a noticeable flow velocity increase that can be seen in the circled area. The circled area’s elevation is shown in the boxed graph. The elevation graph shows that maximum flow velocity increased in the raised section in the middle, which can be explained by fluid behavior in that flow velocity is greater in areas that would cause fluids to compress (i.e., a corresponding velocity increase inWater smaller 2019, 11 areas, 144 with a constant fluid volume). 15 of 20

−1 FigureFigure 11. 11.Map Mapof ofmaximum maximum stormstorm surgesurge velocityvelocity (ms(ms−1).). Storm Storm surge surge inland inland flow flow direction direction is is shown shown withwith blue blue arrows.arrows. InsetInset panelpanel showsshows surface elevation versus storm storm surge surge height, height, with with velocity velocity of of transecttransect (red (red arrow) arrow) increasing increasing asas∆ ∆HH decreases. decreases. 3.2.3. Effect of Coastal Shape on Storm Surge Flow Depth 3.2.3. Effect of Coastal Shape on Storm Surge Flow Depth Coastal formation characteristics can also affect an incoming storm surge’s extent and depth. Coastal formation characteristics can also affect an incoming storm surge’s extent and depth. Figures 12 and 13 show the coastal shape’s effect on storm surge flow depth and extent. Storm surge Figures 12 and 13 show the coastal shape’s effect on storm surge flow depth and extent. Storm surge flooding extents are farther away in coastal areas with concave coasts than those with straight-line flooding extents are farther away in coastal areas with concave coasts than those with straight-line coasts (circled areas), even at the same elevation. Fluid is more likely to be dispersed when hitting a coasts (circled areas), even at the same elevation. Fluid is more likely to be dispersed when hitting a convex coast, whereas fluid hitting a concave coast is likely to accumulate in the center, leading to a convex coast, whereas fluid hitting a concave coast is likely to accumulate in the center, leading to a greater extent of flooding. greater extent of flooding.

Water 2019, 11, 144 16 of 20 Water 2019, 11, 144 14 of 18 Water 2019, 11, 144 16 of 20

Figure 12. Effect of coastal shape on flow depth and extent in areas with the same elevation (Marabut, FigureSamar). 12. TheEffect right of coastal panel showsshape on inland flow ﬂow inundation, depth and extent whereas in areas areasthe left with panel the showssame same elevation elevationthe elevation (Marabut, contour Samar).(pink Theline isright flooding panel extent),shows inland proving inundation, that the inundated whereas theregions left panel (circled shows areas) the are elevation low-lying contour coasts. (pink line is ﬂooding extent), proving that the inundated regions (circled areas) are low-lying coasts. (pink line is flooding extent), proving that the inundated regions (circled areas) are low-lying coasts.

Figure 13. Effect of coastal shape on ﬂow depth and extent in areas with the same elevation (Ajuy, Figure 13. Effect of coastal shape on flow depth and extent in areas with the same elevation (Ajuy, Iloilo). The right panel shows inland inundation, whereas the left panel shows elevation contour (pink FigureIloilo). 13. The Effect right of panel coastal shows shape inland on flow inundation, depth and whereas extent the in leftareas panel with shows the same elevation elevation contour (Ajuy, (pink line is ﬂooding extent), proving that inundated regions are low-lying coasts. Iloilo).line isThe flooding right panel extent), shows proving inland that inundation, inundated whereas regions the are left low-lying panel shows coasts. elevation contour (pink line is flooding extent), proving that inundated regions are low-lying coasts.

Water 2019, 11, 144 17 of 20 Water 2019, 11, 144 15 of 18 3.2.4. Effect of Coastal Shape on Storm Surge Velocity

3.2.4.We Effectalso found of Coastal storm Shape surge on differed Storm Surge in response Velocity to the shape of the coast. Figure 14 shows velocity behavior differences in areas with dissimilar coastal characteristics and demonstrates that the maximumWe also velocity found stormincreases surge in the differed center in of response catchments tothe with shape equal of distance the coast. from Figure storm 14 surge shows inflows.velocity Vector behavior velocity differences grids in in the areas boxed with areas dissimilar of Figure coastal 14 characteristicsshow uniform andstorm demonstrates surge discharge that the frommaximum all directions, velocity which increases thenin accumulate the center ofin catchmentsthe center. withThus, equal the flow distance velocity from increases, storm surge because inflows. theVector fluid now velocity has more grids momentum in the boxed to areas flow ofin Figurea single 14 direction. show uniform storm surge discharge from all directions, which then accumulate in the center. Thus, the flow velocity increases, because the fluid now has more momentum to flow in a single direction.

Scale in meters

FigureFigure 14. 14. MaximumMaximum velocities velocities (ms (ms−−11)) at constant constant elevation, elevation, blue blue arrows arrows showing showing inland inland flow direction. flow direction.Lower panelsLower displaypanels display zoomed-in zoomed-in regions showingregions showing directional directional velocity ofvelocity flow, green of flow, and green yellow and areas yellowhaving areas higher having velocity higher flow velocity compared flow compared with the blue with ones. the blue ones.

