Harold Impact Assessment Report

Tropical Cyclone Harold Impact Assessment Report

Zulfikar Begg, Naomi Jackson, Antonio Hermosa, Tomasi Sovea and Stephen Meke Geoscience Energy and Maritime Division Pacific Community.

Suva, , 2021 © Pacific Community (SPC) 2021

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Original text: English

Pacific Community Cataloguing-in-publication data

Begg, Zulfikar

Tropical Cyclone Harold impact assessment report / Zulfikar Begg, Naomi Jackson, Antonio Hermosa, Tomasi Sovea and Stephen Meke

(SPC Technical Report SPC00066 / Pacific Community)

1. Cyclones – Fiji. 2. Cyclones – Tropics. 3. Cyclone damage – Fiji. 4. Cyclones – Environmental aspects – Fiji. 5. Cyclones – Social aspects – Fiji. 6. Hurricanes – Fiji.

I. Begg, Zulfikar II. Jackson, Naomi III. Hermosa, Antonio IV. Sovea, Tomasi V. Meke, Stephen VI. Title VII. Pacific Community

551.5513099611 AACR2

ISBN: 978-982-00-1396-4 TABLE OF CONTENTS

1 EXECUTIVE SUMMARY 1 INTRODUCTION 2 1.1 Background 2 2 SURVEY OVERVIEW 3 2.1 Site Description and Survey Results 4 2.1.1 Komave Village 4 2.1.2 Shangri-La Resort 6 2.1.3 Intercontinental Resort 7 2.1.4 Rendezvous Surf Camp 7 2.1.5 Momi Community 9 2.1.6 Wailoaloa Beach 10 2.1.7 Nila Beach Hotel 11 2.1.8 Nasoata Settlement 12 2.1.9 Typeria Settlement 13 2.2 Field Data Analysis 14 2.3 Asset Impact Data 15 3 CALCULATING INUNDATION OF SEVERE TROPICAL CYCLONE HAROLD 17 3.1 Modelling wind, waves, and 17 3.2 Verification of model using measured inundation profiles 22 4 DISCUSSION 24 4.1 Approach 24 4.2 Hazard parameters 25 4.3 Data collection devices 25 4.4 Building assessment 26 5 CONCLUSION 26 6 REFERENCES 26 TROPICALTROPICAL CYCLONE CYCLONE HAROLDHAROLD IMPACT IMPACT ASSESSMENT ASSESSMENT REPORT REPORT TROPICAL CYCLONE HAROLD IMPACT ASSESSMENT REPORT

EXECUTIVE SUMMARY

Severe Tropical Cyclone Harold developed close to the south east of , and strengthened as it passed through Solomon Islands, , Fiji and .

The Regional Specialized Meteorological Centre in , Fiji (RSMC Nadi) reported Tropical Cyclone (TC) Harold as a Category 5 passing through Vanuatu and later weakening to a Category 4 system on 7 April 2020 as it approached the Fiji group, later moving towards the north-east of Nukualofa, Tonga. It was predicted that Fiji would be severely affected by strong winds, storm surges and inundation, destroying building structures, infrastructure and other assets on low-lying coastal areas.

This report focuses on both the field assessment undertaken along the southern coast of to assess the inundation impact and the state-of-the-art numerical models to simulate wave and storm tide as TC Harold passed through the region.

The field survey and modelling undertaken as part of the TC Harold impact assessment will inform and improve understanding of tropical cyclone risk in the Pacific region and guide decisions and investments to minimise and reduce future risk.

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Figure 1. Track map of TC Harold prepared by the Fiji Meteorological Service indicating locations of Pacific sea level and tsunami monitoring stations and wave buoys

INTRODUCTION

1.1 BACKGROUND

In early April 2020, Tropical Cyclone Harold caused severe damage in the South Pacific as it tracked through Solomon Islands, Vanuatu, Fiji and Tonga (Figure 1). Reported impacts across the four countries were mostly wind-related or caused by wave inundation. The swells, together with high tides, were destructive to most coastal communities and businesses. In response to a request from the Fiji Meteorological Service (FMS), the Pacific Community deployed a team to collect data on inundation extent and run-up, including impact on buildings. The primary reason for the request was to map the impact of coastal inundation caused by TC Harold. These results will also inform the Coastal Inundation Forecasting Demonstration (CIFDP) project, which is developing a robust early warning system that will integrate real time monitoring with high resolution wave models to better predict coastal inundation.

