climate change on UAE - Stockholm Environment Institute-US Center

water height of each tidal day observed over the
National Tidal Datum Epoch. Unfortunately,
Mmsl data is publicly available for only three
years for the UAE, (PSMSL, 2008) and MHHW
was not available from sources consulted. Since
available tide data is considered insufficient for
a tidal analysis, this report relies on existing
analysis of tidal dynamics found in the literature,
as referenced below, to best understand how
tidal variation may shift with <strong>climate</strong> <strong>change</strong>
induced sea level rise.
Mean sea level is not just influenced by
tidal dynamics but by Arabian Gulf thermal
expansion as well. For a detailed explanation
of those dynamics it is suggested to revisit
sections “‎2.3 Sea-surface temperatures, thermal
expansion, and thermosteric sea level rise” and
“‎2.4 Increase in the tidal variation around the
mean”. Separate from these processes, which
have altered sea levels for millennia, based
on the available literature, net sea level rise is
defined as the historical sea level rise rate plus
the accelerated rate due to global warming
minus the estimated accretion rate for each
type of tidal wetland calculated annually over
the 200 year time period (Jones and Strange,
2008).
For lack of better elevation data, the analysis
on SRTM data is from the CGIAR-CSI
(Consortium for Spatial Information) GeoPortal
which provides processed SRTM 90m DEM for
the entire world. Produced by NASA originally,
the data made available by CGIAR-CSI was
processed to fill data voids and to facilitate its
ease of use by a wide group of potential users.
This was found to be the most comprehensive
and consistent elevation data for the United
Arab Emirates available publicly at the time
of the analysis. Also, it is worth noting that the
SRTM data is also only available in integers
the implications of which, with respect to the
floodfill model, limits the scenarios we can run
to the values in the existing data set.
The flood fill program considers the elevation
values in the cells of a grid, and then assesses
the elevation differential from the elevation in
neighboring cells. Take the example grid cell
shown in Figure ‎4‐8. Those cells with a value of
“0” signify mean sea level. When one adds a 1
meter scenario to the grid in Figure 4-8b, the
flood fill program calculates which neighboring
cells in 8 directions (N, S, E, W, NW, NE, SW,
SE) the water could possibly travel, based on
elevation. With 1 meter rise, inundation will
extend to the blue-shadec cell. In the case
where sea level rises by 2 meters in Figure 4-8c,
inundation extends throughout the shaded 1
meter and 2 meter cells, and are only blocked by
the 3- and 4-meter cells inland. Our reliance on
the SRTM dataset, also determines where mean
sea level is in our model. The SRTM vertical
datum is mean sea level and is based on the
WGS84 Earth Gravitational Model (EGM 96)
geoid. The EGM 96 is the closest approximation
of the geoid in most areas, and therefore mean
sea level (since sea level mirrors the geoid as
explained by Figure ‎4‐5).
4.5. GIS and a flood-fill algorithm
As explained in Section 4.2, there are two main
sea level rise (SLR) modeling approaches
typically drawn upon. Either one is suitable
for GIS analysis. Inaccuracies arise, however,
when deriving a vulnerable zone based on the
contour-method because it does not consider
contiguous cells the way that a pour-point or
flood-fill model would. SEI developed a floodfill
program to calculate flooded areas adjacent
Impacts, Vulnerability & Adaptation for
Coastal Zones in the United Arab Emirates
Figure 4-8a (0m)
1 1 2 2 2
0 2 2 1 3
4 1 2 2 3
4 1 1 2 0
4 1 2 2 0
Figure 4-8b. (1m)
1 1 2 2 2
0 2 2 1 3
4 1 2 2 3
4 1 1 2 0
4 1 2 2 0
Figure 4-8c (2m)
1 1 2 2 2
0 2 2 1 3
4 1 2 3 3
4 1 1 2 0
4 1 2 2 0
Figure ‎4‐8 a,b,c. Example flood-fill process.
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