Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

SPC Applied Geoscience and Technology Division (SOPAC)

September 2013

Hervé Damlamian, Jens Kruger, Maleli Turagabeci & Salesh Kumar

SPC SOPAC Technical REPORT (PR176)

Ocean and Islands Programme © Copyright Secretariat of the Pacific Community (SPC), 2013

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Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

SPC SOPAC TECHNICAL REPORT (PR176)

Hervé Damlamian, Jens Kruger, Maleli Turagabeci & Salesh Kumar

September 2013

Ocean and Islands Programme DISCLAIMER

While care has been taken in the collection, analysis, and compilation of the data, it is supplied on the condition that the Secretariat of Pacific Community Applied Geoscience and Technology Division (SOPAC) shall not be liable for any loss or injury whatsoever arising from the use of the data.

Important Notice

This document has been produced with the financial assistance of the European Union through the Supporting Disaster Risk Reduction in Pacific Overseas Countries and Territories, th9 European Development Fund – C Envelope.

The contents of this document are the sole responsibility of the Secretariat of the Pacific Community and can under no circumstances be regarded as reflecting the position of the European Union.

The SPC Applied Geoscience and Technology (SOPAC) Division undertook the work in collaboration with ‘Service de I’Urbanisme’ of French Polynesia TABLE OF CONTENTS

ACKNOWLEDGEMENTS...... 2

EXECUTIVE SUMMARY...... 3

1 INTRODUCTION...... 4

2 meTHODOLOGY...... 6

2.1 Physical processes...... 6

2.2 Characteristic cyclone sea state...... 7

2.3 Oceanographic data collection...... 8

2.4 XBeach model...... 9

3 CALIBRATION...... 11

3.1 Sensitivity analysis...... 11

3.2 Calibration...... 12

3.3 Other modelling considerations...... 13

4 reSULTS...... 20

4.1 Rangiroa...... 21

4.2 Apataki...... 29

4.3 Kauehi...... 33

4.4 Manihi...... 37

4.5 Arutua...... 42

5 CONCLUSION...... 49

6 reFERENCES...... 51

7 APPENDICES...... 52

APPENDIX A: 3D COMPUTATIONAL DOMAIN...... 52

APPENDIX B: MODEL RESULT PLOTS...... 55

1 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

ACKNOWLEDGEMENTS

The success of this study is due to the assistance of a number of individuals from the Government of French Polynesia, especially Emilie Nowak and Emmanuel des Garets. We also acknowledge the guidance received from Scott Stephens, National Institute of Water and Atmospheric Research, New Zealand, and Dano Roelvink, United Nations Educational, Scientific and Cultural Organization Institute for Water Education, the Netherlands.

2 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

EXECUTIVE SUMMARY

This study is a component of a larger project aimed at assessing the storm surge hazard in the Tuamotu Archipelago of French Polynesia and at providing information that would aid the inclusion of this hazard into future development plans and risk prevention solutions.

This study used forcing conditions generated by a 12 m wave impacting on the atolls of Apataki, Arutua, Kauehi, Manihi and Rangiroa at high tide. The inundation was computed using the XBeach hydrodynamic model. Such a wave correlates to about a 50 year return interval that has a 2 per cent chance of occurring in any one year.

Prior to applying the above input parameters, the XBeach model was initially set up and calibrated based on baseline and observational data collected in the field from June to December 2011. This report covers the results of the two- dimensional cyclone wave model of the five targeted sites set up under this study. A companion report of the fieldwork component, including details on the oceanographic data collected and used in the calibrations, is available, as is a report on the bathymetry survey for Rangiroa.

Model results were mapped into four risk categories of weak, intermediate, strong and very strong. All modelled sites that were adjacent to the ocean showed very strong risk to extreme cyclone-generated significant wave heights of 12 m. Lagoon sides that were mapped as having intermediate to weak levels of risk should be considered with caution, because inundation could also come from the lagoon side through storm surge, which was not investigated in this study. We found that the model results to be very sensitive to wave direction. It is noted that the effects of variable incident wave directions that deviate from the perpendicular could result in higher risk in some areas, especially near a pass or hoa.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 3 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia 1 INTRODUCTION

Following the impact of Cyclone Alan on the Society Islands in 1998, the Government of French Polynesia requested the systematic study and mapping of natural hazards in all of the island groups (through the ARAI Programme1). This project received 3.7 million euros in funding and was implemented by the Bureau de Recherches Géologiques et Minières (the French Geological Society) from 2002–2006. The natural hazards examined included tsunamis, floods, landslides, ocean swell waves and earthquakes.

Given ARAI’s findings, the Government of French Polynesia decided to introduce the systematic use of risk prevention plans known as PPR or plan de prévention des risques. The PPR is a document produced by the Government of French Polynesia with the goal of regulating land use based on natural hazards. The document comprises the following information for each zone: • Introductory report, containing an analysis of hazards and a study of their impact on people and property, both currently and in the future. The report also highlights the principles of developing the PPR • Map atlas of historical events and hazards, and including zoning maps on a scale of 1:10,000 and 1:25,000, which specifies the areas controlled by the PPR • Regulations specifying the rules for each zone • Methodology

The PPR is most comprehensive and detailed in the high islands, especially urban Tahiti, and less so in the low-lying and less populated regions of the Tuamotu Archipelago, where development is restricted primarily through coastal setback rules. As part of the European Union-funded project ‘Supporting disaster risk reduction in Pacific countries and territories”, the Applied Geoscience and Technology Division of the Secretariat of the Pacific Community, in collaboration with the Government of French Polynesia, undertook a large study to reduce the data gaps and refine the development regulations in the Tuamotus for a strategic area for tourism and pearl farming – two of the country’s main sources of revenue.

Rangiroa is one of the world’s largest atolls, and comprises 16% of the Tuamotu Archipelago’s population of more than 18,000 (en.wikipedia.org, accessed July 2013). Rangiroa and its neighbour atolls are already subject to development regulations that take into consideration the hazard of storm surge. The impacts of this hazard are, however, not well known for this area. An assessment of this hazard would make it not only possible to refine development regulations that would achieve greater security for people and property, but also to produce benefits for tourism, pearl culture and the environment.

The purpose of the project was, therefore, to study the storm surge hazard in the Tuamotu Archipelago of French Polynesia and to provide information that would aid the inclusion of this hazard into future development plans and risk prevention solutions. For this purpose, five atolls were chosen: Apataki, Arutua, Kauehi, Manihi and Rangiroa (Figure 1).

The reasons for this choice are: • they are among the most highly populated atolls within the archipelago; • they are representative of environmental factors (e.g. Kauehi – Fakarava Biosphere Reserve, listed and recognised by UNESCO) and economic considerations (Apataki, Arutua and Manihi have good pearl culture potential, which is the French Polynesia’s second largest economic resource, after tourism); • they are representative of the various atoll morphologies occurring in French Polynesia; and • Rangiroa is French Polynesia’s largest atoll, and the second largest atoll in the world. It is also a prime world scuba diving destination.

The vulnerability assessment of the key sites, mainly airports and villages, was undertaken in the five targeted atolls by developing numerical models. The XBeach open-source model (see: oss.deltares.nl/web/xbeach/) was used to investigate the potential inundation area generated from the characteristic extreme cyclone wave as defined within the ARAI project:

• significant wave height, Hs = 12 meters (m)

• peak wave period, Tp = 13 seconds (s) • an incident wave direction perpendicular to the shoreline

1 ARAI = Aléas Risques naturels, Aménagement et Information.

4 SPC SOPAC TECHNICAL REPORT (P176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 1: Tuamotu Archipelago. Red arrows point to the five targeted atolls: Rangiroa, Arutua, Kauehi, Apataki and Manihi.

Prior to applying the above input parameters, the XBeach model was initially set up and calibrated based on baseline and observational data collected in the field from June to December 2011. This report covers the results of the two- dimensional cyclone wave model of the five targeted sites set up under this study. A companion report of the fieldwork component, including details on the oceanographic data collected and used in the calibrations (Kruger et al. 2013) is available, as is a report on the bathymetry survey for Rangiroa (Kumar et al. 2013).

SPC SOPAC TECHNICAL REPORT (P176) SPC SOPAC TECHNICAL REPORT (P176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia 2 METHODOLOGY

2.1 Physical processes Investigating the area of potential inundation from tropical cyclone-generated sea states on the coasts of atolls requires a good understanding of the hydrodynamics induced by severe weather conditions and the nearshore hydrodynamic processes of reef environments.

Tropical cyclone-generated sea states are separated into two main components in order to assess potential inundation: • storm surge, which is a rise in water level resulting from a change in atmospheric pressure and high winds exerting pressure on the sea surface; and • storm waves that result from cyclone winds exerting pressure on the sea surface.

On atolls and, more generally, steep volcanic islands, tropical cyclone inundation potential is predominantly driven by wave impact and is less influenced by wind-driven wave surge as it is for continental shelf environment. The contribution of surface wind stress to surge is determined by the width and depth of the continental shelf. Atolls have reef-dominated shorelines and do not generally have geomorphic features such as shelves; therefore, the contribution of wind setup2 to total surge and subsequent land inundation is minimal. The primary forces driving inundation on reef-fringed coastlines are wave generated, and result from wave setup and runup3 as open ocean waves propagate over the reef and onto the land (Dean and Darymple 1991).

