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- Editorial board
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- For authors
- For reviewers
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**Research article**
28 Jun 2019

**Research article** | 28 Jun 2019

LES modeling of tsunami-like solitary wave processes over fringing reefs

^{1}School of Hydraulic Engineering, Changsha University of Science and Technology, Changsha, Hunan 410114, China^{2}Ocean College, Zhejiang University, Zhoushan, Zhejiang 316021, China^{3}Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, Changsha 410114, China^{4}Key Laboratory of Coastal Disasters and Defense of Ministry of Education, Nanjing, Jiangsu 210098, China

^{1}School of Hydraulic Engineering, Changsha University of Science and Technology, Changsha, Hunan 410114, China^{2}Ocean College, Zhejiang University, Zhoushan, Zhejiang 316021, China^{3}Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, Changsha 410114, China^{4}Key Laboratory of Coastal Disasters and Defense of Ministry of Education, Nanjing, Jiangsu 210098, China

Abstract

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Many low-lying tropical and subtropical reef-fringed coasts are vulnerable
to inundation during tsunami events. Hence accurate prediction of tsunami
wave transformation and run-up over such reefs is a primary concern in the
coastal management of hazard mitigation. To overcome the deficiencies of
using depth-integrated models in modeling tsunami-like solitary waves
interacting with fringing reefs, a three-dimensional (3-D) numerical wave
tank based on the computational fluid dynamics (CFD) tool
OpenFOAM^{®} is developed in this study. The Navier–Stokes
equations for two-phase incompressible flow are solved, using the large eddy simulation (LES) method for turbulence closure and the volume-of-fluid (VOF)
method for tracking the free surface. The adopted model is firstly validated
by two existing laboratory experiments with various wave conditions and reef
configurations. The model is then applied to examine the impacts of varying
reef morphologies (fore-reef slope, back-reef slope, lagoon width,
reef-crest width) on the solitary wave run-up. The current and vortex
evolutions associated with the breaking solitary wave around both the reef
crest and the lagoon are also addressed via the numerical simulations.

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Yao, Y., He, T., Deng, Z., Chen, L., and Guo, H.: Large eddy simulation modeling of tsunami-like solitary wave processes over fringing reefs, Nat. Hazards Earth Syst. Sci., 19, 1281-1295, https://doi.org/10.5194/nhess-19-1281-2019, 2019.

1 Introduction

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A tsunami is an extremely destructive natural disaster, which can be generated by earthquakes, landslides, volcanic eruptions and meteorite impacts. Tsunami damage occurs mostly in the coastal areas where tsunami waves run up or run down the beach, overtop or ruin the coastal structures, and inundate the coastal towns and villages (Yao et al., 2015). Some tropic and subtropical coastal areas vulnerable to tsunami hazards are surrounded by coral reefs, especially those in the Pacific and Indian oceans. Among various coral reefs, fringing reefs are the most common type. A typical cross-shore fringing reef profile can be characterized by a steep offshore fore-reef slope and an inshore shallow reef flat (Gourlay, 1996). There is also possibly a reef crest lying at the reef edge (e.g., Hench et al., 2008) and/or a narrow shallow lagoon existing behind the reef flat (e.g., Lowe et al., 2009a). Over decades, fringing reefs have been well known to be able to shelter low-lying coastal areas from flood hazards associated with storms and high surf events (e.g., Cheriton et al. 2016; Lowe et al., 2005; Lugo-Fernandez et al., 1998; Péquignet et al., 2011; Young, 1989). However, after the 2004 Indian Ocean tsunami, the positive role of coral reefs in mitigating the tsunami waves has attracted the attention of scholars who conduct postdisaster surveys (e.g., Chatenoux and Peduzzi, 2007; Ford et al., 2014; Mcadoo et al., 2011). There is consensus among the researchers that, in addition to the establishment of the global tsunami warning system, the cultivation of coastal vegetation (mangrove forest, coral reef, etc.) is also one of the coastal defensive measures against the tsunami waves (e.g., Dahdouh-Guebas et al., 2006; Danielsen et al., 2005; Mcadoo et al., 2011). Numerical models have been proven to be powerful tools to investigate tsunami wave interaction with the mangrove forests (e.g., Huang et al., 2011; Maza et al., 2015; Tang et al., 2013, and many others). Comparatively speaking, their applications in modeling coral reefs subjected to tsunami waves are still very few.

Over decades, modeling wave processes over reef profiles face several challenges such as steep fore-reef slope, complex reef morphology and spatially varied surface roughness. Local but strong turbulence due to wave breaking in the vicinity of the reef edge needs to be resolved. Among various approaches for modeling wave dynamics over reefs, two groups of models are the most pervasive. The first group focuses on using the phase-averaged wave models and the nonlinear shallow water equations to model the waves and the flows, respectively, in field reef environments, and typically the concept of radiation stress (Longuet-Higgins and Stewart, 1964) or vortex force (Craik and Leibovich, 1976) is used to couple the waves and the flows (e.g., Douillet et al., 2001; Kraines et al., 1998; Lowe et al., 2009b, 2010; Van Dongeren et al., 2013; Quataert et al., 2015). As for modeling tsunami waves at a field scale, we are only aware of Kunkel et al.'s (2006) implementation of a nonlinear shallow water model to study the effects of wave forcing and reef morphology variations on the wave run-up. However, their numerical model was not verified by any field observations. The second group aims at using the computationally efficient and phase-resolving model based on the Boussinesq equations. This depth-integrated modeling approach employs a polynomial approximation to the vertical profile of velocity field, thereby reducing the dimensions of a three-dimensional problem by one. It is able to account for both nonlinear and dispersive effects at intermediate water level. At a laboratory scale, Boussinesq models combined with some semiempirical breaking wave and bottom friction models have been proven to be able to simulate the motions of regular waves (Skotner and Apelt, 1999; Yao et al., 2012), irregular waves (Nwogu and Demirbilek, 2010; Yao et al., 2016, 2019) and infragravity waves (Su et al., 2015; Su and Ma, 2018) over fringing reef profiles.

