Sea-Level Rise Threats in the Caribbean
Data, tools, and analysis for a more resilient future

Benjamin Strauss
Scott Kulp
Climate Central, Princeton, NJ

February 2018

© Inter-American Development Bank, 2018
The opinions expressed in this publication are those of the authors and do not necessarily reflect theviews of the Inter-American Development Bank, its Board of Directors, or the countries they represent.
The publication benefited from the coordination and inputs provided by Jose Jorge Saavedra, Gerard
Alleng, Michael Donovan and Camilo Pecha.

TABLE OF CONTENTS
Scope and Executive Summary ................................................................................................................... 1
Companion tools ......................................................................................................................................... 2
Sea-level rise and coastal flood hazards ..................................................................................................... 2
Sea-Level Projections ............................................................................................................................. 3
Tides and Floods ..................................................................................................................................... 4
Permanent Inundation ........................................................................................................................ 4
Coastal Flooding .................................................................................................................................. 4
Other Key Assumptions....................................................................................................................... 5
Hazard exposure ......................................................................................................................................... 6
Elevation data ......................................................................................................................................... 7
Population and economic data ............................................................................................................... 7
Exposure analysis .................................................................................................................................... 8
Long-term consequences for climate choices ............................................................................................ 9
References ................................................................................................................................................ 10

LIST OF TABLES
Table 1. Probabilistic sea-level projections .............................................................................................. 12
Table 2. Median sea-level projections according to two models under two scenarios ........................... 12
Table 3. Standard flood heights derived from the Global Tides and Surge Reanalysis............................ 13
Table 4. Annual percentage risk of coastal flooding above fixed heights ................................................ 14
Table 5. Performance of CoastalDEM compared to other satellite-based datasets and to lidar. ........... 15
Table 6. Low-lying land area ..................................................................................................................... 15
Table 7. Population on low-lying land. ..................................................................................................... 16
Table 8. Population on low-lying land: LandScan vs. WorldPop .............................................................. 16
Table 9. Internet access points on low-lying land .................................................................................... 17
LIST OF FIGURES
Figure 1. Surging Seas Risk Zone Map ...................................................................................................... 18
Figure 2. Surging Seas Risk Finder ............................................................................................................ 19
Figure 3. Mapping Choices........................................................................................................................ 20
Figure 4. Photorealistic illustration of different locked-in sea levels in Kingston, Jamaica ..................... 21
Figure 5. Performance maps of CoastalDEM vs. SRTM ............................................................................ 22
Figure 6. Median sea-level projections around the Caribbean basin....................................................... 23
Figure 7. The heights of annual (“one-year”) floods around the Caribbean basin .................................. 24
Figure 8. Poverty rates vs. Internet access points per capita in Kingston, Jamaica ................................. 25ii

Scope and Executive Summary
This report describes methodology used to develop extensive assessments of sea-level rise and coastal
flood likelihoods and hazards in the Caribbean Basin, and summarizes high-level findings. The Surging
Seas suite of online tools provides detailed complete results for local through national administrative units.
The specific geography covered includes The Bahamas, Barbados, Jamaica, Trinidad and Tobago,
Suriname, Guyana, Dominican Republic and Haiti, Dominica, Grenada, Saint Lucia, St Kitts and Nevis,
Antigua and Barbuda, St Vincent and the Grenadines, Corn Island (Nicaragua), and San Blas (Panama).
Analysis incorporates local sea-level rise projections from the recent scientific literature at 13 tide gauges
in the region. It develops future flood-likelihood forecasts at a series of standard heights by integrating
sea-level projections with published local flood-probability statistics based on historical data andmodeling. Finally, population and indexed economic activity are assessed in each location on land below
each standard flood height. Economic activity or wealth/poverty is indexed by the density of Internet
access points (e.g. wireless routers) on a fine spatial grid. These screening-level assessments are based onland elevations and projected water surface heights, and do not include dynamical modeling of storm surge,
waves, structural damage, or potential natural habitat responses to rising sea levels such as wetland
accretion or migration.
This is the first study to employ Climate Central’s proprietary new CoastalDEM™ digital terrain model
for land elevation data. CoastalDEM provides much greater vertical accuracy than other datasets available
at Caribbean Basin scale, including NASA’s Shuttle Radar Topography Mission (SRTM) data, and theAW3D30 dataset from the Japanese aerospace agency, both of which overestimate coastal elevations by
more than 2 m on average – and thus underestimate sea-level rise and coastal flood threats.
Based on analysis using CoastalDEM and the best available population data for each location, roughly
one million people live on land less than one vertical meter above local high tide lines within thegeographic scope of this study. More than 600,000 occupy land less than 0.5 m above the tides. Guyana
accounts for more than one-third of these totals; Haiti, Suriname, The Bahamas and the Dominican
Republic are the other major contributors, in descending order. Among these, however, The Bahamas
confront by far the greatest proportional threat: 32% of land, 25% of population, and 13% of Internet
access points are below 0.5 m.
Integrated sea-level rise projections and flood risk analysis indicate that floods reaching at least 0.5 m
above high tide line at shore will become common events throughout most of the Caribbean within half acentury, and more likely sooner. Floods above 1 m may become common by the end of the century, andpermanent sea-level rise exceeding this threshold is possible. Recent research suggests that Antarctic ice
sheets may be less stable than previously anticipated. In this case, scenarios of unabated climate pollution
lead to sea-level projections exceeding 1.5 m by 2100 across the Caribbean. Swift and sharp cuts in climate
pollution, however, could reduce these projections by roughly 1 m.
In the much longer run, contemporary carbon emissions leading to 4 C warming could lock in more than
8 m of sea-level rise unfolding across centuries. Limiting warming to 2 C could limit sea-level increases
to roughly 4 m, and projections translate 1.5 C warming to about 3 m.
Humanity’s carbon choices will thus have profound consequences for the Caribbean both in this century
and far beyond. For many islands, some pathways will be manageable, and others will not.
1Companion tools
This report is companion to a suite of online tools providing granular local maps and projections, plus
analyses for national and subnational units within the study area. Different elements are designed tosupport planning for coastal resilience and to illustrate the stakes for the Caribbean of different carbon
pollution and climate change pathways. Caribbean tools and analyses all employ Climate Central’s
proprietary elevation dataset, CoastalDEM™. The tools comprise:
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Surging Seas Risk Zone Map (ss2.climatecentral.org; Figure 1) shows areas vulnerable tosubmergence or flooding at different water levels. In the Caribbean, the map is based on
CoastalDEM, and includes high-resolution population and Internet access point density layers.
Surging Seas Risk Finder (riskfinder.org/caribbean; Figure 2) quantifies land, population andInternet access points exposed at different water levels for all Caribbean nations in the study area,
as well as hundreds of subnational units. It additionally provides detailed local sea-level and floodlikelihood projections under multiple scenarios. Risk Finder is home to the comprehensive analytic
results produced by this project, and offers extensive data table, map and figure downloads.
Mapping Choices (choices.climatecentral.org; Figure 3) maps the long-term sea level
consequences of different climate pathways, including carbon emissions leading to 1.5ºC, 2ºC, 3ºC
or 4ºC warming.
Picturing Choices (climatecentral.org/news/sea-level-stakes-for-the-caribbean-in-pictures-21770;
Figure 4). Three pairs of photorealistic images compare potential future Caribbean scenes basedon 4ºC vs. 2ºC warming, and the long-term sea level commitments shown by Mapping Choices.

