282746 impact2c quantifying projected impacts under...
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282746 IMPACT2C
Quantifying projected impacts under 2°C warming
Instrument Large-scale Integrating Project
Thematic Priority FP7-ENV.2011.1.1.6-1
D14.1 Estimates of impacts and costs of sea-level rise for the selected climate scenario
Due date of deliverable
March 2015
Actual submission date
July 2015
Start date of the project 01.10.2011
Duration 48 months
Organisation name of lead
contractor for this deliverable
SOTON
Revision: FInal
Project co-funded by the European Commission within the Seventh Framework Programme
Dissemination Level PU Public x PP Restricted to other programme participants (including the Commission Services) RE Restricted to a group specified by the consortium (including the Commission
CO Confidential, only for members of the consortium (including the Commission Services)
IMPACT2C: The impact of sea-level rise
on small island nations – A case study
of the Maldives
Deliverable for WP14 “Impact, vulnerability and
adaptation in most vulnerable regions: Small islands
(the Maldives)”
Sally Brown, Robert J Nicholls, Matthew Wadey
University of Southampton, UK
Ali Shareef, Zammath Khaleel
Ministry of Environment and Energy, Government of the Maldives, Maldives
Daniel Lincke, Jochen Hinkel
Global Climate Forum, Germany
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Summary
The Maldives is a low-lying island atoll nation in the Indian Ocean at risk from sea-level rise.
Sea-level rise poses one of the greatest threats to island nations as even if temperatures
stabilise under climate mitigation in a 2°C world, sea-level continues to rise for many
decades.
Hulhumalé is an artificial, almost flat island built 2m above mean sea-level and completed in
2002. Its purpose is to relieve population pressure from the nearby capital city and become a
new national hub of housing and development. A flood risk assessment was undertaken to
determine if and when sea-level rise could affect the island, assuming the island’s present
defences are not upgraded and the reef does not keep pace with sea-level rise. Using tide
gauges and hindcast data to envisage storm conditions, augmented by topographic and
bathymetric data, an overtopping model was run. Storm conditions were replicated from
hindcast conditions due to long period waves generated in the Southern Ocean that resulted
in flooding over several days in numerous natural islands in May 2007.
Under present conditions, flooding caused by an extreme sea level event, would not be
expected on Hulhumalé. With sea-level rise, nuisance flooding may be expected when
combined with long period swell wave conditions and 0.4m to 0.6m of mean sea-level rise. A
rise of 1m in mean sea-level may result in minor damage to buildings. These conditions
would not be anticipated under a 2°C world, but could occur under higher emissions
scenario that are projected towards to end of the 21st century. Given the level topography of
the island, flood extent could extend very rapidly if the islands is inundated over long time
periods. Flood risk is likely to increase into the 22nd century, where adaptation may have to
occur. Further research needs to consider model uncertainties and natural reef responses to
sea-level rise.
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Table of Contents Summary ............................................................................................................................... i
List of figures ........................................................................................................................ iii
List of tables .......................................................................................................................... v
Abbreviations ........................................................................................................................ v
Units and symbols ................................................................................................................. vi
1. Introduction .................................................................................................................... 1
2. Background .................................................................................................................... 2
2.1 Physical and human setting .................................................................................... 2
2.2 Oceanography and atmospheric processes ............................................................ 5
2.3 Human setting ......................................................................................................... 9
2.4 Case study of Hulhumalé ...................................................................................... 11
3. Data sources ................................................................................................................ 13
3.1 Land-based data ........................................................................................................ 14
3.2 Defences ................................................................................................................... 15
3.3 Marine-based data ..................................................................................................... 16
4. Methodology ................................................................................................................ 23
4.1 Step 1: Land and defence data .................................................................................. 24
4.2 Step 2: Marine data ................................................................................................... 25
4.3 Step 3: Overtopping ................................................................................................... 27
4.4 Step 4: Flood extent .................................................................................................. 30
4.5 Step 5: Impact assessment ....................................................................................... 33
5. Results .......................................................................................................................... 33
5.1 Step 1: Land-based data ........................................................................................... 33
5.2. Step 2: Marine data .................................................................................................. 34
5.3 Step 3: Overtopping ................................................................................................... 39
5.4 Step 4: Flood extent .................................................................................................. 40
5.5 Step 5: Impact assessment ....................................................................................... 43
6. Synthesis ........................................................................................................................ 44
6.1 Implications of findings .............................................................................................. 44
6.2 Adaptation ................................................................................................................. 46
6.3 Other climatic effects ................................................................................................. 47
7. Conclusions .................................................................................................................... 49
8. References ..................................................................................................................... 50
Acknowledgements ............................................................................................................. 60
Appendix ............................................................................................................................. 60
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List of figures
Figure 1 Location of the Maldives.......................................................................................... 3
Figure 2 The Maldivian capital, Malé. .................................................................................... 4
Figure 3 Projected population growth in the Maldives ........................................................... 5
Figure 4. Example of a 2 week segment (date shown on x-axis) of predicted tides at Malé-B
Hulule. .................................................................................................................................. 7
Figure 5. The global distribution of tropical cyclones ............................................................. 8
Figure 6. Malé, and the proposed reclamation area for Hulhumalé (Phase 1 and Phase 2). 11
Figure 7. Land use development on Hulhumalé Phase 1 .................................................... 12
Figure 8. Revetment used for beach protection (left). Sand accreted onto of the revetment
(right). ................................................................................................................................. 12
Figure 9. Hulhumalé Phase 2. Building the new island. ....................................................... 13
Figure 10. Data required for analysis of extreme events and sea-level rise on Hulhumalé. . 13
Figure 11. Position of 8,706 points of elevation recorded from dGPS data on the land,
together with photographs taken from around the island ..................................................... 14
Figure 12. Infrastructure and land use on Hulhumalé.. ........................................................ 15
Figure 13. Examples of defences and foreshore, Hulhumalé.. ............................................ 16
Figure 14. Projected patterns of global sea-level rise in 2100 with respect to 1985-2005,
based on a median level of ice melt. ................................................................................... 18
Figure 15. Sea-level rise plotted against an increase in global mean temperatures to
illustrate the commitment to sea-level rise using the HadGEM2-ES projections. ................. 19
Figure 16. Sea-level for the Maldives throughout the 21st century ...................................... 20
Figure 17. Location of the Malé-B Hulhule tide gauge. ........................................................ 21
Figure 18. Example of the WAVEWATCH III grid in the Indian Ocean. ............................... 22
Figure 19. Example of the profile and user interface of the SWAB model............................ 29
Figure 20. Example of boundary ‘inflow’ points at the edge of the LISFLOOD-FP model’s
DEM .................................................................................................................................... 31
Figure 21. Digital elevation model of the land height on Hulhumalé. ................................... 33
Figure 22. Representative profile through Hulhumalé from west to east based in topographic
and bathymetry measurements. .......................................................................................... 34
Figure 23. Time series marine data at Malé ........................................................................ 35
Figure 24. Sea-level trends, Malé........................................................................................ 35
Figure 25. The May 2007 event .......................................................................................... 36
Figure 26. Coastal sea-level effects caused by tides, storm surge and wave processes ..... 37
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Figure 27. Wave direction roses for 2005-2014 for significant wave height and wave period
........................................................................................................................................... 39
Figure 28. Locations of the bathymetry profiles used to run the SWAB model .................... 39
Figure 29. Overtopping Volumes plotted against sea-level rise above mean sea-level for
each profile analysed. ......................................................................................................... 40
Figure 30. Inundation results at Hulhumalé in terms of land area inundated ....................... 41
Figure 31. The incremental progression of inundation on Hulhumalé from all sides of the
island assuming no waves or set-up ................................................................................... 42
Figure 32. Example of peak water depth distribution on Hulhumalé .................................... 43
Figure 33. Increasing likelihood with overtopping on Hulhumalé with sea-level rise for the
ensemble range of RCP2.6, RCP4.5 and RCP8.5 in 2050 and 2100. ................................. 45
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List of tables
Table 1. Hard protective measures for different engineering problems. ............................... 10
Table 2. Soft protective measures for different engineering problems. ................................ 10
Table 3. Project methodology. ............................................................................................. 24
Table 4. Results from the extremes analysis relative to the return period. ........................... 38
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Abbreviations
AMM Annual Maxima Method
AMSL Above mean sea-level (1992-1993)
CMIP5 Coupled Model Intercomparison Project 5
DEM Digital elevation model
dGPS differential Geographic Positioning System
DIVA Dynamic Interactive Vulnerability Assessment
ENSO El Nino Southern Oscillation
GCM Global Circulation Model
GEV Generalised extreme value
GIS Geographical Information System
IPCC Intergovernmental Panel on Climate Change
MSL Mean sea-level
MSLR Mean sea-level rise
NOAA–NCEP National Oceanic and Atmospheric Administration–National Centers for
Environmental Prediction
RCP Representative Concentration Pathway
SLR Sea-level rise
SWAB Shallow-water and Boussinesq
UNCLOS United Nations Convention on the Law of the Sea
UNFCCC United Nations Framework Convention on Climate Change
WW3 WAVEWATCH III
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Units and symbols
din = depth of the wave input;
g = gravity;
h = water depth;
ht = the maximum water depth;
ht = water depth at time, t;
htflow = depth of water available to flow;
htij = depth of water in the cell (i,j);
i,j = the cell for which the calculation is being applied;
L = wavelength;
n = Manning’s friction coefficient;
Q = discharge;
qt = flow per unit width;
t = time;
T = wave period;
Tb = bed shear stress;
u = depth averaged velocity;
x = location in profile or grid domain;
y = location in profile or grid domain;
z = bed elevation;
Zb = bed level above datum level;
α = a dimensionless coefficient varies between 0.2 and 0.7;
ρ = the density of water;
∆t = model time step;
∆x = model grid resolution in the direction x.