The solitary wave has been employed in many laboratory/numerical studies to model the leading wave of a tsunami. Compared to the aforementioned regular/irregular waves, the numerical investigations of solitary wave interaction with the laboratory reef profile are much fewer. Roeber and Cheung (2012) was the pioneer study to simulate the solitary wave transformation over a fringing reef using a Boussinesq model. Laboratory measurements of the cross-shore wave height and current across the reef as conducted by Roeber (2010) were reproduced by their model. More recently, Yao et al. (2018) also validated a Boussinesq model based on their laboratory experiments to assess the impacts of reef morphologic variations (fore-reef slope, back-reef slope, reef-flat width, reef-crest width) on the solitary wave run-up over the back-reef beach. Despite the above applications, several disadvantages still exist in using the Boussinesq-type models: (1) Boussinesq equations are subjected to the mild-slope assumption – thus using them for reefs with a steep fore-reef slope is questionable, particularly when there is a sharp reef crest located at the reef edge; (2) wave breaking could not be inherently captured by Boussinesq-type models – thus empirical breaking model or special numerical treatment is usually needed; (3) Boussinesq models could not resolve the vertical flow structure associated with the breaking waves due to the polynomial approximation to the vertical velocity profile.

To remedy the above deficiencies of using Boussinesq-type models to
simulate the solitary processes (wave breaking, bore propagation and run-up)
over the fringing reefs, we develop a 3-D numerical wave tank based on the
computational fluid dynamics (CFD) tool OpenFOAM^{®} (Open Field Operation and Manipulation) in
this study. OpenFOAM^{®} is a widely used open-source CFD code in
the modern industry supporting two-phase incompressible flow (via its solver
interFoam). With appropriate treatment of wave generation and absorption, it
has been proven to be a powerful and efficient tool for exploring
complicated nearshore wave dynamics (e.g., Higuera et al., 2013b). In this
study, the Navier–Stokes equations for an incompressible fluid are solved.
For the turbulence closure model, although large eddy simulation (LES) demands more computational
resources than Reynolds-averaged Navier–Stokes (RANS) equations, it computes the large-scale unsteady motions
explicitly. Importantly, it could provide more statistical information for
the turbulence flows in which large-scale unsteadiness is significant (Pope,
2000). Thus the LES model is adopted by considering that the breaking-wave-driven flow around the reef edge/crest is fast and highly unsteady. The free-surface motions are tracked by the widely used volume-of-fluid (VOF) method.

In this study, we first validate the adopted model by the laboratory experiments of Roeber (2010) as well as our previous experiments (Yao et al., 2018). The robustness of the present model in reproducing such solitary wave processes as wave breaking near the reef edge/crest, turbulence bore propagating on the reef flat and wave run-up on the back-reef beach is demonstrated. The model is then applied to investigate the impacts of varying reef morphologies (fore-reef slope, back-reef slope, lagoon width, reef-crest width) on the solitary wave run-up. The flow and vorticity fields associated with the breaking solitary wave around the reef crest and the lagoon are also analyzed by the model results. The rest of this paper is organized as follows. The numerical model is firstly described in Sect. 2. It is then validated by the laboratory data from the literature as well as our data in Sect. 3. What follows in Sect. 4 are the model applications for which laboratory data are unavailable. The main conclusions drawn from this study are given in Sect. 5.

2 Numerical methods

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To simulate breaking-wave processes across the reef, the LES approach is
employed to balance the need of resolving a large portion of the turbulent
flow energy in the domain while parameterizing the unresolved field with a
subgrid closure in order to maintain a reasonable computational cost. The
filtered Navier–Stokes equations are essential to separate the velocity field
that contains the large-scale components, which is performed by filtering
the velocity field (Leonard, 1975). The filtered velocity in the *i*th spatial coordinate is defined as

$$\begin{array}{}\text{(1)}& {\stackrel{\mathrm{\u203e}}{u}}_{i}\left(x\right)=\int G(x,{x}^{\prime}){u}_{i}\left({x}^{\prime}\right)\mathrm{d}{x}^{\prime},\end{array}$$

where *G*(*x*, *x*^{′}) is the filter kernel, which is a localized
function. The eddy sizes are identified using a characteristic length
scale, Δ, which is defined as

$$\begin{array}{}\text{(2)}& \mathrm{\Delta}=(\mathrm{\Delta}x\cdot \mathrm{\Delta}y\cdot \mathrm{\Delta}z{)}^{\mathrm{1}/\mathrm{3}},\end{array}$$

where Δ*x*, Δ*y* and Δ*z* are the grid size in streamlines,
spanwise and vertical directions, respectively. Eddies that are larger than Δ are roughly considered as large eddies, and they are directly
solved. Those which are smaller than Δ are small eddies.