Sea-level rise and coastal flood hazards
Climbing global temperatures bring rising sea levels worldwide. Melting glaciers and collapsing ice sheets
swell the oceans, and increasing ocean temperatures cause seawater to expand. While rising tides are athreat to all coastal places, the Caribbean Basin is unusually vulnerable, due to the flat, low-lying
topography, porous limestone bedrock, and tropical cyclones common to the area.
In 2017, the punishing hurricanes Irma and Maria provided sharp reminders of this vulnerability. Rare
extreme events like these are already catastrophic to lives and infrastructure in the low elevation coastal
zone, and will become even more so as sea-level rise continues to add to storm surge heights. At the sametime, the Caribbean also faces threats from chronic low-grade flooding aggravated by sea-level rise, andeventually permanent inundation due to ever higher sea levels.
Global society must ultimately choose a carbon emissions pathway to follow over the coming years anddecades, and this choice will have profound consequences for the Caribbean in the second half of the
century. The difference between pathways could be amplified even further if recent research is correct insuggesting greater-than-expected instability in Antarctica. In such a case, low emissions scenarios could
prevent the Caribbean from experiencing an additional 1 meter of sea level rise by 2100.
In almost any case, floods reaching 0.5 m above contemporary high tide levels appear likely to becomecommon throughout the Caribbean within the next several decades to half-century. Floods reaching 1 m
above today’s high tide lines may become the new routine later in the century, especially in the case of
rapid Antarctic ice loss.
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Sea-Level Projections
The Intergovernmental Panel on Climate Change (IPCC) provides an authoritative voice in climate science.
The most recent IPCC assessment report published “likely” ranges of sea-level-rise trajectories for thiscentury, meant to approximate the 17th to 83rd percentile ranges of potential rise for each greenhouse gas
emissions pathway evaluated (Church et al. 2013). The assessment did not include more extreme outcomes,
which would be driven by ice loss from Greenland or Antarctica, because understanding of ice sheet
behavior was considered too limited at the time by its authors.
This report considers two different sea-level models that build on top of the IPCC’s work, and which
provide local projections for the 13 tide gauges in this study. Kopp et al. (2014) used a very similar
framework as IPCC, but added its missing extremes by using results from a structured expert elicitationfrom the world’s leading ice sheet and glacier experts, eliciting estimated probabilities for different
potential Greenland and Antarctic contributions (Bamber and Aspinall 2013). The overall outcome wasan effectively probabilistic set of projections through the 99.9th percentile. Furthermore, Kopp et al. (“K14”hereafter) developed physically-based local projections based on the combined processes modeled, as wellas on non-climatic local background effects such as sinking or rising land.
Local effects and variation are generally modest in the Caribbean. For example, under a high carbon
emissions scenario, Representative Concentration Pathway 8.5 (Riahi et al. 2007), the median 21st-century
sea-level-rise projection ranges from 0.74-0.83 m across 12 tide gauges in this study (Table 1; Figure 6.
Median sea-level projections around the Caribbean basin for the year 2100 assuming unabated climate
pollution (RCP 8.5), according to Kopp et al. 2014. Omits the study tide gauge at Belem, Brazil.Error!
Reference source not found.). The remaining gauge is a modest outlier at 0.91 m, and is far from mostof the areas in this study (the gauge is at Puerto Castilla, on the coast of Honduras).
K14 has been widely cited and used for local sea-level projections in the United States from city throughfederal levels. Recently, the State of California adopted K14 for its updated sea-level-rise guidance(Griggs et al. 2017), and the U.S. National Oceanic and Atmospheric Administration used K14 to assign
probabilities to its latest sea-level scenarios, and to build local projections (Sweet et al. 2017).
K14 and the expert elicitation informing it were published before key new research emerged suggestingthat Antarctica could lose ice faster than previously anticipated (DeConto and Pollard 2016). Kopp et al.
(2017) (hereafter “K17”) represents an early attempt to incorporate this new Antarctic science into local
sea-level projections at global scale. K17 is the second sea-level model considered here. K17 does notprovide probabilistic projections, as K14 does, because a probabilistic distribution of Antarctic outcomesbased on DeConto and Pollard (2016) has not yet been developed. However, K17 does offer a frequency
distribution of results from simulations based on this research, sampling a large, non-probabilistic range
of input parameter values to the Antarctic model, and thus providing a range of plausible outcomes.
Median (50th percentile / central) results should be considered more robust than the tails.
Both K14 and K17 make sea-level projections relative to the year 2000. For each model, this analysis
considers standard high, moderate and low greenhouse gas emissions scenarios, respectively named
Representative Concentration Pathways (RCPs) 8.5, 4.5, and 2.6, and widely used in climate modeling(Riahi et al. 2007; Clarke et al. 2007; van Vuuren et al. 2007). RCP 8.5 corresponds to an unabated increasein emissions; RCP 4.5 corresponds to reduced emissions roughly in line with fulfilling the Paris
Agreement, and RCP 2.6 corresponds to large and immediate cuts, leading to net zero and then negative
annual emissions in the second half of the century.
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Table 2 compares median projections under K14 vs. K17 with unabated emissions or aggressive cuts.
(Risk Finder provides more complete details.) The results indicate almost no difference in medianprojections across emissions scenarios through 2050 (just 0.01-0.04 m), and relatively little difference
between models for the same period (0.01-0.09 m). However, divergences become important in thesecond half of the century. For example, under RCP 8.5, median projections according to K17 are 0.670.80 m greater than according to K14. Using K17, median projections are up to ~1 m greater under RCP
8.5 than RCP 2.6. The differences are substantially larger for higher percentile projections (e.g. 95th
percentile). 95th percentile results for K17, included in Risk Finder, should, however, be interpreted withcaution.
The tide gauges used in this study were selected because of the availability of local sea-level rise
projections, and because each gauge is the nearest neighbor of at least one study area in this project, even
if the gauge itself is not within the study area.