The morphology of a typical barrier reef consist of three main sections (Figure 2 on page 9), a very steep reef slope, a shallow reef flat (<1 m in depth, with a width ranging from 60–280 m), and a low-lying land area (<5 m in height). As waves propagate over the reef, they undergo several transformations (Lowe and al. 2009). An offshore wave begins shoaling as soon as its wave length is equal to the water depth. This results in energy dissipation through bottom friction and an increase in significant wave height. When the wave height reaches a threshold determined by the ratio of its height to water depth, the wave becomes unstable and breaks. This depth-limited breaking generally occurs when the height of the wave is equal to 78% of the water depth (Dean and Dalrymple 2002). On a fringing reef with a steep outer slope and a shallow reef flat, the surf zone or area where wave breaking occurs is narrow and confined near the reef crest under normal weather conditions. Wave breaking reduces the wave height in the surf zone, which is balanced by a rise in water level, or wave setup. This concept is called ‘radiation stress’, and was developed by Longuet-Higgins and Stewart (1962), is analogous to light developing a radiation pressure when shining on an object The remaining waves that propagate onto the reef flat are subject to significant dissipation due to bottom friction (Lowe and al. 2009).

The mechanism of swell wave transformation over a fringing reef is increasingly being examined. Field studies provide a good understanding of wave transformation into mean wave-current over a reef flat due to wave breaking and bottom friction (Lowe et al. 2009; Taebi et al. 2011).

Furthermore, previous studies have shown that the energy spectrum on the reef flat is dominated by motions at the infragravity frequency level (Lugo-Fernandez et al. 1998; Young 1989; Brander et al. 2004). Those infragravity waves, which are low frequency waves (0.005–0.04 Hz), are created by the non-linear interaction of wave groups as bound long waves (Longuet-Higgins and Stewart 1962). More recently, field studies have shown the significant involvement of infragravity waves in the water level variation on the reef flat (Pequinet et al. 2009). Both studies collected data that suggest that infragravity waves over a reef flat are generated in the surf zone by the radiation stress gradient resulting from the wave group forcing frequencies (Symonds et al. 1982).

Infragravity waves are believed to be preponderant in causing inundation in a fringing reef environment during a cyclone wave event. Infragravity waves are also highly modulated by the water depth on the reef flat. During a cyclone event, wave setup and storm surge potentially increase water depth over the reef flat, positively influencing the contribution of the infragravity wave. A field study of Ningaloo Reef in Australia showed dominant infragravity waves on the reef flat to be as high as 15% of the incident offshore wave height during high tide (Pomeroy et al. 2012). Additionally, a field study on Ipan Reef in Guam showed a greater potential for the resonance of infragravity waves on the reef flat as water level rises (Pequinet et al. 2009).

2 The increase in the stillwater surface near the shoreline, due to the presence of breaking waves. 3 The rush of wave water up a slope or structure.

6 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

2.2 Characteristic cyclone sea state In order to integrate this work with previous efforts to develop French Polynesia’s risk prevention plans, this study was requested to model the impact from a cyclone-induced significant wave height of 12 m perpendicular to the shore. The storm surge associated with this characteristic event is 1 m (des Garets, 2005). The tidal level is spring high tide.

2.2.1 Probability of an event

A recent probabilistic cyclone hazard assessment (Scott Stephens, Coastal Modeller, NIWA, 2012) attributes a 50- year return period to a 12 m wave for Tahiti. This result could be extended to the Tuamotu Archipelago. However, the probability of a 12 m wave being generated at one specific site within the Tuamotus would be much higher. Furthermore, the joint probability for such a wave to impact a specific site in the Tuamotus at spring high tide is again even greater. Some of the offshore wave heights that are believed to have occurred during the 1982–1983 tropical cyclone season are listed in Table 1, and there is no doubt that studying the risk from such an event is relevant to the archipelago.

Table 1: Wave heights reported by eye-witnesses during the 1982–1983 tropical cyclone season.

Tropical Cyclone Location Reported wave height (m) Reva Rangiroa 5–6 Veena Manihi 6 Ahe 6 Tahiti 5–7 Moorea 5–7 William Hao, Puka Puka, Reao 8–9 Nano Hao 15 Rea 5–7 Hereheretue 5–7 Tureia 16–18 Orama Takapoto 6–8 Manihi 6–8 Ahe 6–8 Anaa 12

Source: BRGM/RP-55038-FR, des Garets, 2005.

2.2.2 Storm surge

Surge can be divided into a pressure-driven component and a wind setup component.

The increase in ocean water level due to a drop in atmospheric pressure can be approximated using Bernoulli’s equation.

Where:

Equation 1

! ! 𝑃𝑃! 𝑣𝑣! 𝑃𝑃! 𝑣𝑣! + + 𝑧𝑧! = + + 𝑧𝑧! P is the pressure,𝛾𝛾 2 𝑔𝑔 is the weight𝛾𝛾 of the 2fluid𝑔𝑔 pg( ), v is the velocity of the fluid, andz is the water level. Because waves and currents in the ocean create only negligible changes in the pressure field, Equation 1 can be reduced to its static form so that: 𝛾𝛾

Equation 2

𝑝𝑝! − 𝑝𝑝! 𝑧𝑧!!𝑧𝑧! ! 𝛾𝛾

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 7 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Applying Equation 2 to an extreme cyclone condition, with a drop in atmospheric pressure to 980 mbar (intense mid- latitude cyclone), the ocean water level would be expected to rise to about 35 cm. For a Category 3 cyclone with an atmospheric pressure of about 950 mbar, the water level would rise to about 62 cm. For a Category 4 cyclone, such as Cyclone Orama-Nisha or Veena, which devastated the Tuamotus during the 1983–1984 cyclone period, the minimum atmospheric pressure is believed to have reached 925 mbar. Such low pressure would have created a water level rise of about 88 cm.

The following empirical relation (Equation 3), which is based on the momentum balance of wind stress and water surface gradient, provides a simple expression to estimate the wind contribution to setup over a fringing reef. Equation 3

Where W is the width of the reef flat, Cd is the wind drag coefficient, U is the wind speed, K is an empirical coefficient, hr is the still-water depth over the reef flat, and is the wave setup. K is in the range of 800–1200 (Alejandro Sánchez et al. 2007).

As observed by Powel et al. 2003, Cd decreases during extreme cyclone conditions due to generated foam covering the sea surface, and the authors recommend a coefficient of 0.002.

Equation 3 clearly shows that the wind setup for the targeted sites is proportional to the width of their reef flat. All sites feature a narrow reef flat between 60 m and 80 m, except for Apataki whose reef flat ranges from about 200–280 m.

The wind setup contribution can be estimated to about 0.15 m with W = 80 m, U = 60 m/s, hr + = 5m, Cd = 0.002, and K = 800. Hence, wind setup contribution as calculated represents only 3% of the total water level on the reef flat. For Apataki, the wind setup contribution to the surge could reach between 0.37 cm and 0.52 cm due to its relatively wide reef flat.

In view of the approximated wind setup and water level rise due to a change in atmospheric pressure, a surge of 1 m seems to be a reasonable hypothesis. However, the surge could be noticeably underestimated for Apataki.

2.3 Oceanographic data collection In November 2011, a four-week oceanographic survey was undertaken to collect data in the main settlements of four targeted atolls: Apataki, Avatoru, Kauehi and Manihi. In each of the villages, four oceanographic instruments were deployed along a transect, extending from the reef slope to the shoreline (Figure 2). A similar deployment was undertaken in Avatoru, Rangiroa for about five months, between July 2011 and November 2011 (see the companion report by Baleilevuka et al. 2013 for details). Three pressure sensors (RBRTWR-2050), recording water level, wave height and wave period were deployed on the reef slope, reef crest and near the shoreline. The tide and wave recorder (TWR) deployed on the reef slope records incident wave and ocean tide data. Data recorded from TWRs on the reef flat can be analysed to extract generated wave setup and wave attenuation. Set-up parameters of those instruments are shown in Table 2. A current meter (Nortek Acoustic Doppler Current Profiler, or Aquadopp) was deployed in the middle of the reef flat to collect wave-generated currents. Setup parameters of this instrument are shown in Table 3. Finally, each profile was surveyed using a theodolite and staff following standard land survey techniques.

Table 2: Main setup parameters of pressure sensor (TWR).

3 Tide 4 Wave Sampling period (min) Averaged period (min) Burst length Burst rate (Hz) Burst sampling period (min) 5 1 2048 2 60

Table 3: Main setup parameters of current profiler (Aquadopp).

5 Profile interval (s) 6 Averaged interval(s) 7 Cell size (m) 600 60 0.10 m

8 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 2: Diagram of standard deployment for wave transformation data collection. Four instruments were deployed along transect; three pressure sensors (TWRs), recording water level and wave height and one current profiler (Aquadopp). The morphology of the fringing reef is (from left to right) reef slope, reef flat, and land.

2.4 XBeach model A recent and well tested open source model, XBeach (Roelvink et al. 2009) was developed including a new process- based and time dependent 2DH (depth averaged) model of the nearshore and coast. XBeach is a good fit for this study because it resolves coupled short-wave energy, flow and infragravity wave generation, and propagation. While a brief description is given below, highlighting the key parameters used in this study, an in-depth description can be found in Roelvink et al (2009).

XBeach solves the time-dependent, short-wave action balance on the scale of wave groups as follows: Equation 4

With the action wave A = E w / σ , E w is the wave energy and σ the intrinsic wave frequency; Cx and Cy represent the respective components of the wave group velocity. The velocity in directional space c θ takes into account refraction due to the sea bottom and currents. Importantly, the energy dissipation due to waves breaking (Dw) is modelled according to Roelvink (1993) as: Equation 5

With,

With being the wave dissipation coefficient, being the wave energy, the density of water, the water depth α Ew ρ h st th and the breaker index (for calibration), and Trep is the mean period defined by the 1 and 0 moments of the wave spectrum.

