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Severe Flooding in the Atoll Nations of Tuvalu and Kiribati Triggered by a Distant Tropical Cyclone Pam

Abstract and Figures

Tropical cyclone (TC) Pam formed in the central south Pacific in early March 2015. It reached a category 5 severity and made landfall or otherwise directly impacted several islands in Vanuatu, causing widespread damage and loss of life. It then moved along a southerly track between Fiji and New Caledonia, generating wind-waves of up to approximately 15 m, before exiting the region around March 15th. The resulting swell propagated throughout the central Pacific, causing flooding and damage to communities in Tuvalu, Kiribati and Wallis and Futuna, all over 1,000 km from TC Pam’s track. The severity of these remote impacts was not anticipated and poorly forecasted. In this study, we use a total water level (TWL) approach to estimate the climatological conditions and factors contributing to recorded impacts at islands in Tuvalu and Kiribati. At many of the islands, the estimated TWL associated with Pam was the largest within the ∼40-year period of available data, although not necessarily the largest in terms of estimated wave setup and runup; elevated regional sea-level also contributed to the TWL. The westerly wave direction likely contributed to the severity, as did the locally exceptional storm-swell event’s long duration; the overall timing and duration of the event was modulated by astronomical tides. The findings of this study give impetus to the development, implementation and/or improvement of early warning systems capable of predicting such reef-island flooding. They also have direct implications for more accurate regional flood hazard analyses in the context of a changing climate, which is crucial for informing adaptation policies for the atolls of the central Pacific.
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fmars-07-539646 January 21, 2021 Time: 11:57 # 1
published: 22 January 2021
doi: 10.3389/fmars.2020.539646
Edited by:
Robert Timothy McCall,
Deltares, Netherlands
Reviewed by:
Christian M. Appendini,
National Autonomous University
of Mexico, Mexico
Rui Caldeira,
Agência Regional para o
Desenvolvimento da Investigação
Tecnologia e Inovação (ARDITI),
Ron K. Hoeke
Specialty section:
This article was submitted to
Coastal Ocean Processes,
a section of the journal
Frontiers in Marine Science
Received: 02 March 2020
Accepted: 27 October 2020
Published: 22 January 2021
Hoeke RK, Damlamian H, Aucan J
and Wandres M (2021) Severe
Flooding in the Atoll Nations of Tuvalu
and Kiribati Triggered by a Distant
Tropical Cyclone Pam.
Front. Mar. Sci. 7:539646.
doi: 10.3389/fmars.2020.539646
Severe Flooding in the Atoll Nations
of Tuvalu and Kiribati Triggered by a
Distant Tropical Cyclone Pam
Ron K. Hoeke1*, Herve Damlamian2, Jérome Aucan3and Moritz Wandres2
1Climate Science Centre, Commonwealth Science and Industrial Research Organisation (CSIRO), Aspendale, VIC, Australia,
2Geoscience, Energy and Maritime Division, Pacific Community (SPC), Suva, Fiji, 3Laboratoire d’Ecologie Marine Tropicale
des Océans Pacifique et Indien (ENTROPIE), Institut de Recherche pour le Développement (IRD), Nouméa, New Caledonia
Tropical cyclone (TC) Pam formed in the central south Pacific in early March 2015.
It reached a category 5 severity and made landfall or otherwise directly impacted
several islands in Vanuatu, causing widespread damage and loss of life. It then moved
along a southerly track between Fiji and New Caledonia, generating wind-waves of up
to approximately 15 m, before exiting the region around March 15th. The resulting
swell propagated throughout the central Pacific, causing flooding and damage to
communities in Tuvalu, Kiribati and Wallis and Futuna, all over 1,000 km from TC Pam’s
track. The severity of these remote impacts was not anticipated and poorly forecasted.
In this study, we use a total water level (TWL) approach to estimate the climatological
conditions and factors contributing to recorded impacts at islands in Tuvalu and Kiribati.
At many of the islands, the estimated TWL associated with Pam was the largest within
the 40-year period of available data, although not necessarily the largest in terms of
estimated wave setup and runup; elevated regional sea-level also contributed to the
TWL. The westerly wave direction likely contributed to the severity, as did the locally
exceptional storm-swell event’s long duration; the overall timing and duration of the
event was modulated by astronomical tides. The findings of this study give impetus
to the development, implementation and/or improvement of early warning systems
capable of predicting such reef-island flooding. They also have direct implications for
more accurate regional flood hazard analyses in the context of a changing climate, which
is crucial for informing adaptation policies for the atolls of the central Pacific.
Keywords: tropical cyclones, flooding, sea level, wave climate analysis, coastal hazards, atolls
Coastal areas are perceived to be at risk of increasingly frequent and severe flooding and erosion
impacts associated with sea level rise (SLR). This has led to concern that island nations are especially
vulnerable to coastal flooding (Nicholls et al., 2007;Seneviratne et al., 2012), particularly the atoll
nations of Kiribati, Tuvalu, Marshall Islands and Maldives (Barnett and Adger, 2003;Nicholls
et al., 2011). There has been some debate on the nature and timing of these impacts, however.
Several studies have presented evidence that, despite sea level rise on the order of 20 cm over the
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Hoeke et al. Atoll Flooding by Distant Tropical Cyclones
last century, atoll islands have not (yet) experienced net erosion
and that many may have some capacity to keep up with future
SLR (e.g., Webb and Kench, 2010;Beetham et al., 2017;Tuck
et al., 2019) through the deposition of reef-derived materials
during storm over wash events (e.g., Maragos et al., 1973;
Smithers and Hoeke, 2014). Other studies have suggested that
rates of SLR will outstrip reefs accretion rates (Perry et al., 2018),
and that in any case, the increased frequency and severity of
inundation will make most atoll islands uninhabitable within
a few decades (e.g., Storlazzi et al., 2018). Regardless, there is
widespread agreement that overall vulnerability to SLR will be
heterogeneous due to differences in reef/island morphology and
exposure to extreme sea level drivers (viz sea level variability,
storm waves, storm surges, and astronomic tides), and that more
detailed event-based case studies are needed to understand the
role of background sea level (and SLR) compared to these drivers
(Woodroffe, 2008;Beetham and Kench, 2018;Idier et al., 2019).
Such case studies can also better inform the development of
early warning forecast systems, which are essential in the face
of likely increased flooding and erosion events (Storlazzi, 2018),
by identifying the relative importance of the extreme sea level
processes and indications of local vulnerability.
In this study, we investigate extensive flooding and erosion
impacts which occurred in Tuvalu and the Gilbert Islands of
Kiribati during March 2015. Unlike the case studies of Hoeke
et al. (2013),Wadey et al. (2017),Ford et al. (2018), and Wandres
et al. (2020), all of which focused at least primarily on events
triggered by distance-source swell waves generated by mid-
latitude (temperate) cyclones in the western Pacific (primarily the
Federated States of Micronesia, Papua New Guinea, and Solomon
Islands), the Maldives, Majuro Atoll (Marshall Islands) and Fiji,
respectively, the events in Tuvalu and Kiribati described here are
associated with the passage of tropical cyclone (TC) Pam. TC
Pam formed in the central south Pacific in early March 2015. It
reached a category five severity and made landfall or otherwise
directly impacted several islands in Vanuatu, causing widespread
damage and loss of life (Gov. of Vanuatu, 2015). It then moved
along a southerly track between Fiji and New Caledonia, exiting
the region around March 15th. The resulting swell propagated
throughout the central Pacific, causing flooding and damage to
communities in Tuvalu, Kiribati, and Wallis and Futuna, all over
1,000 km from TC Pam’s track (Damlamian et al., 2017;Figure 1).