Tides and Floods
This report considers two hazards. First, as defined here, permanent inundation of land occurs when risingseas push the local high tide line above the land’s elevation. Second, coastal flooding becomes higher andmore frequent as sea level increases.
Permanent Inundation
Sea-level rise projections are water elevation differences. In order to identify land at risk, these differencesmust be added to some baseline water elevation. This study uses precise measurements of local mean sea
level averaged across a 19-year (1993-2012) satellite altimetry record on a 2-arcminute resolution grid
(Aviso 2015). An offset is then added to this average to approximate the local high tide line. The precise
offset is the local difference between the standard tidal levels Mean Higher High Water (MHHW) andMean Sea Level (MSL), based on the tidal model TPXO8 (Egbert et al. 2002). Model results were
provided by Mark Merrifield, University of Hawai’i, at 2-arcminute resolution. Bilinear interpolation wasused to upsample 2-arcminute results to 1-arcminute resolution, and nearest neighbor interpolation wasthen used to upsample to 1-arcsecond resolution (roughly 30 m, at the equator).
In other words, measured historical mean sea level, plus the modeled difference between mean sea leveland mean high tide, plus projected sea-level rise, together form the basis for projecting future high tidelines and, from these, land in danger of permanent inundation under different scenarios. Surging Seas Risk
Zone Map shows elevations relative to the baseline local high tide line (or more precisely, MHHW). Thus,
for example, when the slider is set to 1 meter, the map colors land blue that is less than 1 m above MHHW.
After 1 m of sea-level rise, these areas would be below the local high tide line.
Coastal Flooding
Long before permanent inundation, the same areas would be subject to increased flooding caused by risingseas interacting with tides, storm surge, and waves to cause temporary extreme sea levels. This study andSurging Seas Risk Finder use modeled local relationships between flood height and flood probability(called return level curves), together with sea-level projections, to estimate the chances of different eventsin different time frames. For example, if a flood 0.5 m above MHHW has a 10% annual probability, thenafter 0.5 m of sea-level rise, a flood 1 m above MHHW will have a 10% annual probability. (This report
uses flood height or flood as a synonym for extreme sea level, thus indicating flood height at the shore;inland flood heights during individual events vary spatially, as described in the next section, Other Key
Assumptions.)
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Return level curves are taken from the Global Tides and Surge Reanalysis (GTSR) (Muis et al. 2016). A
critical caveat is that GTSR appears to underestimate most flood heights in most areas globally, including
the Caribbean, where, for example, the heights of 10% annual probability floods are underestimated by
0.10-0.50 m (Muis et al. 2016). This underestimation increases for rarer extremes (e.g. 1% annual chancefloods) in areas such as the Caribbean that are affected by tropical cyclones (Muis et al. 2016; Buchanan
et al. 2017). Therefore, results presented here and in Risk Finder (for single- and multi-year flood
likelihoods) should be treated more as lower bounds for risk, not risk estimates. This report accordingly
focuses on milder high-frequency floods, and Risk Finder presents projections for sea-level rise plus mild
or moderate (but not extreme) floods. Such lower-grade floods do not pose the same immediate safety andstructural threats as major floods driven by hurricane storm surges. However, chronic floods can threaten
infrastructure, homes, home values, and habitability, and in general, they affect people much more often.
Table 3 and Figure 7 report GTSR flood heights for standard flood probabilities at study tide gauges acrossthe region. Height differences between “annual” flood levels (heights exceeded on average once per year
– sometimes more, sometimes less) and decadal flood levels (heights exceeded on average once per decade,and with roughly 10% probability annually) are universally small, ranging from 0.02-0.11 m. For all but
one gauge, 0.08 m or less of sea-level rise is enough to turn a “10-year” flood into an annual event on
average. The height differences between 10% and 1% annual probability floods are even smaller.
These small differences explain how, as seen in Risk Finder, the annual probability of a flood exceedinga given elevation threshold can jump from near 0% to near 100% within one decade: because the jumpshould typically require on the order of just 0.10 m of sea-level rise. Since flood heights are underestimatedby GTSR, however, each flood level is actually more likely than what the tool presents, and could
approach 100% annual probability sooner than shown. Developing and employing improved return level
curves for the Caribbean should be a high priority for future work.
Based on the curves available from GTSR, Table 4 presents annual likelihoods of floods exceeding the
fixed levels of 0.5 m or 1 m MHHW in certain years, integrating sea-level projections from different
models, and assuming RCP 4.5 (roughly consistent with meeting the emissions goals of the Paris climateagreement). Because GTSR underestimates risk, values should be considered lower bound estimates.
Table 4 thus indicates that floods exceeding 0.5 m MHHW are likely to become common at most
Caribbean locations in fewer than 50 years, and may undergo rapid transitions from being rare to frequent
events. Floods exceeding 1 m MHHW are likely to become common by the end of the century in the caseof more rapid Antarctic collapse scenarios (model K17), and also without collapse at any locations where
GTSR may underestimate flood heights by ~0.5 m. Floods exceeding 1.5 m will probably remain rare thiscentury in the absence of rapid Antarctic collapse or other drivers of faster-than-expected sea-level rise.
The exposure analysis in this report thus focuses on 0.5 and 1 m.
For description of methods used in Risk Finder to integrate sea-level projections with return level curves,in order to generate annual (single-year) and also accrued (multi-year) flood probabilities, see Tebaldi etal. (2012) and Buchanan et al. (2016), upon which the approach is based.
Other Key Assumptions
Each national and subnational area analyzed in Risk Finder is paired with a nearby tide gauge, in order toprovide local sea-level rise and flood likelihood projections pertaining to the area. Neighboring areas canhave different probabilities for the same flood height due to a variety of factors, such as differing local
topography, bathymetry and shore orientation. Local sea-level rise should be more consistent across5

