Coastal Flood Hazard Assessment of Ghana

COASTAL FLOOD HAZARD ASSESSMENT OF GHANA

A Capstone Submitted in Partial Fulfillment of the

Requirements for the Award of the Degree of

MASTER OF SCIENCE IN GIS

Peter Adortse

Under the Supervision of

Prof. Marcos Luna and Prof. Stephen Young

The Department of Geography

Salem State University

May 2019 Table of Contents

List of Tables ...... iii List of Figures ...... iv Abstract ...... 1 Introduction ...... 2 Literature Review ...... 4 Study Area ...... 9 Methods and Data ...... 11 DEM Processing ...... 11 Land Area and Population Inundated...... 12 Results and Discussion ...... 13 Conclusion ...... 24 References ...... 25

List of Tables

Table 1. Area inundated at 2ft, 6ft, and 10ft sea level rise scenarios...... 16 Table 2: Population impacted at 2ft, 6ft, and 10ft sea level rise scenarios...... 18 Table 3: Percentage of population impacted at 2ft, 6ft, and 10ft sea level rise scenarios...... 20

iii

List of Figures

Figure 1: Map of Coastal Zones of Ghana ...... 9 Figure 2: Map of districts along the coast of Ghana ...... 10 Figure 3: Map of SRTM DEM with three different sea level rise scenarios at 2 feet, 6 feet and 10 feet...... 13 Figure 4: Impacted coastal surface area by district ...... 15 Figure 5: Flooded coastal populations by districts ...... 17 Figure 6: Percentage of population impacted by sea level rise scenarios ...... 19 Figure 7: Population impacted by 2 feet sea level rise...... 21 Figure 8: Population impacted by 6 feet sea level rise...... 22 Figure 9: Population impacted by 10 feet sea level rise ...... 23

Abstract

As the planet continues to warm up at an alarming rate, the effect of increasing frequency of coastal flooding is already being felt in most coastal communities. The coastal cities of Ghana are no exception to this effect. Most cities on the eastern shorelines of Ghana’s coast already experience frequent inundation from regular high tides.

Ghana like most African countries have heavily populated coastal cities due to the concentration of most of the country’s economic infrastructure on the coast. The 550km coastline of Ghana is estimated to be occupied by about one fourth of the country’s population and host about 80% of the country’s industrial infrastructure (Armah and Amlalo, 1998).

Prolonged and frequent flooding in these coastal cities will undoubtedly lead to devastating socio-economic effects. Besides the devastating loss of human capital, coastal flooding also, results in extensive destruction of infrastructure, agricultural lands and wetlands.

It is, therefore, imperative to develop reliable and sound systems to monitor the annual recession of shorelines to the ocean in order to accurately forecast future impact of coastal flooding resulting from rising sea levels.

This study seeks to provide an assessment of the flood hazard of the shorelines of Ghana using satellite imagery in Remote Sensing and GIS to provide a model for planning and managing coastal areas. Digital Elevation Model (DEM) from Shuttle Radar Topography Mission (SRTM) will be used to estimate the impact of 3 different scenarios of sea level rise at 2ft, 6ft, and 10ft inundation of the coast of Ghana. To ascertain the impact of coastal flooding on population the most recent population data of the coast of Ghana will used.

Introduction

The occurrence of flood in coastal areas is one of the most devastating natural catastrophes that continues to plague coastal communities (Nadeem et al. 2014; Gigović et al. 2017; Udin et al. 2018 p. 47). It has been predicted by the Intergovernmental Panel on Climate Change (IPCC) that the mean sea level around the globe will reach unprecedented heights in the 21st century and beyond as a result of climate change (Church et al., 2001) with devastating effects on coastal populations and ecosystems (McLean et al., 2001). Generally, coastal areas around the globe are characterized by high population densities due to the concentration of socio-economic activities as well as industrial infrastructure on the coast (Vitousek, S. et al. 2017; Wahl, T. et al. 2017).