The energy𝛾𝛾 dissipation due to bottom friction is: Equation 6

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 9 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

With fw being the short-wave friction coefficient. The roller energy balance is coupled to the wave action/energy balance where dissipation of wave energy serves as a source term for the roller energy balance.

The total roller energy dissipation is given by (Reniers et al. 2004): Equation 7

With, Er being roller energy, c being phase velocity, and β being breaker slope coefficient. Using these wave action formulations, it is possible to solve directionally spread infragravity waves and time varying currents.

Surface elevation and flow, including infragravity waves and unsteady wave induced currents, are solved using the shallow water momentum and mass balance equations. To include short wave-induced mass fluxes and return flows in the shallow water equation, XBeach uses the generalised Lagrangian mean formulation (Andrews and Mcintyre 1978).

10 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia 3 CALIBRATION

3.1 Sensitivity analysis A sensitivity analysis was carried out to extract dominant XBeach model parameters when studying wave transformation over fringing reefs. This process enables the calibration on a set of dominant parameters, which drastically reduces computational time. Additionally, this sensitivity analysis is a quick assessment of XBeach model’s behaviour.

A one-dimensional (1D) XBeach model was created, representing the cross-shore profile in front of Avatoru, Rangiroa, where the oceanographic instruments were deployed (Figure 3). The model was set up to simulate two measured independent events; boundary condition of wave and water level were taken from the pressure sensor on the reef slope. Simulated wave setup and wave-generated current were extracted and compared with the measured data, and a root mean square error (RMSE) was calculated.

The sensitivity of a given parameter was estimated by the variation of the RMSE of simulated and measured data between two model runs; with the parameter’s minimum and maximum values, while all other parameters were set to their default values.

While this sensitivity analysis only considered the model sensitivity for each parameter independently to the combination of the other parameters values, it gives a clear indication of the dominant parameters to be considered in the calibration process.

The output of this automated sensitivity study was synthesised into Figure 4, and resulting in five dominant parameters.

As expected, the bottom friction (Cf) in the flow equation, the wave dispersion coefficients alphaa ( ), the breaker parameter gamma ( ), and the friction coefficient in the wave action f( ) are largely controlling the model’s behaviour. ɣ w Additionally, the breaker slope coefficient, beta, appears to play an important role.

Figure 3: Map showing oceanographic instrument deployment along a transect in front of Avatoru, Rangiroa. Right panel shows the location of transect. The left panel is a more detailed map of the instruments. The backdrop is a 2002 Ikonos satellite image.

Figure 4: Sensitivity analysis plots, from top to bottom: Root mean square error (RMSE) for Speed; RMSE wave height, RMSE water level. Plots represents the variation in RMSE in the model (y-axis) caused by modifying a given parameter (x-axis) value from its minimum to its maximum, leaving all other parameters to their default value. From bottom to top, the figures show the involvement of each parameter on the variation of error (RMSE) in wave setup, wave attenuation and wave-generated current over

two wave events. The parameters are from left to right: cf: flow friction coefficient, gamma: breaker parameter, alpha: Wave dissipation coefficient, nuh: horizontal background viscosity, nuhfac: viscosity coefficient for roller-induced turbulent horizontal

viscosity, n: Power in Roelvink dissipation model, Break: Option breaker model, fw: short wave friction coefficient, beta: breaker slope coefficient. SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 11 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

3.2 Calibration The dominant mechanism of wave dissipation across a fringing reef is dependent on the incident wave height. Lowe et al. (2005) estimate the contribution of incident wave energy dissipated by a barrier reef due to wave breaking and bottom friction separately. They found that the contribution of dissipation by breaking increases with wave height: for small waves (root mean square wave height, or Hrms, <0.6) dissipation by friction is dominant and is accountable for about 80% of the total incident wave energy dissipation. For larger waves (significant wave height, or Hs = 1.6), the contribution of dissipation by breaking becomes dominant and dissipation by friction only represents about 30% of the total incident wave energy dissipation.

This study focuses on an extreme wave of Hs = 12 m. Wave breaking is, therefore, expected to be the main wave energy dissipation mechanism even if dissipation by friction is still significant. Thus, calibration needs to be undertaken on data recorded from large incident wave events.

Unfortunately, only four wave events with a significant wave height larger than 1.6 m were recorded during the data collection campaign and were suitable for calibration purposes. All of those events were recorded on the reef slope of Avatoru on Rangiroa. Thus, the one-dimensional calibration was undertaken on a profile captured in front of Avatoru and the set of calibrated parameters were used to model wave impact at all sites.

The fully automated calibration process (an in-house routine developed as part of this study) was performed on the set of dominant parameters extracted from the sensitivity analysis (Section 3.1).

Each of the five dominant parameters, namely bottom friction (Cf) in the flow equation, the wave dispersion coefficients alpha (a), the breaker parameter gamma ( ), and the friction coefficient in the wave action (f ), is given a range of ɣ w possible values leading to a large number of possible parameter value combinations. A one-dimensional XBeach model was run for each combination of parameter values for the four relevant offshore wave events (Hs>1.6 m). Each combination of parameters was assessed by comparing the model output with the current speed, water level and short wave height observed on the reef flat (Figure 5).

Figure 5: Calibration plot. The bottom plot shows the combination of parameter values. The top plot shows the percentage of error for each combination of parameter values in terms of current speed (blue), water level (red) and wave height (green). See text for additional explanatory notes.

Wave energy dissipation due to wave breaking is controlled by the breaker parameter, gamma, and the wave dissipation

coefficient, alpha. The wave energy dissipation due to friction is controlled by the short-wave friction parameter,f w.

12 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

The outcome of the calibration process (Figure 5) was analysed to extract the best set of parameter values. The main findings are detailed below. • Low value of the breaker slope coefficient ( β ) leads to unrealistic shoreward shift of wave breaking. Combination of parameters with low beta values are not considered (all combination on the left side of the vertical blue line in Figure 5)

• The percentage of error increases with the short wave friction coefficient, fw, as indicated by the dashed violet line. This is in agreement with findings from Lowe et al. (2005). An increase inf w, leads to more energy being dissipated by friction, whereas for large waves, dissipation by breaking is dominant

• For high value of fw, the influence of gamma is overridden as shown by the red boxes in Figure 5. The influence of wave energy dissipation by breaking decreases relative to the dissipation by friction. To be in agreement with

Lowe et al. (2005), for large waves, fw should be constrained below fw<0.5

• When the flow and short-wave friction coefficients, cf and fw, respectively, are low, high gamma causes rapid wave energy conversion into setup, resulting in a divergence in the RMSE percentage error for wave height and wave setup. In Figure 5, the variation of gamma is shown as a black line in the bottom plot. Its influence on model output is highlighted by the orange arrows in the top plot • The wave dissipation coefficient alpha is less significant

Several sets of parameters that were giving reasonable calibration results were extracted. To pick the final set, additional considerations were taken into account.

• Water flow is attenuated on very rough bottoms (coral reef) so that cf needs to be high

• fw should be larger than 0 but smaller than 0.5 (Lowe et al. 2005) • Calibrated parameters should emphasise and accurate representation of current speed and wave setup rather than wave attenuation

The chosen combination of parameter values was: C [ fw=0.25, f=0.065, alpha=1, gamma=0.55, beta=0.15]

While this combination does not give the lowest RMSE found in the calibration process, it follows all the above

considerations. As a note, best combination was found for fw=0. However, Lowe et al. (2005) suggest that for large waves, friction is still an important dissipation factor of the wave energy. It is important to note that no observation of wave transformation on a barrier reef for a 12 m wave is avalaible. It is possible that for such a wave, the friction coefficient becomes irrelevant, leading to a different pick of calibrated parameters. However, we chose to base our analysis on avalaible field observations and findings rather than a hypothesis.

The RMSE percentage error with the set of calibrated parameters is: [wave setup: ~5%, current speed: ~12%, wave height: 18%]

Unfortunately, we have not observed any inundation event during our oceanographic deployment period. Additionally, available inundation information from past cyclone events found in various press releases or technical reports for the targeted sites, are only indicative or anecdotal and cannot be used for calibration purposes.

Bottom roughness is the main tuning parameter controlling the behaviour of the water flow on land. Due to the lack

of inundation data, we will use a constant bottom friction in all domains. The adequate flow friction c( f) determined for simulating the flow of water on the reef flat will also be used on land. Bottom roughness on land, particularly due to vegetation and building, is expected to be significantly higher resulting in an over-prediction of the actual inundation. However, this is a conservative approach typically used in this type of study when no inundation data are available.

3.3 Other modelling considerations In addition to the calibration for observed waves, other modelling considerations were also envisaged and these are discussed in detail below.

3.3.1 Simulation period

The goal of this modelling study is to derive maximum inundation depths and maximum current speeds over the targeted areas generated from a cyclone sea state generating a 12 m wave height at high tide. Because XBeach is computationally expensive (at least relative to our current computer resources, which consist of an eight core desktop), a small investigation was undertaken to compare two model outputs: the inundation from the modelling of a full cyclone time series, against the inundation from a simulation that focuses only on the peak of the time series.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 13 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 6: Storm surge time series (after Steetzel 1993). The top plot shows the evolution of the surface elevation as the cyclone passes through. The middle and bottom plots show the evolution of the wave height and period, respectively.

On a profile, two XBeach models were setup with the calibrated parameters (see Section 3.2). For one model (case A), the offshore boundary condition was forced by wave and water level extracted from a simulated time time series (Figure 6). For the other model (case B), only the wave (Hs=12 m) and surface elevation ( η =1.5 m) from the peak of the time series series was forced into the model for a one our duration.