3.2.5.3.2.5. Effect Effect of Angle of Angle of Approach of Approach on onFlow Flow Depth Depth DifferencesDifferences in inthe the typhoon’s typhoon’s angle angle of ofapproach approach to tothe the coast coast can can also also affect affect the the extent extent of ofstorm storm surge.surge. Figure Figure 15 15shows shows that that extent extent of inundation of inundation was was farther farther in areas in areas that that were were directly directly hit hitby bythe the typhoon.typhoon. Typhoons Typhoons that that move move onshore onshore perpendicular perpendicular to to the the coast coast tend tend to produce to produce higher higher storm storm surges surgesthan than those those moving moving parallel parallel to or to at or an at oblique an oblique angle angle to the to coast the [coast23]. [ 24[23].] also [24] conducted also conducted research researchregarding regarding the response the response in storm in surgestorm heightsurge height to wind to direction wind direction for cities for exposed cities exposed at the endat the of end a long of inlet,a long as inlet, compared as compared to cities to exposed cities exposed in straight in coastline.straight coastline. Changes Changes in storm in surge storm heights surge and heights extents andwere extents observed were by observed simulating by changes simulating in the changes angle of in directional the angle wind. of directional The same wind. methodology The same was methodologyapplied by [was25] toapplied this event, by [25] supporting to this event, the resultssupporting that the the orientation results that of the coastal orientation area relative of coastal to the areatyphoon’s relative to wind the directiontyphoon’s is wind a major direction factor inis a simulating major factor expected in simulating storm surge. expected storm surge.

Water 2019, 11, 144 18 of 20 Water 2019, 11, 144 16 of 18

Figure 15. Effect of typhoon’s approach angle on storm surge ﬂowflow depth. The area delineated by the red box shows coasts directly perpendicular to the typhoon’s wind direction, whereas the blue boxed area shows the coast that is parallel to the wind direction.

4. Conclusions We simulated Typhoon Haiyan’sHaiyan’s stormstorm surge height and inundation on the most affected coastal provinces in the Philippines, using the JMA storm surge surge model and and the the FLO-2D FLO-2D flood flood routing routing model, model, respectively. The reliability of the models used was proven through validation using actual storm surge records for Typhoon Haiyan.Haiyan. Note that we only conducted qualitative analysis for the effect of quantitative analysis, because of lack of validation data.data. Storm surge model results identified identified Iloilo, Samar, Leyte, Palawan, and Eastern Samar provinces as having the greatest storm surge heights during Typhoon Haiyan, with the maximum storm surge height as high as as 3.9 3.9 m. m. We We subsequently subsequently used used these these province province catchments catchments for for FLO-2D FLO-2D flood flood modeling. modeling. We analyzed analyzed the the aggravation aggravation of storm of storm surge depth surge and depth extent and in terms extent of in the terms coast’s of topographical the coast’s topographicalcharacteristics. characteristics. Analysis shows Analysis significant shows effects significant of elevation, effects coastal of elevation, shape, and coastal inland shape, approach and inlandangle on approach flood depth angle and on stormflood depth surge velocity.and storm The surge detailed velocity. findings The detailed are as follows: findings are as follows:

Water 2019, 11, 144 17 of 18

• Flow depth analysis in terms of elevation indicated that deeper flooding events were experienced in areas with lower ground elevations (estuaries, low elevation sand lines), whereas coastal areas with higher elevations were not inundated even when the area was directly located on the coast. • Flow velocity analysis in terms of elevation indicated that the maximum velocity increased in the center of catchments with higher elevation and equal distance from storm surge inflows; floods flowed faster in areas that would drive the fluid to compress. • Flow depth analysis in terms of coastal shape indicated that some areas are flooded more than others, even with the same elevation, due to differences in coastal shape. This is due to the fact that fluid is more likely to be dispersed when hitting a convex coast, whereas fluid hitting a concave coast is likely to accumulate in the center, leading to a greater extent of flooding. • Flow velocity analysis in terms of coastal shape revealed that in some regions with same level of elevation, flood velocity appeared to be faster in the central areas of catchment that had inflow parameters in all directions. • Flow depth analysis in terms of a typhoon’s angle of approach indicated that extents are farther in the areas that are directly hit by the typhoon; coastlines that are perpendicular to the to the typhoon’s directional approach displayed a greater tendency to produce storm surges to a greater extent than those that are parallel to the coast.

Through these analyses, we can conclude that not all coastal areas are vulnerable to storm surges. Some regions may be more inundated than others depending on their physical inland characteristics, which can be used to improve early warning systems and long-term coastal urban planning.

Author Contributions: The simulations, field data comparison and writing research draft was done by L.D.; Conceptualization, model analysis and overall organization was done by S.L.; Analysis and final research writing were done by L.D. and S.L. Acknowledgments: This research was supported by the APEC Climate Center. The models and the validation data used in this research were provided through the author’s affiliation with the Philippine Nationwide Operational Assessment of Hazards (UP NOAH Center). The authors would like to acknowledge the contribution and support of the UP NOAH Center Team-Executive Director Mahar Lagmay and the Research and Development Chief Science Research Specialist John Kenneth Suarez. The authors would also like to express gratitude to the reviewers’ insightful comments that had contributed to the further improvement of quality of this paper. Conflicts of Interest: The authors declare no conflicts of interest.

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