Here, we focus on the remote impacts to Tuvalu and Kiribati,
which were poorly forecast or otherwise anticipated (Taupo and
Noy, 2017). Multiple reports were collated (Table 1) and then
a combination of astronomical tidal predictions and numerical
wind-wave and sea level hindcasts, spanning from 1979 to the
present, were combined using a total water level (TWL) approach
(e.g., similar to Dodet et al., 2019), to estimate the climatological
conditions and factors contributing to recorded impacts. The
accuracy of these TWL components were independently assessed
using tide gauge and satellite altimetry observations; it also
includes an empirical wave runup estimate. This resulting proxy
for flooding severity was compared to the recorded flooding and
erosion impacts; the relative contribution of waves, tides and
background sea level to TWL were assessed, as was event duration
and wave direction.
Summary of Impacts to Islands and
Information on which island and atolls within Tuvalu and
Kiribati were impacted (or were not), and types of impacts
were collated from a number of sources, primarily disaster relief
agencies. These are summarized in Table 1; these locations are
also plotted in Figure 2. Note that (hand drawn) “inundation”
and “erosion” maps, and notes provided by the Tuvalu Public
Works Department to the authors are provided in this article’s
“Supplementary Material” section.
Geophysical Data
To assess the timing and location of regional TCs, particularly
TC Pam, the IBTrACS (Knapp et al., 2010) database (version
04r0)1was interrogated for TCs tracks; any portion of which
occurred between longitudes 155E and 170W and latitudes of
30S and 10N and between the years 1979–2019 (the time period
of the wave hindcast used in this study, see next section) were
retained and defined as “regional” TCs. TC Pam’s track is given
in Figure 1.
To assess impacts, three different categories of sea
level information were used in this analysis: wind-waves,
astronomical tides, and non-tidal sea-level. These are detailed in
following sub-sections.
Hourly significant wave height (Hs), integrated wave energy flux
(CgE), peak wave period (Tp) and peak wave direction (Dp)
for years 1979–2019 were extracted from the CAWCR Wave
Hindcast, a validated global wind-wave hindcast with increased
(approximately 7 km) spatial resolution around the Pacific island
nations (Durrant et al., 2014)2and related collections. Previous
global and regional comparisons with satellite altimetry and wave
buoy data have found the overall hindcast Hsbias and overall
root-mean-square (RMS) difference to be on the order of 5%
and <10%, respectively, in the central Pacific (Durrant et al.,
2014;Hemer et al., 2016). However, in this study, a further
verification of the hindcast’s ability to estimate the extreme waves
associated specifically with TC Pam was performed by comparing
hindcast Hsto calibrated Hsmeasured by multiple satellite
altimeters, provided via the Ribal and Young (2019) dataset
(sourced from Australias Integrated Marine Observing System)3.
While overall correlation coefficients (R) and errors between
hindcast Hsand that estimated by various altimeters/bands
indicate good agreement (R>0.9, std.err. <0.004 m) for
the area and time period indicated in Figure 1, the hindcast
overestimated the most extreme waves (Hs>12) near TC Pam’s
eye wall compared to the altimeter data by up to several meters
(Supplementary Figure 1). This divergence is at least partially
attributable to (1) the known tendency of the WaveWatch III
model to overestimate energy of waves traveling in the oblique
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FIGURE 1 | Tropical Cyclone (TC) Pam’s track (black line), with maximum hindcast significant wave height (Hs) during the TC’s passage (March 6–17, 2015)
indicated in color shading. Island nations/archipelagos in the area are labeled and their exclusive economic zones (Flanders Marine Institute, 2019), indicated with
gray lines. The locations in Table 1 are indicated with black +’s.
and opposing winds (as would occur in a tight-radius TC,
including with the “ST4” physics package used in the hindcast)
and; (2) uncertainty in the radar altimeters estimation of Hs
in such extreme conditions: Ribal and Young (2019) limit their
calibrations to values of Hs<9.0 m. This overestimation (or
divergence between hindcast and altimetry observations) of Hs
does not occur at all for the same time period in the area
indicated by Figure 2 (which corresponds to the area of flooding
and erosion impacts researched in this paper), and statistics in
this area are modestly improved (R>0.92, std.err. <0.002 m,
Supplementary Figure 2). For further information on the details
and discussion of this hindcast/satellite altimeter comparison,
please refer to Supplementary Material, however, the authors
have confidence that the CAWCR wave hindcast represented the
propagation of the wave field associated with TC Pam, and its
representation within the overall extreme value statistics of the
hindcast, throughout the Gilbert Islands and Tuvalu sufficiently
to support the paper’s conclusions.
Astronomical Tides
Hourly astronomic tidal predictions (ηtide) were calculated for
a time period corresponding to that of the wave hindcast
(years 1979–2019) from TPXO tidal constituents (version 9.1,
Egbert and Erofeeva, 2002) at all locations in Table 1. To
assess the skill of TPXO-based tidal predictions used in this
study, they were compared to tidal predictions based on
hourly tide gauge observations from Tarawa and Funafuti
(years 1988–2018 and 1979–2017, respectively) sourced from the
University of Hawaii Sea Level Center (4Caldwell et al., 2015).
Root-mean-square-differences (RMSD) between tide gauge and
TPXO predictions at Funafuti and Tarawa were 0.01 and 0.02 m,
respectively. More importantly for this analysis, 99.5th quantiles
differences between tide gauge and TPXO predictions at Funafuti
and Tarawa were 0.021 and 0.025 m, respectively. The tidal
software UTide (Codiga, 2011) was used both to estimate
constituents from tide gauge observations and to calculate
predictions for both the TPXO and tide gauge constituents.
Non-tidal Sea-Level
The computation of TWLs and associated extreme value analysis
(see next) requires an uninterrupted time span of non-tidal
sea-level data that corresponds to the wave hindcast (years
1979–2019) and the astronomical tides calculated in section
“Astronomical Tides”. This precludes using sea surface heights
measured by satellite altimetry, which only start around 1992.
We evaluated three global gridded sea level products, all available
from publicly accessible data repositories, which meet this time
span requirement:
1. NCEP Climate Forecast System Reanalysis Reforecast
(CFSR) 6-hourly sea surface height (Saha et al., 2013);
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TABLE 1 | Documented remote impacts in Tuvalu and Kiribati.
Island/ Atoll Morphology notes Summarized hazard and impacts* TWL Hb
(m) AEP (years) (m) AEP (years)
Tarawa (KI) Atoll/lagoon on western side,
main settlements on southern
Severe: “widespread coastal flooding”; “damage to major
throughway bridge”1,2
2.30 40(620) 4.93 40 (162)
Nonouti (KI) Atoll/lagoon on western side,
some western-exposed villages
Severe/moderate: unclear from reporting12.39 40 (6397) 5.04 40 (141)
Onotoa (KI) Atoll/lagoon on western side,
some western-exposed villages
Severe/moderate: unclear from reporting12.36 40(425) 5.27 40 (169)
Tamana (KI) Reef island/no lagoon, village(s)
on west side;
Severe: “worst hit” “80 per cent of wells were badly affected”
“65 homes completely destroyed, 42 damaged,”
2.44 40(196) 5.55 40 (53)
Arorae (KI) Reef island/no lagoon, some
village(s) on west side;
Severe/moderate: “inundations”; unclear from reporting1,22.40 40(65) 5.45 41 (51)
Nanumea (TV) Atoll, but village on western
Severe/Moderate: 60% households affected, although
some discrepancy in reporting1,2,3,4Severe erosion on the
western side, beach receded by 6–8 meters5.