Maximum water level (Figure 8), maximum wave height (Figure 7) and maximum current speed (Figure 9) were derived from the two runs and compared. The maximum water level and maximum wave height show no significant difference between the two simulations. However, the maximum current speed is noticeably different near the back boundary toward the lagoon. The back of the boundary is forced by water level that is identical to the offshore boundary, and when inundation occurs water is flushed into the lagoon. The difference in current speed is due to the different water levels in the lagoon in the two models when the water enters the lagoon. In case B, the water level is 1.5 m. In case A the water level is lower and the hydraulic jump is higher, creating a higher current speed.

In light of those results, restricting the simulation to the peak of the surge is acceptable in order to extract maximum inundation variables.

Figure 7: Comparison between maximum wave height extracted from a model forced by the surge time series (blue) and a model forced by the peak of the time series for one hour (red stars) only. The topography of the profile is represented by the green line.

14 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 8: Comparison between maximum water level extracted from a model forced by the surge time series (blue) and a model forced by the peak of the time series for one hour (red stars). The topography of the profile is represented by the green line.

Figure 9: Comparison between maximum current speed extracted from a model forced by the surge time series (blue) and a model forced by the peak of the time series for one hour (red stars). The topography of the profile is represented by the green line.

3.3.2 Sensitivity for extreme scenarios

The behaviour of an extreme wave such as a 12 m significant wave height on a fringing reef is not well known as no such data have been collected in the field. We therefore rely on the XBeach model to predict its behaviour.

The goal of this modelling activity is to verify that calibration is needed when modelling an extreme wave event. To do so, another sensitivity analysis was performed identical to the one in Section 3.1 except for the offshore boundary condition replaced by a 12 m significant wave height.

In order to quantify the sensitivity of the model towards each parameter, the result from a one-dimensional XBeach model setup with default parameters is taken as reference so that a percentage variation can be calculated for each model output. To do so, current speed, wave height and water level were extracted in three locations along the profile (Figure 10). SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 15 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 10: Diagram of extraction point location on a profile.

Figure 11: Sensitivity analysis for an extreme wave condition.

The outcome of this sensitivity analysis (Figure 11) shows that even for an extreme event, the model is still sensitive to changes in parameter values. A proper calibration was indeed needed. The model shows a strong sensitivity towards

the breaking dissipation parameter (alpha and gamma) as well as the flow friction coefficient,C f.

Note that the wave energy dissipation due to friction, related to fw, does not seem to play a dominant role anymore, which can be related (but not only) to the higher water depth on the reef flat. The beta parameter was constrained to a range of high value, as a low value brings an unsatisfactory result (see Section 3.1). However, this constraint virtually limits its sensitivity in the final outcome.

3.3.3 One-dimensional to two-dimensional

The calibration process (Section 3.1) was performed on a 1D profile and involved more than 1,000 model runs. However, in order to better capture the cyclone wave impact, especially near the islet’s passes, a two-dimensional (2D) model was thought to be better suited.

Considering the computational resources available for this project, the exhaustive calibration process could not have been undertaken on a 2D computational domain. XBeach being a coupled wave and flow model, it is computationally demanding, even if it largely benefits from its multi-threading capability. 16 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Prior to using the calibrated parameters on a 2D XBeach model, we undertook a quick check to compare results from a 1D model and from an identical profile extracted from a 2D model in a region away from any channel.

A local company, GéoPolynésie, was contracted to collect single beam bathymetry and real time kinematic global positioning system (GPS) topographic data in each of the targeted sites of this project.

The baseline data were first interpolated onto a flexible mesh domain using MIKE Zero software, then onto a variable rectangular grid built in MATLAB using the XBeach toolbox. The choice of using MIKE Zero Mesh Generato (featuring prioritisation of scatter data and breaklines) as an intermediate step provided an improved dataset and allowed better control in the final interpolation onto the variable rectangular grid (Figure 12, Figure 13, and Figure 14).

A 2D XBeach model was set up to run on the variable rectangular grid of Manihi, using the calibrated parameters with the offshore wave condition forced by a stationary wave (Hs=8 m, Tp=13 s, perpendicular to shore). Additionally, a cross-shore transect was extracted from the 2D domain and used as a computational 1D domain into XBeach. By doing so, we ensured that the comparison is free of grid size disturbances. The 1D model was then set up identically to the 2D model.

The outcome of this brief investigation shows the consistency of the XBeach model in its 1D and 2D calculation. Maximum water level and maximum wave height show no significant discrepancy between both runs (Figure 15 and Figure 16). As expected, 1D XBeach output shows a noticeable higher cross shore current speed, inevitably due to the 2D formulation of water flow. Wave-generated current in the 1D formulation is fully transferred to the cross shore current, while in the 2D calculation the energy, depending on the morphology of the reef, is shared between both component of the current (U,V).

To conclude, the outcome of this investigation suggests the acceptability of transferring the set of calibrated parameters from the 1D model into a 2D model. While the action plan for this project only recommends the development of a 1D model, we decided to upgrade the modelling approach by developing 2D models for each of the targeted sites. While this upgrade brings better understanding of the potential offshore wave impact, the 2D model is limited by the necessary interpolation of baseline data (bathymetry and topography) onto a 2D terrain model.

Figure 12: Image of collected baseline data in Manihi village overlayed on Quickbird satellite imagery.

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Figure 13: Image of mesh generation in Manihi village. Meshes are represented by white triangles. Meshes in polygons (red dotted lines) are constrained to a user-defined maximum area.

Figure 14: Final terrain model of Manihi village. Bathymetry is shown in metres.

18 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 15: Maximum wave height comparison between a 1D (red) and 2D (blue) model on an identical profile (green).

Figure 16: Maximum water level comparison between a 1D (red) and 2D (blue) model on an identical profile (green).

Figure 17: Maximum cross-shore current comparison between a 1D (red) and 2D (blue) model on an identical profile (green).

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 19 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia 4 RESULTS

A 2D domain was created for each site by interpolating the baseline data from the flexible mesh onto a curvilinear grid using open-source the DELFT3D toolbox. The final curvilinear computational domain is composed of a range of grid sizes, ranging in the cross-shore direction from more than 10 m at the offshore boundary to about 2 m around the surf zone. The offshore boundary is about 1.5 km away from the shore and a constant depth of 50 m is applied on the ocean side.

The model is setup using the calibrated parameters and leaving all other parameters to their default value. A JONSWAP (Joint North Sea Wave Project) shape wave group spectrum, characterising a 12 m significant wave height and 13 s period perpendicular to the shore, is forced into the model through the offshore wave condition.

The water level forced in the offshore boundary is constant and refers to our characteristic event-associated surge of 1 m (des Garets 2005) on top of spring high tide (0.5 m). The simulated period is set to one hour.

Forcing the water level into the model should be considered carefully. While this work focuses on cyclone wave impact on the ocean side of the targeted sites, water level at the lagoon boundary of the model (back boundary) plays an important role in the final output due to the low-lying nature of the land on the lagoon side, which is usually lower than 1.5 m relative to the ocean side.

Estimating lagoon water levels during such a scenario is beyond the scope of this study because it would require studying the entire atoll system in order to particularly assess wave pumping into the lagoon and wind setup. Nevertheless, few considerations can be made. The atmospheric pressure, the dominant component of surge on a fringing reef system, is applied at a large scale so that it is considered to be identical on the lagoon side and the ocean side. However, the generated water level in the lagoon would depend on the degree of aperture, which controls the flux of water between ocean and lagoon through an atoll rim (Andrefouet et al. 2001). In all of the targeted atolls, the degree of aperture is relatively low in normal weather conditions, limited to few narrow channels. However, during extreme cyclone conditions occurring at high tide, the combined surge and tide would submerge the atoll rim and increase the exchange rate of water between the lagoon and the ocean. The response of the lagoon’s water level toward such a severe drop in atmospheric pressure is complex and would require the consideration of the entire atoll system. However, it is likely that in such a weather condition, the pressure-dependant component of the surge and the tidal level would be rapidly transferred into the lagoon water level (with an unavoidable time lag).The difference in water level between the lagoon and ocean is then mainly attributed to the wind setup/set down component, and the wave pumping into the lagoon.

Wave pumping can create a significant increase in water level on the order of 1 m within the lagoon. This phenomenon has been observed on Rangiroa during our deployment on 29 August 2011, where a 4 m southerly swell wave event created a water level rise of about 1 m in the lagoon (Baleilevuka et al. 2013). In the lagoon, the wind stress during an extreme cyclone event is expected to create a water level gradient, resulting in a potential high water level on the exposed shore. However, the imposed scenario, with wave and wind impacting perpendicular to the ocean shore, would lead to a wind set down on the targeted lagoon shore. Thus, water level in the lagoon side can potentially be in the same order or even greater than in the ocean during such event. However, the topographic data collected shows large areas on the lagoon side being lower than 1.5 m. A constant 1.5 m water level induces large initial inundation before wave impact, with often only the beach berm emerging out of the water.

In order to isolate the inundation generated from the ocean side, all sites with no channel are run with a water level of 0.5 m forced into the back boundary, and a water level of 1.5 m forced into the offshore boundary. This, in turn, results in a higher hydraulic jump at the lagoon shore, leading to a higher current velocity. Furthermore, a model with a 1.5 m constant water level forced in both the front and the back boundaries is run. The results for the latter scenario are gathered into Appendix B as indicative information.