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1. Introduction Small island nations are scattered around the world’s oceans. They are remote and
subsequently many have a limited resource base, and rely on the maritime environment for
their survival. Small islands have variable morphologies and origins, including volcanic, coral
and rocky outcrops. Corals make them attractive for tourism and fisheries due to their rich
ecosystems and warm environments. Corals atolls, such as the Maldives (the case study for
IMPACT2C) are low-lying and vulnerable to extremes and changes in sea-level, waves, and
environment (e.g. increases in sea surface temperature that can lead to coral bleaching). In
response to sea-level rise coral islands are well-known to accrete vertically (e.g. Marshall
and Jacobsen, 1985), but there are widespread concerns that this growth will not keep pace
with rapid rates of sea-level rise (SLR) (e.g. Woodroffe, 2008).
Sea-levels have been changing for thousands of years. The instrumental record of modern
sea-level change indicates an onset of global mean sea-level rise (MSLR) during the 19th
century (e.g. Church et al. 2013). From 1901 to 2010, global average sea-level, recorded
from tide gauges, rose 1.7±0.2 mm/yr (Church and White 2011; Church et al. 2013). From
1993 to 2010, global mean sea-levels from tide gauge and satellite altimetry data indicated a
higher rate of rise of 2.8±0.8mm/yr and 3.2±0.4 mm/yr respectively. The Intergovernmental
Panel on Climate Change (IPCC) Fifth Assessment Report (Church et al. 2013) concluded
that over the remainder of the 21st century it is “very likely” that mean sea-levels will increase
at a greater rate than already observed. Projected rates of sea-level rise from Church et al.
(2013) range from 0.26m for a climate mitigation scenario to 0.98m for a high emissions
scenario in 2100 with respect to 1986-2005. The rate of sea-level rise is complex to project
and will not occur uniformly world-wide, due to the influences of thermosteric (expansion in
ocean volume) and halosteric (volume increase resulting from a freshening of the water
column circulation, which has more of a regional than global influence) circulation (Pardaens
et al. 2011). Additionally, ice melt from ice caps, glaciers and ice sheets will also result in
non-uniform changes to sea-level due to gravitational changes and oceanic mixing.
The relationship between global mean temperature rise and subsequent sea-level rise is not
linear, as the oceans take decades to centuries to absorb changes in atmospheric
temperature down to their bottom layers (Meehl et al. 2012). This process is known as the
‘commitment to sea-level rise’ (Wigley and Raper, 1993; Schaffer et al. 2012). Additionally,
ice also takes time to melt in response to a warming temperature. Thus the rises in global
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mean surface temperatures recorded since pre-industrial times may only now be showing in
sea-level records.
The impacts of sea-level rise include saltwater contamination of groundwater, shoreline
erosion, changing sediment patterns and increased likelihood of flooding. In the worst-case
scenario, land may need to be abandoned and people forced to move. Due to commitment
to SLR it is important to plan ahead for environmental changes and options for adaptation.
This report assesses the potential impacts of SLR in the Maldives. Rather than considering
the whole nation1, a single island, Hulhumalé, located adjacent to the capital city, Malé, has
been studied in detail. Hulhumalé is a reclaimed island and is undergoing rapid development,
so will become an important national hub. Hence it is important to assess risk over the long-
term (i.e. end of the century).
The report is structured as follows: First the background to the physical, oceanographic and
human setting of the islands is reviewed, together with more detail of Hulhumalé (Section 2).
Data sources are described in Section 3, and the methodology in Section 4. Section 5
reports on the potential impacts of SLR. Implications and adaptation are then explored.
2. Background
2.1 Physical and human setting Reefs form when volcanoes become extinct and erode and subside. Coral is created from
the interaction between physical, chemical, biological and geological influences. In warm,
shallow water, corals form from calcium carbonate structures, together with coral skeletons
form reef environments. A fringing reef grows around the edge of the subsiding volcano,
creating a shallow lagoon environment inside. Over time, these lagoons deepen, creating a
circular atoll of numerous islands (e.g. Stoddart and Steers, 1977). Corals grow best in warm,
shallow, clear waters, but are sensitive to small changes in their environment, such as light,
exposure, sea surface temperature and pH. The reefs offer natural protection to the islands
because they allow waves to break seaward of the shoreline, therefore dissipating their
1 The IMPACT2C Description of Work originally planned to analyse the whole nation using the Dynamic Interactive Vulnerability Assessment (DIVA) modelling framework. Due to issues associated with data resolution, it was not possible to do this as the results would be subject to large errors. Instead, this report focuses on a more detailed study of a large and growing population centre, where if affected by sea-level rise, large impacts and damages could occur.
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energy and reducing erosion. Coral reefs, are some of the largest biological constructions in
the world (e.g. Viles and Spencer, 1995). The Maldives, is the seventh largest such system
(NAPA 2007). The Maldives are located in the Indian Ocean, comprising 1,192 small, low-
lying coral islands, divided into 26 atolls (Figure 1).
Figure 1 Location of the Maldives. The square in the main image indicates the location of the capital city, Malé. Locality of the tide gauges are indicated by a cross. Image sources: base map: Titus (1989), top right aerial: Google Earth.
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Although the land area in the Maldives is relatively small (298km2), the islands span an area
860km (north to south) and up to 100km (west to east), covering a total area of
approximately 107,500km2. 325,000 people are located on 198 inhabited ‘local’ islands.
Population distribution varies substantially, with small villages of less than 300, to the capital
city, Malé (Figure 2) which contains more than 100,000 people and has a population density
of over 17,000 people/km2.
Figure 2 The Maldivian capital, Malé. (http://commons.wikimedia.org/wiki/File:Malé_maldives.jpg)
From 1990 to 2030 the Maldives is expected to have the world’s twelfth largest increase in
urbanisation (133%, cf. UN-Habitat, 2013). The nation has an annual population growth rate
of 1.9% (World Bank, 2015), including expatriates, many who work in the construction and
health industries. By 2050 approximately 500,000 people are projected to live in the country
(Figure 3). Furthermore, approximately 100 islands are dedicated to tourist resorts, which
apart from fisheries, are a major source of income for the country.
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Figure 3 Projected population growth in the Maldives (Source: Department of National Planning, 2009)
2.2 Oceanography and atmospheric processes
2.2.1 Tides, surges and mean sea-level
This section describes the main oceanographic and meteorological processes relevant to
coastal flooding in the Maldives, including an overview from the existing literature plus new
data where required.
Coastal floods in the islands arise from a combination of sources:
• Tides: the predictable daily (or twice daily, dependent on location) rise and fall of still
water level mainly caused by the gravitational pull of the moon and sun;
• Surges: a meteorologically-induced temporary rise in water level, caused by wind and
low pressure;
• Gravity waves: generated by the wind that propagate towards the beach. These can be
either actively forced by the wind (wind waves) or they can have left their area of
generation (swell waves);
• Fluctuations in mean sea-level: the change to sea-level on scales longer than individual
storms or tides (although definition of mean sea level varies). This can be due to global
effects (e.g. warming of the oceans) or regional patterns in weather systems, currents or
inter-annual tidal fluctuations.
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Other processes also affect coastal water levels, including seiches, shelf-waves, infra-gravity
waves, but these are not focused on here. Tsunamis (caused by underwater landslides,
earthquakes, volcanic activity and asteroid impacts) can also cause major flood events,
including the 26th December 2004 Indian Ocean tsunami which severely affected the
Maldives.
Surges and tides move across the ocean predominantly as Kelvin waves or ‘long waves’
whose propagation is dissipated by bottom friction in shallow water on continental shelves.
Wavelengths are large (hundreds of kilometres) compared with the water depth (c.f. Bode
and Hardy, 1997). With the exception of bores and currents, the effects of tides and surges
generally appear at the coast as a slowly fluctuating still water level. Local enhancements in
surge height can occur due to resonance producing large tidal ranges (Pugh, 1987) and in
such situations the relative timing of a surge peak and tidal high water is critical to coastal
flooding. Tides are generated by gravitational forces acting over the whole water column in
the deep ocean. For example, in the UK there are semi-diurnal (twice daily) high-low waters,
whilst in some other locations (e.g. Gulf of Mexico) there is a diurnal (one high-low cycle per
day) tide (Pugh, 1987). The heights of tides vary over a monthly cycle: at new moon and full
moon the Moon is aligned with the Sun which causes the vertical difference between high
tide and low tide (tidal range) to be large (spring tides). The minimum tidal ranges are on
neap tides which occur when the moon is at right angles to the sun. There are also inter-
annual cycles such as the 18.6 year and 4.4 year cycles, which can influence flood risk
(Haigh et al, 2011a). In the Maldives, the tidal regime is semi-diurnal with diurnal inequalities
– there are two high tides and two low tides of different heights a day, with successive spring
tides being approximately 12 hours and 25 minutes apart. Mean spring tidal range is 0.96m,
0.76m, and 0.70m at Gan (located 200km south of Malé, see Figure 1), Malé, and
Hanimaadhoo (located 300km north of Malé, see Figure 1) respectively (Woodworth, 2005).
The time between periods of successive spring tides (i.e. times at which the risk of flooding
would be greater due to the more frequently raised tidal water level) is approximately 14 ¾
days (see Figure 4, and c.f. Owen et al. 2011).
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Figure 4. Example of a 2 week segment (date shown on x-axis) of predicted tides at Malé-B Hulule. Note the two different daily tides and vertical difference between neaps (far left and right) and springs (centre). Data extracted from UHSLC (2015).
Meteorological surges are well-known as the storm induced component of extreme sea-
levels and coastal flooding in many locations (e.g. Pugh, 1987). Surges are the sea-level
response to wind stress and the horizontal atmospheric pressure gradient at the sea surface.