2.73 40(158) 6.75 20(17)
Niutao (TV) Reef island/no lagoon, village(s)
on west side;
Moderate: 32% households affected1,2,3,4erosion of about
10 m on the western side5.
2.55 13(29) 6.25 14 (15)
Nanmanga (TV) Reef island/no lagoon, village(s)
on west side;
Moderate: 15% households affected1,2,3,42.80 40(91) 6.96 20 (20)
Nui (TV) Atoll; village on western side Severe: 98 % households affected.1,2,3,4All of settlement
flooded with inundation coming from both ocean and lagoon
side. Aggregate deposition on the south eastern side of the
atoll. Shipping channel damaged5.
2.69 40(35) 6.72 14 (21)
Vaitupu (TV) Atoll (very small lagoon)
village(s) on west side;
Moderate: although some discrepancy in reporting1,2,3,4.
First row of houses near the ocean shoreline were destroyed.
Inundation extend about 50 m from ocean coastline. Erosion:
beach on western side receded by 15–20 metres5.
2.32 04(06) 5.96 10 (15)
Nukufetau (TV) Atoll; village on western side,
but behind another seaward
Minor: Damaged seawall, with boulders deposited in the
main shipping channel—Inundation on ocean and lagoon side
2.36 05(13) 6.04 10 (17)
Funafuti (TV) Atoll, settlements on eastern
None reported. 2.10 03(02) 5.58 08 (12)
Nukulaelae (TV) Atoll; village on western side Severe: 66% households affected1,2,3,42.34 05(05) 6.62 10 (16)
Niulakita (TV) Reef island/no lagoon, village
on west side
None reported 2.48 10(10) 7.17 20 (20)
The Island/Atoll column is sorted according to latitude from north to south; country is indicated in parentheses, KI for Kiribati and TV for Tuvalu. The maximum value for
total water level (TWL) and breaking wave height (Hb) during the passage of TC Pam is given in meters, the following columns indicate the empirical annual exceedance
probabilities (AEP) of the TWL and Hb at each location, with fitted AEPs indicated in parentheses (see section Materials and Methods” for how these quantities are
calculated). *Summarized Impacts are from the following sources: 1ICRC, 2018;2USAID, 2015;3OCHA, 2015;4Taupo and Noy, 2017;5inundation/erosion maps from
the Tuvalu Public Works Department (see Supplementary Material). Note that for Tuvalu, “households affected” percentages are “of households reported that surges
from the TC Pam entered their homes” from Taupo and Noy, 2017.
2. CSIRO monthly sea-level reconstruction (Church and
White, 2011); and
3. ORAS5 Ocean ReAnalysis System 5 (ORAS5) monthly sea
surface height5.
These sea level products were assessed for best representing
background non-tidal sea levels (ηSL) by comparing them to the
30-days median low-pass filtered tide gauge residual sea level at
Tarawa and Funafuti (see section “Astronomical Tides”). All sea
levels, (including tide gauge residuals) where adjusted such that
mean sea level = 0 between 1999 and 2009 prior to comparison.
Correlation coefficients (R) and RMSD calculated against the
low-pass filtered tide gauge residuals are shown in Table 2.
5 reanalysis
ORAS5 clearly outperforms the other two sea surface
height products in comparison to the (observed) Tarawa and
Funafuti tide gauge residuals. ORAS5 monthly values are linearly
interpolated to the (hourly) times of the wave hindcast (years
1979–2019) to represent non-tidal sea levels (ηSL); throughout
the remainder of this article, ηSL refers to that derived from
ORAS5 sea surface height values. It should be noted that thus
ηSL omits high-frequency (timescales <1 month) sea level
variability. However, the variance of the 30-days median low-pass
filtered (observed) residuals is 0.005 m at Tarawa and 0.010 m
at Funafuti. The high-frequency (remainder) residual variance
at both sites is 0.001 m, i.e., 5–10 times less than that of the
monthly variance; which is fairly typical of low-latitude Pacific
locations where sea level variability is dominated by seasonal
and interannual (e.g., ENSO) dynamics (Merrifield et al., 1999;
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FIGURE 2 | Region and locations indicated in Table 1. The maximum hindcast significant wave height (Hx) during TC Pam’s passage (March 6–17, 2015) is shown
in subplot (a), which is normalized by the local 1-in-20 year annual exceedance probability (AEP) shown in subplot (b). Note Hs, rather breaking wave height (Hb) or
total water level (TWL) as these latter quantities follow wave shoaling and breaking across shallow fringing reef, respectively, and the area of Figure 2 (apart from the
scattered islands and reefs) contains abyssal ocean depths.
TABLE 2 | Comparison of CFSR, CSIRO and ORAS5 sea surface height values
with monthly-median low-pass filtered non-tidal (residual) tide gauge observations.
Tarawa 0.70 0.045 m 0.89 0.027 m 0.93 0.024 m
Funafuti 0.76 0.055 m 0.85 0.041 m 0.94 0.030 m
Church et al., 2006;Zhang and Church, 2012). Thus, the lack of
inclusion of higher-frequency sea level fluctuations is assumed to
introduce a relatively small error into this analysis. Future studies
may evaluate introducing inverse barometer estimates, based on
global sea level products to further ameliorate this error.
Extreme Value Analysis and Total Water
Level (TWL)
To reduce complexity of the extreme value (statistical) analysis,
we focus on four of the previously defined variables: CgE(wave
energy flux), Dp(peak wave direction), ηtide, and ηSL as well as
event duration, which arises from the analysis. CgE, rather than
Hs, is chosen as a primary diagnostic variable for the analysis,
since (as a measure of power, rather than energy), it tends
to scale more linearly with nearshore processes such as wave
runup and setup.
Extreme events are identified for each site in Table 1 by
choosing a threshold and identifying exceedances of CgEabove it;
an event begins when the CgEexceeds the threshold, and a new
event is assumed only when CgEremains below the threshold for
more than 12 h (i.e., “declustering” events). The threshold for this
analysis is the 99.5% quantile.
Event duration is then calculated as the amount of time that
each declustered CgEevent remains above the threshold. The
Dp(peak wave direction) associated with the event is taken as
that occurring at the time of the peak (maximum) CgE. The ηSL
and ηtide associated with the event are the respective maxima of
each occurring during the storm duration, i.e., not necessarily
the water level at the time of maximum CgE. To further
aid in interpretation, empirical annual exceedance probability
(AEP), also commonly called return periods or return levels,
were empirically calculated from the declustered storm event
maximum CgEvalues at both locations.