The collection of post-disaster impact data provides invaluable information to correlate how exposed assets respond to hazards. Using the technical advice and support provided through initiatives, such as the Climate and Ocean Support Programme in the Pacific and thePacific: Catastrophe Risk Assessment and Financing Initiative (PCRAFI), up-to-date vulnerability models for buildings and infrastructure exposed to a range of hazards will be developed to help better understand future disaster risk.

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SURVEY OVERVIEW

In response to a request from the Government of Fiji, a survey team from SPC and FMS was mobilised to conduct an impact assessment from 18–23 May 2020. Given the COVID-19 travel restrictions put in place by the Government of Fiji and SPC, special provisions were established to ensure the safety of the field team and the communities that were being assessed.

Prior to the survey, the GEM Oceanography team developed a high-resolution wave and storm tide model of TC Harold as it passed through the Fiji group. The field survey focused on 27 locations along the southern coast of Viti Levu, as identified from the model (see Figure 26).Of the 27 sites, nine were surveyed by the team, as shown in Figure 2. The team expected, prior to leaving, that a lot of the debris and evidence of impact in and around communities would have been cleaned up.

The survey team assessed several parameters (Figure 7). These included the extent of inundation, the wave run-up and the impact on infrastructure, such as damage to buildings. The equipment used for the survey included an automatic level, measuring tape and a staff to assess inundation extent and run-up, and a tablet to collect and store the assessment data in a Kobo toolbox, a data collection tool.

Figure 2. Locations of sites surveyed during the field campaign

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Figure 4. Survey team measuring the extent of inundation

Figure 3. Survey team recording measurements on top of Figure 5. Taking height measurement using a dumpy level the beach berm using a dumpy level and a tide staff at Komave Village, , Fiji

Figures 3 to 5 show the team conducting the survey in each location of interest. A 100 m tape was used to measure the distance from the water level to the extent of inundation (Figure 4). A 4 m tide staff was used for height measurements. It was placed at the water level and along any geomorphological features, such as the toe of beach, the beach berm, the edge of vegetation etc., up to the extent of inundation (Figure 3). A dumpy level mounted on a tripod was used to get the height along the transect (Figure 5). Local time at the water level was also recorded. In addition to the field measurements, the team also conducted on-site interviews to record eyewitness accounts of the event. Aside from the field logs used by the team, forms in the Kobo toolbox systematically stored building damage assessment and photos taken at each site.

2.1 SITE DESCRIPTION AND SURVEY RESULTS

2.1.1 KOMAVE VILLAGE

The first site visited was Komave village on the Coral Coast, which experienced the event in the afternoon of 7 April 2020 at low tide. There were three evacuation sites in the village where people took shelter when the water level extended into the village boundaries. The inundation extent in Komave was measured and found to be 122.4 metres inland from the water level.

Figure 6. Village Headman Mr Josaia Totonavosa showing the mark of the water level during TC Harold at Komave Village, Coral Coast, Fiji

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Figure 7. Survey team configuring the devices for asset data collection at Komave Village, Coral Coast, Fiji

Table 1. Field measurements taken at Komave village. The distance from the water level to the inundation extent was measured, as well as any geomorphological features, in this case the toe of the beach

Resort/ Village Komave Date 19/05/2020 Time of reading 12.00 p.m. Water level Readings (m) Top 4.68 Middle 4.25 Bottom 3.82 Toe of beach Top 3.538 Middle 3.388 Bottom 3.243 Inundation extent level Top 2.481 Middle 2.302 Bottom 2.113 Floor depth 0.354 Inundation extent from water level 122.4

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2.1.2 SHANGRI-LA RESORT

This was the second location measured, as there were eyewitness accounts of high waves causing inundation of parts of the hotel (Figure 8). There were wind-generated waves with minimal flooding, causing no major damage. It is believed that the inundation occurred at approximately 10:30 a.m. on 7 April 2020 and again around 3.00–4.00 p.m. with sediment and vegetation debris washing up and filling the swimming pool and the hotel lawn. The survey team collected in situ inundation about the hazard as shown in Table 2.

Table 2. Readings taken at Shangri-La Resort

Resort/ Village Shangri-La Date 20/05/2020 Time of reading 11.00 a.m. Water level Readings (m) Top 4.029 Middle 3.899 Bottom 3.902 Top of seawall (20 m from Toe of Beach) Top 1.567 Middle 1.54 Bottom 1.512 Inundation extent level Top 2.26 Middle 1.852 Bottom 1.45 Floor depth 0 Inundation extent from water level 107.5

Figure 8. Withered grass due to inundation at the Shangri-La Figure 9. Impact from wind and waves causing the tree Resort to be uprooted in front of the dive shop

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2.1.3 INTERCONTINENTAL RESORT

The third site surveyed was the Intercontinental Resort, which experienced coastal flooding resulting in major damage to the boulder protection (Figure 10), gardens, and beach cabanas. Sand deposits buried seating areas by the beach. The people interviewed highlighted debris, including tree logs, being brought in from the ocean and stuck on reefs, and there was extensive change to the beach morphology, where large volumes of sand were deposited above the beach berm (Figure 11). Height readings and measurements were collected for run-up analysis as shown in Table 3.