Wind stress over shallow water is the most influential of surge formation processes, whilst in
deep water surge elevations are approximately hydrostatic, with a 1hPa decrease in
atmospheric pressure giving about 1cm increase in surge elevation (e.g. Flather, 2000).
Storm surges can be generated from mid-latitude depressions (extra-tropical cyclones), or
smaller and more intense tropical storms (hurricanes, cyclones and typhoons). Figure 5
depicts global regions where tropical storms occur (and their respective names in these
regions). With its equatorial and open ocean situation, the Maldives is not known to be
associated with large storm surges.
The tropical Pacific and Indian Ocean regions have considerable inter-annual and decadal
sea-level variability associated with the El Niño-Southern Oscillation (ENSO), the Asian–
Australian monsoon, and the North Pacific Decadal Oscillation (e.g. Church et al. 2006). In
short tide-gauge records, this variability may obscure any longer-term sea-level change, or
the variability may be misinterpreted as a regional change. For example, annual mean sea-
level at some locations can change by as much as 0.2m to 0.3m (when comparing year on
year) (Church et al. 2006).
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Figure 5. The global distribution of tropical cyclones (Source: Met Office, 2013)
2.2.2 Waves, run-up and overtopping
Wind and swell waves (higher frequency waves than tides and surges and whose primary
restoring force is gravity) are the dominant source of energy in the nearshore zone. The
magnitude of these waves is dependent upon wind strength, fetch length and the track of the
driving low-pressure pattern. Upon generation they propagate in a spread of directions with
wavelengths small or comparable to offshore water depth. The characteristics of gravity
waves are generally divided into three main zones: (a) Offshore: wave generation and the
interaction of waves with each other; (b) nearshore: where the seabed influences wave
propagation and includes shallow water effects such as shoaling, depth refraction,
interaction with currents and depth-induced wave breaking; (c) shoreline response:
responses and interactions of waves with beaches and structures.
In the Maldives, wave run-up is an important component of coastal flooding. Run-up is the
vertical displacement of water above the still water level, which results from the effects of
swash (residual wave energy at the shoreline) and asymmetrical wave motion, that overall
enables water to propagate onto ‘dry’ areas of a beach or defence. This effect can be
substantial under energetic conditions, and can cause flooding by overtopping or by causing
rapid foreshore erosion. In some cases, wave set-up occurs as waves cause water to pile up
in the surf zone (the region of breaking waves), causing water level increases at the coast.
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Run-up is sometimes viewed as the vertical displacement of water at the shoreline caused
by swash combined with set-up. Locally generated components containing higher
frequencies (and smaller periods) are known as ‘wind-sea’. In contrast, swell waves are
generated when non-linear interactions among wave components results in the transfer of
energy from high to lower frequencies. This allows waves to travel large distances and
impact coasts far from the site of generation, and with larger periods of between 10 and 30
seconds. It has been noted from observations and scale tests just how dramatically wave
period can exacerbate wave run-up, i.e. unusually longer periods can cause much greater
than expected run-up than extreme wave heights (e.g. Mason et al, 2007).
Coastal flooding due to overtopping is caused by the combined effects of extreme water
levels and waves allowing water to enter a floodplain at a faster rate than it can drain away.
This mode of failure can occur when the still water level is below the height of the defence
crest or floodplain. Overtopping rates generally increase with larger wavelengths, incidence
of attack, and with decreasing freeboard (the distance between the crest and still water level)
and the structure’s width although combinations of wave frequency, water depth and
structural configurations can be highly complex influences (e.g. EurOtop Manual. 2007). The
relationship between incident wave steepness and wave slope is important for sloping
defences (beaches, embankment sea walls), whereas wave size and water depth are more
significant influences upon overtopping behaviour for vertical structures.
2.3 Human setting
Most Maldivian islands are around 1m above mean sea-level (MSL) (the highest point being
2.4m above MSL) and subject to erosion, accretion and period flooding. Many populated
islands are protected by artificial means. Defences help maintain island shape and decrease
flooding. These management approaches can be divided into hard measures (based upon
engineering such as sea walls and breakwaters) and soft measures (e.g. re-nourishment of
beaches). Some of these techniques are listed in Table 1 and Table 2 (based on Shaig,
2011). Although these methods are used to reduce erosion and flooding, there is no
guarantee that they will protect against the most extreme water levels.
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Table 1. Hard protective measures for different engineering problems.
Erosion control and prevention
Infrastructure Land shortage
Foreshore sea walls and bulkheads Nearshore breakwaters Revetments Groynes Ad-hoc reclamation
Breakwater Quay wall Groynes Jetties Causeways Bridges
Land reclamation
Table 2. Soft protective measures for different engineering problems.
Short term measures Long term measures
Beach replenishment (particularly for tourist islands) Temporary sea walls and groynes Ad hoc sea walls and ridges
Land use controls and setbacks Coastal vegetation retention Ridge maintenance Artificial reefs Drainage adjustments Coastal structures on stilts Submerged sand-filled geotextile tubes
Land claim is common in many islands and regions worldwide, for example territorial
expansion has been an essential part of economic growth for Singapore (e.g. Economist,
2015). Although this practice has obvious benefits, reclaimed land can be more liable to
flooding – particularly if extreme events and SLR are not factored into the design
specifications. This is a problem for land reclaimed many decades ago when there was less
awareness and knowledge about MSLR (c.f. RIBA & ICE, 2009). Land claim is prevalent in
the Maldives, with reclamation projects typically building land 1.2m to 2m above MSL with
reclaimed land generally 0.2m to 0.3m higher than the existing land (Shifaz, pers comm.). In
Malé, competition for land to develop upon has been fierce, and has resulted in numerous
land reclaims. Land was also claimed in the south-west of Malé to house the local population
from a nearby island when the island was redeveloped for other purposes. Today, the city’s
land is reclaimed nearly to the edge of the reef flat. Since the reef attenuates wave energy,
any loss of reef would expose the shoreline to larger waves (and therefore extreme events).
Hence, the potential for future land claim in Malé is limited, further exacerbating land use
pressures in the city. Hence whilst the capital city is threatened by SLR, reclaiming a new
island from the sea provides opportunity to adapt the cityscape to a growing population.
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2.4 Case study of Hulhumalé
To overcome land-use and population pressure in Malé a new island was constructed in the
late 1990s. Named, Hulhumalé, the island is located 4km north-east of the capital, and is
connected by a causeway to the nation’s international airport on Hulhule Island (Figure 6).
Hulhumalé is expected become a new economic hub, and serve as a catalyst for broad-
based investments in the fields of commerce, education, health, recreation, tourism, fisheries
and a number of other related areas by both foreign and national parties.
Figure 6. Malé, and the proposed reclamation area for Hulhumalé (Phase 1 and Phase 2). Map outline source: Ministry of Environment and Energy, Maldives and Hulhumalé Development Cooperation.
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Phase I (Figure 7) resulted in 244 hectares of land reclaimed on the reef flat which began in
1997 and was completed in 2002, costing US$32 million and mainly funded by a loan from
the Bank of Ceylon (Haveeru Online, 2015). The island was reclaimed to a height of
approximately 2m above MSL.
Figure 7. Land use development on Hulhumalé Phase 1 (http://www.localyte.com/attraction/35701--Hulhumalé--Maldives--Maale--Maale and http://www.maldivesbest.com/Hulhumalé)
Sheet piling has been used to protect the shore on all sides of the island but on the eastern
side there is a sloping revetment and nourished beach used for recreational purposes
(Figure 8). The island’s population is growing, but at a slower rate than Malé. For example,
in 2006 there were 5,100 applications for just 100 apartments. Population census at 2006
was approximately 5,000 and within five years, increased to 20,000 people. Apartments are
still under construction.
Figure 8. Revetment used for beach protection (left). Sand accreted onto of the revetment (right).
Phase 2 (Figure 9) of reclamation started in 2014, where the island has been extended
northwards by constructing of a new island which will be joined by bridges to Phase 1. This
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adds another 240ha to Hulhumalé. The required 6 million m³ of sand was dredged from
depths of over 60m and pumped through a system of floating, sinker and shore lines. The
Hulhumalé Phase 2 project brings residential development, a business district, a light
industrial park, a marina and cruise terminal, a knowledge and technology park and a
heritage and tourism district. It will accommodate about 100,000 people (Dredging Today,
2015).
Figure 9. Hulhumalé Phase 2. Building the new island.
3. Data sources To determine the effects of extreme events and MSLR on Hulhumalé, data was required
from the marine and terrestrial environment. Additionally, data was required on the
hinterland between these environments containing the defences (Figure 10). Vertical datums
between data sources have been checked and corrected (see Appendix).
Figure 10. Data required for analysis of extreme events and sea-level rise on Hulhumalé.
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The land, defences and marine environment will be reviewed in turn.
3.1 Land-based data
Land based data includes land elevation, infrastructure and population. Land elevation was
undertaken via a differential Geographic Positioning System (dGPS) survey. A dGPS survey
measures a fixed point to within centimetre accuracy. This is particularly important for
vertical elevation in low-lying environments where such accuracy could potentially represent,
under certain climatic conditions, the difference between a flooded and non-flooded environ.
9,512 measurements were taken along the islands roads, defences and on the beach. Raw
elevation data, with respect to mean sea-level (1992-1993) is shown in Figure 11. The data
indicates that the island’s elevation is approximately 2m above MSL, with an area of lower
ground in the industrial area in the south-west of the island. The western and southern sides
are quay areas, with retaining structures. Two thirds along the western side of the island, is
where the ferry terminal is located.
Figure 11. Position of 8,706 points of elevation recorded from dGPS data on the land, together with photographs taken from around the island (position of photographs on figure is adjacent or nearby to where that area is represented in the figure). Data points provided by the Ministry of Environment and Energy, Maldives. Based map: Google maps.