It is estimated that the number of people flooded in a typical year by storm surges would increase 6-times and 14-times given a 0.5 and 1.0m rise in global sea levels, respectively (Nicholls et al., 1999). The number of people who are affected by flooding will also increase due to growing coastal populations, including net coastward migration across the globe (WCC’93, 1994; Bijlsma et al., 1996). Coastal flooding also, has significant impact on wetlands. Coastal wetlands are made up of saltmarshes, mangroves and associated unvegetated intertidal areas. These areas serve a very vital role in the coastal ecosystem such as the assimilation of waste, nursery for fisheries, nature conservation and flood protection. It is therefore of paramount importance to develop sound flood hazard models for better flood management and protection of coastal communities.

The recession of coastal shorelines in West Africa countries along the Gulf of Guinea due to erosion is a serious environmental concern (Hinrichsen 1990). The coastal research report by Tilman et al.1989 on the Bight of Benin (the bay of the coast of West Africa) extending eastward for about 400 miles (640 km), from Cape St. Paul (Ghana) to the Nun outlet of the Niger River (Nigeria), stressed the need for coastal management along the region due to coastal erosion. Ghana's coast continues to suffer from coastal erosion and flooding which in many cases has led to loss of infrastructure thus posing

additional cost to its conventional development agenda (Jayson-Quashigah et al., 2013; Appeaning Addo 2011; Amlalo 2006; MoWH 2011).

As global seal level continues to rise, there is a dire need at the country and local levels to determine appropriate response measures to counteract the socio-economic effects on coastlines.

Literature Review

Although flooding is a nationally widespread phenomena, each flood event is highly localized and dependent on the ever-changing local landscape of which only certain portions are inundated with each flooding episode (FEMA, 1992). Those portions of land area affected by floods are usually very important to human life and enterprise that, protection against the hazard inspires regional systems of inter-agency cooperation, national legislation, and national funding.

Coastal flood hazard mapping has come a long way from static models of historic flood events usually used by planners as a guide (Coller et al., 2018). In recent decades, methods for predicting the susceptibility of coastal areas to flooding have dramatically improved with the use of computer models in GIS.

Many researchers have attempted to study the sea level rise and its impacts on the coastal areas and the infrastructure associated with it. Such studies are extremely important in order to develop informed policy making related to climate change in general and seal level rise in particular.

Li et al., (2009), developed a comprehensive global model to analyze and visualize the impacts of sea level rise. The prominent feature of their research was to locate the potentially inundated area, the spatial extent of inundated area, type of land-cover being affected, and the effects on the population. The methods used by Li et al., (2009) were developed by Weiss and Overpeck (2003). The DEM was the foundation for their analyses. In the first step raster, cells adjacent to the ocean were selected. From these selected cells, cells with the elevation at or below the projected sea level rise increment were extracted. Selected cells were then converted to the ocean layer and thus the inundation zones from this layer were generated. Next, the area for the inundated zones was calculated using the formula developed by Bjorke and Nilsen (2004). The results showed that with 1-m increase of sea level rise, an area of 1.055 million km2 was under water while at 6 m the area is increased to 2.193 million km2. The population at risk due to potential inundation ranges from 107.9 million people at 1 m to 431.4 million people at 6 m.

There were, however, a few limitations in the methodology. The first limitation was that the method did not take into consideration the pressure of sea level rise on high tide levels which is important to compute accurate flood risk maps (Anthoff et al.2006). The second limitation was that the existing protections, such as dikes were not considered in the methods (Tol et al., 2006; McGranahanet al., 2007). Lastly, the use of uniform sea level rise ignores the fact that changes in sea level do not occur uniformly around the world IPCC (2007). The economic impacts were also not part of the analyses.

Gravelle and Mimura (2008) developed a model which not only presented the impacts of the sea-level rise on the coastal areas, but it also represented the areas which would be affected due to storm surges from sea level rise. The model was developed in three stages, in the first stage design water level components were created to show possible flooding and inundation around the coastline based on the sea level rise and storm surge scenario. The layer was generated from the tidal information and the storm surge scenarios were created from the residual water levels before and after the storm and from the tide gauges. In the second step, based on the IPCC projections, sea level rise values were added to the design water level layer. Finally using ArcGIS, the DEM, and the DWL layer was overlaid to show the extent of the inundation.