In order to better understand how these variables (CgE,Dp,
ηSL, and ηtide ) combine to create extreme sea level events, we
follow a “total water level” (TWL) approach (Dodet et al., 2019),
and calculate a further variable (η2), defined as the 2% exceedance
water level at the shoreline above the “still” water level, as a
function of wave setup and variable swash (“runup”) due to both
wind-waves and infra-gravity/longwave dynamics associated with
shoaling and breaking of wave groups at the reef edge. The TWL
is simply calculated as:
TWL =ηSL +ηtide +η2(1)
To calculate η2, we follow the simpler of the two approaches
described by Merrifield et al. (2014), viz:
Where Hbis “breaking wave height,” calculated using a simplified
relation to account for wave refraction and shoaling to breaking
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FIGURE 3 | Time series plot of estimated breaking wave height (Hb, red), wave setup/runup (η2, black) and total water level (TWL, blue) for selected islands listed in
Table 1, from March 6—March 20, 2015. The 99.5th quantiles (percentiles) for the entire hindcast timeseries (1980–2019) are shown as dashed red and blue lines
for Hband TWL, respectively.
depth from the (deep water) Hsprovided by the wave hindcasts:
Hb= [H2
In Equation 2, the two empirical coefficients b1and b0are
assumed a value of 0.3 and –0.1, both near the mean value and
within the 95% confidence limits for the two study sites examined
in Merrifield et al. (2014). While these sites were both in the
Marshall Islands, it is arguable that the Tuvalu and Kiribati sites
in this study share similar atoll/narrow fringing reef morphology
to the Marshall Islands sites, although morphological effects are
discussed in more detail in the following sections. In Equation 3,
γis the ratio of breaking Hbto breaking water depth and Dnis
the local shore-normal angle.
Time series of total water level (TWL) and breaking wave height
(Hb) centered on a 2-week period of the peak of Hbduring the
time period of TC Pam’s passage, is presented in Figure 3. From
these plots, it is clear that most impacted locations experienced a
sustained period of 5 or more days of Hbabove the 99.5th quantile
threshold (based on the entire 40-year timeseries); TWL was
similarly elevated during successive high tides during the same
period. These highly elevated values (above the threshold) are
noticeably absent for Funafuti, where no impacts were reported
during the period; however, Nukufetau and Niulakita also did
not report impacts, but experienced highly elevated TWL and Hb,
similar to highly impacted neighboring Tuvaluan islands/atolls
(Table 1 and Figure 3). Potential reasons for this discrepancy are
discussed later.
As previously mentioned, the timing of high tides in relation
to the timing of maximum Hb(and CgE) appear to have
modulated the timing of impacts at the different islands.
However, astronomical tides were not abnormally high (mostly
roughly between spring and neap, Figure 3) during the event at
any of the impacted islands. This tidal modulation could explain
the lesser impacts reported in the southern islands of Tuvalu
(except Nukulaelae) where the peak of Hb(and CgE) was of
shorter duration and occurred predominantly during neap tides.
To better examine the relative contribution of CgE, tides
(ηtide), background sea level (ηSL ), other multivariate aspects
of this event and its relation to other TWL extreme events,
Figures 4,5present “scatterplot matrix” diagrams for Tamana
and Nui, which were selected as representative of impacted
locations within Kiribati and Tuvalu, respectively. These
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FIGURE 4 | Scatterplot matrix of Tamana (Kiribati) event-based data identified by declustering extreme sea level events with integrated wave energy flux (CgE) above
the 99.5 percentile threshold. The variables are: within-event maximum CgE, peak wave direction (Dp) during the hour of maximum CgE, within-event maximum
astronomic tide maximum (ηtide), background sea level (ηSL), and event duration (hours). W ithin each subplot the event closest to March 15, 2015 (i.e., associated
with TC Pam) is highlighted with a red circle.
scatterplot matrices allow the visualization of the co-occurrence
CgE,ηtide, and ηSL , as well as wave direction (Dp)and
event duration for all events above the (99.5th quantile)
TWL threshold within the hindcast record, particularly when
combined with annual exceedance probability (AEP) plot of all
locations (Figure 6).
At Tamana (Kiribati), maximum CgE(and thus estimated
wave setup/runup) was by far the highest in the entire hindcast
record, on the order of 40% higher than the next highest
event in terms of CgE(Figure 4). The event also had the
longest or second longest duration at the impacted islands. This
probably contributed to the noted erosion at Tarawa, in addition
to recorded severity of inundation at the impacted islands in
Kiribati. At Nui (Tuvalu), maximum CgEwas not the highest
in the entire hindcast record, it was third highest (Figure 5).
However, when combined with ηSL and ηtide it created the highest
TWL in the record (Figure 5). Event duration at Nui (Figure 5)
was also among the top three events, and likely contributed
to the extensive erosion recorded at many of the locations in
Tuvalu, as in Kiribati.
Overall, TWL and Hbwere the highest in the hindcast
period, with a calculated empirical AEP of 40 years (the highest
possible for the time duration of the hindcasts used in this
study) for all islands in Kiribati with recorded impacts (Table 1
and Figure 6). These results are more varied in Tuvalu: Hb
empirical AEPs generally ranged between 10 and 20 years, in the
northern islands, elevated ηtide and particularly ηSL increase TWL
empirical AEP values above those of Hb. In the southern islands,
the aforementioned later onset of elevated Hb, closer to neap
tides, lead to lower TWL empirical AEP values relative to that
of Hb. Never the less, all islands in Tuvalu which recorded severe
impacts experienced greater than a 10-year AEP TWL, excepting
Nukulaelae (Table 1 and Figure 6).
In addition to the TWL and Hbevent AEP levels of indicated
in Figure 6, wave direction (Dp) associated with each event is
also indicated by an arrow indicating the direction waves are
traveling at the time of peak Hb. This indicates that all large
events (AEP >10 years) are from the west in Kiribati, but not
in Tuvalu. At some (but not all) Tuvalu locations, TC Pam was
the largest westerly event. To further investigate the drivers the
storm wave events, all regional TCs with maximum sustained
winds greater than 48 knots (the lower threshold for category 2
TCs according to the southern hemisphere intensity scale) were
queried. Hband TWL events which occurred during or up to 5
days after TCs which occurred within ±5 days of empirical AEP
events are labeled as such in Figure 6 and these TCs tracks are
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Hoeke et al. Atoll Flooding by Distant Tropical Cyclones
FIGURE 5 | Scatterplot matrix of Nui (Tuvalu) event-based data identified by declustering extreme sea level events with wave energy flux (CgE), above the 99.5
percentile threshold. The variables are the same as in Figure 4. Within each subplot the event closest to March 15, 2015 (i.e., associated with TC Pam) is
highlighted with a red circle.
plotted in Figure 7. This illustrates that despite TC tracks never
crossing the Gilbert Islands and only very rarely crossing the
Tuvalu islands within the 40-year hindcast period, all Hbevents
(and most TWL events) greater than the 10-year AEP value at
all locations excepting Tarawa (the northernmost of the study
site), are associated with TCs. All of the associated TCs’ tracks lay
primarily well to the south and west (or north and west in one
case) of the Gilbert Islands and Tuvalu, and most, though not
all, attained at least a TC category 4 maximum sustained wind
intensity. Of note is TC Kina which occurred in early January,
1993. It is associated with the largest Hband TWL at many of
the southern Tuvalu islands in our analysis; severe flooding and
destruction of homes were reported on Nanumea, Nanumaga,
Niutao, Nui, and Vaitupu6.
Finally, it should be reiterated that at all locations, background
sea levels (ηSL) were the highest or among the highest on
record (+0.1 m) during the TC Pam events. This definitely
contributed to the overall severity of the event, particularly in
Tuvalu. What portion of that is attributable to sea level rise
(SLR) vs. seasonal and/or inter-annual (e.g., ENSO) sea level
variability is beyond the scope of this paper, since higher order
techniques (such as multivariate regression or empirical function
6 nina-jan- 1993-dha-undro-
situation-report- 1
analyses) are needed to separate these two components (Zhang
and Church, 2012;Albrecht et al., 2019). This will be addressed
in future studies.