In sites that feature passes, such as the villages of Apataki, Arutua, the Avatoru–Tiputa stretch of Rangiroa, and Manihi, the lagoon and the ocean are linked, and imposing a lower water level in the lagoon would result in the generation of an artificial hydraulic gradient leading to an incoming current in the channel. Instead, closer to what was initially requested (1D modelling), we built a domain for each of those sites away from the pass so that lagoon and ocean are well separated and an offshore sea state generating inundation can be extracted. Additionally, conceding the current lack of understanding of the lagoon sea state, a model with a 1.5 m constant water level forced in both the front and the back boundaries is run for each of those sites in order to gain a better understanding of possible inundation near the channels.

The results highlight the extreme vulnerability of all sites to the potential impact of an extreme cyclone delivering a 12 m significant wave height, which refers to an incident wave group potentially composed by a wave with a maximum height of about 20–24 m. The combination of wave setup, tide and surge on the reef flat results in a maximum water level of about 5 m throughout all sites. Current velocities at 1 m depth in the surf zone reach a maximum of at least 3 m/s in all sites. The energy released during wave breaking can potentially break and transport quantities of reef onto

20 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

land. Most importantly, the model shows that the 12 m cyclone wave scenario produces inundation extending across the land from the ocean side to the lagoon in all sites.

Vulnerability already arises from the surge component alone. As discussed above, many areas especially on the lagoon side are below 1.5 m, which is the imposed water level scenario that includes the contribution of tide, atmospheric pressure and wind setup (Appendix B). Moreover, this water level does not reflect the potential higher water level that could occur during such an event on the lagoon side due to wave pumping or the possible larger wind setup.

The model outputs are derived into three maps of: • maximum inundation depth • maximum current speed • risk according to the risk matrix (Table 4 below)

Table 4: Inundation risk matrix used by the Government of French Polynesia.

8 Water depth/speed 9 V<0.5 m 10 V>=0.5 m 11 H<0.5 m 12 Category 1: 14 Category 2: 13 Weak 15 Intermediate 16 0.5 m1 m 22 Category 3: 24 Category 4: 23 Strong 25 Very strong

4.1 Rangiroa This modelling study for Rangiroa focuses on the urban area, including Avatoru and Tiputa (Figure 18).

Figure 18: THEOS image of Rangiroa showing site location (red box) for this modelling work.

Data collected along the Avatoru–Tiputa stretch of the atoll shows a reef edge of about 0.3 m, ranging from 0.15– 0.5 m (see Figure 19). The maps show coastal terrain data collected along the Avatoru–Tiputa stretch of the atoll. The elevation (L) is grouped into five categories: L>=2.5 m: red dots, 2.0<=L<2.5: orange dots, 1.5<=L<2.0: yellow dots, 0.5<=L<1.5: blue dots, L<=0.5 green dots. The beach berm is relatively high in places such as near passes, with a height often higher than 4.0 m (e.g. Avatoru village and Tiputa village). In other parts of the domain, the beach berm height ranges between 3 m and 4.0 m. The height of the airport runway ranges from 2.5 m to less than 3.0 m. The airport is protected by a beach berm lower than 4.0 m in most places.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 21 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 19: Top panel — Image showing baseline data collected (dots) over the western side of Avatoru–Tiputa stretch (including Avatoru village), overlayed with IKONOS imagery.

Middle panel — Image showing baseline data collected (dots) over the centre of Avatoru–Tiputa stretch (including airport), overlayed with IKONOS imagery.

Bottom panel — Image showing baseline data collected (dots) over the eastern side of Avatoru–Tiputa stretch (including Tiputa village), overlayed with an IKONOS imagery.

22 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

First, the inundation in the urban areas near the two channels was investigated using a constant 1.5 m water level. Two domains were built: one model showing wave impact from a 12 m wave coming parallel to the Avatoru channel (Figure 20); and another model showing a 12 m wave coming parallel to Tiputa channel (Figure 21). The maximum flow depths extracted from those simulations are presented in the top panels of Figure 20 and Figure 21. Maximum flow depth in Avatoru is around 1 m in most places, with maximum of about 2 m and minimum of less than 0.1 m. Around the pass of Tiputa, maximum flow depth is globally lower, with large areas showing depths less than 0.5 m.

The maximum velocity extracted from the models is presented in the middle panels of Figure 20 and Figure 21. The models show higher maximum velocity in Avatoru, with a current speed of about 1 m/s in most places. Maximum current speed on land around the Tiputa passage is less than 0.5 m/s in large areas on both sides of the channel.

The risk mapped for each domain is presented in the bottom panels of Figure 20 and Figure 21. In Avatoru village, the risk ranges from Category 3 to 4 with a lower risk near the channel. On both sides of Tiputa channel, the risk ranges from Category 1 to 3. In order to isolate the inundation generated from the cyclone wave on the ocean side alone, and prevent initial wetting (or inundation) of land lower than 1.5 m, the passes were cut off from the study area in a subsequent model domain. Due to the different orientation between the islets, from Avatoru to the airport (Figure 22) and each side of the Tiputa channel (Figure 23), three domains were built to easily assess inundation from a wave coming perpendicular to the shore.

Figure 22 and Figure 23 show two distinct levels of vulnerabilities. Land near the passes is wider and feature higher beach berms leading to a risk of Category 1 and 2 in places. Farther away from the passes, land is much narrower and inundation is more severe (flow depth >1 m and velocity > 0.5 m/s), leading to a risk Category ranging from 3 to 4.

It is interesting to compare the model output for Avatoru village in Figure 20 versus those shown in Figure 23. The significant difference between the results highlights the sensitivity of the model towards three factors: • lagoon water level; • channel influence; and • wave direction (~25° difference between the two wave conditions; parallel to the channel and perpendicular to the shore).

The sensitivity of the results to wave direction is great, especially near a pass, and can be better assessed by comparing results from Figure 20 to those of Figure 21 with model outputs of the entire Avatoru–Tiputa stretch (including the channels), with a wave condition perpendicular to the shore (Appendix B).

Inundation for the output shown in Figure 23 was generated from two separate models created for the villages of Tiputa and Otetou under the conditions discussed above. The results show a higher risk than previously presented in Figure 21, especially for Otetou on the western side of the Tiputa channel. This increase of risk is believed to be generated by the same factors enumerated above. The topographic data collected along the lagoon shore in Otetou shows a height well above 1.5 m, and about 2 m high at a distance of 10 m from the coast. Thus, no significant initial inundation was generated in the model presented in Figure 21. Even in such a case, lagoon water level plays an important role in the final output because it dictates the height of the hydraulic jump when water is flushed into the lagoon. Additionally, the exclusion of the channel and a change of 15° in the wave direction are factors influencing the model outputs. In fact, the model presented in Figure 21 simulated a wave field that generates a flow, with a higher eastward component, that can partially be transferred into the channel flow.

In order to gain a better understanding of the role of these three factors as well as gain more confidence in the results gathered in Figure 23, four more simulations were run for Otetou (Figure 24), each with a different combination of lagoon water levels (1.5 m or 0.5 m) and wave direction input (parallel to the channel or perpendicular to shore; and a difference of about 15°). In light of the risk maps derived from those four scenarios (panels A to D in Figure 24), inundation is very sensitive to wave direction and lagoon water level. Wave direction has a dominant role in the categorisation of risk on the western side of the domain while the hydraulic jump-generated current is the primary cause of the increase in risk on the eastern side.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 23 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 20: Results for Avatoru village with a 1.5 m constant water level and a wave direction parallel to the channel. Top panel: Map of maximum inundation depth (m) for Avatoru village.

Middle panel: Map of maximum current velocity (m/s) for Avatoru village.

Bottom panel: Risk map of Avatoru village. Risk is categorised by four levels (Table 4): blue = weak, green = intermediate, yellow = strong, red = very strong.

24 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 21: Results around the Tiputa channel with a 1.5 m constant water level and a wave direction parallel to the channel. Top panel: Map of maximum inundation depth (m) around Tiputa channel.

Middle panel: Map of maximum current velocity (m/s) around Tiputa channel.

Bottom panel: Risk map around Tiputa channel. Risk is categorised by four levels (Table 4): blue = weak, green = intermediate, yellow = strong, red = very strong.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 25 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 22: Results for the Avatoru–Tiputa stretch (part 1) with a 1.5 m and 0.5 m constant water level, respectively, forced into the ocean and lagoon boundaries. Top panel: Map of maximum inundation depth (m) for the centre of the Avatoru–Tiputa stretch.

Middle panel: Map of maximum current velocity (m/s) for the centre of the Avatoru–Tiputa stretch.

Bottom panel: Risk map of the centre of the Avatoru– Tiputa stretch. Risk is categorised by four levels (Table 4): blue = weak, green = intermediate, yellow = strong, red = very strong.

26 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 23: Results for the Avatoru–Tiputa stretch (part 2) with a 1.5 m and 0.5 m constant water level, respectively, forced into the ocean and lagoon boundaries. Those figures combine two different model runs: a model was run for each side of the channel with wave direction perpendicular to the shore. The channel is not included in those domains. Top panel: Map of maximum inundation depth (m) around Tiputa channel.

Middle panel: Map of maximum current velocity (m/s) around Tiputa channel.

Bottom panel: Risk map around Tiputa channel. Risk is categorised by four levels (Table 4): blue = weak, green = intermediate, yellow = strong, red = very strong.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 27 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 24: Test results for the model’s sensitivity to lagoon water level and wave direction. Panel A: Risk map of Otetou with a wave perpendicular to the shore, lagoon water level is 0.5 m.

Panel B: Risk map of Otetou with a wave perpendicular to the shore, lagoon water level is 1.5 m.

Panel C: Risk map of Otetou with a wave parallel to the channel, lagoon water level is 0.5 m.

28 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Panel D: Risk map of Otetou with a wave parallel to the channel, lagoon water level is 1.5 m.