Figure 10. Damage to boulder protection at the InterContinental resort, Fiji

Figure 11. Concrete pavements were covered with sand, which had to be removed

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Table 3. Field measurements taken at Intercontinental Resort

Resort/ Village Intercontinental Date 20/05/2020 Time of reading 1.33 p.m. Water level Readings (m) Top 4.668 Middle 4.405 Bottom 4.152 Beach berm (16 m from toe of beach) Top 2.39 Middle 2.24 Bottom 2.089 Inundation extent level Top 0.91 Middle 0.855 Bottom 0.799 Floor depth 0 Inundation extent from water level 61.5

2.1.4 RENDEZVOUS SURF CAMP

Rendezvous Surf Camp is located approximately 25 minutes from Nadi town on the coast. During TC Harold, coastal, flooding (Figure 12) and strong waves caused damage to boats and water sports equipment. A lot of debris was washed inland. Witness accounts shared experiences of waking up in the morning to find boats parked by the door to the staff dormitories or closer than usual (Figure 13). This coastal flooding was experienced at about 6.00–8.00 a.m. on 7 April 2020. The survey team collected data for run-up analysis as shown in Table 4.

Figure 12. Withered grass due to inundation at the Rendezvous Surf Camp

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Figure 13. Boat brought in by strong currents during TC Harold at the Rendezvous Surf Camp

Table 4. Field measurements taken at Rendezvous Surf Camp

Resort/ Village Rendezvous Date 21/05/2020 Time of reading 11.21 a.m. Water level Readings (m) Top 3.605 Middle 2.85 Bottom 2.01 Reef Flat 100 m from water mark Top 3.278 Middle 2.938 Bottom 2.586 Inundation extent level Top 0.782 Middle 0.563 Bottom 0.33 Floor depth 0 Inundation extent from water level 217.3

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2.1.5 MOMI COMMUNITY

The Momi Community is about 600 metres south of the Marriot Resort at Momi Bay. According to the property owner, the area is quite exposed, as there are no sheltering mangroves and they have experienced flooding during past natural disasters. The location surveyed sustained major damage to the sea-walls, boats washed up to the mangroves a few kilometres from the property and witness accounts confirm that the damage sustained was the highest ever experienced. Railway tracks near the property were also destroyed. The information gathered from the survey at Momi is shown in Table 5.

Table 5. Height readings at the Momi Bay community

Resort/ Village Momi Date 20/05/2020 Time of reading 3.42 p.m. Water level Readings (m) Top 3.622 Middle 3.511 Bottom 3.402 Top of slope at 17 m Top 1.622 Middle 1.598 Bottom 1.572 Inundation extent level Top 1.475 Middle 1.405 Bottom 1.332 Floor depth 0 Inundation extent from water level 36

Figure 14. Visible erosion scarp and debris Figure 15. Sea-wall damaged by strong waves at Momi Bay

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2.1.6 WAILOALOA BEACH

The Wailoaloa beach is a public beach with constructions on neighbouring properties. During the field visit to this site six weeks after TC Harold, there was still evidence of debris. There was debris from nearby construction sites, impacts on vegetation, evidence of the eroded beach and debris washed 20 metres inland. The inundation information collected for the inundation hazard analysis is shown in Table 6.

Figure 17. Withered grass in Wailoaloa beach

Figure 16. Erosion scarp observed at Wailoaloa Beach. Figure 18. Debris brought in with the inundation

Table 6. Field measurements taken at Wailoaloa

Resort/ Village Wailoaloa Beach Date 21/05/2020 Time of reading 1.59 p.m. Water level Readings (m) Top 4.182 Middle 3.853 Bottom 3.524 Reef Flat 100 m from water mark Top 0 Middle 0 Bottom 0 Inundation extent level Top 1.382 Middle 1.241 Bottom 1.1 Floor depth 0 Inundation extent from water level 95

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2.1.7 NILA BEACH HOTEL

Nila Beach Resort is about 500 metres from the First Landing Resort/Vuda Marina and approximately 15 minutes’ drive from City. The survey team collected inundation hazard on this property, as shown in Table 7. Even though the hotel property experienced inundation on the lawn (Figure 19), no properties were damaged. Witness accounts reported that a lot sand was deposited onto the lawn (Figure 20).