The results not only showed permanently inundated areas but also presented the areas that were affected by the storm surge pressure. The final output showed the high- risk areas based on the different scenarios. The major limitation of the study was that the results were generated at a very broad scale, in a sense that only high-risk areas were identified. The areas were not quantitatively assessed to produce tables or graphs showing the impact on land, infrastructure, and population.

El-Nahry and Doluschitz (2010) used GIS to determine the impacts of sea level rise on the Nile Delta. Their analyses were mainly concerned about the loss of land and alteration of soil characteristics on the delta. The analyses were heavily based on the field work through which the actual coastline was generated. Additionally, they used Landsat and ASTER imagery to derive digital elevation model and to conduct change detection for the coastline. Sea level rise scenarios were then generated using a quadratic equation as a sum of global sea level rise, regional oceanic effects, and vertical land movement.

Sr, t = Sg, t + So, t + Vt where Sr, t = relative SLR in year t (m), Sg, t = global SLR in year t, So, t = regional sea level change induced by oceanic changes in year t (m), V = vertical land movement (m/year), and t = number of years in the future (base year 1990).

Through the above methodology, three sea level rise scenarios were created including 1 m rise, 1.5 m rise, and 2 m rise. With 1 m rise area of 6900 km2 would inundate which will include cropland, wetland, and fish ponds rep-resenting 28.93 % of the total area of the Nile Delta. At 2 m rise area of 8425 km2 will be lost representing 35.33 % of the total Delta area. Finally, at 3 m rise 121,106 km2 area will be lost representing 50.78 % of the total area of the Nile Delta.

Al-Jeneid et al., (2008) quantitatively assessed the impact of sea level rise on Bahrain. To accomplish their goals, the researchers integrated the use of Remote Sensing and GIS. As the first step in their methodology, they used the QuickBird satellite imagery to classify the image using unsupervised classification algorithm. Through the classification process and field work to enhance the results a land-use-land-cover map was created. Next, a high-resolution DEM (595 m) was developed using the contours, height, and Bathymetry points. The contours, on the other hand, were manually digitized. Using the elevation points from the DEM and projections from IPCC, sea level rise scenarios were created. The scenarios were divided into three parts: low (0.5 m) moderate (1 m), and high (1.5 m).

Natesan and Parthasarathy (2010) analyzed the potential impacts of sea level rise along the coastal zone of Kanyakumari District in Tamilnadu, India. The methodology in the following study was divided into five parts. In the first part, GIS data was generated by digitizing the study boundary and coastal villages. In the second step the contours were created and in the third part of the methodology, DEM was created. The DEM was generated using the elevation points and interpolation method. Next, using the DEM inundation zones were derived by setting the value 0.5 m and 1 m for the sea level rise scenario. Finally, the inundated areas were overlaid with land-use, village, tourism, and sensitive areas data to analyze the impact.

From the preceding paragraphs, it is evident that, there are several flood models and techniques for coastal flood hazard mapping, most of which employ hydrological, meteorological and geomorphological data but not all of these approaches are applicable in developing countries largely due to the lack of adequate and accurate hydrological and meteorological data. The use of geomorphologic data for flood hazard mapping in coastal areas is more widespread in developing countries because they are easy to obtain and generally more effective and appropriate (Wolman, 1971; Lastra et al., 2008). This method involves the use of aerial imagery, mainly satellite imagery to provide a better understanding of different land cover and land use types in a study area.

The use of satellite imagery such as Landsat and SRTM DEM in flood hazard modelling has proven to be an economical and efficient method especially in areas where there are inadequate data (Wang et al., 2002). Combining land use classifications from a Landsat Image with SRTM DEM classifications has been in use for coastal flood hazard mapping (Demirkesen et al., 2006; Willige, 2007). Umitsu et al. (2006) shows how important it is to combine SRTM with GIS in flood and land use analysis.