For incompressible flow, the filtered continuity and momentum equations are as follows:

$$\begin{array}{}\text{(3)}& {\displaystyle}& {\displaystyle \frac{\partial {\stackrel{\mathrm{\u203e}}{u}}_{i}}{\partial {x}_{i}}}=\mathrm{0},\text{(4)}& {\displaystyle}& {\displaystyle \frac{\partial \mathit{\rho}{\stackrel{\mathrm{\u203e}}{u}}_{i}}{\partial t}}+{\displaystyle \frac{\partial \left(\mathit{\rho}{\stackrel{\mathrm{\u203e}}{u}}_{i}{\stackrel{\mathrm{\u203e}}{u}}_{j}\right)}{\partial {x}_{i}}}=-{\displaystyle \frac{\partial \stackrel{\mathrm{\u203e}}{p}}{\partial {x}_{i}}}+\mathit{\rho}{g}_{i}+\mathrm{2}\mathit{\mu}{\displaystyle \frac{\partial {\stackrel{\mathrm{\u203e}}{S}}_{ij}}{\partial {x}_{j}}}-{\displaystyle \frac{\partial {\mathit{\tau}}_{ij}^{r}}{\partial {x}_{j}}},\end{array}$$

where *ρ* is the water density, *μ* is the dynamic viscosity,
$\stackrel{\mathrm{\u203e}}{p}$ is the filtered pressure, ${\stackrel{\mathrm{\u203e}}{S}}_{ij}$ is
the strain rate of the large scales defined as

$$\begin{array}{}\text{(5)}& {\stackrel{\mathrm{\u203e}}{S}}_{ij}={\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\left({\displaystyle \frac{\partial {\stackrel{\mathrm{\u203e}}{u}}_{i}}{\partial {x}_{j}}}+{\displaystyle \frac{\partial {\stackrel{\mathrm{\u203e}}{u}}_{j}}{\partial {x}_{i}}}\right)\end{array}$$

and ${\mathit{\tau}}_{ij}^{\mathrm{r}}$ is the residual stress approximated by using subgrid-scale (SGS) models to get a full solution for the Navier–Stokes equations.

The residual stress is usually calculated by a linear relationship with the rate-of-strain tensor based on the Boussinesq hypothesis. The one-equation eddy viscosity mode, which is supposed to be better than the well-known Smagorinsky model for solving the highly complex flow and shear flow (Menon et al., 1996), is employed in the present study. Based on the one-equation model (Yoshizawa and Horiuti, 1985), the subgrid stresses are defined as

$$\begin{array}{}\text{(6)}& {\mathit{\tau}}_{ij}^{\mathrm{r}}={\displaystyle \frac{\mathrm{2}}{\mathrm{3}}}{k}_{\mathrm{S}}{\mathit{\delta}}_{ij}-\mathrm{2}{v}_{t}\left({\stackrel{\mathrm{\u203e}}{S}}_{ij}-{\displaystyle \frac{\mathrm{1}}{\mathrm{3}}}{\stackrel{\mathrm{\u203e}}{S}}_{kk}{\mathit{\delta}}_{ij}\right),\end{array}$$

where *δ*_{ij} is the Kronecker delta, and *ν*_{t} is the SGS eddy viscosity, which is given by

$$\begin{array}{}\text{(7)}& {\mathit{\nu}}_{t}={C}_{k}\mathrm{\Delta}\sqrt{{k}_{\mathrm{S}}},\end{array}$$

and the SGS kinetic energy *k*_{S} needs to be solved by

$$\begin{array}{}\text{(8)}& {\displaystyle \frac{\partial {k}_{\mathrm{s}}}{\partial t}}+{\stackrel{\mathrm{\u203e}}{u}}_{i}{\displaystyle \frac{\partial {k}_{\mathrm{s}}}{\partial {x}_{i}}}={\displaystyle \frac{\partial}{\partial {x}_{i}}}\left({\displaystyle \frac{\mathit{\mu}}{{P}_{\mathrm{r}}}}{\displaystyle \frac{\partial {k}_{\mathrm{s}}}{\partial {x}_{i}}}\right)-{\displaystyle \frac{{\mathit{\tau}}_{ij}^{\mathrm{r}}}{\mathit{\rho}}}{\displaystyle \frac{\partial {\stackrel{\mathrm{\u203e}}{u}}_{j}}{\partial {x}_{i}}}-{\displaystyle \frac{{C}_{\mathit{\epsilon}}{k}_{\mathrm{s}}^{\mathrm{3}/\mathrm{2}}}{\mathrm{\Delta}}},\end{array}$$

where *C*_{k}=0.094, *C*_{ε}=0.916 and *P*_{r}=0.9 as
suggested by the OpenFOAM^{®} user guide (OpenFOAM Foundation, 2013).

The presence of the free-surface interface between the air and water is
treated through the commonly used VOF method (Hirt and Nichols, 1981), which
introduces a volume fraction and solves an additional modeled transport
equation for this quantity. The general representation of fluid density *ρ* is written as

$$\begin{array}{}\text{(9)}& \mathit{\rho}=\mathit{\alpha}{\mathit{\rho}}_{\mathrm{1}}+(\mathrm{1}-\mathit{\alpha}){\mathit{\rho}}_{\mathrm{2}},\end{array}$$

where *ρ*_{1}=1000 kg m^{−3} is the density of water, *ρ*_{2}=1 kg m^{−3} is the density of air and
*α* is the volume fraction of water contained in a grid cell. The distribution of *α* is modeled by the advection equation

$$\begin{array}{}\text{(10)}& {\displaystyle \frac{\partial \mathit{\alpha}}{\partial t}}+\mathrm{\nabla}\cdot \left(\mathit{\alpha}{\stackrel{\mathrm{\u203e}}{u}}_{i}\right)+\mathrm{\nabla}\cdot \left[\mathit{\alpha}(\mathrm{1}-\mathit{\alpha}){u}_{i}^{\mathrm{r}}\right]=\mathrm{0}.\end{array}$$

The last term on the left side is an artificial compression term, avoiding the excessive numerical diffusion and the interface smearing; the new introduced ${u}_{i}^{\mathrm{r}}$ is a velocity field suitable to compress the interface.