Figure 19. Withered grass from the inundation Figure 20. Large volumes of sand deposited on the lawn Table 7. Field measurements taken at Nila Beach Resort

Resort/ Village Nila Beach Resort Date 21/05/2020 Time of reading 4.08 p.m. Water level Readings (m) Top 3.026 Middle 2.941 Bottom 2.852 Top of slope 11 m from water mark Top 1.721 Middle 1.686 Bottom 1.051 Inundation extent level Top 1.51 Middle 1.418 Bottom 1.323 Floor depth 0 Inundation extent from water level 34.2

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2.1.8 NASOATA SETTLEMENT

Nasoata Settlement is located on the coast a few minutes’ drive from the Lautoka city centre. According to the village headman, the community has about 82 buildings and a population of approximately 400 people. The Nasoata community began to experience strong winds and high waves, causing inundation between 5.00 and 7.00 a.m. on 8 April 2020. Water entered homes but no major damage was reported. A video interview was conducted with the village headman with TC Harold experience. The survey team gathered information, as shown in Table 8.

Figure 21. Coral rubble and debris accumulated at the inundation extent

Table 8. Field measurements taken at Nila Beach Resort

Resort/ Village Nasowata Village Date 22/05/2020 Time of reading 10.07 a.m. Water level Readings (m) Top 4.178 Middle 3.548 Bottom 2.921 Base of beach 110.7 m from water mark Top 3.678 Middle 2.988 Bottom 2.915 Inundation extent level Top 1.769 Middle 1.669 Bottom 1.565 Floor depth 0.55 Inundation extent from water level 146

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2.1.9 TYPERIA SETTLEMENT

Typeria Settlement is an informal coastal settlement 5–10 minutes’ drive from Lautoka City. The settlement experienced coastal inundation between 4.00 a.m. and 7.00 a.m. on 8 April 2020. It damaged the foundation of houses and destroyed outhouses (outdoor kitchen, outdoor bathrooms, etc.).

Witnesses reported that this was the first time they had experienced such high waves. The inundation extent, run-up and inundation depth measurements are shown in Table 9. The building impact survey was conducted on a few buildings that were affected by coastal flooding.

Figure 22. Witnesses reported that waves covered the Figure 23. House-owner showing flood level during TC mangroves during the event Harold

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Table 9. Field measurements taken at Typeria Settlement

Resort/ Village Typeria Village Date 22/05/2020 Time of reading 11.47 a.m. Water level Readings (m) Top 3.945 Middle 3.405 Bottom 2.882 Toe of beach 89 m from water mark Top 2.875 Middle 2.785 Bottom 2.698 Inundation extent level Top 2.245 Middle 1.59 Bottom 0.9 Floor depth 0.45 Inundation extent from water level 0

2.2 FIELD DATA ANALYSIS

Heights measured using the dumpy level were adjusted to the observed water level. During the survey, a staff was held at the inundation extent, as well as on the water level at a particular time. The height measured at the water level was then used to adjust heights to the observed water level. Tidal levels were then extracted for each measurement at the water level for the respective time and this was used to correct heights above the observed sea level. As the tidal level is referenced to the mean sea level, the heights above the corrected sea level are now referenced to the mean sea level, as seen in Table 10.

Table 10. Height readings at inundation extent, water level and additional geomorphological features measured during the field survey at all sites

Data and Location Description Distance from Height (m) Height above Height Time event beach base observed sea above occurred (m) level (m) corrected sea level (m) 7 April 2020 Komave Village Inundation 122.40 2.302 1.948 1.42 2.00–3.00 p.m. extent Top of scarp 4.25 3.72 Base of beach 3.388 0.862 0.33 Water level -0.53 4.250 0 3.721 8 April 2020 Nasoata Inundation 146.00 1.669 1.879 1.69 4.00–8.00 a.m. (Settlement) extent Top of scarp