Remote sensing technology has been used to identify, monitor and assess coastal changes in various places using mapping techniques (Lim et al., 2008; Chen et al., 2005). The mapping methods adopt techniques that extract the shoreline positions from data sources. The conventional data sources consist of historical maps, aerial photographs and repeated field measurement, while the current sources are obtained from remote sensing technologies using airborne, spaceborne and land-based techniques. Remote sensing advancement has thus enabled the provision of a continuous monitoring of the shoreline globally. This enables shorelines mapped in situ and extracted from aerial photographs to be compared to, detect, measure and analyze change. Historic rate of change information and estimated sea level rise enable the future shoreline positions over time to be estimated and its impact on the coastal environment identified.

Satellite images have shown that, the extent of Arctic sea ice has declined by about 8.5% per decade from its size in 1979 (Mastrandrea and Schneider, 2008). According to (Meehl et al., 2008), if the observed increases in ice discharge rates from the Greenland and Antarctic ice sheets were to increase linearly with global mean temperature change,

this would add a 0.05 to 0.11 m rise for the A1FI scenario over the 21st century. The global average rate of sea level rise for the past century has been estimated to be about 10 to15 cm, which could rise to about 1 m over the next century (Houghton et al., 2001). The effect would vary locally due to prevailing factors such as isostatic adjustment of the mantle and variations in oceanic level change (Gornitz. V, 2000).

The identification of coastal areas that are prone to seal level rise is very important in starting the planning process. It is often quite challenging to identify vulnerable coastal areas due to varying spatial distribution of coastal landscapes. Localized modelling of complex coastal processes is therefore, required.

Available models typically require large, detailed datasets and are computationally intensive (Chadwick et al., 2015; Barnard et al., 2014). So, it is common for local communities to develop SLR adaptation guidance based on either no modeling; or on a simple passive (or “bathtub”) terrain flood model (Hinkel et al., 2014). The passive flood model is a projection of a flood surface onto a digital elevation model (DEM). The flood surface, in its simplest form, is a horizontal plane of pre-determined height or elevation (e.g. SLR). It is easy to implement and provides the requisite spatial specificity but does not give a comprehensive picture of coastal flooding hazard due to sea level rise as it does not consider dynamic processes such as tides, storm surge and land cover, which have been shown to have a significant impact on coastal communities.

In this study, factors such as population and land area will be modelled to derive a more comprehensive quantification of land area exposed to sea level rise at the local level as well as the proportion of population impacted.

Study Area

The coast of Ghana is generally divided into 3 geomorphological zones namely: the west coast, central coast and eastern coast (Ly, 1980). The West Coast is about 95 km and is geologically made up of fine sand, gentle beaches, coastal lagoons. The Central Coast is about 321 km and is made up of embayed coast of rocky headlands, rocky shores, littoral sand barriers, coastal lagoons. The East Coast is about 149 km and comprises of sandy beaches, deltaic estuary of Volta River situated halfway in-between.

The coastal zone of Ghana represents about 6.5% of the total land area of the country but is occupied by about 25% of the country’s population and hosts about 80% of the country’s industries. Over 70% of the shoreline of 550km is sandy (Armah and Amlalo, 1998).

Figure 1: Map of Coastal Zones of Ghana

Currently there are 28 administrative districts located along the coast Ghana. These are shown in figure 2 Below:

Figure 2: Map of districts along the coast of Ghana

Methods and Data

The impact of sea level rise has been highlighted and analyzed following closely the bathtub model to build the sea level rise scenarios and to assess the vulnerability of the coastal areas using GIS geoprocessing approaches. This study identifies vulnerable coastal districts and population and assesses the degree of future risk posed by SLR in order to enhance the coastal towns’ capacity to tackle the possibility of accelerated sea level rise.

The methodological framework applied in the study can be divided into two sections; the first section deals with analyzing the impacted areas through spatial analysis, while the second part simulates the impact and presents the results in the form of simulation. All analyses were carried out using ArcMap.