4.2 Apataki The modelling work for Apataki focuses on the urban area, located on an islet in the southwest corner of the atoll (Figure 25).

Figure 25: IKONOS satellite image of Apataki, showing the location of the study site (red box).

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 29 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Data collected on Apataki shows a reef edge of about 0.5 m (0.4 m–0.6 m) and a reef flat about 0.3 m in height. From the topographical data (Figure 26), the highest elevations collected on Apataki are outside of the urban area, on both sides of the channels. In the urban area, the runway is about 3 m in height. In the village, elevation varies between 1.0 m and 2.5 m.

Figure 26: Image of Apataki village showing topographic baseline data collected (dots) overlayed with an IKONOS imagery. Elevation (L) is grouped into five categories: 1) L>=2.5 m: red dots; 2) 2.0<=L<2.5: orange dots; 3) 1.5<=L<2.0: yellow dots; 4) 0.5<=L<1.5: blue dots; 5) L<=0.5: green dots.

A model of Apataki was run using a constant 1.5 m water level (Figure 27). A large area of Apataki village is lower than 1.5 m, resulting in a significant initial inundation when applying a 1.5 m constant surface elevation.

Model shows a gradient of maximum flow depth over Apataki village and runway, ranging from 2.0 m to less than 0.5 m from the ocean to the lagoon. The velocity in the village is high, mainly greater than 0.5 m/s. Figure 28 shows the output from a model built without the channel to force a lower water level of 0.5 m on the lagoon side and to avoid initial flooding of the land. However, because the water flows over the land and into the lagoon, it generates a hydraulic jump on the lagoon’s shore, which leads to higher current velocity.

Risk categories derived from the model outputs show the lagoon shore and the area near the wharf as Category 1 and 2. However, only a study of the overall atoll system (ocean + lagoon) could give credibility to this result.

30 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 27: Results for Apataki village with a 1.5 m constant water level. Top panel: Map of maximum inundation depth (m) for Apataki village.

Middle panel: Map of maximum current speed (m/s) for Apataki village.

Bottom panel: Risk map of Apataki village. Risk is categorised by four levels (Table 3): blue = weak, green = intermediate, yellow = strong, red = very strong.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 31 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 28: Results for Apataki village with a 1.5 m and 0.5 m constant water level, respectively, forced into the front and back boundaries. Top panel: Map of maximum flow depth (m) for Apataki village.

Middle panel: Map of maximum current speed (m/s) for Apataki village.

Bottom panel: Risk map of the Apataki village. Risk is categorised by four levels (Table 3): blue = weak, green = intermediate, yellow = strong, red = very strong.

32 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

4.3 Kauehi 4.3.1 Kauehi village

Kauehi village is located on a relatively wide (850 m) area of land on the lagoon side (Figure 29), in a general area that ranges from 1–1.5 m in height. The village is protected from offshore waves by a relatively high beach berm (3–4 m in height). The reef flat has an average height of 0.2 m and is rather narrow (~75 m). Data collected over the reef crest suggests a height of about 0.5 m (Figure 30).

Figure 29: Ikonos satellite imagery of Kauehi Atoll showing airport site (upper red box) and village site (lower red box).

Figure 30: Image of Kauehi village showing topographic baseline data collected (dots) and overlayed with IKONOS imagery. Elevation (L) is grouped into five categories: 1) L>=2.5 m: red dots; 2) 2.0<=L<2.5: orange dots; 3) 1.5<=L<2.0: yellow dots; 4) 0.5<=L<1.5: blue dots; 5) L<=0.5: green dots.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 33 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 31: Results for Kauehi village with a 1.5 m and 0.5 m constant water level, respectively, forced into the ocean and lagoon boundaries. Top panel: Map of maximum flow depth (m) for Kauehi village.

Middle panel: Map of maximum velocity (m/s) for Kauehi village.

Bottom panel: Risk map of the Kauehi village. Risk is categorised by levels (Table 3): blue = weak, green = intermediate, yellow = strong, red = very strong.

34 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Model outputs show that a 12 m wave induced by cyclonic winds, propagating over a 1.5 m still water level and coming perpendicular to the shoreline, will generate a flow of water flowing through Kauehi village and into the lagoon.

Figure 31 focuses on the ocean wave impact, without the initial inundation inherent from a 1.5 m high water level. The model shows that offshore wave induced flow depth is lower than 0.5 m in most of the village area. High current velocity initially generated in the surf zone is significantly reduced by bottom friction before reaching Kauehi village, leading to a current velocity lower than 0.4 m/s within the populated area. Currently, risk in the village is mostly mapped as Category 1 (weak). However, during such an event, the village area would be exposed to a threat from the lagoon side, which is not taken into account in this study.

4.3.2 Kauehi airport

Figure 32: Image of Kauehi airport showing baseline data collected (dots) and overlayed with IKONOS imagery. Elevation (L) is grouped into five categories: 1) L>=2.5 m: red dots; 2) 2.0<=L<2.5: orange dots; 3) 1.5<=L<2.0: yellow dots; 4) 0.5<=L<1.5: blue dots; 5) L<=0.5: green dots.

From the topographic data collected (Figure 32), the primary natural protection against offshore waves is from the combined reef crest (from ~0.7–1.0 m), reef flat (mostly greater than 0.5 m) and beach berm (from ~3.5–4.8 m) is relatively high. The height of the runway, located behind the bump, is gradually increasing from about 1.6 m on the western side to about 4.0 m on its eastern end. Inversely, the distance between the reef flat and the runway increases from east to west, from about only 60 m to about 250 m.

Figure 33 shows the inundation from the wave action alone by applying a 0.5 m water level boundary condition on the lagoon side. Wave setup combined with high tide and surge generates a water level on the reef flat of about 4.0–5.0 m and a current speed reaching about 3 m/s in the surf zone. Wave induces a flow depth on the western side of the runway of less than 0.5 m. However, due to a much higher proximity of the eastern runway to the surf zone, higher flow depth (from 0.5–1.0 m) and velocity (from 0.4–0.8 m/s) is expected.

The difference between both sides of the runway is also shown by the risk map, with the western side shown as Category 1 (weak) and the eastern side shown as Category 3. Additionally, the lower region adjacent to the west of the runway permits larger wave-induced flow to penetrate inland, which produces high velocity on the western end of the continued next page runway, resulting in an increase of risk from Category 1 to Category 2. SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 35 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 33: Results for Kauehi airport with a 1.5 m and 0.5 m constant water level, respectively, forced into the front and back boundaries. Top panel: Map of maximum flow depth (m) for Kauehi airport.

Middle panel: Map of maximum current velocity (m/s) for Kauehi airport.

Bottom panel: Risk map of Kauehi airport. Risk is categorised by four levels (Table 3): blue = weak, green = intermediate, yellow = strong, red = very strong.

36 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

4.4 Manihi

Figure 34: IKONOS satellite imagery of Manihi Atoll showing airport site (upper red box) and village site (lower red box).

4.4.1 Manihi village

Manihi village presents an inconsistent reef flat width as well as few deposited boulders, suggesting the occurrence of past extreme events, possibly tropical cyclones. Manihi, being on the outer boundary of the Tuamotu Archipelago, can be directly impacted by ocean waves without disturbance from other islands or bathymetric features. Cyclone Orama- Nisha in 1983 is believed to have generated a 6–8 m wave at Manihi, which devastated the village (Table 1). It is no surprise that the model shows dramatic impact from the imposed characteristic wave of 12 m. The seawall, rising to about 3 m, does not significantly improve Manihi’s resistance to such a wave.

The results presented in Figure 36 and Figure 37, highlights the extreme vulnerability of Manihi village.

Figure 35: Image of Kauehi airport showing baseline data collected (dots) and overlayed with IKONOS imagery. Elevation (L) is grouped into five categories: 1) L>=2.5 m: red dots; 2) 2.0<=L<2.5: orange dots; 3) 1.5<=L<2.0: yellow dots; 4) 0.5<=L<1.5: blue dots; 5) L<=0.5: green dots.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 37 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 36: Results for Manihi village with a 1.5 m constant water level. Top panel: Map of maximum flow depth (m) for Manihi village.

Middle panel: Map of maximum velocity (m/s) for Manihi village.

Bottom panel: Risk map of Manihi village. Risk is categorised by four levels (Table 3): blue = weak, green = intermediate, yellow = strong, red = very strong.

38 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 37: Results for Manihi village with a 1.5 m and 0.5 m constant water level, respectively, forced into the front and back boundaries. Top panel: Map of maximum flow depth (m) for Manihi village.

Middle panel: Map of maximum velocity (m/s) for Manihi village.

Bottom panel: Risk map of Manihi village. Risk is categorised by four levels (Table 3): blue = weak, green = intermediate, yellow = strong, red = very strong.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 39 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

4.4.2 Manihi airport

Figure 38: Image of Kauehi airport showing baseline data collected (dots) and overlayed with IKONOS imagery. Elevation (L) is grouped into five categories: 1) L>=2.5 m: red dots; 2) 2.0<=L<2.5: orange dots; 3) 1.5<=L<2.0: yellow dots; 4) 0.5<=L<1.5: blue dots; 5) L<=0.5: green dots.

The western side of the runway is protected by a beach berm that is greater than 4.0 m in height, with a maximum measured height of 5.0 m. The height of the beach berm decreases to about 3.0 m on its eastern side. The height of the runway rises to about 4.0 m on its western end and gradually drops down to less than 2.8 m on its eastern end.

The topographical changes between the western and eastern side of the computational domain leads to a significant difference in the level of risk faced from the impact of a 12 m wave. Figure 39 gathers the processed model outputs.