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Base of beach 110.70 2.988 0.560 0.368 Water level -0.19 3.548 0 0 Typeria Inundation 1.590 1.815 1.77 (Settlement) extent 8 April 2020 Top of sea- 3.30–8.00 a.m. wall Base of beach 0.00 3.355 Water level -0.05 3.405 0 2.242 8 April 2020 Rendezvous Inundation 217.30 0.563 2.29 1.679 3.00–4.00 a.m. Surf Camp extent Top of seawall 100.00 2.938 -0.09 -0.696 Water level -0.61 2.850 2.242 8 April 2020 Wailoaloa Inundation 95.00 1.241 2.61 2.123 3.30–8.00 a.m. Beach extent Edge of vegetation Base of beach Water level -0.49 3.853 0 3.364 8 April 2020 Nila Beach Inundation 34.20 1.418 1.167 1.329 3.30–8.00 a.m. extent Beach berm 11.00 1.686 0.899 1.061 Base of beach Water level 0.16 2.585 2.747 8 April 2020 Shangrila Inundation 107.50 1.825 2.074 1.50 2.00–5.00 p.m. extent Top of sea- 20.00 1.540 2.359 1.79 wall Base of beach Water Level -0.57 3.899 0 3.327 8 April 2020 Intercontinental Inundation 61.50 0.855 3.55 3.12 2.00–5.00 p.m. (From the extent sitting area in front of the Bar) Top of berm 16.00 2.240 2.165 1.74 Base of beach 0.00 Water level -0.43 4.405 0 3.976 8 April 2020 Momi Inundation 36.00 1.405 2.106 2.279 2.00–5.00 p.m. Communities extent Beach berm 17.00 1.598 1.913 2.086 Base of beach Water level 0.17 3.511 3.684

2.3 ASSET IMPACT DATA

The PCRAFI Program is a regional initiative led by SPC with the aim of updating national risk information, including asset data collection capacity, to enable countries to update and maintain national risk databases for evidence-based policy-making and to inform decision-making processes.

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As part of PCRAFI, countries from the region in collaboration with SPC agreed on standard attributes that informed the development of impact data collection templates for a wide range of assets (e.g. buildings, roads, etc.). This survey focused on collecting physical characteristics and impacts of wind and inundation on affected buildings. Tables 11 and 12 show a template of the survey from the Kobo toolbox and the different fields of data collected to measure the physical characteristics and impacts of wind and inundation on buildings.

Table 11. The table show the attributes of the building data collection template

Attributes Building Survey Shangri-La start 2020-05-20T11:28:24.843+12:00 end 2020-06-02T16:30:42.199+12:00 Collect GPS point -18.137139159571635 177.42054268759645 50.55884851887822 4.0 _Collect GPS point latitude -18.137139159571635 _Collect GPS point_longitude 177.42054268759645 _Collect GPS point_altitude 50.55884851887822 _Collect GPS point_precision 4.0 Usage/Occupancy Class Commercial Point and shoot! Use the camera to take a photo 1589930963076.jpg Subuse N/A Urban/Rural Rural Foundation type Concrete slab Foundation bracing Concrete wall Building type Single storey Building structure Concrete columns Roof shape Gable Roof material Wooden shakes Roof pitch Steep Wall opening Wall Wall material Concrete Wall material/concrete 1 Window type Bay/rectangle windows Window type/bay/rectangle windows 1 Window protection No Min floor height above ground >1.0m (50cm increment) Max floor height above ground <1.0m (25cm increment) Floor type / material Option 3 Foundation condition Under construction Roof condition Good condition Wall condition Minor cracks Building age Medium (5-20yrs) Number of storeys 1 Under storey level No Hanging balcony presence No

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Utilities Electricity Septic tank Yes Water tank No Gutter No

Table 12. The table shows the attributes of impacted building

Attributes Information for thebure at Shangri-La Hazard parameter Coastal flooding Building damage Minor (< 50%) Wall damage Minor (< 50%) Foundation Minor (< 50%) Habitability <50% of the building Business or service disruption Yes Max. water level Debris Sediments Debris impact Yes Comment Sand and debris swept through the building

3 CALCULATING INUNDATION OF SEVERE TROPICAL CYCLONE HAROLD

3.1 MODELLING WIND, WAVES, AND STORM SURGE

The GEM Oceanography team developed a high-resolution wave and storm tide model of TC Harold as it passed through the Fiji group, with the aim of providing timely and reliable information regarding the areas severely affected by TC Harold for consideration in selection of survey locations. ADCIRC+SWAN was the numerical model selected, as it is a highly vetted complex hydrodynamic spatial model commonly used in storm surge assessment. ADCIRC (Advanced Multi-Dimensional Circulation Model, Luettich et al. 1992; Westerink et al. 1992) uses a finite element-based unstructured mesh, which allows a high resolution near the coasts, islands, atolls and small islets. The model fully describes the complex physical processes associated with storm surge.