In the present solver interFoam, the algorithm PIMPLE, which is a mixture of
the PISO (Pressure Implicit with Splitting of Operators) and SIMPLE
(Semi-Implicit Method for Pressure-Linked Equations) algorithms, is employed
to solve the coupling of velocity and pressure fields. The MULES
(multidimensional universal limiter for explicit solution) method is used
to maintain boundedness of the volume fraction independent of the underlying
numerical scheme, mesh structure, etc. The Euler scheme is utilized for the time
derivatives, the Gauss linear scheme is used for the gradient term and the Gauss linear corrected scheme is selected for the Laplacian term. The detailed implementation
can be founded in the OpenFOAM^{®} user guide (OpenFOAM Foundation, 2013).

Wave generation and absorption are essentials for a numerical wave tank, but
they are not included in the official version of OpenFOAM^{®}.
Therefore, supplementary modules were developed by the other users, e.g.,
waves2Foam (Jacobsen et al., 2012) and IH-FOAM (Higuera et al., 2013a). In
this study, the IH-FOAM is selected because it employs an active wave
absorbing boundary and does not require an additional relaxation zone as
used by waves2Foam. Meanwhile, it supports many wave theories including the
solitary wave theory. The free surface and velocity for a solitary wave
generation in IH-FOAM are (Lee et al., 1982)

$$\begin{array}{}\text{(11)}& {\displaystyle}& {\displaystyle}\mathit{\eta}=H\mathrm{sec}{h}^{\mathrm{2}}\left(\sqrt{{\displaystyle \frac{\mathrm{3}H}{\mathrm{4}{h}^{\mathrm{3}}}}}X\right),\text{(12)}& {\displaystyle}& {\displaystyle \frac{u}{\sqrt{gh}}}={\displaystyle \frac{\mathit{\eta}}{h}}\left[\mathrm{1}-{\displaystyle \frac{\mathrm{1}}{\mathrm{4}}}{\displaystyle \frac{\mathit{\eta}}{h}}+{\displaystyle \frac{h}{\mathrm{3}}}{\displaystyle \frac{h}{\mathit{\eta}}}\left(\mathrm{1}-{\displaystyle \frac{\mathrm{3}}{\mathrm{2}}}{\displaystyle \frac{{z}^{\mathrm{2}}}{{h}^{\mathrm{2}}}}\right){\displaystyle \frac{{\mathrm{d}}^{\mathrm{2}}\mathit{\eta}}{\mathrm{d}{X}^{\mathrm{2}}}}\right],\text{(13)}& {\displaystyle}& {\displaystyle \frac{w}{\sqrt{gh}}}={\displaystyle \frac{-z}{h}}\left[\left(\mathrm{1}-{\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}{\displaystyle \frac{\mathit{\eta}}{h}}\right){\displaystyle \frac{\mathrm{d}\mathit{\eta}}{\mathrm{d}X}}+{\displaystyle \frac{\mathrm{1}}{\mathrm{3}}}{h}^{\mathrm{2}}\left(\mathrm{1}-{\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}{\displaystyle \frac{{z}^{\mathrm{2}}}{{h}^{\mathrm{2}}}}\right){\displaystyle \frac{{\mathrm{d}}^{\mathrm{3}}\mathit{\eta}}{\mathrm{d}{X}^{\mathrm{3}}}}\right],\end{array}$$

where *η* is the free-surface elevation, *H* is the wave height, *h* is
the water depth, $X=x-ct$, $c=\sqrt{g(h+H)}$ is the wave
celerity, and *u* and *w* are the velocities in the streamwise and vertical
directions, respectively.

3 Model validation

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The first set of laboratory experiments serving as validation is from Roeber (2010), who reported two series of experiments conducted at Oregon State University, USA, in separate wave flumes. In this study, we only reproduce their experiments in the large wave flume, which is 104 m long, 3.66 m wide and 4.57 m high. As illustrated in Fig. 1a, the two-dimensional (2-D) reef model, starting at 25.9 m from the wavemaker, was built by a plane fore-reef slope attached to a horizontal reef flat 2.36 m high followed by a back-reef vertical wall. Both the waves and flows across the reef profile were measured by 14 wave gauges (wg1–wg14) and 5 ADVs (acoustic Doppler velocimeters), respectively. Only two scenarios for the reef with and without a trapezoidal reef crest subjected to two incident waves are reported in this study (see also Table 1). The large wave flume experiments enable us to test our model's ability to handle relatively large-scale nonlinear dispersive waves together with wave breaking, bore propagation and associated wave-driven flows. For a more detailed experimental setup, see Roeber (2010).

The second set of 2-D reef experiments for model validation comes from our previous work (Yao et al., 2018). These experiments were conducted in a small wave flume 40 m long, 0.5 m wide and 0.8 m high at the Changsha University of Science and Technology, P. R. China. As shown in Fig. 1b, a plane slope was built 27.3 m from the wavemaker and it was truncated by a horizontal reef flat 0.35 m high. A back-reef beach of 1:6 was attached to the end of the reef flat. The surface elevations were measured at eight cross-shore locations (G1–G8) and no flow measurement was performed. However, a CCD camera was installed to record the process of water uprush on the back-reef slope. Thus the model's robustness to capture the whole process of solitary wave transformation over the reef flat and run-up on the back-reef beach can be evaluated. In this study, we only simulate the tested idealized reef profile with and without a lagoon at the rear of reef flat subjected to the same wave condition (see also Table 1). The lagoon was formed by two 1:1 slopes connecting the reef flat and the toe of the back-reef beach to the flume bottom. The dimensions of the fore-reef slope and the reef flat, the water depths over the reef flat and the incoming wave heights were designed according to the Froude similarity with a target geometric-scale factor of 1:20. See Yao et al. (2018) for the detailed laboratory settings.