The relatively high beach berm on the western side significantly reduces the inundation. In most areas behind that bump, model shows a flow depth less than 0.5 m and current velocity less than 0.5 m/s, leading to a risk Category of 1 (weak).

On the eastern side, where the topographical features are lower, wave-generated flow largely inundates the land, with flow depth close to 1.0 m on the runway and a current velocity exceeding 1.0 m/s.

40 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 39: Results for Manihi airport with a 1.5 m and 0.5 m constant water level, respectively, forced into the front and back boundaries. Top panel: Map of maximum inundation depth (m) for Manihi airport.

Middle panel: Map of maximum current speed (m/s) for Manihi airport.

Bottom panel: Risk map of Manihi airport. Risk is categorised by four levels (Table 3): blue = weak, green = intermediate, yellow = strong, red = very strong.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 41 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

4.5 Arutua 4.5.1 Arutua airport

The airport runway on Arutua is 3.5 m in height, which is the highest topographical feature within the computational domain. The reef flat is about 60 m wide with a reef crest height between 0.2 m and 0.3 m. Processed modelling outputs are shown in Figure 42.

The runway is mainly mapped as risk Category 2, with maximum flow depth ranging from 0.5–1.0 m. Maximum velocity along the runway is mainly less than 0.4 m/s, but reaches nearly 0.6 m/s in places. Behind the runway, the slope generates an increase in velocity to about 0.6 m/s, but rapidly decreases to less than 0.4 m/s afterwards in most places. The maximum flow depth is relatively well contained below 1.0 m with large patches below 0.5 m.

Figure 42 shows the risk category behind the runway is mainly Category 2, but ranges from Category 1 to Category 3. West of the runway, the topography is lower and allows a larger flow of water to penetrate inland (Category 4).

Figure 40: IKONOS satellite imagery of Kauehi Atoll showing airport site (upper red box), village site (lower red box) and marina (middle red box).

Figure 41: Image of Arutua airport showing baseline data collected (dots) and overlayed with IKONOS imagery. Elevation (L) is grouped into five categories: 1) L>=2.5 m: red dots; 2) 2.0<=L<2.5: orange dots; 3) 1.5<=L<2.0: yellow dots; 4) 0.5<=L<1.5: blue dots; 5) L<=0.5: green dots.

42 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 42: Results for Arutua airport with a 1.5 m and 0.5 m constant water level, respectively, forced into the ocean and lagoon boundaries. Top panel: Map of maximum flow depth (m) for Arutua airport.

Middle panel: Map of maximum velocity (m/s) for Arutua airport.

Bottom panel: Risk map of Arutua airport. Risk is categorised by four levels (Table 3): blue = weak, green = intermediate, yellow = strong, red = very strong.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 43 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

4.5.2 Arutua marina

The topographical data collected on the marina shows a low terrain, representing very limited natural protection against ocean wave action. The beach berm on the ocean side is mainly less than 1.5 m in height. The highest topography is along the lagoon shore with a height between 2 m and 2.5 m.

Figure 44 shows the model outputs. The beach berm is low, and therefore, a 12 m incident wave impacting on the ocean side would create a devastating flow over the domain. The entire domain is mapped as Category 4.

Figure 43: Image of Arutua airport showing baseline data collected (dots) and overlayed with IKONOS imagery. Elevation (L) is grouped into five categories: 1) L>=2.5 m: red dots; 2) 2.0<=L<2.5: orange dots; 3) 1.5<=L<2.0: yellow dots; 4) 0.5<=L<1.5: blue dots; 5) L<=0.5: green dots.

44 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 44: Results for Arutua marina with a 1.5 m and 0.5 m constant water level, respectively, forced into the ocean and lagoon boundaries. Top panel: Map of maximum flow depth (m) for Arutua marina.

Middle panel: Map of maximum velocity (m/s) for Arutua marina.

Bottom panel: Risk map of Arutua marina. Risk is categorised by four levels (Table 3): blue = weak, green = intermediate, yellow = strong, red = very strong.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 45 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

4.5.3 Arutua village

From the data collected (Figure 45), the village of Arutua appears to be lower on its southern side. Apart from a small area along the ocean side that has a height between 2 m and 2.5 m, the data collected shows that topography is less than 2.0 m with large areas less than 1.5 m. At the northern part of the village, data collected shows slightly higher ground, but mainly less 2.5 m. Additionally, a seawall was built along the ocean side of the village. Data collected on the seawall show a height of about 2.3 m on the southern side and about 1.7 m on the northern side.

The modelling outputs are gathered into Figure 46 and Figure 47. Results show that the seawall is not high enough to effectively protect the village against an extreme event. Applying a 1.5 m water level in the domain (Figure 46) generates an initial inundation in most part of the village (before the simulated waves reach the shore). Maximum flow depth can reach extreme values between 2.0 m and 3.0 m in the south-centre part of the village. However, this area shows relatively low current velocity (less than 0.5 m/s). Even when investigating inundation generated from the ocean side alone (Figure 47), maximum flow depth within Arutua village is greater than 1.0 m except along the lagoon shore, which would be subject to wave forcing from the lagoon side.

Finally, the power plant, located at the northern tip of the village is on very low-lying ground (below 1.0 m) and only protected by a seawall of about 1.7 m in height. The model shows high maximum flow depth in this area (greater than 2.0 m). The risk generated from an extreme event for Arutua village is mapped as Category 3 and Category 4.

Figure 45: Image of Arutua village showing baseline data collected (dots) and overlayed with IKONOS imagery. Elevation (L) is grouped into five categories: 1) L>=2.5 m: red dots; 2) 2.0<=L<2.5: orange dots; 3) 1.5<=L<2.0: yellow dots; 4) 0.5<=L<1.5: blue dots; 5) L<=0.5: green dots.

46 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 46: Result for Arutua village with a 1.5 m constant water level. Top panel: Map of maximum inundation depth (m) for Arutua marina.

Middle panel: Map of maximum current speed (m/s) for Arutua village.

Bottom panel: Risk map of Arutua village. Risk is categorised by four levels (Table 3): blue = weak, green = intermediate, yellow = strong, red = very strong.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 47 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 47: Results for Arutua village with a 1.5 m and 0.5 m constant water level, respectively, forced into the ocean and lagoon boundaries. Top panel: Map of maximum inundation depth (m) for Arutua marina.

Middle panel: Map of maximum current speed (m/s) for Arutua village.

Bottom panel: Risk map of Arutua village. Risk is categorised by four levels (Table 3): blue = weak, green = intermediate, yellow = strong, red = very strong.

48 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia 5 CONCLUSION

This modelling study is based on topographic and bathymetry data collected by GéoPolynésie. Oceanographic data was collected on Rangiroa by the Secretariat of the Pacific Community as well as the recent advancement in the understanding of wave transformation over fringing and barrier reefs through XBeach.

The models were calibrated with the best available data. Unfortunately, only the oceanographic deployment at Rangiroa recorded large enough swells needed for the calibration. Due to the highly similar geomorphology of all sites, we considered it acceptable to use the calibrated parameters for Rangiroa for all sites. It is also important to note that the inundation or the behaviour of water flowing on land is not calibrated because the necessary information is not available. Instead, we used a constant bed roughness over the entire domain. This is a conservative approach that can potentially result in an over-prediction of the actual inundation. Furthermore, this study does not take into consideration the erosion that would certainly occur on the beach berm during such a significant event. Such a level of energy impacting on the oceanic shore would result in a potentially high erosion rate over the beach berm, the main protection against inundation. Additionally, high energy in the surf zone could result in the excavation of large boulders projected on the reef flat.

While the models for the nine targeted sites were upgraded to 2D in order to gain a better understanding of the inundation generated from wave impact on the ocean side, it leaves questions pending on the risk generated from the lagoon. This risk includes possible water level rise due to wave pumping, inverse barometric pressure, wind setup, and wind generated lagoon waves and possible seiching. We consider that the exclusion of the lagoon-generated risk within this study to be especially reductive when modelling sites with an adjacent channel. For example, the unknown current within the pass could potentially play a significant role in wave transformation in the vicinity of the channel. In fact, a possible channel outflow would increase incident wave steepness and potentially trigger wave breaking, allowing better protection for land near the channel. On the other hand, an incoming current would potentially create more damage.

All model sites show severe risk towards extreme cyclone-generated significant wave heights of 12 m. Even areas mapped as an intermediate to weak level of risk should be considered with caution because they are located near the lagoon. A study including the lagoon is likely to uncover a higher level of risk in those areas. Additionally, we found that the model is very sensitive to wave direction. It is noted that the effects of variable incident wave directions that deviate from the perpendicular could result in higher risk in some areas, especially near a pass.

This study showed that all villages, except Kauehi, would be severely impacted by a 12 m significant wave generated on the ocean side. This result correlates well with reported damage that occurred during past cyclone event in the Tuamotus. A graphic example is the various witness testimonies we collected on Manihi, which all reported a complete inundation of Manihi village, with water flowing from the ocean into the lagoon, destroying most houses (in combination with wind forcing) during Cyclone Orama-Nisha. A similar observation was made by Dupont (1987) reporting that the village of Manihi had been completely devastated after Cyclone Orama-Nisha. Additionally, he deducted that flow depth in villages reached 1.0–1.5 m in areas impacted by Cyclone Orama-Nisha. The wave was reported to be between 6 m and 8 m in height (des Garets 2005).

As indicative information, we tested our calibrated parameters against the information gathered on the impact of Cyclone Orama Nisha on Manihi village. For a wave height of 6 m coming perpendicular to the village, and a combined tide and surge level of 1 m, the maximum flow depth ranges from 1.4 m to less than 0.5 m within the village, which is well in agreement with the information collected.