14
Population and infrastructure data was provided by the Maldivian Ministry of Environment
and Energy, National Bureau of Statistics and the Hulhumalé Development Cooperation.
Land use is shown in Figure 12. Infrastructure includes utilities, schools, building plots and
parks. Additional data included storey height of buildings and plot costs. The figures illustrate
landscape characteristics around the island, including residential properties, guest houses
(along the eastern side, which was planned to be a residual area), mosque, Development
Cooperation, community area and park. The number of storeys in a building is limited due to
proximity of the airport. On the eastern side, buildings are typically three to four storeys high.
Figure 12. Infrastructure and land use on Hulhumalé. Map outline source: Ministry of Environment and Energy, Maldives.
3.2 Defences
Information about the type of defence was included in the topographic data provided by the
Ministry (as shown in Figure 11), noting each point as whether on ground level or quay wall.
15
Further verification has been undertaken with aerial photographs and site visits. From the
dGPS survey, the defences are approximately 1.9m above MSL on the eastern side, with
beaches decreasing to 0.2m above MSL (i.e. the limits of the survey). On the western side is
a quay wall, with the surface located between 1.8m to 1.9m above MSL. Along the northern
side, there are shallow banks over a retaining structure (which now has limited access due
to Phase 2 of the Hulhumlalé development). The southern side of the island has retaining
structures and looks out onto a small harbour. Figure 13 are photographs illustrating typical
the defences and foreshore at each side of the island.
Figure 13. Examples of defences and foreshore, Hulhumalé. Note that Phase II can be seen to the north of the North photograph. In the east, nourished beach material typically covers the defences.
3.3 Marine-based data
Marine based data includes sea-level rise scenarios, observed (tide gauge) sea-level data,
wave characteristics and bathymetry.
3.3.1 Sea-level rise scenarios In IMPACT2C, projections were used generated from Hinkel et al. (2014), with a range of
global mean projections from 0.25m to 1.23m in 2100. These scenarios comprise of a
16
contribution of thermal expansion (an increase in ocean volume due to warming), ice melt
from ice caps, glaciers, and ice melt from the large ice sheets of Greenland and Antarctica.
The thermal expansion (steric component) was generated by a General Circulation Model
(GCM). The contribution of the world’s glaciers and ice caps (excluding those in Antarctica
and Greenland) was taken from glaciers in the Randolph Glacier Inventory (Arendt et al.
2012). Past global surface mass balance was modelled by Marzeion et al. (2012), and then
validated and forced with monthly precipitation and temperature data from New et al. (2012).
Future changes were projected by comparing historic measurements in precipitation and
temperature data against future projections based on 15 GCMS from the Coupled Model
Intercomparison Project 5 (CMIP5). For ice melt from Greenland, surface mass balance was
taken from Fettweis et al. (2012). The model was then forced from output from three GCMs
from CMIP5. Ice melt from Antarctica is more challenging to project, so global mean
temperatures from 19 GCMs from CMIP5 were scaled to oceanic surface temperature
outside of the ice-shelf cavities. This temperature was translated into basal ice-shelf melting,
which then forced five different continental ice sheet models, each reflecting different ice-
melt processes (further described in Levermann et al. 2012). This did not take account of
changes in basal lubrication or surface mass balance, but these factors are thought to be
small in comparison. The estimates from Greenland and Antarctica were then regionalised
by undertaking gravitational-rotation fingerprinting derived from a model by Bamber and Riva
(2011). It was assumed that there was uniform mass loss over the ice sheets, and any ice
melt resulted in an instantaneous fingerprint due to local uplift due to gravitational changes.
Patterns are illustrated for four climate models, HadGEM2-ES, IPSL-SM5A-LR, MIROC-
ESM-CHEM and NorESM-1, in Figure 14. For each model, a different Representative
Concentration Pathway (RCP) is shown, from RCP2.6 representing a world of climate
mitigation to RCP8.5 representing a world of high emissions.
17
Figure 14. Projected patterns of global sea-level rise in 2100 with respect to 1985-2005, based on a median level of ice melt. Data extracted from Hinkel et al. (2014).
As noted in Section 1, there is a time lag between atmospheric (surface) warming and a rise
in global MSL. Taking HadGEM2-ES as an illustrative example, Figure 15 plots projected
global mean SLR against an increase in global mean temperature. The figure illustrates that
sea-level will continue to rise, even if temperatures stabilised. Therefore, sea-level rise at
around 2°C could be between 0.11m (in 2035 under RCP8.5 high) to 0.54m (in 2100 under
RCP2.6 low). Additionally, it must be noted that with higher emissions and a RCP8.5
pathway, sea-levels at 2.0°C will not necessarily be higher than on a lower emissions or
climate mitigation pathway, despite reaching 2.0°C in an earlier timeframe. However, with a
RCP8.5 pathway, there is, over the long-term a greater potential to rise more than a lower
emission pathways. Hence it is important to consider SLR over long time period, taking
account of a range of temperature rise.
18
Figure 15. Sea-level rise plotted against an increase in global mean temperatures to illustrate the commitment to sea-level rise using the HadGEM2-ES projections.
Regional projections for the Maldives were downscaled from the global projections as shown
in Figure 16. These illustrate that across the four models, by 2100, sea-levels could rise up
to 1.33m (as an upper end RCP8.5 scenario projected by MIROC-ESM-CHEM and
represented by a 5.7°C rise in global mean temperatures with respect to pre-industrial). This
is greater than the global mean. Conversely, at the lower end under climate mitigation
(RCP2.6 scenario), sea-level rise could be as low as 0.26m, from a 1.8°C rise in global
mean temperatures projected by IPSL-CM5A-LR. Therefore, there is potential range of rise
of 1.07m by 2100. These numbers represent a greater range of sea-level rise than previous
projections for the Maldives, including the Maldives National Communication to the United
Nations Framework Convention on Climate Change (UNFCCC) (as cited in RIMES, 2011
where projections were 0.48 and 0.94m of sea-level rise in 2100). This is partly due to the
advancement in science as the projections of RIMES (2011) projections are based on the
IPCC Third Assessment Report, whereas the projections from Hinkel et al. (2014) contain
the latest projections of ice melt. Ice melt has particularly seen an increase in its potential
contribution to sea-level rise in recent years (Church et al. 2013).
19
Figure 16. Sea-level for the Maldives throughout the 21st century (data extracted from Hinkel et al. 2014).
3.3.2 Observed sea-level data Still water levels (i.e. the water level that appears to the observer as still, and is primarily
controlled by tide, surges and MSL) were analysed by a time series of water level heights. A
sea-level time series from 1989 to 20142 was extracted from hourly tide gauge records at
Malé-B Hululé (Figure 17) available from the University of Hawaii Sea-level Center (UHSLC,
2015).
2 Research quality data was used from 1989 to 2012. This data has been checked for errors, such as data spikes or time shifts. To extent the data set, non-research quality data was used, following in-house checks.
20
Figure 17. Location of the Malé-B Hulhule tide gauge. Map outline source: Ministry of Environment and Energy, Maldives.
3.3.3 Wave characteristics
In many world regions, monitoring and analysis of wave characteristics (wave direction,
significant wave height, wave period) is undertaken using wave buoy data (e.g. Bradbury et
al. 2004). Recent and historical time series data is usually logged and stored in local or
national archives. However, in the Maldives there are no wave buoys recording long-term
data. To overcome this, hindcast data was used, generated from WAVEWATCH III (WW3),
an ocean surface wave model (e.g. Tolman, 2009). This was developed at the National
Oceanic and Atmospheric Administration–National Centers for Environmental Prediction
(NOAA–NCEP)3. Hindcasting is a method which projects past conditions based on datasets
where data is known, to localities where is not known. The WW3 hindcast covers the entire
globe, and was downloaded in the Indian and Southern Ocean at 3 hourly temporal
resolution. WW3 has been used in numerous research projects to study surface wave
dynamics (c.f. Tolman, 2009), and as an operational wave model for global and regional
3 http://polar.ncep.noaa.gov/waves/ensemble/download.shtml
21
wave forecasts. WW3 outputs wave height, period and direction from 2006 to 2014, so can
provide a time series of when extreme events have potentially occurred. Figure 18 illustrates
an output from WW3, with a plot of wave period during the 15th May 2007 in the Indian
Ocean.
Figure 18. Example of the WAVEWATCH III grid in the Indian Ocean. The example here is 12:00 15 May 2007 showing the long period swell waves propagating from the south-southwest (the parameter shown in this example is wave period). The location of the cell where data was extracted from 2006-2014 in the Maldives case study, is shown by a blue circle and cross. This is centred on Hulhumalé (73.5412°, 4.205°), using the closest grid data available from the 30 arc-minute global grid.
WW3 explicitly accounts for wind input, wave-wave interaction, and dissipation due to white-
capping and wave-bottom interaction. It solves the spectral action density balance equation
for directional wave number spectra, with the implicit assumption that the medium depth and
current as well as the wave field varies on time and space scales that are much larger than
the corresponding scales of a single wave. The multi-grid WW3 model is run as a mosaic of
grids that are 2-way nested. The spectral parameters are computed on each of these grids.
The physics included in the model does not cover conditions where waves are
severely depth-limited. Hence the model is typically applied on spatial scales greater than
22
1–10km and outside the surf-zone. The coarse resolution of the grid and 3-hourly outputs
are suitable to infer incoming waves from the offshore area, but not to resolve individual
wave heights in shallower, and nearer the shoreline. In this context, there are limitations in
the application of this data to infer inshore wave conditions, for calculations such as run-up
and overtopping at the Maldivian atoll shorelines.
