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CLIVAR Exchanges No. 71, Feb 2017 32
Valentine K. Ochanda, Daniel K. Irurah
School of Architecture and Planning, University of the Witwatersrand, Private Bag 3, P.O. Wits,
Johannesburg, South Africa
Contact e-mail: valochanda@gmail.com
Introduction
Sea Level rise (SLR) and the encroachment of the sea
waters into adjacent built environment and primarily
residences, has caused negative impacts in coastal
areas around Asia and some parts of Africa much to the
inconvenience and threat to the residents (Walker et
al., 2007). Current projections of sea level rise associate
with climate change scenarios (Richard et al., 2007;
Stocker et al, 2013) show that current century minimum
of 1.8m poses a challenge to coastal communities in
Kenya and especially for Mombasa island. The threat
of climate change and SLR will most likely place the
longevity of the highly rated star hotels, and government
investments in infrastructure and services at risk. The
unfortunately, are a new concept for the developing
implications of coastal areas as compared for example to
the developed economies.
tides have led to the relocation and forced migration of
some local communities inland. Except for South Africa,
few developed countries have implemented several
in port cities to protect the investments and the
communities living in these areas. These include Durban,
in South Africa, and portions of the Nile delta.
The world is working to reduce the world temperatures
to a maximum of 2o
require countries to adopt cleaner options of energy,
reduce the reliance on fossil fuels, besides reducing
their carbon footprint through several other initiatives.
African countries, on the other hand, are forcefully
coming into the oil and gas exploration market with the
have been a privilege of the developed countries. In East
Africa, Kenya, Uganda and Tanzania are developing and
mining fossil fuels, more so to meet the growing energy
demands in their economies which increase the exposure
eight African countries again are at an advanced stage of
building the Lamu Port South Sudan Ethiopia Transport
Corridor (LAPSSET), to facilitate the transfer of resources
within these countries. The results of this investment
warming, and will paradoxically have further severe
SLR related consequences for these same countries and
impacts of climate change, therefore increasing their
vulnerability and exposure to climate change induced
impacts (Hallegatte et al., 2013).
coastal population, most located in some important
coastal cities. Many of these coastal cities are also
important ports for national and regional trade,
imports and exports (UN-Habitat, 2012). In the event
the unprotected average population and developed
assets in these coastal regions are much higher.
This study investigates the vulnerability and inundation
levels for the island city of Mombasa and Lamu. Failed
assessments and planning are putting the lives of
communities, government investments and people under
threat from accelerated SLR. The island city of Mombasa
is a well-documented tourist destination with several
world class star rated hotels that contribute to the
national Gross Domestic Product (GDP) in Kenya. The city
is an important port city for the east and central African
region, making it crucial in the eastern and central Africa’s
business port. As well as being the city targeted under the
Kenya 2030 development blueprint that intends to push
Kenya's development agenda higher, one of the pillars
that are foreseen to improve the development of Kenya is
hinged on tourism which is mainly in Mombasa and Lamu
coastal cities. Based on the primary objective of assessing
the impacts of climate-change-induced SLR for Mombasa
this century almost 50% of Mombasa island and almost
71% of Lamu island falls under threat of inundation from
SLR enhanced storm surges of a one storm surge in 100
years.
Shoreline integrated SLR impact prediction in
Mombasa and Lamu islands in Kenya
33 CLIVAR Exchanges No. 71, Feb 2017
Methods
Primary data sources
Required primary data for the analysis of the areas under
threat in the two islands, included the initial spatial
and land uses along the coastline that is within the low-
elevation coastal zone (LECZ) mark. This area, and
selected infrastructural point data for the assessment of
sectors that is low lying, were supplemented by data from
the mini-charrette process, where community members
of primary data collection was to gather both quantitative
and qualitative information to be used in the vulnerability
analysis. Point data were collected using Garmin GPS,
while the land use data were gathered from the ministry of
and areas of interest for the mapping of the area and
threatened critical zones.
Secondary data sources
Secondary data collection was for the analysis of the
quantitative data on the mapping and the positioning of
Mombasa and Lamu island about SLR. Data collected in
this phase included a complete and detailed inventory
of the critically exposed assets and resources (land,
population, and urban infrastructure) along the cities for
qualitative analysis with the inundation zones, projected
for a current 1-in-100-year storm surge. Also sourced
were spatially-disaggregated data sets from various
public sources such as the National Aeronautics and Space
Administration (NASA), the US Geological Survey (USGS),
Dynamic Interactive Vulnerability Assessment (DIVA), the
World Wildlife Fund (WWF) and Centre for International
Earth Science Information Network (CIESIN).