As a physics-based model, it is commonly used for tidal studies, cyclone-driven storm surge and flooding applications. ADCIRC was coupled with the unstructured Simulating Waves Near- shore (UnSWAN, Zijlema, 2009) wave model to consider the contribution of the wave set-up to the coastal flood process. The SWAN wave model was selected because it has the capability of running in the same unstructured mesh as ADCIRC, avoiding the need of any interpolation to share elevations and wave radiation stresses between both models.

Atmospheric forcing is the principal driver of storm surge. This forcing is included in the storm surge model in the form of surface pressure and surface wind fields (at 10-m elevation), which can be derived from various data and methods. In this simulation, the best track data provided by the Fiji Meteorological Service were used to obtain wind and sea level pressure fields as produced by the Generalized Asymmetric Holland Model (Gao et al. 2017; Dietrich et al. 2018). Tropical cyclone wind field asymmetries were corrected and validated, based on wind measurements as provided through the COSPPac project before forcing the storm surge model.

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Astronomical tides were forced on the mesh open-ocean boundary with the major dominant astronomical tidal constituents, which include the following eight: K1, O1, P1, Q1, M2, N2, S2, and K2. These constituents have been obtained from the TPXO9 tidal atlas developed by Oregon State University (Egberg and Erofeeva 2002).

Bathymetry was acquired from the SRTM_15_PLUS database, which combines bathymetric predictions from the latest global gravity model from CryoSat-2 and Jason-1, along with494 million carefully edited depth soundings at 15 arcsecond resolution. This SRTM15_PLUS provides the foundational bathymetry layer for Google Earth and is freely available at: (topex.ucsd.edu).

Figure 24. Top: Produced unstructured mesh with the considered islands and islets in green. Middle: Bathymetry. Bottom: Mesh resolution

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The models selected work in an unstructured triangular mesh. For this task, the team used the OceandMesh2D tool (Roberts et al. 2018), especially designed to assemble two-dimensional triangular meshes used in finite element numerical simulations.

The produced mesh (see Figure 24) contains 180.340 nodes, covering 3.400 km in longitude by 1.970 in latitude. Resolution ranges from 15 km in the deep ocean to 300–500 m along the coasts of Vanuatu, Fiji and Tonga (see Figure 24). Note that, due to the use of a size-varying mesh, the model is able to properly resolve the iteration of the flow and waves with small islands, which is usually a big constraint when working with regular quadrangular meshes.

The model uses a constant quadratic bottom friction scheme (Cd=0.0025) as no homogeneous information on bottom roughness was available at that time. The Smagorinsky turbulence closure model was enabled with the absolute value of 0.05 m2/s. The wave-circulation coupling was done every 10 minutes. ADCIRC used a Powell wind drag formulation, while SWAN was set up with the Zijlema wind drag formulation, which works better for hurricane wind. Results were stored hourly.

Figure 25 shows the results of the maximum wind, storm tide and wave height during TC Harold. The top panel illustrates the evolution in size and intensity of the cyclone in terms of maximum wind speed. The storm tide elevation shown in the middle panel is the result of the astronomical tide plus the storm surge, which were all simulated together. Of note, the red areas show those locations where the cyclone coincided with high tide. Mostly, the storm surge produced by TC Harold was the product of the inverse barometer effect, due to the cyclone´s pressure deficit in its . Nonetheless, the wind set-up was also relevant in certain areas, such as the west coast of Viti Levu and Vanua Levu, despite the large distance from the cyclone´s centre.

Figure 25. Top: Cyclone Harold wind swath. Middle: storm tide. Bottom: significant wave height

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Figure 26. Zoom of the maximum storm tide results over Viti Levu

Finally, the bottom panel of Figure 25 shows the maxima significant wave height, which clearly exceeded 12 metres through most of TC Harold´s lifetime. Waves up to four metres high also reached more distant locations, such as Rotuma and the southernmost islands of Tuvalu.

Figure 26 shows a zoom of the storm tide in Viti Levu; here the two main mechanisms responsible for the storm surge were the wind and wave set-up on the west face of the Yasawa island group, which was more exposed to the extremely high waves. On the other hand, the wind set-up dominated the surge (up to 1.5 m) from Momi Bay to . Some other small patches of wave set- up dominated surge were observed in Sigatoka and between Komave and Pacific Harbour due to their exposure to the massive waves generated by the cyclone.

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Figure 27. Time series of modelled and measured water surface elevation in (Vanuatu), Lautoka (Fiji) and Nukualofa (Tonga) during Cyclone Harold

Figure 27 shows the time series of modelled and predicted water levels at different tide stations. It is observed that SWAN+ADCIRC fairly reproduce not only the intensity but also the duration of the storm surge associated with Harold.