distance, but may also vary due to factors such as local rates of land subsidence. Table 1, Table 3, Figure
6 and Figure 7 give a sense of these variations. Overall, integrated sea-level and flood-likelihood
projections show a notable degree of regional consistency within the Caribbean, suggesting that nearestneighbor tide gauges should provide robust projections for most areas.
(The most likely exception to this rule is for Guyana and Suriname, which are paired with the distant but
nonetheless nearest-available tide gauge in Belem, Brazil. This tide gauge appears to experience muchgreater water level departures than all the Caribbean gauges in this study (Table 3), possibly due to adifferent tidal regime. No data were available for this study to indicate whether the regimes in Guyana
and Suriname are more aligned with Belem or the Caribbean, or are intermediate or otherwise distinct.)
Flood-likelihood projections are developed from sea-level projections combined with extreme water-levelstatistics based on analysis of historic water levels and their reconstruction. This method assumes no futurechanges in storm patterns. Some climate studies project increasing major storms and surges in some areas– in particular, increasing frequency of major hurricanes – suggesting greater future threats (e.g. Lin et al.
2012, Grinsted et al. 2012, Grinsted et al. 2013).
Because GTSR is already known to underestimate the current probability of extreme storm surges andflooding, projections in Risk Finder may generally be treated as lower limits or underestimates of true
flood probabilities.
For integrated assessments considering both flood probability and exposure, this bias is mitigated because
hazard exposure in this study (see next section) is computed based on all land below a specified elevation,such as 1 m MHHW. This approach provides robust exposure estimates for permanent sea-level rise, but
generally overestimates exposure for transient flood events. During a flood, water takes time to propagate
from the shore to inland areas, and friction forces resist it as it does. As a result, water surfaces slope
slightly downward in the inland direction, unless the peak flood height at the shore is sustained forsufficiently long. In other words, extreme sea levels at the shore are generally higher than the flood heights
seen inland. Overestimated flood exposures may thus counterbalance underestimated flood probabilities
in the ensemble of this analysis. Regardless, the use of consistent methodologies across the entire
Caribbean basin should provide useful relative indices of threat in different areas.
This study does not include data or consideration of any protective levees or seawalls which may bepresent.

Hazard exposure
Threats from sea-level rise and coastal flooding depend not only on the timing, severity and likelihood ofthese hazards, but also upon the land, population and economic infrastructure exposed to them. Assessing
the latter depends importantly on accurate elevation data. This is the first study to employ CoastalDEM,
a high vertical accuracy elevation dataset developed by Climate Central, to assess coastal inundation
exposure in the Caribbean.
Based on CoastalDEM and demographic data, The Bahamas, Guyana and Suriname have the largest
fractions of their populations occupying land below 0.5 m or 1 m MHHW, by wide margins among all
study areas. Guyana, Haiti and the Dominican Republic have the largest total populations on low-lying
land, with The Bahamas and Suriname not far behind.
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The Bahamas, Saint Lucia, Suriname and Guyana appear to have the largest fractions of economic
infrastructure exposures in the group.
More extensive and detailed results for national and also subnational units are available in Risk Finder,
and include analyses of extra variables in Kingston and St. Andrew’s parishes, Jamaica, and in Nassau(New Providence), The Bahamas.

Elevation data
CoastalDEM30™ is a 1 arc-second (~30 meter) horizontal resolution digital elevation model based on
SRTM 3.0, a near-global dataset derived from satellite radar during a NASA mission in 2000. SRTM is
known to contain significant error caused by factors such as topology, vegetation, buildings, and random
noise (Shortridge & Messina 2011; LaLonde et al. 2010). On average, SRTM elevations are too high, andcause major underestimation of coastal sea-level and flood threats (Kulp and Strauss 2016). Climate
Central’s analysis indicates that the performance of the Japanese elevation dataset AW3D30 in coastal
areas is slightly inferior to SRTM, with higher biases and error scatter.
Climate Central has estimated SRTM elevation error in coastal areas between (and including) 1-20m in
nominal SRTM elevation. Each pixel in a CoastalDEM raster represents the adjusted elevation at thatpoint - the result of subtracting estimated error from SRTM 3.0. CoastalDEM was built to improve analysis
related to coastal flood exposure due to sea-level rise and storm surge (Kulp and Strauss 2017).
Table 5 gives CoastalDEM30 vertical error rates in the contiguous United States, Australia, Puerto Rico,and Nassau in The Bahamas, and shows dramatic improvements over SRTM, and Figure 5 maps estimatederror. Error is evaluated through extensive comparison against high-accuracy airborne lidar-based
elevations (high-resolution satellite stereophoto-derived elevations in Nassau) using very large numbersof data points for each listed geography (n > 1,000,000; except n = 233,553 in Nassau). Similar
improvements outside of these areas are generally expected, but not guaranteed. Results for Puerto Ricoand Nassau may be particularly indicative for other parts of the Caribbean with similar vegetation,
development patterns and topography as either place. However, the metrics given are large-scale averages,
and performance may vary across small spatial scales, as Figure 5 shows.
When checked against elevations from NASA’s ICESat space lidar product, median global bias incorrected areas drops from 1.93m (SRTM) to -0.24m (CoastalDEM). CoastalDEM performance asmeasured by ICESat throughout the Caribbean shows a slightly greater improvement (Table 5; n370,000). However, these numbers are sensitive to noise, outliers, and gaps in coverage in ICESat, and socomparisons to elevations derived from airborne lidar are much more reliable.

Population and economic data
For consistent Caribbean-wide population exposure assessment, this report employs 2010 data fromLandScan, a 1 km horizontal resolution ambient population density grid (Bright et al. 2011).
Risk Finder substitutes preferred sources where available, including WorldPop data for 2015 at 100m
resolution; and the most recent census data available for Kingston and Saint Andrew parishes in Jamaica
and Nassau (New Providence) in The Bahamas (Jamaica 2011; The Commonwealth of The Bahamas2012). In Kingston and Saint Andrew, exposure of population above and below the poverty line isadditionally assessed, also based on the census. In Nassau, exposure of housing (total, occupied, andvacant) is additionally assessed.
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Economic exposure is analyzed across the entire study area using a 100m resolution grid of the density ofInternet access points (APs) provided by Skyhook Wireless, covering the years 2015-2017. APs include
wireless routers and cellular towers. High AP densities commonly align with core urban business districts,
whereas zero or low densities in populated areas more likely correspond to relatively impoverished areas.
Figure 8 illustrates this relationship by plotting AP density per capita against percentage of population
below the poverty line, by Census district in Kingston, Jamaica.