3.3.4 Bathymetry
Bathymetry data was available up to 600m offshore along a 1,500m exposed eastern section
of the southeast corner of Hulhumalé, and provided by a Maldivian survey company. This
was in the format of 0.5m vertically spaced contour lines. A series of representative cross-
section were extracted as this area. The bathymetry data also provided the reef position.
The reef location was important to identify as it affects wave breaking, wave energy and
subsequently overtopping.
4. Methodology
To investigate the impact of extreme events and overtopping, the following steps were
undertaken (Table 3):
23
Table 3. Project methodology.
Step Description
1
Data pre-processing: Land and defence data
a) Use topographic data to generate a digital elevation model (DEM).
b) Generate a representative one-line topographic and bathymetry profile, which is typical of the eastern side of the island.
2
Data pre-processing: Marine data
a) Using data from the Malé-B tide gauge to undertake an extremes analysis to determine the magnitude of extreme events and their constituent parts. Included in this is extraction of the predicted astronomical tide.
b) Use the WAVEWATCH III hindcast data to determine significant wave heights, period, and direction of extreme events which are linked to flood events.
3
Data analysis: Overtopping
a) Determine overtopping rates using an overtopping model, using input values from Stages 1 and 2,using a range of sea-level rise scenarios (see Section 3.3.2).
4
Data analysis: Flood extent
a) Determine the flood extent on Hulhumalé with inputs from the overtopping assessment
5
Data analysis: Impact assessment
a) Overlay flood extent onto island infrastructure and assets.
b) Assess impacts through a Geographic Information Systems (GIS) analysis to determine when sea-level rise could become detrimental to critical infrastructure.
4.1 Step 1: Land and defence data
4.1.1 Digital elevation model
A digital elevation model (DEM) was created using the 8,706 land based topographic
measurements, by interpolating elevation between measurement points in GIS. DEMs can
be created using several statistical methods including spline, kriging, Natural Neighbour and
Inverse Distance Weighting (c.f. Childs, 2004). Due to the irregular spacing between points
and flat elevation Natural Neighbour interpolation was used. This method finds the closest
subset of input samples to a query point and applies weights to them based on proportionate
24
areas to interpolate a value (Childs, 2004). The data were resolved to 10m (i.e. each point
on a regular grid will lie 10m apart). A higher resolution would not be commensurate with the
larger gaps in the data coverage. Additionally, this allows for good computational run-time in
the flood modelling extent in step 4.
4.1.2 One dimensional profile
To undertake an overtopping analysis (Step 3), a representative one dimension profile was
taken and appended the corresponding bathymetric data (described in Section 3.3.4).
4.2 Step 2: Marine data
4.2.1 Tide gauge analysis
The observed sea-level record at the Malé-B tide gauge was separated into its main
components (Pugh, 2004):
• Mean sea-level: to isolate the contribution of sea-level changes caused by individual
storm events (rather than longer term seasonal or inter-annual changes in meteorology),
the MSL component was derived using a 30 day running mean of the observed sea-level
time-series.
• Astronomical tide; The tidal component was estimated using the T-TIDE harmonic
analysis software (Pawlowicz et al., 2002). Analyses were undertaken for each calendar
year with the standard set of 67 tidal constituents.
• Non-tidal residual (i.e. mainly surge): this was calculated by subtracting the MSL and
tidal component from the total measured sea-level.
Then, the observed high water was extracted (i.e. the peak observed sea-level of each tidal
cycle) and the simultaneous wave conditions (height, period and direction, at the time of high
water) from the WW3 hindcast.
To approximate how often extreme water levels or wave conditions may be expected various
forms of extreme value analysis can be applied to observations or model simulations (e.g.
Coles et al., 1999). For coastal flooding, joint probability analysis is applied to assess the
likelihood of two or more partially related variables (e.g. tide and surge, or sea-level and
waves) occurring simultaneously. The resultant return periods give an estimate of the
25
probability that a given water level and/or wave height will occur in any one year. Hence a 1
in 200 year sea-level has a 1 in 200 probability of occurring in any one year, although this
does not mean the given sea-level will only occur once in a time span of 200 years. It is
actually possible that extremes will occur more closely together in time than expected (e.g.
Wadey et al, 2014). Rising sea-levels can increase the probability of extreme water levels
(e.g. Haigh et al., 2011b; Wahl et al., 2011). Thus with SLR, extreme events are projected to
happen more frequently.
Extreme sea levels were calculated using a method based upon extreme value theory – the
Annual Maxima Method (AMM) (e.g. Gumbel, 1958). This and other methods are described
and compared by Haigh (2009). In the case of AMM, based on a result from probabilistic
extreme value theory, a series is generated (i.e. the maximum peak sea level from each year
of the record). A distribution function is approximated by a member of the generalized
extreme value (GEV) family of distribution functions, and the AMM for extreme sea level
takes the GEV to be the distribution function of the maximum sea level in each year of
observations. From the estimated distribution, it is possible to obtain the sea levels (i.e.
return levels) corresponding to chosen return periods.
The result of applying the AMM to the Malé-B tide gauge data set gave exceedance
probabilities for the corresponding AMM data, from which return periods and water levels
were obtained. As described later in Section 5.2.1, surges and tides are very small in the
Maldives – especially in comparison to local mean sea-level variability and wave run-up. It
should be noted that these factors, and the availability only of short data records could make
a detailed return period analysis of sea-level misleading. Furthermore, the WW3 data has its
limitations in providing accurate inshore wave heights (see Section 3.3.3). Therefore to
understand extreme events and to determine what the mechanisms of flooding are relevant
to Hulhumalé it is appropriate to develop a design storm, with oceanographic conditions
which can be used for the modelling procedures and impact assessment. The design storm
has caused actual flood events on other islands. Conditions associated with the storm will be
extrapolated into the future with MSLR (i.e. to determine potential impacts with higher sea
level combined with the same wave conditions, which may cause greater overtopping and
flooding).
4.2.2 Hindcast analysis
26
Overtopping which progresses to coastal flooding is most likely to occur in storm conditions.
Long period swell waves (from distant storms) causes greater run-up than locally generated
waves. Hence reference to storm conditions in the context of coastal flooding in the Maldives
refers to storms that have generated swell waves that travel a considerable distance (1000s
km) and can impact these shorelines.
To simulate an overtopping event, a storm, accompanied by an increase in still water levels
and associated run up has been modelled. Notable coastal flood events in the Maldives
occurred in April 1812, December 1819, December 1821, January 1955, July 1966, April
1987, May 1991, month 2005 and May 2007 (Jameel, 2007; Shaig 2009). Other significant
events which resulted in widespread flooding in the atolls occurred in 1987 and 1988
(Harangozo, 1992). Local knowledge and records indicate that since Hulhumalé was
constructed it has not seen a large enough storm event to cause any overtopping and
flooding, despite flood events occurring in the capital and on other islands. The most recent
flood event was 15th to 19th May 2007 where 90 inhabited islands were flooded (including the
atolls of Gaafu, Dhaalu, Thaa and Laamu) by long period swell waves originating from the
Southern Ocean. 579 housing units were damaged, 649 people were evacuated, and 33
islands were affected by salt water intrusion causing damage to crops and vegetation
(Reliefweb, 2007; OCHA, 2007).
The May 2007 is the only event that falls within the timespan of the WW3 hindcast and tide
gauge data sets. This event has been selected and will be treated as a design storm. Using
this one event, the aim is to understand the specific extreme conditions that cause coastal
flooding (i.e. magnitude and duration of surge, tides and swell waves) and replicate these
conditions under different magnitudes of projected MSLR to determine if and when
overtopping could occur.
Significant wave height, wave period and direction was extracted from WW3 during the May
2007 event. The extreme conditions were analysed and input into an overtopping model.
4.3 Step 3: Overtopping
The two main methods to analyse overtopping of coastal defences are: (1) empirical
formulae which provide simple approximations of overtopping rates, or (2) numerical models
which can be configured for a variety of structures and generate realistic simulations of
overtopping processes (e.g. shoaling, breaking on or over the structure, and overturning of
27
waves). To assess wave overtopping for Hulhumalé, a one dimensional representative
topographic and bathymetric profile was constructed (described in step 1). This was then
input to a numerical overtopping model, the ‘Shallow-water and Boussinesq’ model (SWAB)
(McCabe et al. 2013). SWAB is a semi-implicit model that solves the continuity equation
(Equation 1) and momentum equation (Equation 2) in a horizontal direction. It was
developed to account for random wave breaking, impact, and overtopping of steep and
recurved sea walls.
Equation 1
Equation 2
Where:
g = gravity;
h = water depth;
ρ = the density of water.
t = time;
Tb = bed shear stress;
u = depth averaged velocity;
x = location in profile or grid domain;
Zb = bed level above datum level;
A typical profile of Hulhumalé in SWAB in shown in Figure 19. This illustrates the land
(known as the bed), defences (the sea wall, as indicated by the black vertical line) and the
still water level. At 450m horizontally the bed has been flattened for modelling purposes only,
and does not illustrate the true bathymetry.
∂ h∂ t
+ ∂ (hu )∂ x
= 0
∂ (h u )∂ t + ∂ ( hu2 )
∂ x = − g h ∂ h∂ x − g h
∂ zb
∂ x −T b
ρ
28
Figure 19. Example of the profile and user interface of the SWAB model. The bed is flattened at 450m for modelling purposes only. The elevation on the y-axis represents the datum used throughout this document, above mean sea-level (1992-1993).
Tests were carried out to examine the sensitivity of SWAB to the input model parameters,
which included water height, bathymetry, landward extent of island, wave length, wave
height and roughness to determine which parameters were the most sensitive to overtopping.
For example, the length of coast landward of the defences was found to be particularly
sensitive: if this was too small then overtopping would simply fill up the land area too quickly,
and would not be representative of actual conditions.