In this study, we link a widespread series flooding, island
overwash and erosion events in 2015 in the atoll nations of Tuvalu
and Kiribati to locally extreme values of total water level (TWL),
composed of regional sea-level, astronomic tide and wind-wave
components. Similar to other studies (e.g., Hoeke et al., 2013;
Wadey et al., 2017;Ford et al., 2018;Wandres et al., 2020),
extreme wind-waves associated with a distant storm (swell) was
the proximate cause, or trigger, of the flooding and erosion
reported here. In contrast to (or perhaps complimenting) these
other studies, the swell triggering this event was generated not
by distant mid-latitude storms, but by a distant tropical cyclone
(TC Pam). Like these other studies, however, local TWL extremes
(and impacts) were highly modulated by the timing of swell
arrival relative to local tidal phase (Table 1 and Figure 3) and
by regional sea level anomalies. Other than the swell generated by
it, meteorological phenomena directly associated with TC Pam,
e.g., local wind setup and inverse barometer effect, appear to have
played little, if any, role.
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FIGURE 6 | Annual exceedance probability (AEP) plots for selected islands listed in Table 1. Black and blue dots indicate empirical AEPs for estimated breaking
wave height (Hb), wave runup (η2) and total water level (TWL), respectively; black and blue lines indicate a maximum likelihood estimate curve using a generalized
Pareto tail model for the same variables, respectively. 95% confidence limits are indicated with dotted lines. Peak wave direction (Dp) for each event is indicated by
red arrow on the HbAEPs, respectively. Where TWL or Hbempirical AEP events have occurred during or up to 5 days after the occurrence of a TC in the region (see
Figure 6) are highlighted with green circles and labeled with the TC name; the event associated with TC Pam is highlighted with a red circle. For events with RI >10
years not matched with a TC are labeled with their date.
The relative severity of the flooding and erosion impacts
amongst the islands and atolls included does not appear to
be simply linked to the local values of TWL or breaking
wave heights (Hb): e.g., maximum TWLs and Hbheights were
significantly smaller (approximately 1.5 m and 30 cm smaller,
respectively) in the southern Gilbert Islands (Kiribati) than in
Tuvalu (Table 1 and Figure 6), but reported impacts in the
affected Gilbert Islands appear to be nearly as bad, perhaps
worse in some cases. Local annual exceedance probabilities
(AEPs), however, align much better with the reported impacts;
e.g., significant wave heights (Hs), TWL and HbAEPs in
the southern Gilberts were almost universally higher (despite
scalar height values being lower) compared to those in Tuvalu
(Figures 2,6and Table 1). The exceptional duration of the
event (greater again in the Gilberts than in Tuvalu, Figures 4,5)
undoubtedly also played a role. In other words, when extreme
water levels associated with TC Pam are taken into local
historical context (through extreme value analysis), they align
better with the reported impacts. This makes sense from
a geomorphology perspective, since local storm ridges and
other reef island features are highly dependent on local wave
climate (e.g., Woodroffe, 2008;Vila-Concejo and Kench, 2017).
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Hoeke et al. Atoll Flooding by Distant Tropical Cyclones
FIGURE 7 | TC tracks in region of the Gilbert Islands (Kiribati) and Tuvalu
(1979–2019). TCs which were matched with TWL and/or Hbevents above the
99.5th quantile threshold (i.e., those plotted in Figures 4,5) are plotted in
color, indicating along-track maximum sustained winds; other cyclones are
plotted in gray.
It also makes sense from a coastal engineering perspective
(coastal defenses are typically designed using wave and/or
water level exceedance probabilities); and also with respect
to traditional island settlement patterns, although inconsistent
coastal management and contemporary changes in settlement
patterns may cloud this picture somewhat (Duvat, 2013;
Kench et al., 2018).
There is also further reason to use such an approach, i.e.,
to describe (or predict) an island flooding and/or erosion event
in the context of an extreme value analysis (EVA) of historical
baseline data. In many cases, the EVA can establish a proxy
or threshold for anticipated impacts, viz rather than simply
predicting flood levels of so-and-so many meters, they can
be predicted in terms of a known historical event or (e.g.)
the 1-in-50 year level. For example: we predict the flooding
impacts of TC Pam will be similar to/less than/greater than
(depending on island) TC Kina in 1993 (which did indeed
cause similar impacts in many of the southern Tuvalu islands,
see “Results” section). This offers an opportunity to timely,
easily understood information toward an increased efficiency
of post disaster emergency efforts, and also provide a pathway
for the development of a simple regional coastal inundation
forecasting (or warning) system for locations where heights and
datums of local coastal defenses may not be well know, and with
computational need tailored to the current local resources.
Both applications would require further investigation to
determine the reliability of existing global or regional wave
forecast products to adequately predict TC- or mid-latitude storm
generated waves and their subsequent propagation thousands of
kilometers away: the wave hindcast used (and verified for TC
Pam) in this study is approximately an order of magnitude higher
spatial resolution than wave products available from the major
numerical weather prediction centers for the region of this study.
Ongoing efforts by regional organizations to provide downscaling
of these global wave forecast products for Kiribati and Tuvalu
could significantly contribute to the generation of island
community scale actionable warning information; however, bias
correction between forecast products and hindcast/historical
products is necessary, and better overall skill assessment of both
are needed. To this end, an increase in in situ wave observations
and monitoring is required, perhaps to at least the level currently
provided for sea level monitoring via the tide gauge network.
Also required is improved consistency and detail in the reporting
of flooding and erosion impacts; despite gathering reports from
multiple sources in this study, local impacts at many islands
is unclear because of inconsistencies in reporting (Table 1).
Establishment and widespread adaptation of reef island flooding
reporting guidelines would be extremely helpful in this regard.
While this TWL extreme value analysis approach appears to
have been largely successful at describing the severity of this event
at island scale, a number of issues are evident. For example, severe
flooding was at Nukulaelae (Tuvalu), while TWL empirical AEP
level was amongst the lowest of the impacted sites; conversely
Niulakita (Tuvalu) which recorded no impacts had a (much
higher) event TWL AEP of 10 years. Such discrepancies may
of course be attributable to inaccuracies associated with the wave
and/or sea level hindcasts. More likely, however, it is due to the
highly simplistic approach taken in estimating wave runup and
setup (η2), which neglects any details of local reef morphology, as
well as (other) natural or man-made coastal defenses or shoreline
orientation (although the method is capable of the latter). It
thus neglects any approximations of local wave transformations
required to predict sub-island scale extreme water levels and
subsequent flooding. A number of researchers are developing
meta-modeling methods (e.g., Pearson et al., 2017;Beetham and
Kench, 2018;Rueda et al., 2019) capable of including local details
of reef morphology; a number of statistical/analytic methods
based purely on a limited number of nearshore observations
(Merrifield et al., 2014;Wandres et al., 2020) are also under
development. Both of these approaches show promise for much
more accurate estimation of local wave transformations over
reefs (as relevant to island flooding) compared to the extremely
simple approach taken in this study. They all also come
with a low enough computational cost to be widely applied
to the approach presented in this paper, e.g., by using the
kind of long-term hindcast data presented here as boundary
condition to establish much more locally relevant extreme value
analysis-thresholding. However, these meta-model and statistical
nearshore wave transformation models require detailed spatial
data on reef morphology (reef flat widths, shoreline orientations,
and slopes, etc.) and/or local in situ observations of water
levels and waves, which are currently available only in very
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Hoeke et al. Atoll Flooding by Distant Tropical Cyclones
few locations. Also, further research into how to incorporate
reef passes and lagoons into such hybrid (statistical-dynamical)
approaches is needed.
