Conference PaperPDF Available
991
TOWARDS A FLOOD RISK ASSESSMENT ON A REEF-LINED
COASTLINE
ANA RUEDA1, LAURA CAGIGAL1,2, DYLAN ANDERSON6, CURT STORLAZZI3,
AP VAN DONGEREN4, STUART PEARSON4, JOHN MARRA5,
PETER RUGGIERO6, FERNANDO MÉNDEZ1
1. Surf and surge research group. E.T.S.C.C.P. Universidad de Cantabria. Av. Los
Castros s/n 39005. Santander, Spain. ruedaac@unican.es; mendezf@unican.es
2. School of Environment, University of Auckland, 23 Symonds Street, Auckland,
New Zealand. lcag075@aucklanduni.ac.nz
3. Pacific Coastal and Marine Science Center, United States Geological Survey, Santa
Cruz, CA, USA. cstorlazzi@usgs.gov
4. Department of Applied Morphodynamics, Unit of Marine and Coastal Systems,
Deltares, Delft, Netherlands. Ap.vanDongeren@deltares.nl;
Stuart.Pearson@deltares.nl
5. NOAA NESDIS National Centers for Environmental Information (NCEI), 1845
WASP Blvd., Building 176, Honolulu, HI, USA. john.marra@noaa.gov
6. College of Earth, Oceanic, and Atmospheric Sciences, Oregon State Univ.,
Corvallis, OR 9733. anderdyl@oregonstate.edu; pruggier@ceoas.oregonstate.edu
Abstract: The assessment of coastal flood risk on a reef-lined coastline presents
several challenges. From the probabilistic side, we need to consider all possible
events that could occur in the system, taking into account the different contributions
of waves, storm surges, and tides that contribute to the total water level. To estimate
reliable flood extents, we need to accurately model the complex wave processes that
occur across the reef. To explore the multivariate nature of coastal flooding, we rely
on a climate emulator that accounts for climate variability and simulates time series
of all the variables involved. Due to the computational constraints to numerically
simulate thousands of events, we explore the feasibility of using a recently developed
tool, the HyCreWW (Hybrid Coral Reef Wave and Water level) meta-model to
estimate wave run-up and flooding extents. Limitation of a 1D assessment are
analyzed and results compared with 2D modeling.
Introduction
Wave-induced flooding is a major coastal hazard on tropical coasts fronted by
coral reefs. In most of these locations, coastal flooding episodes can be caused
either by tropical cyclone events or as a result of “sunny day” swell events
generated by storms farther away (Hoeke et al., 2013). To perform a robust
flood risk assessment, it is necessary to account for all possible events that might
occur in the system. However, sea level observations, from which the
probability of the events can be assessed, are often limited. This leads to the
need of performing stochastic simulation of multivariate events (Serafin et al.,
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2016, Rueda et al., 2017), to account for events that may be unrecorded, but are
statistically possible. Once the statistics are well-captured, the synthetic and
historic events need to be downscaled to obtain the associated flood extent and
magnitude. The model XBeach (Roelvink et al., 2009,2017) has been previously
proved to accurately reproduce the hydrodynamics in reef environments, making
it suitable for the purpose. However, it is computationally expensive for
probabilistic assessments requiring thousands of simulations.
In this study we attempt to explore the feasibility of using a recently developed
tool to estimate wave run-up in reef line shorelines, the HyCreWW (Hybrid
Coral Reef Wave and Water level, Rueda et al. (under review)) metamodel to
estimate flood extent. HyCreWW is defined for an idealized reef profile.
Therefore, the limitations of a 1D assessment will be analyzed and the results
compared with 2D modeling.
Hybrid Coral Reef Wave and Water level: HyCreWW
HyCReWW metamodel was developed for providing wave-driven run-up
estimations along coral reef-lined shorelines under a wide range of fringing reef
morphologies and offshore forcing characteristics. The metamodel is based on
two models: (a) a full factorial design of recent XBeach Non-Hydrostatic
simulations under different reef configurations and offshore wave and water
level conditions (Pearson et al., 2017); and (b) Radial Basis Functions (RBFs)
for approximating the non-linear function of run-up for the set of multivariate
parameters.
Fig. 1. Idealized reef (adapted from Pearson et al., 2017).
The reef schematization is shown in Fig. 1. The hydrodynamic variables defined
are offshore water level (0), significant wave height (H0), and wave steepness
(H0/L0); the reef morphologic parameters include fore reef slope (f), reef flat
width (Wreef), beach slope (b), and seabed roughness (cf). L0 is the deep water
wave length L0=gTp2/2, and Tp is the peak period. Beach crest elevation (zb)
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was fixed at a height of 30 m to focus on run-up as a proxy for coastal
inundation. The parameter ranges are represented in Table 1.
Table 1. Primary XBNH model input parameters and their values.