The most important parameter affecting overtopping was the wave input location (i.e. the
cross-shore location at which waves are prescribed in the model domain, shown by the
green dot in Figure 19). McCabe et al. (2013) suggests that at least one wavelength offshore
from the wave input location is allowed to accommodate the ‘sponge layer’ which allows for
the correct wave reflection and propagation offshore. The tests showed if the input location
was placed in deep water the waves that broke on the shore were very small. The opposite
was true the further onshore the input location was placed, the waves became larger and
more erratic. The wavelength (L) was calculated using Equation 3 using linear wave theory
then, allowing for a small degree of error, this value was used as the wave input location, the
onshore flat bathymetry section was determined by a quarter of the wavelength. For
example if the wavelength was 200m, the offshore flat area would be 200m and the onshore
flat area would be 50m. To ensure a dampening effect did not occur and that the model
equations were valid the ratio of the depth of the wave input (din) and wavelength (L) could
not exceed 0.5 (Equations 4 and 5).
29
Equation 3
Equation 4
Equation 5
Where:
din = depth of the wave input;
g = gravity.
L = wavelength;
T = wave period;
SWAB was run using input parameters from the design storm conditions extracted from
WW3. Still water level was varied according to today’s conditions plus set magnitudes of
sea-level rise (e.g. 0.1m, 0.2m, in increments of 0.1m up to 1.8m of sea-level rise). These
generated over overtopping volume of water per metre run of sea-wall for the duration of the
storm. A look-up figure was then generated to convert sea-level to overtopping volume. This
data was used as boundary conditions to determine the flood distribution and depth on the
island.
4.4 Step 4: Flood extent
Once an overtopping volume per metre run was ascertained from the design storm event,
the distribution of flood water based on the DEM derived in Step 1 was determined, via two-
dimensional hydraulic modelling. The software use was the inertial version of the raster-
based inundation model LISFLOOD-FP (Bates et al, 2010). Input locations (known as inflow
points) were added to the DEM where overtopping could occur. These were spaced every
10m along the eastern boundary of the island, as shown in Figure 20. Water level time
series at inflow locations were based upon the tide-surge water level time-series of the May
2007 storm.
L=(g T 2
2π )× tanh (2 π d i n
L )O n s h or e= L
4
d i n
L> 0.5
30
Figure 20. Example of boundary ‘inflow’ points at the edge of the LISFLOOD-FP model’s DEM
The distribution of floodwater was then calculated. In LISFLOOD-FP, input water levels were
applied at the boundary of the model, and spread to adjacent cells by solving of a continuity
and momentum equation in each direction (2D) (Equations 6 and 7 – not the same as
Equation 1). Flow between neighbouring floodplain cells is calculated as in Equation 7:
3/102 )/(1(
)(
tflow
ttflow
ttflow
t
tt
hqtnghx
zhtghqq
∆+∆+∆
∆−=∆+ Equation 6
Where:
g = gravity;
ht = water depth at time, t;
htflow = depth of water available to flow;
n = Manning’s friction coefficient;
qt = flow per unit width;
t = time;
x = location in profile or grid domain;
z = bed elevation;
∆t = model time step;
∆x = model grid resolution in the direction x.
31
Water depths were updated at each timestep using Equation 7:
211
, xQQQQ
thhtyij
tyij
txij
tjxit
ijtt
ji ∆
−+−∆+= −−∆+ Equation 7
Where:
htij = depth of water in the cell (i,j);
i,j = the cell for which the calculation is being applied.
Qx = the volumetric flow rates between floodplain cells in the direction of x;
Qy = the volumetric flow rates between floodplain cells in the direction of y.
x = location in profile or grid domain;
y = location in profile or grid domain;
∆t = model time step;
∆x = model grid resolution in the direction x;
The stability criterion for this numerical model is given by the Courant–Freidrichs–Levy
condition for shallow water flows such that the stable model time step, ∆t, is a function of the
grid resolution and the maximum water depth within the domain:
tghxt ∆
=∆ αmax Equation 9
Where:
g = gravity;
ht = the maximum water depth;
x = location in profile or grid domain;
α = a dimensionless coefficient varies between 0.2 and 0.7;
∆t = model time step;
∆x = model grid resolution in the direction x.
LISFLOOD-FP outputs grids of water depth at user defined periods in the simulation. The
outputs used to assess flooding for any given scenario was a grid of the maximum water
depth predicted by the model for each pixel over the course of the simulation.
32
4.5 Step 5: Impact assessment Land area affected for each inundation scenario was quantified, as well as average flood
water depth. These outputs were intersected with the island infrastructure (shown in Figure
12) and population using a GIS analysis to determine what assets would be at greatest risk
of inundation.
5. Results
5.1 Step 1: Land-based data
Using Natural Neighbour to interpolate the topographic measurements, the DEM generated
is shown in Figure 21. The figure illustrates that the island is extremely flat, with most of the
land surface in the range 1.8m to 2.0m above MSL, with slightly lower elevation in the south-
west corner where the fishery and industrial units are based.
Figure 21. Digital elevation model of the land height on Hulhumalé, generated from the ground survey points which were interpolated to a 10m resolution grid in ArcGIS.
33
A representative cross section (west to east) of Hulhumalé is shown in Figure 22, based on
topographic and bathymetry data. Typical of reef environments, water depths are shallow
from the foreshore to the reef edge (approximately 160m from the sea-wall on the eastern
side), an area known as the surface zone. Water depths then decrease rapidly away from
the reef or outer lagoon.
Figure 22. Representative profile through Hulhumalé from west to east based in topographic and bathymetry measurements.
5.2. Step 2: Marine data
5.2.1 Tide gauge analysis
The time series of hourly sea-levels at the Malé-B gauge are shown in Figure 23a.
34
Figure 23. Time series marine data at Malé, (a) water level from the Malé-B tide gauge; (b) significant wave height (Hs), (c) wave period (Tp) and (d) direction. The latter three parameters were extracted from WW3.
The figure illustrates changes in the observed sea-level rise, tide and surges. Superimposed
is the 30 day running mean sea-level and the linear trend of sea-level rise over the
instrumental record. Figure 24 illustrates there is an increase in sea-level of 0.11m over the
25-year record. Taking the mean trend, this equates to a SLR of 4.4±0.2mm/yr, and annual
extreme trend level at a slightly higher rate of 4.9±0.2mm/yr.
Figure 24. Sea-level trends, Malé, showing the change in annual extreme sea-levels and the annual mean level, extracted from the tide gauge records.
35
Surge heights of 0.1m to 0.2m surges could be considered extreme conditions in the
Maldives, as larger surges are not found in the tidal record. This is in contrast to extreme
storm surge regions such as the Bay of Bengal or Gulf of Mexico, where surge heights can
exceed 10m, or the North Sea coasts of Europe where surges can exceed 3m (c.f. Pugh,
1987). The figure also illustrates that MSL can have a year-on-year variability of 0.1m to
0.2m. The analysis shows how the higher of the days two tidal high waters is approximately
0.25m larger than the smaller tidal high water of the day on springs, and approximately 0.1m
larger on neaps (hence the highest spring tide high waters are approximately 0.35m larger
than the lowest neap tide high waters). Whilst tides are relatively small in the Maldives, this
daily and monthly water level variation allows windows of opportunity for increased coastal
flood risk (i.e. by swell wave run-up). There is also considerable inter-annual and decadal
sea-level variability is associated with ENSO.
The vertical and temporal scale of these sea-level components iterates that wave run-up is
important, and the duration of extreme water levels events (i.e. if during a period of spring
tides and elevated mean sea-level) is important for identifying possible flood events.
Abdalazeez (2012) has approximated that wave run-up on Maldivian beaches is frequently
0.5m or less, although in extreme cases it could be greater than 1m. Figure 25 illustrates a
time series of sea-level rise, significant wave height, wave period and direction in May 2007.
The dashed box represents the time period where flooding was known to occur, and thus
represents the design storm conditions.
Figure 25. The May 2007 event (the approx. interval of flooding shown by the dashed box), the top x-axis is the day of the month; (a) Observed sea level (blue), tide (grey), surge (red) and MSL (yellow); (b) wave height; (c) wave period, and (d) wave direction.
36
Given the highest still water level that is known to have occurred during the May 2007 event
(0.58m above mean sea-level 1992-1993 (AMSL)) (Figure 25a)), the waves are likely to
have generated approximately 1m of run-up to overtop the beaches and defences.
Harangozo (1992) describes the flood event in 1987 where 0.15m set-up (the change in
coastal still water level induced by waves breaking at the coast) was observed at the harbour
at tide gauge, caused by waves that were breaking on the open coast (where set-up nearer
the site of the largest breaking waves may have been twice this value). Figure 26 shows the
vertical displacement of water by different components of sea-level and waves, which could
affect the south-eastern side of Hulhumalé during extreme events. Hence wave run-up, as
well as set-up (from waves breaking against the shoreline and raising the still water level
around the coast) are important mechanisms in coastal flooding. Hindcast data reveal these
conditions originated from long period swell in the Southern Ocean.
Figure 26. Coastal sea-level effects caused by tides, storm surge and wave processes
The AMM analysis is provided to indicate an extrapolation of extremes beyond the observed
data set (Table 4). This approximates that a 1 in 100 year extreme water level (99%
probability of non-occurrence) is 0.72m AMSL. The largest observed sea-level in the tide
gauge record was during 6th May 2012 (0.70m AMSL), and not known to have caused a
flood event. This was because the wave height and period at the time of high tide were
relatively low at 0.68m and 8 seconds respectively (whereas these were 1.43m and 19.32s
on 17th May 2007). The AMM analysis highlights the small vertical difference between
smaller and larger return period sea levels is a consequence of the small tides and surges. It
is possible that this may be misleading if the 25 year tide gauge time-series has not yet been
coincident with a larger surge event during this period.