In summary, the type of TWL event-driven extreme value
analysis presented in this paper shows promise to be a
valuable tool toward improved understanding and prediction
of inundation and erosion events, at both weather forecasting
and for longer term future climate timescales. Improved
understanding through development of such tools is generally
seen as imperative to understand the continued habitability of
atoll islands in the face of sea level rise (Kench et al., 2018;
Storlazzi, 2018). Although it is beyond the scope of this paper to
quantitatively attribute what role sea level rise may have played
in the events reported here, it does implicate positive sea level
anomaly, and forms a basis to examine attribution of sea level
rise in reef island flooding events in future studies.
The datasets used in this study can be found in online
repositories. The names of the repository/repositories
and accession number(s) can be found in the article/
Supplementary Material.
RKH conceived the study and preformed most of the analysis
and writing. HD, JA, and MW contributed data and other
information, as well as assisting with writing and analysis.
All authors contributed to the article and approved the
submitted version.
RKH was supported in this research by the CSIRO Climate
Science Centre and by the Earth Systems and Climate Change
(ESCC) Hub, funded through the Australian Government’s
National Environmental Science Program.
Many thanks to following: Alec Stephenson (CSIRO) who
originated pieces of code used in the statistical analysis;
Claire Trenham (CSIRO) who assisted in data identification
and management; Kathy McInnes and Julian O’Grady
(CSIRO) who provided comments on early versions of this
manuscript; Xuebin Zhang (CSIRO) for recommending use of
ORAS5 sea surface heigh; and Tom Durrant (Oceanum) for
discussion of performance of WaveWatchIII under tropical
cyclone conditions.
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
The handling editor declared a past co-authorship with several of the
authors RKH, JA, and MW.
Copyright © 2021 Hoeke, Damlamian, Aucan and Wandres. This is an open-access
article distributed under the terms of the Creative Commons Attribution License
(CC BY). The use, distribution or reproduction in other forums is permitted, provided
the original author(s) and the copyright owner(s) are credited and that the original
publication in this journal is cited, in accordance with accepted academicpractice. No
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Frontiers in Marine Science | 12 January 2021 | Volume 7 | Article 539646
... For instance, Ranasinghe et al. [45] and Anderson et al. [46] observed shoreline rotation at embayed beaches, and Trombetta et al. [47] observed an alongshore sediment drift reversal with large consequences for coastal zone management and infrastructures. These remote swells can also drive dramatic overtopping [6] even at storm-free areas, such as in the Gulf of Guinea [1,48], facing the South Atlantic storm track, and in the Pacific due to distant tropical cyclones [17,[49][50][51]. ...
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... For some locations, TCs making landfall nearby are most important (e.g., Charchenga), while for other locations extreme tracks passing at larger distance are equally important (e.g., Batticaloa). For example, TC generated swell waves have caused flooding at small islands in the Pacific over 1000 km away from the TC-track (Hoeke et al., 2020). In these environments, even for the SLMPS configuration, it is recommended to use a large selection domain that covers a wide range of tracks. ...
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Tropical Cyclones (TCs) are singular storms causing intense wind, large waves, extreme water levels, and heavy rainfall. TCs prove every year to be one of the most destructive natural phenomena worldwide. The quantitative assessment of the hazards resulting from TCs (i.e., flooding and extreme winds) is challenging since satellite data are only available for recent decades, whereas older historical observations are incomplete and less accurate. In addition, long-term prediction through numerical weather forecasting is still limited. This often results in large uncertainties in the definition of TC hazards associated with events with longer return periods or in areas infrequently impacted by TCs. Even when this information is available, for example through statistical sampling of synthetic TC tracks, the numerical modelling of the associated hazards for all the different TC conditions can lead to computational costs which are often infeasible. Several methodologies that overcome the issues of accuracy and computational efficiency currently exist, but these are not generically applicable, and they tend to focus on specific areas only, for example where TCs typically make landfall. The main contribution of this paper is a novel methodology for the estimation and analysis of TC hydro-meteorological conditions and induced hazards. The method is generically applicable and maximizes accuracy while accounting for computational efficiency. Our approach identifies a smaller but representative set of TC tracks (RTCs) that preserves the information about extremes and the frequency of events of the larger population. The method is successfully applied and validated in a case study in the Bay of Bengal, using a set of synthetic TC tracks representing 1000 years of TC climate. For the best-performing configuration, the required number of scenarios and associated computational costs were reduced by 90% while maintaining accuracy in the simulated offshore storm surges, significant wave height, and windspeeds typically within 10% of the prediction based on the original full set of scenarios. This method is globally applicable and greatly improves the efficiency of TC-related hazard estimation, making it particularly valuable for areas with limited historical data.
... These islands host relatively large populations in some countries and territories of the Indian and Pacific Oceans, including the Maldives (530,953 inhabitants), Kiribati (117,646 inhabitants), the Federated States of Micronesia (113,815 inhabitants), the Marshall Islands (58,791 inhabitants), French Polynesia (15,544 inhabitants) and Tuvalu (11,646 inhabitants). Because of their physical configuration, atoll countries and territories are among the territories that are the most threatened by climate variability and climate change impacts, including especially the combination of gradual sea-level rise (SLR) and increased storm wave heights, and the degradation of coral reefs under both ocean warming and acidification and increased human disturbances (Bindoff et al., 2019;Cornwall et al., 2021;Duvat et al., 2021;Gattuso et al., 2015;Hoeke et al., 2021;Kane and Fletcher, 2020;Mentaschi et al., 2017;Oppenheimer et al., 2019;Perry et al., 2018;Vitousek et al., 2017). ...
This article proposes a comprehensive methodology considering geomorphic, ecological and human variables to assess atoll island physical robustness in the face of climate-ocean pressures. Six variables are considered, including island size, elevation, shape, structure, vegetation and the influence of human activities on island natural capacity to adjust to climate-ocean changes. Each of these variables is ranked on a five-level scale and the rankings are summed up to provide a final estimate in the form of an index of the relative physical robustness of each island. This methodology is applied to twelve islands of Rangiroa Atoll, French Polynesia, considered key to the maintenance of the habitability of the atoll by the local community and exhibiting contrasting physical configurations. The findings emphasize highly contrasting levels of island physical robustness, with indices ranging from 0.33 to 0.75. The main contributors to differences in island physical robustness are, in order of importance, island size and the influence of human activities on island capacity to adjust to climate-ocean changes; island elevation; island shape, structure and vegetation. Some peripheral rural islands that are targeted by the public authorities for future development have a much higher physical robustness than the settled islands. Based on these findings, we advocate, first, for the inclusion of ecological and human variables in assessments of atoll island robustness and modelling studies; and second, for within-atoll relocation of people and human assets to more robust island areas and islands.
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The SW Pacific is an expansive oceanic region with many of its islands difficult to access and relatively unexplored in a geoheritage context. Here we provide a potential framework for systematically assessing geoheritage values of the SW Pacific and creating a holistic and multi-value inventory. We outline the use of digital terrain models as landform classification tools, and other newly available spatial data. While New Zealand is scientifically and culturally recognised as part of the SW Pacific, due to its large landmass and significant scientific data already available, here we excluded it from our analysis. Rather, the aim of our ongoing research is to provide the first comprehensive overview of the geoheritage potential and unique characteristics of small island nations of the SW Pacific. Only those islands were included in this study where geoheritage research has been initiated and include islands reflecting a good spectrum of geodiversity such as Vanuatu (Ambrym, Ambae, Lopevi, Kuwae group, Tanna), Tonga (Tongatapu, Tofua), and Samoa (Savai’i and Upolu). In a geotectonic context the SW Pacific sits on the western extremity of the large Pacific plate, resulting in a broad array of geotectonic situations where convergent plate boundary processes govern the broad geological evolution and geomorphological processes seen on the islands of our study area. Volcanism associated with plate boundaries is one of the most characteristic and recognisable geological processes linked with the region in the public perception of the area. Volcanism is associated with arc-type geosystems forming complex and compound polygenetic volcanism which offers unique geological-geomorphological elements shaping geodiversity of the region. In addition, these volcanic processes have shaped human communities since the earliest days of their establishment. The interaction of Polynesian societies with volcanism provides a unique geocultural perspective based on accumulated oral traditions, forming the basis of cultural practices that remain part of everyday life into the 21st century.