DEM Processing

A 30-meter SRTM DEM was downloaded in tiles to cover the study area and then mosaiced together in ArcMap using the create mosaic dataset tool under Data Management Tools. All possible sinks in the DEM were filled using the fill tool in ArcMap. This process is necessary to remove any imperfections in the original DEM by converting the negative cell values into meaningful elevation. The SRTM DEM was then clipped to the shapefile of the entire coast of Ghana. Using raster calculator under map algebra in the Spatial Analyst Tools, three different sea level rise scenarios of 2 feet, 6 feet and 10 feet were applied to the DEM. This was done by first converting the sea level rise scenarios from feet to meters since the DEM values are in meters. The following formulae was used to calculate the raster cells inundated under each sea level rise scenario:

DEM Raster ≤ 0.61m (2ft) (1)

DEM Raster ≤ 1.83m (6ft) (2)

DEM Raster ≤ 3.05m (10ft) (3)

The above generated rasters consisted of cells with two values, 0 and 1 with 1 representing the cells that meet the query criteria of 2 feet while 0 represented the remaining cells in the raster. Therefore, these rasters were reclassified using the reclassify tool to assign all the 0 value cells as No Data and thus the respective cells had null values. This process was repeated for all three rasters.

Land Area and Population Inundated

The land area inundated was determined from the inundated rasters created in equations 1-3. To calculate the proportion of the land area of each district affected by 2 feet sea level rise, the total area of the 2 feet polygon was determined from the statistics of the column representing the shape area. In the attribute table of the districts shapefile a new field was added, and the field calculator was used to populate it to determine the proportion of the total land area of each district that will be inundated under 2 feet seal level rise scenario. This was repeated for 6 feet and 10 feet sea level rise scenarios. The formula used is shown below:

Total area of 2 feet SLR / Total shape area of each district (4)

The coastal population inundated at the various levels of sea level rise scenarios was derived from the 2019 population estimate for Ghana. This was preferred to the current 2010 data because of changes in district boundaries which are yet to be fully rectified. The population data was obtained from the Ghana Statistical Services and consisted of the total population for each coastal district. The data was obtained in an excel sheet, edited and imported into ArcMap using the “Excel To Table tool under Conversion Tools”. The imported table was joined to the district’s shapefile and the proportion of the population inundated was estimated using the land areas threatened under each sea level scenario. The formula can be represented as follows:

Population affected = area flooded by SLR * total population (5)

Results and Discussion

Sea Level Rise (SLR) above the Mean Sea Level (MSL) may pose a substantial threat to Ghana’s coastal areas because of its generally low-lying physiographical setting.

Critical infrastructures such as major roads, and many socioeconomic activities are concentrated in the coastal areas. These areas support various economic activities including hotels, airport, and industrial and commercial complexes. In addition, significant numbers of residential areas are also concentrated along the coastal areas. With an increase in the sea level, not only the areas along the coast will be affected but also the low-lying adjacent land will also be inundated.

The main objective of this research was to create potentially inundated areas using digital elevation model for the coast of Ghana as shown in figure 3. Through these inundated areas, the impacted areas and population was assessed both qualitatively and quantitatively.

Figure 3: Map of SRTM DEM with three different sea level rise scenarios at 2 feet, 6 feet and 10 feet.

The factors that were considered crucial in the impact assessment included surface area, neighborhoods, and population. The proportion of population impacted was calculated by considering the assumption that population is evenly distributed in every coastal district; therefore, the total district population was used as one of the variables in the equation.

At 2 feet sea level rise scenario, the impact on surface area and population of the coastal districts were generally minimal but quite pronounced for a few coastal districts (tables 1 and 2). The hardest hit district in terms of land area inundated will be La Dade in the Greater Accra Region with 13.890 km2 representing 38% of the total land area of the district, followed by Ledzokuku, Effutu, and Tema districts with 10.520 km2 (22%), 5.878 km2 (7%) and 5.699 km2 (6%), respectively (table 1 and 3; figure 4 and 6).