By considering a balance between the computational accuracy and efficiency,
the computational domain (Fig. 2a) is designed to reproduce the main aspects
of the laboratory settings. We calibrate the model with the principle that the computed leading solitary wave height at the most offshore gauge should
exactly reproduce its measurement. For a solitary wave, wave length (*L*)
can be estimated as a distance containing 95 % of the total mass of the
solitary wave, which yields $L=\mathrm{2.12}h/\sqrt{{H}_{i}/h}$. The largest
offshore wave length according to the wave conditions in Table 1 is
*L*=8.44 and 1.52 m for the scenario of Roeber (2010) and Yao et al. (2018), respectively.
Thus, we reasonably put the numerical wave generation and absorption at a
location 15 and 6 m from the toe of fore-reef slope, which is also the
location of left boundary. Behind the reef flat, transmitted waves are
allowed to run up on the back-reef beach, but they cannot overtop out of the
computational domain due to a solid wall condition at the right boundary. In
addition, we set the top boundary free to the atmosphere. For
the two side faces, we employed the “empty” boundary in OpenFOAM to
simulate the 2-D reef configurations. When solitary waves interact with the
investigated laboratory reefs, strong turbulence is expected to be generated
inside the domain where the wave breaks near the reef crest and propagates on
the reef flat as a moving bore; thus we do not set the inflow boundary
condition with desired turbulence characteristics for the LES at the wave
generation boundary. Meanwhile, since both the laboratory reef surfaces are
very smooth, the flow structure near the bottom is not resolved in our
simulations, and we only impose a no-slip boundary condition at the reef
surfaces by adjusting the velocity near the bottom to satisfy the
logarithmic law of the wall.

Structured mesh is used to discretize the computational domain. The
discretization is kept constant in the spanwise (*y*) direction (one layer of 20 and 10 mm for the Roeber, 2010, and Yao et al., 2018, scenarios, respectively) while it varies in the streamwise
(*x*) direction to reduce the number of the total cells. From the left
boundary to the toe of the fore-reef slopes, Δ*x* decreases gradually
from 100 to 20 mm for the Roeber (2010) scenario and from 24 to 8 mm for the Yao et al. (2018) scenario (see, e.g.,
Fig. 2b and c). The core region (see, e.g., Fig. 2d), covering from the
fore-reef slope to the back-reef wall or beach, maintains constant cell
sizes of Δ*x*=20 mm and 8 mm for the two experiments,
respectively. Grid refinement near the free surface (e.g., Fig. 2b and c)
is conducted at the core region in the *x* direction by reducing the grid sizes
to one-quarter of their original values, e.g., Δ*x*=5 and 2 mm.
For the vertical (*z*) direction, the grid size is initially set to be
Δ*z*=20 and 8 mm across the domain for the Roeber (2010) and Yao et al. (2018)
scenarios, respectively. Grid refinement near the free surface (e.g., Fig. 2b and c) is
also conducted across the domain by reducing the grid sizes to Δ*z*=5 and 2 mm. The total computational mesh consists of 4.87 and 1.18 million cells for the Roeber (2010) and Yao et al. (2018) scenarios, respectively. The simulation
duration is appointed to be 80 and 30 s to guarantee the arrival of the
reflected waves at the most offshore wave gauge in both experiments. The
time step is automatically adjusted during computation for a constant
Courant number of 0.25. Via parallel computing, it takes approximately 16 and 2 d for the Roeber (2010) and Yao et al. (2018) scenarios, respectively, on a cluster server with 44 CPUs
(Intel Xeon, E5-2696, 2.2 GHz). No notable improvement of the results could be
found with further refinement of the grid size.

For LES modeling solitary wave breaking over reefs, it is crucial to
examine the Reynolds number (*Re*) at the incipient breaking point where
strong turbulence is generated. It could be calculated by $Re={u}_{\mathrm{b}}({H}_{\mathrm{b}}+{h}_{\mathrm{b}})/\mathit{\nu}$ with
${u}_{\mathrm{b}}={c}_{\mathrm{b}}{H}_{\mathrm{b}}/{h}_{\mathrm{b}}$ and
${c}_{\mathrm{b}}=\sqrt{g\left({H}_{\mathrm{b}}+{h}_{\mathrm{b}}\right)}$, where *H*_{b}, *h*_{b}, *u*_{b} and *c*_{b} are wave height, water depth, streamwise velocity and wave
celerity at the breaking point, respectively. *Re* is estimated for all
tested scenarios by using *H*_{b}=*H*_{0} and *h*_{b}=*h*_{r}
(i.e., ignoring wave shoaling on the fore-reef slope and assuming wave
breaking at the reef edge), and the values are also given in Table 1. Since
the near-wall eddies are not resolved in this study, the total required grid
number is independent of *Re* (Pope, 2000). The ideal grid size of the LES model
should be down to the Kolmogorov scale, which is not feasible due to the
limitation of computational resources. To test the convergence of grid size,
we take the experiment with smaller wave flume (i.e., Scenario 3 in Table 1)
which requires finer grid resolution as an example. We only examine the grid
across the reef profile (the aforementioned core region) where the effect of
grid size is supposed to be most influential. Both grid sizes (Δ*x*
and Δ*y*) ranging from 8 mm down to 1 mm are tested. The results in terms of the
dimensionless free-surface elevation and streamwise velocity associated with
the leading solitary wave in the inner reef flat (G7) are compared in Fig. 3. Only differences of less than 2 % in terms of wave and flow could be
observed with the grid size varying from 2 to 1 mm, indicating that our
selection of grid size $\mathrm{\Delta}x=\mathrm{\Delta}z=\mathrm{2}$ mm is sufficient for the current simulations.