37
Table 4. Results from the extremes analysis relative to the return period.
Return Period (Years) Observed (m AMSL)
Distribution 10 50 100 200
Estimated Annual Maxima (m AMSL) Largest sea-level:
0.70 (06/05/2012 08:00)
Largest sea-level during May 2007 storm:
0.58 (16/05/2007 08:00)
Gumbel 0.65 0.69 0.72 0.75
95% Upper 0.68 0.77 0.79 0.82
95% Lower 0.61 0.64 0.65 0.67
5.2.2 Hindcast analysis Data from the full hindcasting record show significant wave heights (Figure 25b) in excess of
2m and wave periods of almost 20 seconds (Figure 23c). However, some caution is required
in using this data, as if it was available, higher resolution bathymetry would mean that
shallow features would be better resolved by a wave model. This is important because
waves change as they propagate further inshore. The model’s time-stepping and grid
resolution are considered as limiting factors for WW3’s ability to accurately model waves
inshore (Met Office, 2011).
Peak wave conditions during May 15th – May 17th 2007 are shown in Figure 23 b,c,d.
Significant wave heights of more than 1.6m (Figure 25b) and period of almost 20s (Figure
25c) were reported during this event. The peak conditions (water level: 0.58m AMSL, Hs of
1.67m, period of 19.68s). The wave conditions and still water levels were combined with
different scenarios of SLR. Wave roses (Figure 27) illustrate that these longest period waves
are quite rare and come in from this direction. Persistent long period (16s-20s) waves from
216-226° which caused the overtopping and flooding on other islands, and make this event
stand out from others with similar sea level heights but which did not cause flooding.
38
Figure 27. Wave direction roses for 2005-2014 for significant wave height and wave period, for the WAVEWATCH III grid cell in which Hulhumalé lies (73.5412°, 4.205°).
5.3 Step 3: Overtopping Using the SWAB overtopping model, three representative topographic and bathymetric
profiles were tested which were located in the north-east corner of the island (Figure 28).
Three profiles were selected to test model sensitivity.
Figure 28. Locations of the bathymetry profiles used to run the SWAB model
39
SWAB was run with the conditions of the design storm. Assuming present day MSL, the
resultant overtopping volumes indicate too small an amount to cause flooding, which is
consistent with the knowledge that this event did not flood Hulhumalé. The model was then
rerun accounting for conditions of sea-level rise (in 0.1m increments). Estimates of
overtopping volumes are shown in Figure 29 for each profile. The x-axis indicates the
combined magnitude of tides, surges and sea-level rise associated with each overtopping
volume. As shown, the different configuration of each bathymetric profile distinctly influences
when overtopping occurs. Overtopping exhibits exponential growth as sea-level rises. The
sensitivity of friction input to the model was tested, but found to be less important than the
bathymetry. The results indicate that wave overtopping could potentially occur at
approximately 0.4-0.6m of sea level rise, where the projected overtopping rapidly escalates
(i.e. 1.2 m AMSL)
Figure 29. Overtopping Volumes plotted against sea-level rise above mean sea-level for each profile analysed.
5.4 Step 4: Flood extent
Overtopping volumes can be distributed in the flood plain using the selected flood simulation
model, LISFLOOD-FP. Figure 30 shows area flooded (in km2) against the water level AMSL
in increments of 0.1m. No flooding is reported under 0.6m AMSL (i.e. present day
conditions). Flooding suddenly increases at 1.0m AMSL (equivalent to 0.4m-0.6m of SLR
added to the peak sea level observed during the May 2007 design storm event) as the flood
40
plain is extremely flat, so effectively this height acts as a physical tipping point. The
simulations indicate that by 1.6m AMSL (equating to 1m of MSLR) all of the land surface of
Hulhumalé would be inundated.
Figure 30. Inundation results at Hulhumalé in terms of land area inundated (to any water depth), with both the dynamic water level and LISFLOOD-FP (the red line).
This physical tipping point is also illustrated in Figure 31 which shows how inundation would
progress based upon still water level alone (no waves). Zero is where the still water level is
level with the lowest point on the outer part of the island. The inundation is assumed to come
from all sides of the island. This shows the small range of land surface elevation, and the
lowest lying parts of the island.
41
Figure 31. The incremental progression of inundation on Hulhumalé from all sides of the island assuming no waves or set-up. Here, zero is the first inundation at fringes of island. A physical tipping point occurs between 0.20m and 0.25m of water depth, where the island becomes rapidly flooded.
42
5.5 Step 5: Impact assessment . Figure 32 illustrates flood depths and effected infrastructure from a scenario of the May 2007
storm combined with 0.8m MSLR. Within the model, overtopping enters from the eastern
side of the island. The figure illustrates that many of the residential or guest houses and
parks on the sea front in the eastern side have some of the greatest flood depths. Flooding
affects almost all building plots, schools and the hospital. Flood depths are shallow in this
scenario (0.2m to 0.3m) although this does not capture the water velocity which may be
greater with the action of waves.
Figure 32. Example of peak water depth distribution on Hulhumalé, from a simulation of the waves from the May 2007 design storm superimposed upon a larger sea-level of 1.4 m AMSL (equivalent to approx. 0.8m of MSLR).
Flood depth damage databases and curves normally used in flood risk assessment are not
available in the Maldives so costs cannot be attributed to the events in a similar way to flood
43
risk assessments elsewhere (e.g. Gouldby et al, 2008). However, an important characteristic
of flooding on Hulhumalé is that the threshold between no flooding and serious flooding (i.e.
sea water covering all of the island as a result of wave overtopping). Given the event of the
design storm, a floodplain water depth of 0.1m (more likely to occur with 0.4m-0.6m SLR) is
unlikely to enter buildings and cause major disruptions and therefore could be classed as a
nuisance rather than severely damaging. However, over many decades it could become
more frequent, and requires the need of adaptation to reduce occurrence.
If 0.25m is taken as a critical depth (an estimate based from research elsewhere such as
Penning-Rowsell et al. 2005), for which sea water will enter houses and public buildings (and
potentially interfere with service provision such as electricity, water and sewerage) this
threshold is reached when still water level under the design storm conditions are 1.6m AMSL
(i.e. a 1m MSLR scenario). This threshold could be further refined by assessing the capacity
of residential, commercial and industrial buildings and key services to their current resilience
to flooding.
6. Synthesis
6.1 Implications of findings This report provides data about sea-levels, waves and flooding, combined with overtopping
and flood simulations for Hulhumalé, Maldives. The focus of the flood modelling was
undertaken by reproducing conditions from a flood event occurring on 15th – 19th May 2007.
This event resulted in widespread flooding in many inhabited Maldivian islands. Using
hindcast data for this storm, and combining with projections of sea-level rise, overtopping
and flood extent has been analysed.
Results indicate that under present conditions (assuming no additional adaptation or reef
growth with sea-level rise) a rise of 0.4m-0.6m could result in nuisance flooding. A rise of 1m
may result in floods of a critical depth that could damage selected infrastructure to a depth of
0.25m. These findings are summarised in Figure 33 for the range of RCP scenarios
analysed in Section 3.3.1. Caution should be given to the overtopping and flood analysis as
great uncertainty remains, both in terms of modelled engineering and overtopping, but also
the response of the outer reef to sea-level rise.
44
Putting this in context of a 2°C world, it is unlikely that significant flooding will occur: As
referred to in Section 3.3.1, local sea-level rise in a world of climate mitigation (where
temperatures stabilise at 2°C) is projected to be a maximum of 0.54m in 2100 (with respect
to 1986-2005). However, with higher rises in emissions and temperature the island may be
at risk as up to 1.33m of rise in 2100 with respect to 1986-2005 (projected under the higher
emissions scenario of RCP8.5). Given such a large range of rise, nuisance flooding is
increasing likely towards the end of the century (assuming the reef does not take account of
SLR). Flood risk would continue to increase into the 22nd century, when adaptation
measures may need to be considered.
Figure 33. Increasing likelihood with overtopping on Hulhumalé with sea-level rise for the ensemble range of RCP2.6, RCP4.5 and RCP8.5 in 2050 and 2100.
This data provides the first assessment of flood risk in Hulhumalé. Given the large
uncertainty in model parameters, storm conditions, instantaneous response to flooding, reef
response and the rates of projected SLR, these results are purely indicative. Further work to
better calibrate the model could include testing a greater range of input parameters (i.e.
inshore wave conditions, friction values, and bathymetry covering all of the island’s
nearshore zone).
45
6.2 Adaptation
The cases considered here assume that the island’s defence system will not be upgraded as
environmental conditions change. Traditionally, coastal protection is divided into three
categories of protect (e.g. by defence), retreat (e.g. give land up to the sea) and
accommodate (i.e. living with worsening conditions, but have strategies to cope with the
consequences) (IPCC, 1990). When considering Hulhumalé, protection by hard and soft
means is seen as the first likely climate change adaptation option. This is reinforced by the
experience on Malé, where the Maldivian government is proactive in promoting island
protection through the construction of the sea wall and the addition of tetrapods surrounding
the island after the severe flooding on 1988. Estimates for the cost of extending the Malé
sea wall have been in the region of $4,000 per metre (BBC, 2005). In Hulhumalé,
considering the wall would be a new installation (rather than an addition as is the case in
Malé) the likely cost would be in excess of $10 million (to protect the east side alone to build
a wall around the entire perimeter of the island would cost over $25 million). Such additions
would not be likely until at least the second half of the century.
In Hulhumalé, the reef acts as a barrier between the open water and land. With small
amounts of sea-level rise, the reef can grow and keep pace with water levels (Wong et al.