Exposure analysis
This report (Table 6-Table 9) and Risk Finder assess exposure of land, population, APs and other variables
at a series of fixed heights above local high tide lines. Water may reach each height (e.g. 1 m MHHW)
through sea-level rise, coastal flooding, or a combination of these factors, on different timelines withdifferent probabilities, depending upon the scenario.
These elevation-based assessments are best interpreted as screening for concentration of risk. Whilestatistical flood risk modeling informs estimates of the likelihood of surpassing each study height under
different scenarios and time frames, at the shore, assessments do not include dynamical modeling of storm
surge, waves or structural damage from simulated storms, nor potential natural habitat responses to rising
sea levels such as wetland accretion or migration. More detailed and accurate elevation data, such as may
be derived from airborne lidar, used as input for detailed local dynamical models, can together provide
critical additional information for planning and development on the ground in high-risk areas.
The headline result from the current assessment is that among areas studied, The Bahamas confront by far
the greatest threat to land, population and property, as a fraction of national totals. An estimated 32% ofBahamas land, 25% of population, and 13% of Internet access points are at elevations below 0.5 m MHHW,
and thus at risk of chronic flooding within the coming several decades. Guyana and Suriname follow nextin population exposure, at 14% and 10%; all other study areas are below 2% at this water level. For
absolute count of population exposed, however, Guyana leads, followed by the Dominican Republic andHaiti (each of these three has close to 100,000 people living on land below 0.5 m MHHW), followed by
The Bahamas (77,000) and Suriname (52,000). After The Bahamas, Saint Lucia has a notably large
fraction of APs (6%) below 0.5 m.
The same rankings generally follow for exposures below 1 m, but with larger fractions and totals involved.
Population exposure in the Dominican Republic, however, does not grow as steeply as in some otherplaces, and so it loses its place in the ranking to The Bahamas.
Table 8 compares population exposure estimates based on LandScan vs. WorldPop data in study locations
where both are available. Assuming both datasets represent total population with reasonable accuracy, thehigher-resolution WorldPop should provide more accurate estimates of coastal exposure by distinguishingat a finer scale between populations occupying higher vs. lower parcels of land. (The most accurate
analysis would use even finer data, the footprints of individual homes.) Results are clearly correlated, butsuggest that at 0.5 m, Guyana may face double the exposure indicated by LandScan, propelling it into themost threatened position on both an absolute and relative basis. Suriname’s exposure also increases, while
Haiti is flat and the Dominican Republic appears to have lower exposure. 100 m resolution WorldPop data
are not currently available for The Bahamas and a number of other study areas.
Overall within the geographic scope of this analysis, some 453,000 people live on land less than 0.5 m
above local high tide lines, and 768,000 occupy on land below 1 m, based on LandScan data. Substituting8WorldPop data, where available, these numbers jump to best estimates of 607,000 and 966,000,
respectively.
As the first step in developing these estimates, this analysis classifies all areas as either land or ocean, as
defined by the SRTM water body dataset (SWBD). To estimate exposure to any given water level, theanalysis then counts all land below that elevation. This approach means that wetland areas that SWBD
does not classify as ocean are counted as land area that could be affected, even though already wet. The
approach also includes low-lying areas that appear isolated from the ocean by intervening higher-elevation
areas. Two main considerations justify this choice. First, the bedrock under many Caribbean islands isporous limestone, so water may penetrate through the ground itself. And second, while CoastalDEM
shows much less noise than NASA’s SRTM, upon which it is based, residual speckling is still evident insome areas when an elevation threshold is applied. It is reasonable to expect that part of a speckled area
should be below the elevation threshold applied; hence, isolated speckles below the threshold are counted
as exposed.
Because the elevation grid has a finer resolution than population and AP grids, the latter are upsampled to
the finer grid. If part of a population or AP grid cell extends over present-day ocean or another water body(as defined by the SWBD), zero population or AP density is assigned to this part, and the density of thebalance of the cell is compensated upward to achieve the same cell-wide total value before down-sampling
to the elevation grid.
Exposure assessments are tabulated within the boundaries of administrative units from levels 0 to 2
(national to county equivalent) in the database GADM 2.0 (University of Berkeley et al. 2012).
In interpreting results, it is important to note that if during a high tide or a storm, water reaches a particularlevel at a point along the shore, such as a study tide gauge, that height must be maintained over time before
reaching farther inland. Exposure values in this study and Risk Finder might thus be seen as corresponding
to the maximum area that could be affected by a flood of a certain height, depending on how long it lasts.
It is also the case that actual storms create uneven flood surfaces depending on factors such as winddirection; this analysis cannot capture the dynamics of an individual event. However, these considerations
do not apply when evaluating exposure from sea level rise acting alone, since sea level rise is effectively
permanent.
Analysis does not account for erosion, marsh accretion or migration, future flood controls, newconstruction, population growth or decline, or other dynamic factors that may affect exposure.

Long-term consequences for climate choices
Due to the long half-life of carbon dioxide in the atmosphere, and a range of climate feedbacks, carbon
emissions will cause sea levels to rise not only this century, but also for many more to come. The contrast
between the consequences from high vs. low emissions scenarios is far greater in the long term than by
2100, with sea level increases that may exceed 8 m in 4 C warming scenarios (Strauss et al. 2015). Longterm sea level increases may exceed 4 m in 2 C scenarios, and 3 m after 1.5 C warming. The Mapping
Choices online tool (choices.climatecentral.org) illustrates these contrasts and thus the stakes across the
Caribbean, updated from the original version by using CoastalDEM for the region. Corresponding
photorealistic images for a downtown scene from each of Kingston, Jamaica; Nassau, The Bahamas; andGeorgetown, Guyana, are also available on the web (climatecentral.org/news/sea-level-stakes-for-the9caribbean-in-pictures-21770). Online documentation and the Mapping Choices report provide more detailon the science and core methodology behind these maps and images.
These long-term projections offer the sobering and important reminder that any current measures toincrease coastal resilience should not be considered as complete solutions, but rather as early steps in alonger journey.