The dataset from NASA was necessary for the provision
of the digital elevation model (DEM) for Lamu Island as it
was clearer (less cloud interference and therefore, needed
DEM for Mombasa Island. Other data included land uses,
and subdivision data collected from Kenya Regional Centre
for Mapping of Resources for Development (RCMRD) based
in Nairobi, Kenya. These data allowed for the comparison
of the global DEM versus the localy developed DEM. The
importance of comparison of remotely sensed data was
to allow for the best data with better resolution for the
purpose of analysis. The comparison was, therefore,
necessary to reduce the magnitude of errors that can
result from widely disaggregated data.
The Kenya Meteorological Department (KMD) data were
used to assess the temperature and rainfall pattern for
the two counties. These data were coupled with the SLR
recording stations managed by the Kenya Marine and
Fisheries Institute (KEMFRI), and then used as a guide for
the low and high tide ranges in the two counties as a key
factor when assessing the impact of a storm surge in the
coastal zones. Other secondary data (including the Physical
Planning Act, the county planning and development
documents necessary in the analysis of the desired area
and infrastructure) were from the Ministry of Devolution
and Planning, and the Ministry of Land Housing and Urban
Development as well as their county equivalent within the
respective governments.
A) Study area
Mombasa district is in the South-Eastern part of Kenya. It
is the smallest of the seven areas in the Coastal counties,
covering an area of 294.6 km2. Lamu Island has a total land
area of about 50 km2. About 19 km2 of the island is covered
by a double row of sand dunes located along the entire
length of the coastline and especially from Shella covering
the southern coast. The Island is a UNESCO world heritage
site.
B) Scenarios Used
The inundation analysis was based on two Representative
Concentration Pathways (RCP) scenarios; this is RCP 8.5
(pessimistic scenario) and RCP 2.6 (optimistic scenario).
RCP 8.5 is considered a high emission, high energy-
intensive scenario as a result of high population growth
and a lower rate of technology development (Detlef et al.,
2011). RCP 2.6 (optimistic scenario) which is the lowest
emission and radiative forcing scenario that represents a
set of mitigation measures aimed at limiting the increase
in global mean temperature to 2oC.
The research sites were also selected based on the national
economic blueprint (Kenya’s Vision 2030), aimed at
driving Kenya's economic status in an upward trajectory.
Kenya's Vision 2030 is the government's development
plan designed to make Kenya a global leader, in technology,
and tourism destination. The main projects envisioned
included the LAPSSET project and the redevelopment
of Mombasa port, which currently represent areas that
are important for both conservation and the country’s
economy through the tourism industry.
C) Analysis of inundation areas
SLR scenario modeling using GIS techniques and guided
by the Intergovernmental Panel on Climate Change (IPCC)
under two SLR scenarios of Regional Concentration
Pathways (RCP 2.6 and RCP 8.5), was applied to estimate
the spatial extent, population, and infrastructure under
mapped for the city of Mombasa and the Island of Lamu.
Secondary data helped in identifying the land use pattern’s
and delineate built areas lying within the LECZ. The
analysis of the areas under threat of inundation dueto the
SLR is based on the 1 in 100 years repeat cycle was done
using the Global Mapper and ArcGIS software.
The surface maps including population and urban extent
were analyzed using the “Spatial Analyst” extension of
the ArcGIS 10.2 TM software of the ArcGIS tool which
allowed for the integration of several aspects towards the
CLIVAR Exchanges No. 71, Feb 2017 34
derivation of the most exposed portions. Using Equation
1, the global SLR rates were adjusted for the coastline
of Mombasa and Lamu after considering, uplift and
subsidence, and the chances of a storm surge at the coast.
The adjusted SLR rates provided the data that was input
maps were then overlaid through an overlaying process
to the land areas and land use maps, the two indicators
(population and spatial/urban extent). The synthesized
inundation and demographic data then inputted into the
maps.
D) Calculating Assets and population at risk
The demographic and economic data were important in
the derivation of the population density maps in the cities,
included in the analysis include the point data to identify
areas of interest in Mombasa and Lamu Island. The
population data were projected to 2016 and used in the
analysis of the inundation levels from a baseline of 2009
for comparison purposes and the production of the maps.
The digital elevation model (DEM) for both islands forms
the primary basis of the analysis of inundation zones. DEM
characterization was used to describe the height levels
through the contours and populations distributed along
the contours. The analysis was based on the zero (m)
elevation of the ocean, from which the characterized areas
The population exposed indicator was done by delineating
population living within the Low elevation areas. This was
through the analysis of population grid for the Island and
associated with each grid cell and land area. Estimates for
the population was collected and analyzed from the 2009
census data sets. For the calculation of exposed assets,
the exposed population was translated into the amount
of capital per inhabitant. This capital per inhabitant is
computed from the GDP per capita in each county and an
estimate of the ratio of ”produced capital” to GDP. The ratio
of produced capital to GDP is calculated using the World
Bank dataset published with the ”Changing Wealth of the
Nations” report. Earlier the rate was calculated at 5 times
the GDP his was later scaled down to the ratio equalling
to 2.8 and is applied to the two island (Hallegatte et al.,
2013). The analysis was done on a 30-year cycle.