To evaluate the performance of the model, the model skill value is adopted and calculated by Wilmott (1981):

$$\begin{array}{}\text{(14)}& \mathrm{skill}=\mathrm{1}-{\displaystyle \frac{\sum {\left|{Y}_{\mathrm{model}}-{Y}_{\mathrm{obs}}\right|}^{\mathrm{2}}}{\sum {\left(\left|{Y}_{\mathrm{model}}-\stackrel{\mathrm{\u203e}}{{Y}_{\mathrm{obs}}}\right|+\left|{Y}_{\mathrm{obs}}-\stackrel{\mathrm{\u203e}}{{Y}_{\mathrm{obs}}}\right|\right)}^{\mathrm{2}}}},\end{array}$$

where *Y*_{model} is the predicted value and *Y*_{obs} is the measured
value. The overline indicates that the average value is taken. The higher
the skill number (close to 1), the better performance of the numerical model.

Figure 4 compares the computed and the measured cross-shore distribution of
the free-surface elevations (*η*) at different stages (*t*) for Scenario 1, where *η* is normalized by the offshore still water depth (*h*_{0})
and *t* is normalized by $\sqrt{{h}_{\mathrm{0}}/g}$. The incident solitary wave gets
steeper on the fore-reef slope at $t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{62.3}$ due to the
shoaling effect. Then its front becomes vertical prior to breaking at
$t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{64.3}$. At $t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{65.8}$, a plunging
breaker occurs with air entrainment and splash-up near the reef edge. After
that, the breaking wave starts to travel on the reef flat in the form of a
propagating turbulent bore at $t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{67.1}$. The bore shows a
gradual reduction in amplitude and continues to propagate downstream on the
reef flat at $t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{76.3}$. The numerical results generally
agree well with the laboratory measurements at all stages with the skill
values larger than 0.85, indicating the robustness of the adopted model to
address the solitary wave processes across the laboratory reef profile in
the large wave flume. When comparing the predictions between our
Navier–Stokes-equation-based model and a Boussinesq model adopted by Roeber (2010), it seems that our model better captures the steep slope of the near-breaking wave
($t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{64.3}$) and breaking wave ($t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{65.8}$).

Figure 5 illustrates the computed and measured time series of dimensionless
free-surface elevations (*η*∕*h*_{0}) at different cross-shore locations (*D*) for Scenario 1. It appears that the model reasonably simulates the
transformation processes of the solitary wave on the fore-reef slope
(*D*=35.9 and 44.3 m) and near the reef edge
(*D*=50.4 m) with the skill values larger than 0.9. The skill values
relatively decrease right after the incipient wave breaking point
(*D*=57.9 m) and at the central reef flat (*D*=65.2 m). Such
discrepancies may be primarily due to the air entrainment in measuring both
the breaking wave and the moving bore (Roeber, 2010) as well as the air
bubble effect on free-surface tracking by the VOF method. In addition, the
second peaks in the time series are due to wave reflection from the
back-reef wall, which are well predicted by the present model. Meanwhile, no
notable difference could be found in view of the time-series predictions
between the present model and the model of Roeber (2010), except at
*D*=65.2 m where the bore amplitude decays in our simulation compared
to that at *D*=57.9 m.

Figure 6 depicts the time series of streamwise velocity (*u*) at five
cross-shore locations (*D*) for Scenario 1, in which *u* is normalized by
the local shallow water wave speed ($\sqrt{gh}$). The model satisfactorily
captures the measured velocity offshore (*D*=17.8 m), on the
fore-reef slope (*D*=47.4 m), on the central reef flat
(*D*=72.6 m) and near the shoreline (*D*=80.2 m). A
transition from the subcritical flow ($u/\sqrt{gh}<\mathrm{1}$) to supercritical
flow ($u/\sqrt{gh}>\mathrm{1}$) could be observed right after wave breaking
(*D*=61.6 m), and a less satisfactory prediction (skill values = 0.76) at this location is probably again due to both the effect of
air bubbles on both flow measurements in the experiments and free-surface
tracking in the simulations. Overall, the adopted model outperforms the
Boussinesq model of Roeber (2010) in view of the velocity predictions,
particularly both near the breaking point (*D*=61.6 m) and the
shoreline on the reef flat (*D*=80.2 m).

As previously introduced, the reef profile of Scenario 2 is identical to
that of Scenario 1 except for a reef crest located at the reef edge. The
cross-shore distribution of dimensionless free-surface elevations (*η*∕*h*_{0}) at different stages ($t/\sqrt{{h}_{\mathrm{0}}/g}$) for Scenario 2 is
demonstrated in Fig. 7. The steepened shoaling wave on the fore-reef slope
appears at $t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{65.0}$ and its front becomes almost vertical
prior to breaking at $t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{66.5}$. The breaking wave begins to
overtop over the reef crest ($t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{69.1}$), and it then
collapses on the lee side of reef crest, resulting in a moving turbulent bore
($t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{72.5}$). The bore travels shoreward on the reef flat
with the continuous damping of its magnitude ($t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{80.5}$).
The skill values for all sampling locations in this scenario are larger than 0.9, implying that the adopted model is able to well address the solitary
wave processes over a more complicated reef geometry such as the presence of
a reef crest at the reef edge. Again, the present model predicts the near-breaking wave ($t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{66.5}$) and breaking wave ($t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{69.1}$ and $t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{72.5}$) slightly better than the model adopted by Roeber (2010).

Figure 8 compares the measured and simulated times-series of dimensionless
free-surface elevations (*η*∕*h*_{0}) at various cross-shore locations (*D*) for Scenario 2. The skill values at all locations are larger than 0.85. It suggest again that the present model not only reasonably reproduces
wave propagation offshore (*D*=17.6 m), shoaling on the fore-reef
slope (*D*=35.9 and 44.3 m) and near breaking in front of
the reef crest (*D*=50.4 m), breaking-wave transformation over the
reef crest (*D*=57.9 m) and bore evolution on the reef flat
(*D*=65.2 m), but also captures the tail waves caused by wave
reflection from the back-reef wall (see, e.g., *D*=65.2 m). Overall,
both our model and the model of Roeber (2010) reproduce the time series of
free-surface elevations equally well for this scenario.