References
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Oak Ridge, TN.
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Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds.)].
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the US Climate Change Science Program and the Subcommittee on Global Change Research. Departmentof Energy, Office of Biological & Environmental Research, Washinfton, DC, USA, 154 pp.
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Rising seas in California: An update on sea-level rise science. California Ocean Science Trust.
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1923. Proceedings of the National Academy of Sciences 109(48):19601-5.
Grinsted A, Moore JC, Jevrejeva S (2013) Projected Atlantic hurricane surge threat from risingtemperatures. Proceedings of the National Academy of Sciences 110(14):5369-73.
10

Jamaica (2011) Census 2011. Statistical Institute of Jamaica, Planning Institute of Jamaica.
Kopp RE, Horton RM, Little CM, Mitrovica JX, Oppenheimer M, Rasmussen DJ, Strauss BH, Tebaldi C(2014) Probabilistic 21st and 22nd century sea‐level projections at a global network of tide‐gauge sites.
Earth's Future 2(8):383-406.
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BH (2017) Evolving Understanding of Antarctic Ice‐Sheet Physics and Ambiguity in Probabilistic Sea‐
Level Projections. Earth's Future.
Kulp S, Strauss BH (2016) Global DEM errors underpredict coastal vulnerability to sea level rise andflooding. Frontiers in Earth Science 19;4:36.
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Mission (SRTM) Elevations in Low‐relief Areas. Transactions in GIS.
Lin N, Emanuel K, Oppenheimer M, Vanmarcke E (2012) Physically based assessment of hurricane surge
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Riahi K, Gruebler A, Nakicenovic N (2007) Scenarios of long-term socio-economic and environmental
development under climate stabilization. Technological Forecasting and Social Change 74(7):887-935.
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Tebaldi C, Strauss BH, Zervas CE (2012) Modelling sea level rise impacts on storm surges along US
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11

Table 1. Probabilistic sea-level projections according to K14 and assuming unabated emissions (RCP 8.5). Median values given with credibleintervals (5th - 95th percentiles) in parentheses. Units of meters.

Tide gauge location

2050

2100

2200

Virginia Key, Florida

0.32 (0.19-0.47)

0.83 (0.46-1.32)

1.99 (1.01-3.82)

Settlement Point, The Bahamas

0.32 (0.18-0.47)

0.82 (0.43-1.32)

1.98 (0.98-3.81)

Gibara, Cuba

0.27 (0.14-0.41)

0.74 (0.36-1.22)

1.85 (0.77-3.74)

Guantanamo Bay, Cuba

0.29 (0.16-0.43)

0.79 (0.41-1.28)

1.95 (0.88-3.85)

Port Royal, Jamaica

0.30 (0.10-0.51)

0.81 (0.31-1.40)

2.04 (0.75-4.03)

Port Au Prince, Haiti

0.28 (0.08-0.50)

0.79 (0.29-1.37)

1.97 (0.71-3.93)

Puerto Plata, Dominican Republic

0.28 (0.07-0.50)

0.77 (0.26-1.37)

1.94 (0.66-3.91)

Magueyes Island, Puerto Rico

0.27 (0.16-0.39)

0.76 (0.39-1.24)

1.89 (0.86-3.77)

Pointe-A-Pitre, Guadeloupe

0.28 (0.08-0.49)

0.78 (0.28-1.37)

1.97 (0.66-3.98)

Puerto Castilla, Honduras

0.35 (0.16-0.55)

0.91 (0.41-1.49)

2.18 (0.96-4.15)

Puerto Limon, Costa Rica

0.27 (0.13-0.43)

0.76 (0.34-1.30)

1.90 (0.79-3.85)

Cristobal, Panama

0.31 (0.19-0.43)

0.82 (0.47-1.31)

2.02 (0.98-3.95)

Belem, Brazil

0.27 (0.16-0.40)

0.77 (0.41-1.26)

1.92 (0.87-3.79)

Table 2. Median sea-level projections according to two models (K14, K17) under low (RCP 2.6) and high (RCP 8.5) emissions scenarios.

Year: 2050

Year: 2100

Year: 2200

RCP 2.6

RCP 8.5

RCP 2.6

RCP 8.5

RCP 2.6

RCP 8.5

Tide gauge location

K14

K17

K14

K17

K14

K17

K14

K17

K14

K17

K14

K17

Virginia Key, Florida

0.28

0.27

0.33

0.36

0.54

0.62

0.84

1.63

1.03

1.20

2.00

8.13

Settlement Point, The Bahamas

0.28

0.27

0.32

0.36

0.54

0.61

0.83

1.63

1.04

1.17

1.98

8.13

Gibara, Cuba

0.24

0.28

0.27

0.30

0.47

0.64

0.74

1.54

0.90

1.23

1.86

7.96

Guantanamo Bay, Cuba

0.26

0.25

0.29

0.32

0.51

0.59

0.79

1.58

0.99

1.13

1.96

8.07

Port Royal, Jamaica

0.27

0.28

0.30

0.27

0.54

0.64

0.82

1.49

1.04

1.23

2.05

7.88

Port Au Prince, Haiti

0.26

0.25

0.29

0.32

0.52

0.59

0.79

1.58

1.02

1.13

1.97

8.07

Puerto Plata, Dominican Republic

0.25

0.28

0.28

0.31

0.51

0.64

0.78

1.55

0.99

1.23

1.95

8.01

Magueyes Island, Puerto Rico

0.24

0.28

0.27

0.31

0.49

0.64

0.76

1.55

0.95

1.23

1.90

8.01

Pointe-A-Pitre, Guadeloupe

0.26

0.24

0.29

0.31

0.52

0.57

0.79

1.56

1.02

1.11

1.97

8.04

Puerto Castilla, Honduras

0.31

0.30

0.35

0.37

0.61

0.67

0.91

1.68

1.18

1.28

2.19

8.12

Puerto Limon, Costa Rica

0.24

0.23

0.28

0.30

0.46

0.52

0.77

1.55

0.86

0.99

1.91

7.85

Cristobal, Panama

0.27

0.26

0.31

0.33

0.52

0.59

0.83

1.59

0.99

1.13

2.03

7.97

Belem, Brazil

0.24

0.23

0.28

0.30

0.47

0.53

0.78

1.50

0.88

1.01

1.92

7.46

12

Table 3. Standard flood heights derived from the Global Tides and Surge Reanalysis. All values in meters above local Mean Higher High
Water. GTSR is known to generally underestimate these flood heights, especially extreme (1% annual chance) flood heights in areas subject
to storm surges from tropical cyclones, such as the Caribbean. For reference, 10% chance flood heights listed here may be underestimatedby 0.1-0.5 m, and 1% annual chance flood heights are likely to be farther below actual values.