In terms of proportion of the population impacted at 2 feet sea level rise scenario, the most impacted districts are La Dade, Ledzokuku, Accra, and Tema with 84,475 people (38%), 60,269 people (22%), 53,035 (3%), and 22692 (6%), respectively (table 2 and 3; figure 5 and 6). As can be seen from the figures quoted, even though Accra district come in as the third district with the most population lost, the percentage of the total population lost is low at 3%.

At 6 feet and 10 feet sea level rise scenarios, the impact on the districts follow the same pattern with increasing proportions. The most inundation and population lost will occur at 10 ft sea level scenario.

Area Impacted by Sea Level Rise

Area_2ft Area_6ft Area_10ft

Area Area Flooded (Sq.Km) 10

Ghana Coastal Districts

Figure 4: Impacted coastal surface area by district

Total Area Area (KM2) Area (KM2) Area (KM2) Districts (KM2) Flooded Flooded Flooded 2 ft SLR 6 ft SLR 10 ft SLR Ledzokuku/Krowor 48.043 10.520 18.935 25.207 Ketu South 281.244 1.797 3.235 4.306 Keta Municipal 760.653 0.664 1.1959 1.592 South Tongu 650.351 0.777 1.3988 1.862 Gomoa West 462.756 1.092 1.966 2.617 Effutu 85.981 5.878 10.580 14.084 Gomoa East 544.857 0.928 1.669 2.223 Jomoro 1507.055 0.335 0.604 0.804 Ellembelle 1003.680 0.504 0.906 1.207 Ahanta West 558.312 0.905 1.629 2.169 Nzema East 1092.604 0.463 0.833 1.108

Sekondi Takoradi 193.163 2.616 4.709 6.269

CapeCoast/Metro 123.434 4.095 7.369 9.810 Komenda Edna Eguafo/ Abirem 456.298 1.107 1.994 2.654 Ada East 292.733 1.726 3.108 4.137 Ada West 327.104 1.545 2.781 3.702 Ningo/Prampram 628.648 0.804 1.447 1.926 LaDade/Kotopon 36.386 13.890 25.002 35.217 Shama 195.252 2.588 4.659 6.202 Mfantsiman 303.336 1.666 2.999 3.992 Ekumfi 279.120 1.811 3.259 4.339 Awutu Senya East 109.041 4.634 8.343 11.106 Accra Metropolis 141.048 3.583 6.449 8.586 Ga South 345.253 1.464 2.635 3.508 Tema Metropolis 88.680 5.699 10.258 13.656 Kpone/Katamanso 242.335 2.086 3.754 4.997

Table 1. Area inundated at 2ft, 6ft, and 10ft sea level rise scenarios.

Sea Level Rise Impact on Population

Pop_Flooded2ft Pop_Flooded6ft Pop_Flooded10ft

250000

200000

150000

100000 PopulationFlooded 50000

Shama

Ekumfi

Jomoro

AdaEast

AdaWest

Ellembelle

KetuSouth

Nzema East Nzema

Gomoa East Gomoa

Mfantsiman

South Tongu South

Ahanta West Ahanta

Gomoa West Gomoa

Keta Municapal Keta

Effutu Municipal Effutu

Ningo Prampram Ningo

Accra Metropolis Accra

Tema Metropolis Tema

KponeKatamanso

Ga South Municipal Ga

CapeCoast Metropolis

La Dade Kotopon Municipal La

Awutu Senya East Senya Municipal Awutu

Sekondi Takoradi Metropolis Sekondi

Ledzokuku/Krowor Municipal Ledzokuku/Krowor Komenda-Edina-Egyafo-Abirem… Ghana Coastal Districts

Figure 5: Flooded coastal populations by districts

Total Population Population Population Districts Population Flooded Flooded Flooded 2ft SLR 6ft SLR 10ft SLR Ledzokuku/Krowor 275,239 60269 108482 144412 Ketu South 198,073 1266 2278 3033 Keta Municipal 182,427 159 287 382 South Tongu 109,461 131 235 313 Gomoa West 160,214 378 681 906 Effutu 82,009 5606 10091 13434 Gomoa East 248,679 423 762 1014 Jomoro 194,808 43 78 104 Ellembelle 114,441 57 103 138 Ahanta West 139,188 226 406 541 Nzema East 80,480 34 61 82