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Atoll islands face increasing coastal risks (coastal erosion and marine flooding) due to climate change, especially sea-level rise. To face increasing coastal risks, various adaptation options are considered by atoll countries and territories, including in particular hard protection (preferred option to date), Nature-based Solutions (increasingly used) and island raising (considered a longer-term solution and a potential alternative to international migration, e.g. in the Maldives). Internal relocation within the same atoll island or atoll, which refers to long-term community movement from one threatened island area or island to a safer island area or island, has previously been disregarded by scholars as a potentially relevant climate adaptation strategy. However, in low-lying coastal areas, it offers real potential to address the dual context of increasing climate risks and the shrinking of the solution space. This paper assesses the potential of internal relocation for atolls by applying to Rangiroa Atoll, French Polynesia, Central Pacific, a two-fold assessment framework questioning its physical relevance (are some islands high enough to host settlements in the future?) and its societal feasibility (are the political-institutional and socio-economic conditions in place? Are people willing to relocate?). The findings show that internal relocation is both relevant and feasible on Rangiroa Atoll and should therefore serve as a pillar to develop robust in situ adaptation pathways in this atoll.
Tropical cyclones are associated with extreme winds, waves, and storm surge, being among most destructive natural phenomena. Developing capability for a rapid impact estimate is crucial for coastal applications and risk preparedness. When predicting waves characteristics associated to tropical cyclones, the traditional approach involves a two‐step procedure (1) a Holland‐type wind vortex model and (2) numerical simulations using a wave generation model, using buoy and satellite measurements for validation. In this work, we take advantage of the increasing amount of remote sensing observational data and propose a new empirical model to estimate the wind wave footprint of tropical cyclones. For this purpose, we construct a dataset with over a million satellite observations of waves triggered by tropical cyclones assuming a circular shape of the TC influence area and defining composites of significant wave height as a function of representative parameters of the track characteristics like the minimum pressure, its forward velocity, and its latitude. The validation against buoy data confirms the usefulness of the model for a first and rapid estimation of the wave footprint, although an underestimation of the most extreme events is observed due to the relatively small number of observations recorded. Due to its efficiency, the model can be applied for rapid estimations of wave footprints in operational systems, reconstruction of historical or synthetic events and risk assessments.
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In tropical cyclone (TC) regions, tide gauge or numerical hindcast records are usually of insufficient length to have sampled sufficient cyclones to enable robust estimates of the climate of TC-induced extreme water level events. Synthetically-generated TC populations provide a means to define a broader set of plausible TC events to better define the probabilities associated with extreme water level events. The challenge is to unify the estimates of extremes from synthetically-generated TC populations with the observed records, which include mainly non-TC extremes resulting from tides and more frequently occurring atmospheric-depression weather and climate events. We find that extreme water level measurements in multiple tide gauge records in TC regions, some which span more than 100 years, exhibit a behaviour consistent with the combining of two populations, TC and non-TC. We develop an equation to model the combination of two populations of extremes in a single continuous mixed climate (MC) extreme value distribution (EVD). We then run statistical simulations to show that long term records including both historical and synthetic events can be better explained using MC than heavy-tailed generalised EVDs. This has implications for estimating extreme water levels when combining synthetic cyclone extreme sea levels with hindcast water levels to provide actionable information for coastal protection.
Islands in estuaries, major river deltas, and open-coast environments reduce the severity of hazards, including erosion and flooding from wind-driven waves and extreme water levels, on the nearby habitats and shorelines. Islands may also provide critical ecosystem function for threatened and endangered species and migratory birds while providing access to recreational opportunities and navigation co-benefits. This chapter (Chapter 11) of the International Guidelines on Natural and Nature-Based Features (NNBF) for Flood Risk Management focusses on islands as NNBFs that support coastal resilience. Three types of islands are discussed—barrier islands, deltaic islands (including spits), and in-bay or in-lake islands. These islands may be new construction or, as in most cases, the restoration of island remnants. The degradation and loss of islands through combined processes such as sea-level rise, subsidence, and inadequate sediment input (e.g., upstream impoundments, navigation channels, evolving natural processes) are reducing the coastal resilience benefits of these features.
Future climatic change and sea-level rise threaten the persistence of reef islands potentially making them uninhabitable over the next century. Improved understanding of island morphodynamics at multiple time scales is required for resolving major drivers of shoreline change and predicting future island resilience. Utilising 45 individually mapped shorelines, the morphodynamic change of a shelf-edge reef island (Raine Island, Great Barrier Reef) has been quantified over a 57-year time scale in the context of environmental change and anthropogenic impacts. Results show that between 1963 and 2020, there has been an average net shoreline movement (NSM) of -4.71 m and an average rate of retreat of -0.11 m/y. However, only 23% of the shoreline has exhibited statistically significant erosion while the remaining 77% has remained stable. Importantly, data reveal a remarkable level of long-term shoreline stability despite large seasonal fluctuations in wind and wave climate, episodic storm events, and a regional sea level rise currently outpacing the global mean. Direct human impact, through modifications of the beach profile since 2014, have resulted in localised increases in rates of shoreline change but have only slightly decreased the proportion of stable shoreline (91% since modifications commenced versus 96% prior to modifications). Interestingly, there is a significant decreasing trend in island planform area over the 57-year period and especially over the last decade. However, the length of the observation interval between sampled shorelines has a strong effect on the change in planform area recorded over the interval, suggesting increased sampling frequency, rather than sea level rise, is a more likely cause of the decreasing trend. The findings of this study have direct implications for more accurate assessments of past, present and future shoreline change on reef islands. They also support the concept of 21st century island persistence in the face of climate change although the relative resilience of a given island to change (e.g., sea level, human modifications) will depend on a continuous adequate supply of sediment from organisms living on the surrounding reef, which may or may not be threatened in the future. - Temporary link for free access,3sl3pcyN
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Distant-source swells are known to regularly inundate low-lying Pacific Island communities. Here we examine extreme total water level (TWL) and inundation, driven by a distant-source swell on Fiji’s Coral Coast using observations and a phase-resolving wave model (XBeach). The objective of this study is to increase understanding of swell-driven hazards in fringing reef environments, to identify the contribution of wave setup and infragravity waves to extreme TWL, and to investigate coastal flooding during present and future sea levels. The maximum TWL near the shore was caused by compounding mechanisms, where tides, wave setup, infragravity waves, and waves in the sea swell frequencies contributed to the TWL. Waves and wave setup on the reef were modulated by offshore wave heights and tides, with increased setup during low tide and increased wave heights during high tide. Numerical simulations were able to reproduce the mechanisms contributing to the extreme TWL and allowed an estimation of the inundation extent. Simulations of the same swell under the RCP8.5 sea-level rise scenario suggested the area of inundation would increase by 97% by 2100. A comparison between the numerical model, a multiple linear regression model, and two commonly used parametric models revealed that both XBeach and the linear regression model were better suited to reproduce the nearshore wave setup and TWL than the empirical equations. The results highlight the need for customized, site-specific coastal hazard assessments and inundation forecast systems in the South Pacific.