Tide gauge location

Height ofannual flood

Height of
10% annual
chance flood

Height of
1% annualchance flood

Virginia Key, Florida

0.12

0.19

0.23

Settlement Point, The Bahamas

0.17

0.25

0.29

Gibara, Cuba

0.14

0.18

0.20

Guantanamo Bay, Cuba

0.09

0.16

0.19

Port Royal, Jamaica

0.00

0.08

0.11

Port Au Prince, Haiti

0.11

0.15

0.17

Puerto Plata, Dominican Republic

0.12

0.16

0.17

Magueyes Island, Puerto Rico

0.05

0.11

0.13

Pointe-A-Pitre, Guadeloupe

0.05

0.07

0.09

Puerto Castilla, Honduras

0.00

0.03

0.04

Puerto Limon, Costa Rica

0.00

0.02

0.02

Cristobal, Panama

0.02

0.04

0.05

Belem, Brazil

0.78

0.89

0.94

13

Table 4. Annual percentage risk (0-100%) of coastal flooding above fixed heights, given denoted sea-level rise model and year, for scenario
RCP 4.5. 100% likelihoods include the case of sea-level rise exceeding the given height (permanent inundation). Median estimates are shown,with credible intervals for K14 (5th – 95th percentile) but not for K17 because K17 projections are not probabilistic. Credible intervals are
based on different sea-level projections. K17 estimates are very similar to K14 estimates in 2050 and thus are not shown; also not shown,
K14 median estimates in 2070 and 2100 are all zero for 1m, outside of Belem. Risk Finder allows exploration of additional years and scenarios.
However, all estimates in Risk Finder and this table should be regarded as lower bounds because analysis uses return level curves that
underestimate flood risks in the Caribbean.

Flood height (MHHW):

0.5m

0.5m

0.5m

1m

1m

Model:

K14

K14

K17

K17

K17

Year:

2050

2070

2070

2070

2100

Virginia Key, Florida

6 (0-82)

88 (0-100)

100

0

100

Settlement Point, The Bahamas

39 (0-93)

95 (9-100)

100

0

100

Gibara, Cuba

0 (0-79)74 (0-100)

100

0

98

Guantanamo Bay, Cuba

0 (0-53)58 (0-100)

100

0

92

Port Royal, Jamaica

0 (0-46)

7 (0-100)

20

0

1

Port Au Prince, Haiti

0 (0-97)

70 (0-100)

100

0

98

Puerto Plata, Dominican Republic

0 (0-99)

79 (0-100)

100

0

99

Magueyes Island, Puerto Rico

0 (0-2)6 (0-100)

88

0

66

Pointe-A-Pitre, Guadeloupe

0 (0-72)

0 (0-100)

89

0

64

Puerto Castilla, Honduras

0 (0-100)

15 (0-100)

100

0

100

Puerto Limon, Costa Rica

0 (0-0)0 (0-100)

12

0

0

Cristobal, Panama

0 (0-0)0 (0-100)

100

0

19

100 (100-100)

100 (100-100)

100

99

100

Tide gauge location

Belem, Brazil

14

Table 5. Performance of CoastalDEM compared to SRTM and WorldDEM, referenced against higher accuracy sources. Nassau data provided
by IDB.

Reference DEM
data source

Area
USA

Airborne lidar(continuous
coverage)

Australia
Puerto Rico

WorldView-2
stereo images

Nassau,The Bahamas

ICESat spacelidar (sparse)

Caribbean

Airbus brochurestated claim

Global

Product

Bias (m)RMSE (m)LE90 (m)LE95 (m)SRTM 3.0

3.7

5.4

9.1

11.2

CoastalDEM30 v1.1

0.0

2.4

3.9

5.0

SRTM 3.0

2.5

4.2

6.8

9.0

CoastalDEM30 v1.1

-0.1

2.5

3.9

5.2

SRTM 3.0

2.1

3.5

5.6

6.9

CoastalDEM30 v1.1

0.1

2.8

4.5

5.6

SRTM 3.0

3.1

3.7

5.5

6.5

CoastalDEM30 v1.1

-0.2

1.8

2.8

3.7

SRTM 3.0

2.4

CoastalDEM30 v1.1

-0.3

ICESat data do not support
robust RMSE or LExx estimates

Airbus WorldDEM™ DTM<10

Table 6. Low-lying land area (km2) within 0.5-4 vertical meters of local Mean Higher High Water. Total (tot) and percentage (pct) of land
below each level given.

Place
Antigua and Barbuda
The Bahamas

National
Tot Area
427

0.5m
Tot
10

Pct
2.3%

1m
Tot
17

Pct
4.0%

2m
Tot
41

Pct
9.6%

4m
Tot
100

Pct
23.4%

12,514

4,043

32.3%

5,771

46.1%

8,771

70.1%

11,24089.8%

Barbados

437

-

0.0%

-

0.0%

2

0.5%

11

2.5%

Dominica

757

1

0.1%

1

0.1%

3

0.4%

5

0.7%

47,866

779

1.6%

1,006

2.1%

1,417

3.0%

2,168

4.5%

Grenada

360

3

0.8%

4

1.1%

7

1.9%

15

4.2%

Guyana209,746

3,457

1.6%

4,632

2.2%

6,223

3.0%

7,956

3.8%

Haiti

26,983

126

0.5%

210

0.8%

423

1.6%

794

2.9%

Jamaica

11,013

113

1.0%

160

1.5%

303

2.8%

615

5.6%

Saint Kitts and Nevis

269

-

0.0%

2

0.7%

5

1.9%

10

3.7%

Saint Lucia
Saint Vincent and the
Grenadines
Suriname

617

7

1.1%

9

1.5%

17

2.8%

30

4.9%

399

4

1.0%

5

1.3%

7

1.8%

11

2.8%

145,018

4,113

2.8%

5,467

3.8%

7,621

5.3%

10,316

7.1%

Trinidad and Tobago

5,169

78

1.5%

118

2.3%

206

4.0%

379

7.3%

Dominican Republic

15

Table 7. Population (LandScan 2010) on low-lying land.

Place
Antigua and Barbuda
The Bahamas
Barbados
Dominica
Dominican Republic
GrenadaGuyanaHaiti
JamaicaSaint Kitts and Nevis
Saint Lucia
Saint Vincent and
the Grenadines
Suriname
Trinidad and Tobago

National
Total Pop.
86,710310,015
285,134
72,811
9,812,309
107,817723,1079,660,438
2,847,231
49,898160,742

0.5m
Tot
1,170
76,993240
737
99,3101,021
101,871
97,241
13,700
244
2,725

Pct
1.3%
24.8%
0.1%
1.0%
1.0%
0.9%
14.1%
1.0%
0.5%
0.5%
1.7%

1m
Tot
1,737
128,328526
1,277
127,179
2,108
191,368
164,310
22,681
613
8,659

Pct
2.0%
41.4%
0.2%
1.8%
1.3%
2.0%
26.5%
1.7%
0.8%
1.2%
5.4%

2m
Tot
4,236
176,398
5,643
3,284
232,506
4,859
446,309405,978
75,242
1,996
22,466

Pct
4.9%
56.9%
2.0%
4.5%
2.4%
4.5%
61.7%
4.2%
2.6%
4.0%
14.0%

4m
Tot
12,759247,435
25,335
6,316
574,226
8,728
558,477
852,626
370,732
4,310
31,838

Pct
14.7%
79.8%
8.9%
8.7%
5.9%
8.1%
77.2%
8.8%
13.0%
8.6%
19.8%

104,218

697

0.7%

1,129

1.1%

3,650

3.5%

8,060

7.7%

508,340
1,228,676

52,493
4,426

10.3%
0.4%

102,318
15,38420.1%
1.3%

332,934
66,64065.5%
5.4%

435,907
170,167

85.8%
13.8%

Table 8. Population on low-lying land: Results based on LandScan (1km resolution) vs. WorldPop (100m resolution) for places where bothare available.