As for Scenario 2, Roeber (2010) only reported one location of flow
measurement on the seaside face of the reef crest. Figure 9 presents the
time series of dimensionless streamwise velocity ($u/\sqrt{gh}$) at the
point *x*=54.4 m, and a skewed and peaky velocity profile is
observed to be associated with the leading solitary wave because the position is
very close to the incipient wave breaking point. The two secondary peaks in
the time series are generated by the reflected waves from the reef crest and
from the back-reef wall, respectively. The model captures the temporal
variation of the current fairly well with the skill value of 0.86, and its
prediction is also better than that from the model of Roeber (2010),
particularly for the reflected waves.

The experiments of Yao et al. (2018) only measured the time series of wave records at limited locations (G1–G8) across the reef as well as the maximum wave run-up on the final beach. Figure 10 compares the computed and measured time series of free-surface elevations for Scenario 3. The overall agreement between the simulations and experiments for G1–G8 is very good, with the skill values at all locations larger than 0.9. When the solitary wave travels from the toe (G2) to the middle of fore-reef slope (G3), it gets steeper due to the shoaling effect. Wave breaking starts at a location right before the reef edge (G4), and the surf zone processes extend over the reef flat in the form of a moving bore. Thus, from G5 to G8, the wave time series show saw-shaped profiles and there is a cross-shore decrease in the leading solitary wave height. Such features of the breaking waves are also well captured by the model. Note that the second peak in the time series of G7 is due to wave reflection from the back-reef beach, and the incident and reflected waves are not fully separated from each other at G8 because this location is too close to the beach. The predicted and measured wave run-ups are 0.122 and 0.109 m, respectively, for this scenario. Compared to the Boussinesq model employed by Yao et al. (2018), no significant difference in the predicted time series could be found for the present Navier–Stokes-equation-based model.

Figure 11 depicts the same comparison of wave time series but for the reef profile with a lagoon (Scenario 4). Again, the model performance for this scenario is fairly good (all skill values larger than 0.9). The predicted and measured wave run-ups are 0.123 and 0.116 m, respectively, for this scenario. Notable mismatch only appears for those small wave oscillations generated by the reflected wave propagating out of the lagoon to the reef flat (i.e., from G8 to G6). But our model seems to be superior to the model of Yao et al. (2018) in reproducing those oscillations at G7 and G8. We finally remark that the tail of the leading solitary wave, particularly from G1 to G4, is below the initial water level in the laboratory data, which is due to the water lost to form the generated wave crest around the paddle of the wavemaker. However, such a phenomenon is not observed in the numerical results because we generate a theoretical solitary wave in the numerical domain as indicated by Eq. (11).

4 Model applications

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In this section, we apply the well-validated LES model to examine the
variations of reef morphological parameters (fore-reef slope, back-reef
slope, lagoon width, reef-crest width) that may affect the wave run-up (*R*)
on the back-reef beach. Based on Scenario 3 (1:6 for both the slopes of
fore reef and back reef, 9.6 m for the reef length, no reef crest and no
lagoon) from Yao et al. (2018), we firstly test five slopes (1:2, 1:4, 1:6, 1:8 and 1:10, which all fall within the common range of 1:1 to 1:20 in the
reported field observations; see, e.g., Table 1 of Quataert et al., 2015)
for both the fore reef and the back reef. We then consider the existence of
a lagoon at the rear of reef flat by testing four upper widths of the lagoon
(1.6, 3.2, 4.8 and 6.4 m) and comparing to the case without a lagoon
(lagoon width = 0 m). We finally investigate a trapezoidal reef crest
located at the reef edge with its seaward slope matching the fore-reef
slope and its shoreward slope of 1:1. We examine five reef-crest widths (0.1, 0.2, 0.3, 0.4 and 0.5 m) in view that the dimension of reef crest
at the field scale is on the magnitude of meters (see, e.g., Hench et al.,
2008). During simulations, each run is performed by changing one of above
morphological parameters while keeping other parameters unaltered. All runs
are conducted under a combination of one solitary wave height (*H*_{0}=0.08 m) and two reef-flat water depths (*h*_{r}=0.05 m
and *h*_{r}=0.1 m).

Generally, Fig. 12a shows that *R* is not very sensitive to the change in
the fore-reef slope within the tested range, in that wave breaking for this
scenario occurs close to the reef edge (G4); thus most of the surf zone
processes and associated energy dispassion complete on the reef flat. Only
when the fore-reef slope becomes steeper than 1:8, *R* decreases slightly
under both water depths (*h*_{r}), which is attributed to the increased
fore-reef reflection of the incident wave energy. Figure 12b reveals that *R* is more sensitive to the back-beach slope under both *h*_{r}. It decreases
significantly as the back-beach slope becomes milder, which is consistent
with that found for the plane slope (see, e.g., Synolakis, 1987). Figure 12c
shows the variation of *R* with the lagoon width. Note that the zero width
represents the reef without a lagoon. It appears that *R* increases notably
with the increase in lagoon width because a wider lagoon dissipates less
wave energy partly due to the stoppage of propagating bore and partly due to
the reduction of bottom friction. As for the effect of reef-crest width
(Fig. 12d), although the presence of a reef crest is reported to be an
important factor affecting the wind wave transformation over fringing reefs
(e.g., Yao et al., 2017), it seems to have little impact on the solitary
wave run-up under both *h*_{r}; a slight decline of *R* could only be found under crest widths larger than 0.4. This is because the solitary wave is
very long compared to the reef-crest width; thus most of its energy could
transmit over the narrow reef crest. However, when the reef crest becomes
sufficiently wide, its shallower crest tends to energize the wave breaking
and thus the energy dissipation. To summarize all above analyses, it can be
concluded that coastal regions protected by the fringing reefs with steeper
back-reef slopes and wider lagoons are more valuable to coastal inundation
during a tsunami event.