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Surface gravity waves generated by winds are ubiquitous on our oceans and play a primordial role in the dynamics of the ocean–land–atmosphere interfaces. In particular, wind-generated waves cause fluctuations of the sea level at the coast over timescales from a few seconds (individual wave runup) to a few hours (wave-induced setup). These wave-induced processes are of major importance for coastal management as they add up to tides and atmospheric surges during storm events and enhance coastal flooding and erosion. Changes in the atmospheric circulation associated with natural climate cycles or caused by increasing greenhouse gas emissions affect the wave conditions worldwide, which may drive significant changes in the wave-induced coastal hydrodynamics. Since sea-level rise represents a major challenge for sustainable coastal management, particularly in low-lying coastal areas and/or along densely urbanized coastlines, understanding the contribution of wind-generated waves to the long-term budget of coastal sea-level changes is therefore of major importance. In this review, we describe the physical processes by which sea states may affect coastal sea level at several timescales, we present the methods currently used to estimate the wave contribution to coastal sea-level changes, we describe past and future wave climate variability, we discuss the contribution of wave to coastal sea-level changes, and we discuss the limitations and perspectives of this research field.
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Coastal areas epitomize the notion of ‘at-risk’ territory in the context of climate change and sea level rise (SLR). Knowledge of the water level changes at the coast resulting from the mean sea level variability, tide, atmospheric surge and wave setup is critical for coastal flooding assessment. This study investigates how coastal water level can be altered by interactions between SLR, tides, storm surges, waves and flooding. The main mechanisms of interaction are identified, mainly by analyzing the shallow water equations. Based on a literature review, the orders of magnitude of these interactions are estimated in different environments. The investigated interactions exhibit a strong spatiotemporal variability. Depending on the type of environments (e.g., morphology, hydrometeorological context), they can reach several tens of centimeters (positive or negative). As a consequence, probabilistic projections of future coastal water levels and flooding should identify whether interaction processes are of leading order, and, where appropriate, projections should account for these interactions through modeling or statistical methods.
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This dataset consists of 33 years (1985 to 2018), of global significant wave height and wind speed obtained from 13 altimeters, namely: GEOSAT, ERS-1, TOPEX, ERS-2, GFO, JASON-1, ENVISAT, JASON-2, CRYOSAT-2, HY-2A, SARAL, JASON-3 and SENTINEL-3A. The altimeter data have been calibrated and validated against National Oceanographic Data Center (NODC) buoy data. Differences between altimeter and buoy data as a function of time are investigated for long-term stability. A cross validation between altimeters is also carried out in order to check the stability and consistency of the calibrations developed. Quantile-quantile comparisons between altimeter and buoy data as well as between altimeters are undertaken to test consistency of probability distributions and extreme value performance. The data were binned into 1° by 1° bins globally, to provide convenient access for users to download only the regions of interest. All data are quality controlled. This globally calibrated and cross-validated dataset provides a single point of storage for all altimeter missions in a consistent format.
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Plain Language Summary Sea level change is important in the context of climate change. The global mean sea level has risen about 20 cm in the last century. However, sea level change is not globally uniform but varies regionally due to different factors. These factors are a mixture of natural and human‐induced contributions. Satellite data provide a global picture of sea level change. These observations started in the early 1990s. During the period 1993 to 2015 for which the satellite data are available, a spatial nonuniform trend has been observed in the South Pacific, with a maximum in the western part, east of New Zealand. The main objective of this work is to analyze whether this trend can be explained with natural climate variability or whether it is human influenced. Apart from the observations, model data that simulate the climate of the past century are used in order to answer that question. The results of this work indicate that the observed sea level pattern during 1993 to 2015 cannot be explained by natural variability alone.
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Wave-induced flooding is a major coastal hazard on tropical islands fronted by coral reefs. The variability of shape, size, and physical characteristics of the reefs across the globe make it difficult to obtain a parameterization of wave run-up, which is needed for risk assessments. Therefore, we developed the HyCReWW metamodel to predict wave run-up under a wide range of reef morphometric and offshore forcing characteristics. Due to the complexity and high dimensionality of the problem, we assumed an idealized one-dimensional reef profile, characterized by seven primary parameters. XBeach Non-Hydrostatic was chosen to create the synthetic dataset, and Radial Basis Functions implemented in MATLAB ® were chosen for interpolation. Results demonstrate the applicability of the metamodel to obtain fast and accurate results of wave run-up for a large range of intrinsic reef morphologic and extrinsic hydrodynamic forcing parameters, offering a useful tool for risk management and early warning systems.
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Wave-driven flooding is a serious hazard on coral reef-fringed coastlines that will be exacerbated by global sea-level rise. Despite the global awareness of atoll island vulnerability, little is known about the physical processes that control wave induced flooding on reef environments. To resolve the primary controls on wave-driven flooding at present and future sea levels, we present a globally applicable method for calculating wave overtopping thresholds on reef coastlines. A unique dataset of 60,000 fully nonlinear wave transformation simulations representing a wide range of wave energy, morphology and sea levels conditions was analysed to develop a tool for exploring the future trajectory of atoll island vulnerability to sea-level rise. The proposed reef-island overtopping threshold (RIOT) provides a widely applicable first-order assessment of reef-coast vulnerability to wave hazards with sea-level. Future overtopping thresholds identified for different atoll islands reveal marked spatial variability and highlight distinct morphological characteristics that enhance coastal resilience.
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Sea-level rise (SLR) is predicted to elevate water depths above coral reefs and to increase coastal wave exposure as ecological degradation limits vertical reef growth, but projections lack data on interactions between local rates of reef growth and sea level rise. Here we calculate the vertical growth potential of more than 200 tropical western Atlantic and Indian Ocean reefs, and compare these against recent and projected rates of SLR under different Representative Concentration Pathway (RCP) scenarios. Although many reefs retain accretion rates close to recent SLR trends, few will have the capacity to track SLR projections under RCP4.5 scenarios without sustained ecological recovery, and under RCP8.5 scenarios most reefs are predicted to experience mean water depth increases of more than 0.5 m by 2100. Coral cover strongly predicts reef capacity to track SLR, but threshold cover levels that will be necessary to prevent submergence are well above those observed on most reefs. Urgent action is thus needed to mitigate climate, sea-level and future ecological changes in order to limit the magnitude of future reef submergence.
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Understanding Flooding on Reef-lined Island Coasts Workshop; Honolulu, Hawaii, 5–7 February 2018
Sea-level rise and increased storminess are expected to destabilize low-lying reef islands formed on coral reef platforms, and increased flooding is expected to render them uninhabitable within the coming decades. Such projections are founded on the assumption that islands are geologically static landforms that will simply drown as sea-level rises. Here, we present evidence from physical model experiments of a reef island that demonstrates islands have the capability to morphodynamically respond to rising sea level through island accretion. Challenging outputs from existing models based on the assumption that islands are geomorphologically inert, results demonstrate that islands not only move laterally on reef platforms, but overwash processes provide a mechanism to build and maintain the freeboard of islands above sea level. Implications of island building are profound, as it will offset existing scenarios of dramatic increases in island flooding. Future predictive models must include the morphodynamic behavior of islands to better resolve flood impacts and future island vulnerability.