Place
Antigua and Barbuda
Dominican Republic
GuyanaHaiti
Jamaica
Suriname
Trinidad and Tobago

0.5m
LandScan
1,170
99,310101,871
97,241
13,70052,493
4,426WorldPop
1,165
80,279225,307
97,46019,12888,707
12,0781m
LandScan
1,737
127,179
191,368
164,310
22,681
102,318
15,384

16

WorldPop
1,689
119,624
332,339
168,663
29,534
146,774
25,0272m
LandScan
4,236
232,506
446,309405,978
75,242332,934
66,640WorldPop
4,641
239,877
537,378439,580
68,865
334,497
70,9274m
LandScan12,759574,226
558,477
852,626
370,732
435,907
170,167

WorldPop
12,935556,144626,494
962,830
272,043
424,151
173,951

Table 9. Internet access points on low-lying land.

National
Total APs
45,094

0.5m
Tot
262

Pct
0.6%

1m
Tot
365

Pct
0.8%

2m
Tot
2,564

Pct
5.7%

4m
Tot
13,236Pct
29.4%

The Bahamas

197,748

26,19513.2%

51,18125.9%

80,141

40.5%

134,68168.1%

Barbados

205,483

37

0.0%

118

0.1%

5,878

2.9%

37,763

18.4%

Dominica

29,850

165

0.6%

165

0.6%

659

2.2%

6,384

21.4%

Place
Antigua and Barbuda

Dominican Republic

2,044,686

11,0580.5%

20,162

1.0%

48,034

2.3%

138,155

6.8%

Grenada23,034

198

0.9%

267

1.2%

2,736

11.9%

6,395

27.8%

Guyana90,703

3,584

4.0%

11,49912.7%

69,23476.3%

88,503

97.6%

Haiti

71,468

256

0.4%

281

0.4%

501

0.7%

2,504

3.5%

Jamaica

487,225

3,986

0.8%

9,325

1.9%

36,7607.5%

132,352

27.2%

Saint Kitts and Nevis

26,651

177

0.7%

293

1.1%

3,131

11.7%

10,52339.5%

Saint Lucia
Saint Vincent and the
Grenadines
Suriname

55,599

3,448

6.2%

10,61319.1%

25,49845.9%

34,86062.7%

29,650

58

0.2%

58

0.2%

214

0.7%

2,480

8.4%

191,974

8,284

4.3%

26,43413.8%

155,93981.2%

187,282

97.6%

Trinidad and Tobago

727,614

520

0.1%

5,135

0.7%

30,225

4.2%

103,364

14.2%

17

Figure 1. A screenshot from Surging Seas Risk Zone Map (ss2.climatecentral.org), showing (in blue) land less than 2m above the local hightide line in Nassau (New Providence), The Bahamas.

18

Figure 2. A partial screenshot from Surging Seas Risk Finder (riskfinder.org/Caribbean) showing a portion of the landing page for Nassau,
The Bahamas.

19Figure 3. A screenshot from Mapping Choices (choices.climatecentral.org) showing multi-century sea-level rise just west of Kingston,
Jamaica, as projected to lock in after carbon emissions causing 4 C vs 2 C warming.

20

Figure 4. Photorealistic illustration of different locked-in sea levels in Kingston, Jamaica.

21

NASA’s SRTM 3.0

CoastalDEM™1.1

Nassau,The Bahamas:
Comparison vs.
WorldView2

Caribbean region:
Comparison vs.
ICESatElevation differences vs. reference data

metersFigure 5. Images showing the performance of CoastalDEM vs. SRTM at different scales, as measured against reference datasets believed tohave more accurate elevations. Red tones indicate that SRTM or CoastalDEM is overestimating elevation compared to reference and thus
underestimating exposure to sea-level rise and coastal flooding; blue tones indicate underestimation of elevation and thus overestimationof risk. Darker reds and blues indicate larger absolute errors, and lighter/whiter tones indicate smaller ones. In the first row, medium graydenotes water, and dark gray covers land areas >20m in elevation according to SRTM, which were not modeled in CoastalDEM. In the secondrow, the darkest blue represents water or land not included in CoastalDEM. Second-row error is computed as the median of all errors within
1-degree cells, because of the sparseness and noise in ICESat data. Table 5 provides error statistics corresponding to all four images.

22Settlement Point

Sea-level rise projections

Virginia Key

0.72 - 0.76 m
0.76 - 0.80
0.80 - 0.84
0.84 - 0.88
0.88 - 0.92

GibaraGuantanamo Bay Puerto Plata
Port Au Prince
Port Royal

Magueyes Island
Pointe-A-Pitre

Puerto Castilla

Puerto Limon
Cristobal

Figure 6. Median sea-level projections around the Caribbean basin for the year 2100 assuming unabated climate pollution (RCP 8.5),
according to Kopp et al. 2014. Omits the study tide gauge at Belem, Brazil.

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Figure 7. The heights of “annual” floods (heights exceeded on average once per year) around the Caribbean basin, relative to local high tidelines (Mean Higher High Water), according to the Global Tides and Surge Reanalysis. GTSR generally underestimates flood heights in the
Caribbean. Figure omits the study tide gauge at Belem, Brazil.

24

0.6
n = 423

Access points per capita

0.5

0.4

0.3
209
310

0.2

11

125
94

0.1

8

48
24

4

0
5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

2
55%

2
60%

2

1

65%

70%

Percentage of population in poverty (bin upper limit)
Figure 8. Bars indicate 25th percentile (lower edge), median (red line), and 75th percentile (upper edge) Internet access points per capita for
Census districts in Kingston and St Andrew’s parishes, Jamaica, with different poverty levels. Most areas with more than 0.1 APs per capita
have low rates of poverty, whereas areas with fewer APs per capita are more weighted toward greater poverty.

25