Parameter
Symbol
Units
Values
Offshore water
level
0
m
1, 0, 0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3
Offshore
significant wave
height
H0
m
1, 2, 3, 4, 5
Offshore wave
length
L0
m
-
Offshore wave
steepness
H0/L0
-
0.005, 0.001, 0.050
Fore reef slope
f
-
½, 1/10, 1/20
Reef flat width
Wreef
m
0, 50, 100, 150, 200, 250, 300, 350,
400, 500, 1000, 1500
Beach slope
b
-
1/5, 1/10, 1/20
Coefficient of
friction
cf
-
0.01, 0.05, 0.10
RBFs are a flexible interpolation technique originally developed by Hardi
(1971). They have been previously used as a metamodel of SWAN for wave
propagation problems (Camus et al., 2011, Gouldby et al., 2014) and recently
with 2D surf beat Xbeach simulations on the coral coast of Fiji for coastal
inundation forecasting (Bosserelle, personal communication) with successful
results.
The validation of HyCReWW with existing field and laboratory (Fig. 2)
demonstrates the ability to produce accurate run-up estimates along reef-lined
shorelines over a large range of the parameter space.
Application
The location of study was Roi-Namur Island on Kwajalein Atoll in the Republic
of Marshall Islands (Fig. 3). Its wave climate and reef morphology can be
representative for many atolls and reef-lined coastlines in the Pacific, with the
added value that it is a well-studied location with different field experiments and
hydrodynamic simulations already performed there (Quataert et al., 2015,
Becker et al., 2014, Cheriton et al., 2016).
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Fig. 2. HyCReWW validation, comparing measured and modeled run-up.
Fig. 3. Morphology of the study area. A) Kwajalain Atoll and bathymetric contours. B) Google Earth
image of Roi-Namur, the northernmost part of the atoll, and the location of the cross-shore transect.
C) Cross-shore transect (adapted from Quataert et al., 2015)
After generating thousands of years of multivariate offshore conditions using the
Time-varying Emulator for Short-and-Long-term Analysis of coastal flooding,
TESLA-flood (Anderson et al., in prep), a hybrid downscaling of wave
conditions (using selection algorithms, SWAN, and non-linear interpolation
techniques) was performed to account for the directional wave sheltering and
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995
obtain wave conditions closer to the reef. These results were validated with the
measurements of a 600 kHz Nortek Acoustic Wave and Current Meter (AWAC)
at a depth of 21m depth deployed during three previous campaigns in 2013–
2015 (Cheriton et al., 2016).
Due to the computational constraints of simulating thousands of events with
XBeach, and recognizing 2D effects on the reef, we have selected a number of
cases to simulate with a 2D XBeach Non-Hydrostatic. These form the basis of
comparison with the run-up estimations provided by HyCReWW.
Fig. 4. Left: Return period analysis of a proxy of flooding. Black dots represent historical events, and
red lines are simulated. Right: Example of one simulated hydrograph. The bottom panel gives the
runup estimation from HyCReWW.
In this case, the selection of the events to simulate with a high resolution model,
is based on a proxy of flooding volume (integrating through time the total water
levels above the threshold that initiates overtopping). The left panel of Fig. 4
shows the historical events (black dots) and an ensemble of 10 realizations of
1000 years with TESLA-flood (red lines). The statistical model reproduces the
flooding behavior, and is also capable of extrapolating the tail of the distribution
for low occurrence probabilities (Fig. 4, left), indicating a dramatic change in
the shape of the tail of the distribution (Frechet type). The right panel of Fig. 4
shows an example of a synthetic hydrograph with the associated values of
significant wave height, wave period, wave direction, and water level,
corresponding with a 100-year return period event. The bottom panel on the
right side correspond to the run-up estimation with HyCReWW. These time-
dependent hydraulic boundary conditions are essential for a correct estimation
of the flooding extents with the 2D non-hydrostatic version of XBeach.
Preliminary results show different behaviors in the flooding extents with similar
occurrence probabilities of the multivariate boundary conditions.
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996
Conclusion
Coastal flood risk assessment along reef-lined coastlines is a challenge involving
different input data (bathymetry, topography, wave and sea level forcing),
numerical models (wave transformation processes at regional-SWAN, and local-
scale-XBeach), and statistical models. In this work we have combined the state-
of-the-art of statistical and numerical models on a well-monitored island. The
difficulties faced during the study emphasize the complexity of hydrodynamics
in reef environments and open new research questions such as, what should be
the optimal number of events to simulate with a high resolution model to
perform a robust risk assessment? Will it be valid a single grid for 2D high
resolution simulations in multimodal wave conditions? How can we validate
extreme events, mostly associated with tropical cyclones?
Acknowledgements
This work was critically supported by the US Geological Survey under
Grant/Cooperative Agreement G15AC00426 and from the US Department of
Defense’s Strategic Environmental Research and Development Program project
RC-2644. AvD and SGP were partially funded by the Deltares Strategic
Research Program “Quantifying Flood Hazards and Impacts”.
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