E) Vulnerability to future SLR analysis
Assessing vulnerability to future SLR was a three-setup
approach. Firstly after generating the digital elevation
map, the map was then subjected to inundation scenarios
and alternative storm-surge (wave height) scenarios
analysis. The scenario analysis was after applying
the equation (1) by Nicholls et al. (2007) under the
and 2090. Secondly, the county surface maps for each
exposure indicator were then prepared (population and
urban extent). Third, these surface indicator maps were
overlaid with the inundation zone layer. The overlaying
of the maps helped in the determinat-ion of the spatial
exposure of each of the two indicators (Population,
urban extent) under inundation threat for the Islands.
The calculation of storm surges (extreme sea levels),
followed the method outlined by Nicholls et al. (2007)
and also applied by several researchers Dasgupta et al.
(2011), Hallegatte et al. (2011) in global studies where
calculations for Future Storm Surges (FSS) are as follows:
(Equation 1)
FSS = S100 + SLR +{UPLIFT *100yrs} + SUB + (S100* x),
1000
where
• FSS= S100=1-in-100 years surge height(m),
• SLR=sea-level rise (based on the IPCC results
from the CIMP models),
• UPLIFT=continental uplift/subsidence in mm/
year,
• SUB=0.5mm (applies to deltas only),
• x=0.1, or increase of 10% applied only in coastal
areas currently prone to tsunamis and tropical
cyclones.
F) Calculating assets exposed to SLR effects
For the analysis of exposed assets, values for 2015
National per capita GDP and the Purchasing Power Parity
(PPP) were used. Analysis of exposed assets was done
using the formulae shown in equation (2). The national
per capita is preferred to the local per capita which would
be translated regarding the coastal contribution to the
country GDP (Hallegatte et al., 2013; Nicholls et al., 2007).
In estimating the anticipated economic losses, a method
suggested by Hallegatte et al. (2013) and Nicholls et al.
(2007) is described as follows:
(Equation 2)
Ea = Ep*GDPpercapita(PPP)*2.8
where,
• Ea= Exposed assets
• Ep= Exposed populations
GDPpercapitappp= the nation's per capita
Gross Domestic Product(GDP) purchasing
power parity (PPP)
According to Nicholls et al. (2011), the factor of 2.8
translates to per capita GDP, i.e. the annual production of
the economy divided by population, to the per capita value
of assets. In their argument, annual investments usually
represent, on average, about 25 percent of GDP. Assuming
that per capita asset value in the city is growing by 3
percent a year, a rapid calculation suggests that the value
GDP Consistent with this estimate.
35 CLIVAR Exchanges No. 71, Feb 2017
Results and Discussions
A) Mombasa and Lamu Island
Table I illustrates the impacts of SLR in the two coastal
islands. Mombasa average annual mean sea level changes
are averaged at 4.32m as collected from the SLR measuring
stations. By applying the sea level rise anticipated globally
and the local high water tide to ascertain the inundation
areas within the island.
B) Scenario 1: Under the RCP 2.6
The inundation-prone areas under the optimistic scenario
of RCP 2.6 by the year 2060. The island area of up to 1.4Km2
in Mombasa and 28.3 km2 of Lamu island is low lying
and is susceptible to inundation. The areas under threat
translate into 10% for Mombasa and 42.6% for Lamu land
mass from accelerated SLR. By the year 2090 island area
of up to 2.3 km2 for Mombasa and 35 km2 for Lamu is low
lying and is susceptible to ocean disturbances. A total of
22% and 60.4% respectively of the area of the islands that
will be rendered unusable by the end of the century due to
C) Scenario 2: Under the RCP 8.5
In Mombasa Island, the area of the island under threat
translated to 2Km2 equivalents to the 8.2 % of the
land exposed. Inundation in 2060, this increases to
approximately 2.6Km2 for Mombasa a percentage of 20%
adding up to 28.2% of the island area under threat from
the effects of an extreme event. By 2090 the coastal regions
prone to inundation is 3.1Km2. The land mass under threat
translates to a total of 44.9% of the land mass under threat
from accelerated SLR In Mombasa island.