2014). Evidence of this in reef environment (e.g. Funafuti atoll, Tuvalu, south Pacific) has
been reported in Funafuti atoll, Tuvalu, south Pacific (Kench et al. 2015). Here, between
1896 and 2013, island shape was recorded. Whilst some islands lost land to erosion and
flooding, the majority of islands gained land, leading to a 7.3% increase in net island area.
Naturally surviving island change is more challenging for artificial or protected islands as
engineering constrains natural response. However, engineering can also provide solutions. If
reef environments are unable to keep pace with sea-level rise, engineered options include
increasing the height or changing the angle of the sea-wall, further nourishing beaches with
sand or increasing the height of the reef. A last, but expensive resort could be to periodically
increase the height of the island as infrastructure is renewed. However, this would be a
piecemeal, time-consuming and expensive option.
The Maldives is a rapidly developing economy. Vision2020 (Republic of the Maldives, 2005)
sets the scene for national, cultural, social and economic development for the nation. This
includes policies of population consolidation leading to urbanisation, greater national unity,
improved and longer duration of schooling, movement to a knowledge base economy,
modern technology, increased trade, gender equality and better distribution of wealth.
Simultaneously, the growth of cities, such as Malé and Hulhumalé and their protection is part
46
of the national development plan. This, together with recommendations in Sovacool
(2012a,b) aim to better define hazards to determine vulnerabilities in order to decide upon a
sustainable, adaptive framework. It also assesses barriers of implementing such a plan.
Adaptation goes hand-in-hand with wider development, which can cause additional stresses
on small island states (Nurse et al. 2014). Therefore, adaptation will occur alongside
development goals following Vision2020 (Republic of the Maldives, 2005), the National
Development Plan (Government of the Maldives, 2007) and the broader Millennium
Development Goals (e.g. United Nations, 2015) to improve well-being. Wider adaptation
concepts include:
• Population consolidation;
• Efficient implementation of the Safer Islands strategy (i.e. selectively raising islands
to protect against extreme events);
• Improved land use planning to protect human settlements;
• The capacity to protect the coast and beaches, improving defences so increasing
resilience;
• To offer natural island defence, by protecting the house reef of an island;
• Integration of climate change into the disaster risk management framework.
Furthermore, there are wider development and adaptation strategies, such as those relating
to infrastructure planning, biodiversity, agriculture and fisheries. The nation also aims to
become a low carbon economy by 2020 (Republic of the Maldives 2005). As development
does not happen evenly throughout small island nations (Nurse et al. 2014), so adaptation
support must be given to the whole country, rather than just focusing on the most urbanised
areas. The National Adaptation Programme of Action (Republic of the Maldives 2006) also
ranks adaptation measures in terms of importance. For example, coastal protection is
ranked first and population consolidation is ranked second. However, some adaptation
policies are in potential conflict with one another (e.g. building defences verses the
protection of beaches, as at times defences can exacerbate local erosion). Therefore,
climate change and coastal adaptation requires further integration.
6.3 Other climatic effects
Whilst sea-level rise poses one of the most widely recognised threats to small island nations
(Nurse et al. 2014) through flooding and inundation of islands, it is only one threat to the
Maldives. Additional changes involve increases in surface and sea-temperatures (the latter
affecting fisheries and coral reefs) and changing precipitation patterns.
47
Climate change may affect marine ecosystems by changing light, salinity and temperature
(Wong et al. 2014). Warmer oceanic temperatures could lead to coral bleaching. This was
periodically seen during 1997-1998 El Nino where oceanic temperatures rose leading to a
reduction of 8% of corals in the Maldives-Changos reefs (McClanahan et al. 2000). In the
northern Maldives coral cover at different sites in 2011, several years after the El Nino
ranged from 1.7% to 51% at 3m to 5m water depth. Recovery continued to be slow
(Tkachenko, 2012). Coral reefs also depend on the production and erosion of calcium
carbonate and coral settlement. As atmospheric carbon dioxide is dissolved in the oceans, it
reacts to produce carbonic acid (Wong et al. 2014). This could increase the acidity of sea-
water, which could affect the wider biodiversity of coral reefs. It could also enhance the
production of mangroves through the fertilisation of carbon dioxide. Following an A1B
‘business as usual’ climate change scenario there is high confidence that reefs could
experience at least one bleaching event from 2090 to 2099. If this is persistent, reefs may
significantly decline from some regions of the Maldives. Elsewhere, coral reefs may shift
northwards.
The degradation of coral reefs not only affects biodiversity, but a wider social and economic
wellbeing that rely on the reefs for survival. Reefs play a major role in sediment supply to
island shores and reducing foreshore erosion by dissipating wave energy (Nurse et al. 2014),
which is important for the natural defence or the island. Corals and surrounding aquaculture
also attracts tourists. In 2011, tourism accounted for 29% of gross domestic product (c.f.
Department of National Planning, 2013), making it an important industry. Many tourists come
for diving experiences. Gössling et al. (2012) reports that whilst experienced divers may
notice differences in coral due to bleaching, unexperienced divers and snorkelers are less
likely to. This was noted in Pluket, Thailand after the Indian Ocean tsunami in 2004 (Main
and Dearden, 2007). Tourism may also be affected due to the increased frequency of other
marine events, flooding and erosion. Tourist islands are leased by the government to resorts,
typically on a 40-50 year basis. Land-based infrastructure (e.g. bungalows, restaurants) is
renewed on islands is approximately every 20 years to cope with new tourist trends. Hence
given the periodic renewal of land-based infrastructure, it is less likely to be affected by
erosion or flooding compared with marine infrastructure, such as harbours or groynes.
Changes in temperature are also anticipated to affect the tourist industry. This could
potentially lead to a shift in the main tourist season, or a northward or southward shift in
tourism to account for slight differences in weather.
As the Maldives is situated in the middle of the ocean, limited detailed assessments of
climate change exists, as due to their size, island resolution is not resolved within many
48
climate models. In IMPACT2C, Deliverable 3.2 analysed climate change in non-European
regions (also reported in Déqué and Somot, 2013). Using as 12km model and three grid
points, Déqué and Somot (2013) found that quarterly temperature rise was slightly less than
(by 0.1°C to 0.4°C) than the global mean (of 1.5°C over a 30-year period) in 2031-2060
(RCP8.5) and 2041-2070 (RCP4.5). Quarterly precipitation compared with the mean was far
more variable (in magnitude and direct of change), probably as the model, which unable
resolve land, was not able to take account of soil drying. Caution must be taken in
interpreting these results as only one model was used, and other models may vary.
An increase in temperatures in the Maldives may affect living conditions, with shifts in
behaviour to cope with more extreme weather. A greater use of air conditioning may be
required. With aims to achieve a low-carbon economy by 2020 (Republic of the Maldives
2005) and increase its use of renewable energies (van Alphen et al. 2008), this provides
additional challenges. Changes in precipitation patterns may affect conditions for agriculture.
However, given there are limited projections of temperature and precipitation in the Maldives,
there are few studies regarding impacts and response, thus influencing adaptive capacity.
Permanent island inundation has the potential to reduce the Exclusive Economic Zones
(Houghton et al. 2010) of low-lying nations, such as the Maldives. This has important
economic (e.g. fishing rights) and international consequences given the majority of the
nation’s area is sea. The United Nations Convention on the Law of the Sea (UNCLOS)
defined this as water up to 200 nautical miles from the coast (defined by the water mark at
low tide), with a potential to extend to 350 nautical miles if the nation extends from the
continental shelf (UNCLOS, 1982). With sea-level rise, raising islands is one possibility to
maintain the Exclusive Economic Zone provided the islands are not already submerged.
Thus, with continued sea-level rise, particularly at the high end, coastlines need to be
monitored.
7. Conclusions
As with many small, low-lying islands, the Maldives is at threat from rising sea-levels. In
IMPACT2C, the focus has been on the impacts associated with a 2°C rise in global mean
temperatures. Due to the commitment to sea-level rise, sea-levels will continue to rise even
if temperatures stabilise under conditions of climate mitigation. Hence rising sea-levels are
long-term problems for small island nations. This report has focused on a range of possible
49
sea-level rise, from 0.26m in 2100 with respect to 1986-2005, under climate mitigation to
1.33m of rise under a high emissions scenario in the same time period.
Using tide gauge analysis of extreme events and hindcast storm conditions, an overtopping
model has projected when inundation could occur under a range of sea-level rise scenarios
focusing on one newly reclaimed island, Hulhumalé, built approximately 2m above mean
sea-level. The study used specific set of storm conditions that has resulted in flooding on
other islands due to long period swell waves generated in the Indian and Southern Oceans.
Further analyses determined flood extent and infrastructure affected by extreme conditions.
At present, Hulhumalé is built high enough to not be susceptible to flooding from these
conditions. However, changes to freeboard, i.e. the gap between land height and the level of
still water caused by extreme sea level, could result in flooding. The initial results suggest
than nuisance flooding may occur with a rise of 0.4m-0.6m. Flooding that maybe more
critical to infrastructure may occur with a rise of 1m. These conditions are unlikely under a
2°C world, but could occur by the end of the century under higher emissions scenarios.
Flood risk will continue to increase into the 22nd century, where adaptation, such as the
construction of sea walls, may need to occur. For large rises in sea-level (greater than 2m)
human habitation of the island will become untenable unless land levels are raised.
Improved monitoring of sea level and wave conditions would be advantageous, to better
anticipate the future risks of coastal flooding.
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Acknowledgements
Gratitude is extended to the following students who assisted in the analysis and/or
contributed figures towards this report: Graham Cooper, Peter Grant, Oliver Harvey, Helen
Taylor and Yifan Wang.
Appendix
Datums
Figure A1. Datums applied in the respective sea-level analyses.
MSL (1992-1993). Zero datum for the topographic and bathymetric data.
Tide staff zero = Hawaii tide gauge datum.
1.878
60