One advantage of the current Navier–Stokes-equation-based model over the
depth-integrated models is its ability to resolve the vertical flow
structure under breaking waves, particularly around the complex reef
geometry. Based on the reef profile of Yao et al. (2018), Fig. 13 shows the
simulated wave-driven current and vorticity on the *x*–*z* plane at different
stages ($t/\sqrt{{h}_{\mathrm{0}}/g}$) for the reefs with and without a reef crest
at the reef edge subjected to the same solitary wave condition (*H*_{0}=0.08 m and *h*_{r}=0.05 m). Without the reef crest,
the shoaling wave propagates onto the horizontal reef flat with a uniform
velocity distribution underneath ($t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{25.9}$ and 26.9),
which is typical for the shallow water long waves. Until $t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{27.9}$, wave breaking occurs in the form of a plunging breaker, and
vortex generation gathers mainly around the wave crest. The vortices are
transported further downstream at $t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{28.9}$. When the wave
crest exists, incipient wave breaking moves seaward and it takes place at
the seaside edge of the reef crest ($t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{25.9}$). The breaker
then overtops over the reef crest ($t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{26.9}$) and plunges
onto the reef flat lee side of the reef crest, resulting in a hydraulic jump
($t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{27.9}$). Consequently, the wave-driven current at the rear
part of the reef crest is dramatically increased compared to the same
location without the crest. Both the intensity and the extent of vortex
generation are also enlarged at the lee side of the reef crest ($t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{28.9}$), leading to increased wave energy dissipation compared to the case without the reef crest.

Figure 14 compares the computed wave-driven current and vorticity on the *x*–*z* plane at different stages ($t/\sqrt{{h}_{\mathrm{0}}/g}$) between the reefs in the
presence and absence of the lagoon. Without the lagoon, the propagating bore
arrives with strong vortex motions ($t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{49.4}$). The
vortices are eventfully transported downstream from $t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{54.4}$ to 64.4. However, when the lagoon is present, the current speed
over the depth slows down and additional vortices generate at the seaside
edge of the lagoon as the bore propagates into the lagoon ($t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{49.4}$). The peak value of the vorticity appears at a later time
($t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{54.4}$). After that, the vortices in the lagoon are
primarily diffused by the vortex shedding ($t/\sqrt{{h}_{\mathrm{0}}/g}=\mathrm{59.4}$
and 64.4). Compared to the case without the lagoon, although the existence of
a lagoon dissipates less wave energy by terminating the propagating bore and
reducing the reef-flat friction as previously stated, the vortex generation
and diffusion in the lagoon as demonstrated here are believed to cause local
energy loss. We finally remark that the wave-driven current and vortices
examined in this section could provide a first step to analyze more
sophisticated problems, such as the tsunami-induced sediment/debris
transport over the fringing reefs.

5 Conclusions

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To remedy the inadequacies of using the depth-integrated models to simulate
the interaction between tsunami-like solitary waves and fringing reefs, a 3-D
numerical wave tank, solving the Navier–Stokes equations with the LES for
turbulence closure, has been developed based on the open-source CFD tool
OpenFOAM^{®}. The free surface is tracked by the VOF method. Two
existing laboratory experiments with the wave, flow and wave run-up
measurements based on different fringing reef profiles are employed to
validate the numerical model. Simulations show that the current
Navier–Stokes-equation-based model outperforms the commonly used
Boussinesq-type models in view of its capability to better reproduce the
breaking waves and wave-driven current on the reef flat. The model is then
applied to investigate the impacts of varying morphologic features on the
back-reef wave run-up. The flow and vorticity fields associated with the
breaking solitary wave around the reef crest and the lagoon are also
analyzed via the numerical simulations.

Model results shows that wave run-up on the back-reef slope is most sensitive to the variation of the back-reef slope, less sensitive to the lagoon width, and almost insensitive to the variations of both the fore-reef slope and the reef-crest width within our tested ranges. The existence of a reef crest or a lagoon can notably alter the wave-driven current and vortex evolutions on the reef flat. These findings demonstrate that low-lying coastal areas fringed by coral reefs with steep back-reef slopes and larger lagoons are expected to experience larger wave run-up near the shoreline; thus they are more susceptible to coastal inundation during a tsunami event.

Data availability

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Data availability.

Most of the simulations are made available in the figures of this paper. The raw simulated data used in this study are available upon request (zzdeng@zju.edu.cn).

Author contributions

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Author contributions.

YY and LC performed the data analyses and wrote the paper. TH prepared the experimental data. ZD and HG performed the numerical simulations.

Competing interests

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Competing interests.

The authors declare that they have no conflict of interest.

Financial support

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Financial support.

This research has been supported by the National Natural Science Foundation of China (grant nos. 51679014 and 11702244), the Hunan Science and Technology Plan Program (grant no. 2017RS3035), and the Open Foundation of the Key Laboratory of Coastal Disasters and Defense of Ministry of Education (grant no. 201602).

Review statement

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Review statement.

This paper was edited by Maria Ana Baptista and reviewed by two anonymous referees.

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Short summary

A tsunami can be destructive when it inundates the coastal areas. In recent years, the positive role of coral reefs in mitigating tsunami waves has aroused the attention of scholars. We therefore investigate the tsunami wave interaction with a reef profile via a sophisticated numerical model. We find that the low-lying coastal areas, fringed by coral reefs with milder back-reef beaches and smaller lagoons, are less susceptible to coastal inundation during a tsunami event.

A tsunami can be destructive when it inundates the coastal areas. In recent years, the positive...

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