D) Population and infrastructure under threat for the
two scenarios
that exposure level to the 1:100 storm surge for Mombasa
County at 4m elevation falls between 434,000 and 2.5
million people and over US$ 6.2 billion in assets exposed
by 2090. For Lamu, the exposure is between 38,000
and 481,000 people and over US$ 79.6 million. Under
increased urbanization, vulnerability for both Mombasa
and Lamu increases to over 25.64 million inhabitants, with
infrastructure losses of approximately US$ 614.9 million.
Currently, 48% to 52% of the infrastructure falls within the
Low Elevation Coastal Zones (LECZ) thus highlighting its
extreme vulnerability. The most vulnerable sectors at the
coast that contribute to Kenya's Vision 2030 and long-term
development are at threat to SLR-induced disturbances.
The spatial analysis in vulnerability assessment for
on both the community and critical infrastructure
apart from displacing populations in these islands.
Accelerated SLR can impact Kenya’s GDP where essential
infrastructures such as the Nyali Bridge, the Mombasa
Port, and the LAPPSET project are rendered vulnerable,
and thus mitigation and adaptation techniques need to
be in place to protect threatened assets and communities.
Meanwhile such knowledge should be nationally included
in the planning and implementation of sustainability
interventions for coastal cities such as Mombasa and Lamu
whose residents and infrastructure are under threat from
impacts of accelerated SLR.
Conclusion
As discussed, a 25% of the residents of Mombasa island
are living below the 10m sea level rise area, with a healthy
11.6% living below the centuries 1.8m sea level rise
inundation areas translating into a population of 133,456
as at 2009 and projected to be 417,289 individuals by
2030 and 1.426,537 individuals by 2090. This area under
the inundation zone includes part of the Mombasa port
and essential to the east and central Africa transport hub
as well as several star rated hotels and homesteads around
the islands and those adjacent to it.
The socio-economic damages expected to arise in the
absence of coastal protection barriers is in the area of 9.1
billion USD, well over 8% of the Kenyan budget. In case of
a projected annual increase of 2.8% in population around
the area, a larger number of residents will beunder threat
century-old planning laws are not helping either. The 90m
coastal high water mark that has not changed for a long
time, in the face of a changing climate, will leave most
of the public spaces around this island vulnerable and
exploited for commercial purposes, therefore putting the
lives of many residents, as well as visitors, at risk. The
nexus between businesses interests, the environment and
the use and access to the public space, associated with
Table 1: Population and island exposed to SLR inundation by
the end of the century for Mombasa and Lamu islands
Item Scenario Mombasa Lamu
Area
8.5 8.0Km2
equivalent to 44.9%
103.5Km2 lead-
ing to 71.5%
threatened
2.6 4.2Km2 threatend 81.1Km2 under
threat
Assets 8.5 US$ 14.5 Billion exposed US$ 1.2 Bilion
2.6
US$ 6.4 Bilion assets ex-
posed US$ 435 Milion
People 8.5 9.7 Million exposed 452 Thousands
2.6 6.1 Million people exposed 248 Thousands
exposed
CLIVAR Exchanges No. 71, Feb 2017 36
rapid discussion on adaptation and mitigation strategies.
Critical infrastructure for Mombasa and Lamu islands,
important port city development initiatives that serve
central and eastern African countries, are faced with
inundation threat as a result of accelerated SLR, under
both the optimistic and the pessimistic scenarios. SLR
impacts, especially on the services of Mombasa port as
described, will affect not only the Kenyan economy but
also the neighboring countries that use and depend on the
coastal city for transport and the daily exchange of goods
and services including tourists. The county and national
government thus need to implement plans and programs
to protect this critical infrastructure from the impacts of
the advancing ocean and especially the slow and gradual
SLR. As of now, neither Kenya’s climate change strategy
nor the Mombasa and Lamu county development strategy
explicitly and systematically factor this challenge into
their planning processes or programs. The adaptation and
disturbances are lumped in one disaster preparedness
budget that still lacks systematic preparedness measures
for the sustainability and protection of these two islands
which are important to the economy of Kenya, policies
on adaptation, coastal planning, and protection with SLR
are put into place. Action plans should be included in
the county development plans that are meant to protect
the environment and enhance the protection of critical
infrastructure in these areas. Since the climate action plan
for the country is salient on the matter of SLR and coastal
protection, it's recommended that an addendum to the
legislation for adaptation and mitigation of SLR impacts to
the coastal cities of Mombasa and Lamu
Acknowledgement
Global change and sustainability research institute of Wit’s
living around Mombasa Island are also acknowledged for
allowing us to have you during data collection. The results
of this paper were obtained during my Ph.D. studies at the
University of The Witwatersrand and are also in my thesis
gratitude to my supervisors Daniel Irurah and Dr. Obudho
Omondi whose guidance and support were crucial for the
successful completion of this project.
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