Indicative information about the impact of a 12 m significant wave height on atolls was found in a picture extracted from Dupont (1986) (Figure 49). Cyclone Orama-Nisha is believed to have generated a 12-m wave near Anaa Atoll (des Garets 2005). Figure 49 shows the extent of the damage, clearly indicating that the ocean wave destroyed the village and generated a flow of water going from the ocean to the lagoon.

After the 1983–1984 cyclone season, seawalls were built to protect villages on some atolls. Even though the seawalls were not precisely modelled in XBeach (as some slight smoothing had to be undertaken to remove instabilities that led to an underestimated seawall height of 20–50 cm in places), they appeared to be inefficient against such a severe event. The airport sites are located on higher ground, with 3.5m to 4.0m beach berm in places, and offer a relatively safer location during such an event.

Quantifying the probability of occurrence of such an event is beyond the scope of this study; however a recent regional cyclone probabilistic hazard assessment study conducted by the National Institute of Water and Atmospheric Research, New Zealand (Scott Stephens, Coastal Modeller, National Institute of Water and Atmospheric Research, NIWA, New Zealand, pers. comm., 2012) has shown that a cyclone event producing a 12 m wave has a return period of about 50 years near Tahiti. This correlates very well with a similar study done by Meteo France for which a 12 m cyclone wave event in the Tuamotu Archipelago has a 50 years return period (Meteo France, year unknown). However, this information is not directly relevant to quantifying the return period of such an event for our sites. The distribution of wave

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 49 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

height generated from a severe cyclone passing over the Tuamotu Archipelago is wide. For example, Cyclone Orama- Nisha generated a 12 m wave on Anaa Atoll, but ‘only’ a 6–8 m wave near Manihi. Thus, the joint probability for such a cyclone, developed to its full strength, to be passing within a close enough range of those sites at high tide is still to be determined and is expected to be much higher.

Figure 48: Maximum flow depth on Manihi from a 6 m wave propagating over a 1 m still water level.

Figure 49: Aerial image of Tuuhora village on Anaa Atoll after being hit by a 12 m wave generated by Cyclone Orama-Nisha.

50 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia 6 REFERENCES

Andrefouet, S., Claereboudt, M., Matsakis, P., Pages, J. and Dufour, P. 2001. Typology of atoll rims in Tuamotu Archipelago (French Polynesia) at landscape scale using SPOT HRV images. International Journal of Remote Sensing 22(6)

Andrews, D.G., McIntyre, M.E., 1978. An exact theory of nonlinear waves on a Lagrangian-mean flow. J. Fluid Mech. 89 (4)

Brander, R. W., Kench, P. S., & Hart, D., 2004. Spatial and temporal variations in wave characteristics across a reef platform, Warraber Island, Torres Strait, Australia. Marine Geology, 207(1)

Baleilevuka, A., Kruger, J., Kumar, S., Damlamian, H., Turagabeci, M. and Begg, Z. 2013. Oceanographic Data Acquisition Report: Rangiroa, Kauehi, Arutua, Apataki, and Manihi, French Polynesia. Supporting Disaster Risk Reduction in Pacific Overseas Countries and Territories, 9th European Development Fund – C Envelope. SOPAC Division Data Release Report PR105

Dean, R. G., & Dalrymple, R. A., 2002. Coastal processes with engineering applications Cambridge University Press. New York

Dean, R. G., & Dalrymple, R. A., 1991. Water wave mechanics for engineers and scientists

Dupon, J.-F., 1987. Les atolls et le risque cyclonique, le cas des Tuamotu. Cah. Sci. Hum. 23(3-4)

E. des Garets, 2005. Bilan des connaissances sur les surcotes marines en Polynésie. Rapport BRGM/RP-55038-FR

Kruger, J., Kumar, S., Baleilevuka, A., Damlamian, H., Turagabeci, M. and Begg, Z. 2013. Oceanographic Data Acquisition Report: Rangiroa, Kauehi, Arutua, Apataki, and Manihi, French Polynesia. Supporting Disaster Risk Reduction in Pacific Overseas Countries and Territories, 9th European Development Fund – C Envelope. SOPAC Division Data Release Report PR105

Kumar, S., Kruger, J. and Begg, Z. 2013. Multibeam bathymetry survey of Rangiroa, French Poynesia. Supporting Disaster Risk Reduction in Pacific Overseas Countries and Territories, 9th European Development Fund – C Envelope. SOPAC Division Technical Report (PR106)

Longuet-Higgins, M. S., & Stewart, R. W., 1962. Radiation stress and mass transport in gravity waves, with application to ‘surf beats’. Journal of Fluid Mechanics, 13(04)

Lowe, R. J., Falter, J. L., Bandet, M. D., Pawlak, G., Atkinson, M. J., Monismith, S. G., & Koseff, J. R., 2005. Spectral wave dissipation over a barrier reef. Journal of Geophysical Research: Oceans (1978–2012), 110(C4)

Lowe, R. J., Falter, J.L., Monismith, S.G. and Atkinson, M. J. 2009. Wave-Driven Circulation of a Coastal Reef–Lagoon System. Journal of Physical Oceanography 39

Lugo-Fernandez, A., Roberts, H. H., Wiseman Jr, W. J., & Carter, B. L., 1998. Water level and currents of tidal and infragravity periods at Tague Reef, St. Croix (USVI). Coral Reefs, 17(4)

MeteoFrance, unknown year of publication. Surcotes liées au passage d’un cyclone en Polynésie

Péquignet, A. C. N., Becker, J. M., Merrifield, M. A., & Aucan, J., 2009. Forcing of resonant modes on a fringing reef during tropical storm Man-Yi. Geophysical Research Letters

Powell, M. D., Vickery, P. J., & Reinhold, T. A., 2003. Reduced drag coefficient for high wind speeds in tropical cyclones. Nature, 422(6929)

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Roelvink, D., Reniers, A., van Dongeren, A., van Thiel de Vries, J., and McCall, R., 2010. XBeach Model Description and Manual. Unesco-IHE Institute for Water Edcuation and Deltares and Delft University of Technology, Delft, The Netherlands

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Taebi, S., Lowe, R. J., Pattiaratchi, C. B., Ivey, G. N., Symonds, G., & Brinkman, R., 2011. Nearshore circulation in a tropical fringing reef system. Journal of Geophysical Research: Oceans (1978–2012), 116(C2)

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SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 51 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia 7 APPENDICES Appendix A 3D Computational domain

Figure 50: 3D terrain model of Apataki Village. Bathymetry is shown in metres for this and all subsequent plots.

Figure 51: 3D terrain model of Kauehi Village.

Figure 52: 3D terrain model of Kauehi Airport.

52 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 53: 3D terrain model of Manihi Village.

Figure 54: 3D terrain model of Manihi Airport.

Figure 55: 3D terrain model of Arutua Airport.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 53 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 56: 3D terrain model of Arutua Marina.

Figure 57: 3D terrain model of Arutua Village.

Figure 58: 3D terrain model of Tiputa-Avatoru Islets.

54 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia Appendix B Model result plots Results for Kauehi village with a 1.5 m constant water level

Figure 59: Map of maximum flow depth (m) for Kauehi village.

Figure 60: Map of maximum velocity (m/s) for the Kauehi village.

Figure 61: Risk map of the Kauehi village. Risk is categorised in 4 levels (Table 3), blue: weak, green: intermediate, yellow: strong, red: very strong.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 55 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Figure 62: Map of maximum flow depth (m) for Kauehi airport.

Figure 63: Map of maximum current velocity (m/s) for the Kauehi airport.

Figure 64: Risk map of the Kauehi airport. Risk is categorised in 4 levels (Table 3), blue: weak, green: intermediate, yellow: strong, red: very strong.

56 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Results for Manihi airport

Figure 65: Map of maximum flow depth (m) for Manihi airport.

Figure 66: Map of maximum velocity (m/s) for the Manihi airport.

Figure 67: Risk map of the Manihi airport. Risk is categorised in 4 levels (Table 3), blue: weak, green: intermediate, yellow: strong, red: very strong.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 57 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Results for Arutua airport

Figure 68: Map of maximum flow depth (m) for Arutua airport.

Figure 69: Map of maximum velocity (m/s) for the Arutua airport.

Figure 70: Risk map of the Arutua airport. Risk is categorised in 4 levels (Table 3), blue: weak, green: intermediate, yellow: strong, red: very strong.

58 SPC SOPAC TECHNICAL REPORT (PR176) Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Results for Arutua marina

Figure 71: Map of maximum flow depth (m) for Arutua marina.

Figure 72: Map of maximum velocity (m/s) for the Arutua marina.

Figure 73: Risk map of the Arutua marina. Risk is categorised in 4 levels (Table 3), blue: weak, green: intermediate, yellow: strong, red: very strong.

SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176) 59 Cyclone Wave Inundation Models for Apataki, Arutua, Kauehi, Manihi and Rangiroa Atolls, French Polynesia

Result of simulated inundation over the Avatoru-Tiputa stretch from a 12 m wave field propagating perpendicular to the airport over a still water level of 1.5 m above chart datum.

Figure 74: Map of maximum flow depth (m) for the Avatoru-Tiputa stretch.

Figure 75: Map of maximum current speed (m/s) for the Avatoru-Tiputa stretch.

Figure 76: Risk map of the Avatoru-Tiputa stretch. Risk is categorised in 4 levels (Table 4), blue: weak, green: intermediate, yellow: strong, red: very strong.

60 SPC SOPAC TECHNICAL REPORT (PR176) SPC SOPAC TECHNICAL REPORT (PR176)