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3.2 VERIFICATION OF MODEL USING MEASURED INUNDATION PROFILES

Upon completion of the field survey, the model was used to extract total water level, significant wave height, wave period and direction of the locations that were surveyed, as shown in Table 13. During the survey, run-up information collected was referenced to the level of the sea at the time of the measurement. The run-up information was then reduced to a common vertical reference datum, the mean sea level (MSL) at Maui Bay, in order to compare it with the model outputs (Bosserelle, et al. 2016). Despite the good agreement between the model and the sea levels recorded by the different tide gauges, measured run-ups are not directly comparable. Run-ups data capture not only the steady rise of the water caused by the processes previously described (inverse barometer effect, wind and wave set-up) but also the increase of the water level produced by independent waves – a process known as swash. However, there is no numerical model that can be applied on such a large-scale and at the same time be able to reproduce these small-scale processes. For this reason, we used the empirical formulation proposed by Stockdon et al. (2006) to obtain the swash offset that should be added to the model results for a proper comparison with the measured inundation heights.

Table 13. Wave run-up from field survey and modelled outputs, including total water level (TWL), significant wave height (Hs) and peak period (Tp)

Time Location βf Tide level Height TWL Hs(m) Tp(s) Direction (degrees) at the time above Model (degrees of the MSL (m) (m) North) measurement (m) 12:00 Komave Village 2 -0.5293 1.42 1.42 7.23 10.978 215.67 11:00 Shangri-La 2 -0.5723 1.5 1.478 5.83 10.176 201.22 13:33 InterContinental 6 -0.4287 3.12 2.94 6.17 15.67 277.99 Resort 16:08 Nila Beach 1 0.1621 1.329 1.65 3.51 15.42 302.62 Resort 15:42 Momi 3 0.1733 2.279 2.14 3.81 15.09 305.68 Community 11:21 Rendezvous Surf 2 -0.6077 1.679 1.51 3.85 6.33 328.795 Camp 13:59 Wailoaloa Beach 6 -0.4890 2.123 1.94 2.489 5.591 325.93 10:07 Nasoata 1 -0.1923 1.69 1.75 3.73 14.86 302.28 Settlement 11:47 Typeria 1 -0.6371 1.8 1.74 3.65 15.175 303.79 Settlement

According to Stockdon et al. (2006), the 2% swash height can be determined using the following equation:

1⁄2 R2=0.73βf (H0 L0 )

where H0 and L0 are the corresponding significant wave height and wave length evaluated in deep water and βf is the foreshore reef slope. Due to the absence of a homogeneous high-resolution topo-bathymetric model covering the analysed areas of Viti Levu, the foreshore beach slope was estimated at the time of the fieldwork. Of note is the fact that substantial changes in the

23 TROPICAL CYCLONE HAROLD IMPACT ASSESSMENT REPORT beach morphology may have occurred during TC Harold and therefore, in some locations such as Natadola, slightly higher beach slopes were needed to match model results with observations. Finally, theinformation in Table 13 shows the validation of the run-ups against the model outputs after empirically determining the swash part of the coastal inundation process. As shown in Figure 28, the methodology used demonstrates a high degree of performance to accurately forecast coastal flood heights.

Figure 28. Linear regression of total water level modelled during TC Harold, with wave run-up derived from the field survey for the specific location as illustrated

4 DISCUSSION

4.1 APPROACH

With basic equipment and simple methodology, the survey team was able to collect some very important datasets. Recommendations for future surveys include:

• communicating with the hotel and/ or community focal points to confirm the feasibility of conducting surveys in that area, particularly when investigating whether the location was affected (in this instance, inundated); • conducting the survey soon after the event, noting the need for humanitarian assistance being provided to affected communities as a priority; and • in addition to a handheld GPS for location, a compass is also included to record orientation of transects.

4.2 HAZARD PARAMETERS

Data analysed from the field survey for water level above mean sea level agree when compared with total water level extracted from the model, as in Figure 28. However, a recommendation for future surveys is to collect slope of the beach as well. As per chapter 3, run-up formula (Stockdon

24 TROPICAL CYCLONE HAROLD IMPACT ASSESSMENT REPORT et al. 2006) uses beach slopes as a parameter to calculate run-up. The beach slope was estimated for this report but should be measured in the field. The hazard template needs to include critical parameters – height readings at the various geomorphological features, e.g. height reading at water level, bottom of beach, top of scarp and the inundation extent.

In terms of impact data collection, information such as height reading at water level, base of beach, top of scarp, inundation extent and other geomorphological features are critical parameters to be included to the inundation hazard template.

