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 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,
13,
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:
Section 2 describes the data and the data sources used, as well as the governing equations and procedures behind the storm surge and flood models.
Section 3 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,
Section 4 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.
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:
where
and
are mass fluxes in the x and y directions, defined as
where
and
are the x and y direction velocities,
is the vertical length,
is the water depth below mean sea level,
is the surface elevation,
is the water column height corresponding to the inverse barometer effect,
is the Coriolis parameter,
is the acceleration due to gravity,
is water density,
and
are the x and y components of wind stress on the sea surface, and
are the x and y components of bottom friction stress. Wind stresses are expressed as
where
is the drag coefficient,
is the air density,
is the wind speed, and (
,
) is the wind velocity. The drag coefficient is set using the results of [
18] and [
19]:
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 (region in the northwestern pacific where the Philippines’ national meteorological agency monitors 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.
At the sea–land interface, the normal component of velocity is zero. At open boundaries, the sea surface elevation is given by the inverted barometer effect; a gravity wave radiation condition was used in this study. Tides can be simulated but are excluded, since the major object of this simulation is for surge heights.
Our simulations produced storm surge height time series for each domain. We modified the storm surge height to incorporate astronomical tides computed using WxTide, a Windows-based tide and current prediction program. We determined the coastal provinces targeted for flood simulation from the results of our JMA storm surge simulation. The storm tide (storm surge depth + astronomical tide) time series from the JMA model was used as the input file for our storm surge inundation simulation.
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 (
Figure 2). We then used the catchments of these provinces for our flood inundation simulation.
2.2. Coastal Inundation Model
One of the JMA storm surge model’s limitations is that it does not simulate storm surge inundation. The extent of storm surge flooding predictions is crucial for pre-emptive evacuation and long-term urban planning. Storm surge inundation maps were employed in this study using the FLO2D model, a flood routing model that simulates channel, overland, and street flow over complex topography. We chose the FLO2D model to conduct our flood simulation because of its compatibility with the JMA storm surge model. The FLO2D model’s limitations, such as the absence of wave coefficients and receding parameters, were not deemed significant enough to drastically affect this study’s objective and results. FLO2D’s governing equations are as follows:
where
represents time;
is the flow depth;
is the depth-averaged velocity in one of eight flow directions;
,
is the excess rainfall intensity;
is the friction slope component based on Manning’s equation;
is the bed slope pressure gradient; and
is the acceleration due to gravity. This equation represents a one-dimensional, depth-averaged channel flow. For the floodplain, although FLO-2D is a multi-direction flow model, FLO-2D’s equations of motion are applied by computing average flow velocity across a grid element boundary one direction at a time [
21].
The catchments to simulate for flooding were chosen using the previous storm surge simulation; we selected the top five provinces with the highest simulated storm surge. We applied boundary grids to the province shapefiles to create a catchment. Data with a grid resolution of 20 m × 20 m were used through the provided DTM data. The flood simulation runs smoothly if the storm surge’s estimated peak discharge,
, divided by the grid element surface area,
, does not exceed 3 m
3 s
−1. After considering the simulation time, we set the grid size to 20 m.
Figure 3 shows a sample of inundation results, such as the maximum water surface depth and maximum flow depth, as shown in the FLO-2D 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.
4. Conclusions
We simulated Typhoon Haiyan’s storm surge height and inundation on the most affected coastal provinces in the Philippines, using the JMA storm surge model and the FLO-2D flood routing model, respectively. The reliability of the models used was proven through validation using actual storm surge records for Typhoon Haiyan. Note that we only conducted qualitative analysis for the effect of quantitative analysis, because of lack of validation data.
Storm surge model results 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 3.9 m. We subsequently used these province catchments for FLO-2D flood modeling.
We analyzed the aggravation of storm surge depth and extent in terms of the coast’s topographical characteristics. Analysis shows significant effects of elevation, coastal shape, and inland approach angle on flood depth and storm surge velocity. The detailed findings are as follows:
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.