4.3 DATA COLLECTION DEVICES

A standard questionnaire was developed to capture building and building impact information, uploaded to handheld devices for the survey. The Huawei tablets used for the assessment had a few issues.

• GPS accuracy: 5–10 m off original position. Five tablets were tested and all five gave similar results of about 5-10 m off positioning. • GPS capabilities in the Kobo platform within the five devices took a lot of time to connect to satellites; it would load for at least 15–20 minutes. It was usual for the devices to return a GPS connection error. As an alternative, an android phone was used to collect data. This did not have GPS connection and accuracy issues.

It is recommended that a differential or survey grade GPS for asset data collection be used. The Kobo tool can be used with an improved device option. An android phone can be kept as a backup device for such surveys.

4.4 BUILDING ASSESSMENT

The survey flagged the need to revise the survey forms and attributes to be captured. As the questionnaire was set up using the asset exposure database, several fields represented typologies rather than specific attributes of buildings. For instance, “building type” describes standard building typologies in the Pacific region, which combine several attributes, such as construction type and materials and number of storeys. The decision to use standard typologies or collect individua l building characteristics does need to be determined. Other issues included attributes that did not apply, such as buildings without foundations.

5 CONCLUSION

The Pacific region has been experiencing extreme events, such as tropical cyclones, and itis important to conduct an assessment to collect datasets that could be used for impact-based forecasting and validating forecasts, etc. A high-end model was quickly developed by SPCto investigate the impacts of TC Harold in the region and this was used to identify locations to be investigated. Analysis of data, such as wave run-up, collected from the field survey agrees with the outputs of the model, in this case, total water level. The paucity of impact data in the region directly affects the quality and actionability of risk assessment products. Impact and hazard data collected after an event provide critical baselines that support development of national and regional fragility/damage function. Asset and impact data collection at national and regional scale facilitate major hazard and impact assessment activities in the region. The development ofa standardised regional asset and impact data collection template is crucial for consistent exposure data necessary for risk analysis, planning and decision-making processes.

Several recommendations have been suggested under section 4 which should be considered when a survey such as this is conducted.

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6 REFERENCES

• Bosserelle,C., Lal, D., Reddy, S. and Movono, M. (2016). Waves and coasts in . Pacific Community. • Dietrich, J.C., Muhammad, A., Curcic, M., Fathi, A., Dawson, C., Chen, S., and Luettich, R. Jr. (2018). Sensitivity of storm surge predictions to atmospheric forcing during hurricane ISAAC. J. Waterw. Port Coast. Ocean Eng. 144, 04017035, doi: 10.1061/(ASCE)WW.1943- 5460.0000419 • Egbert, G.D. and Erofeeva, S.Y. (2002). Efficient inverse modeling of barotropic ocean tides. J. Atmos. Oceanic Technol., 19: 183–204. • Gao, J., Luettich, R. A. and Fleming, J. G. (2017). Development and evaluation of a generalized asymmetric tropical cyclone vortex model in ADCIRC. ADCIRC Users Group Meeting, U.S. States Army Corps of Engineers, Vicksburg, MS. • Luettich, R.A., Jr., Westerink, J.J. and Scheffner, N.W. (1992). ADCIRC: An advanced three- dimensional circulation model for shelves coasts and estuaries, report 1: Theory and methodology of ADCIRC-2DDI and ADCIRC-3DL, Dredging Research Program Technical Report DRP-92-6, U.S. Army Engineers Waterways Experiment Station, Vicksburg, MS, 137p. • Roberts, K. J., Pringle W. J. and J.J. Westerink J.J. (2018) OceanMesh2D 1.0: MATLAB-based software for two-dimensional unstructured mesh generation in coastal ocean modeling. Geosci. Model Dev. Discuss [online] Available: https://doi.org/10.5194/gmd-2018-203. • Westerink, J.J., Luettich, R.A., Feyen, J.C., Atkinson, J.H., Dawson, C., Roberts, H.J., Powell, M.D., Dunion, J.P., Kubatko, E.J. and Pourtaheri, H. (2008). A basin- to channel-scale unstructured mesh hurricane storm surge model applied to southern Louisiana. Mon. Weather Rev., 136: 833–864. • Zijlema, M. (2009). Parallel, unstructured mesh implementation for SWAN. Proceedings of the 31st International Conference on Coastal Engineering, pp. 470–482.

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Produced by the Pacific Community (SPC) Pacific Community Suva Regional Office Telephone: +679 337 0733 Email: [email protected] Website: www.spc.int © Pacific Community (SPC) 2021

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