Sekondi Takoradi 726,905 9846 17723 23592

CapeCoast/Metro 186,159 6175 11115 14797 Komenda Edna / Eguafo 159,580 387 697 928 Ada East 86,002 507 913 1215 Ada West 71,133 336 605 805 Ningo/Prampram 85,406 109 197 262 LaDade/Kotopon 221,284 84475 152051 214179 Shama 105,173 1394 2510 3341 Mfantsiman 159,573 876 1578 2100 Ekumfi 61,718 400 721 959 Awutu Senya East 129,629 5510 9918 13203 Accra Metropolis 2,087,668 53035 95461 127079 Ga South 521,162 2210 3977 5295 Tema Metropolis 353,086 22692 40844 54372 Kpone/Katamanso 132,070 1137 2046 2723

Table 2: Population impacted at 2ft, 6ft, and 10ft sea level rise scenarios.

Percentage of population flooded by sea level rise scenarios

% Pop Flooded_2ft % Pop Flooded_6ft % Pop Flooded_10ft

120

100

40 Percent Percent Population 20

Shama

Ekumfi

Jomoro

AdaEast

AdaWest

Ellembelle

KetuSouth

Nzema East Nzema

Gomoa East Gomoa

Mfantsiman

South Tongu South

Ahanta West Ahanta

Gomoa West Gomoa

Keta Municapal Keta

Effutu Municipal Effutu

Ningo Prampram Ningo

Accra Metropolis Accra

Tema Metropolis Tema

KponeKatamanso

Ga South Municipal Ga

CapeCoast Metropolis

La Dade Kotopon Municipal La

Awutu Senya East Senya Municipal Awutu

Sekondi Takoradi Metropolis Sekondi

Ledzokuku/Krowor Municipal Ledzokuku/Krowor Komenda-Edina-Egyafo-Abirem… Ghana Coastal Districts

Figure 6: Percentage of population impacted by sea level rise scenarios

Total Percent Percent Percent Districts Population Population Population Population Flooded Flooded Flooded 2ft SLR 6ft SLR 10ft SLR Ledzokuku/Krowor 275,239 22 39 52 Ketu South 198,073 1 1 2 Keta Municipal 182,427 0 0 0 South Tongu 109,461 0 0 0 Gomoa West 160,214 0 0 1 Effutu 82,009 7 12 16 Gomoa East 248,679 0 0 0 Jomoro 194,808 0 0 0 Ellembelle 114,441 0 0 0 Ahanta West 139,188 0 0 0 Nzema East 80,480 0 0 0

Sekondi Takoradi 726,905 1 2 3

CapeCoast/Metro 186,159 3 6 8 Komenda Edna Eguafo/ Abirem 159,580 0 0 1 Ada East 86,002 1 1 1 Ada West 71,133 0 1 1 Ningo/Prampram 85,406 0 0 0 LaDade/Kotopon 221,284 38 69 97 Shama 105,173 1 2 3 Mfantsiman 159,573 1 1 1 Ekumfi 61,718 1 1 2 Awutu Senya East 129,629 4 8 10 Accra Metropolis 2,087,668 3 5 6 Ga South 521,162 0 1 1 Tema Metropolis 353,086 6 12 15 Kpone/Katamanso 132,070 1 2 2

Table 3: Percentage of population impacted at 2ft, 6ft, and 10ft sea level rise scenarios.

Figures 7, 8, and 9 show maps that represent the number of people that will be impacted at 2 feet, 6 feet, and 10 feet sea level rise scenarios. The maps show the most impacted districts in dark brown, followed by moderately impacted districts in brown and the least impacted districts in yellow.

From figure 7, La Dade, Ledzokuku, and Tema districts on the central coast of Ghana have the greatest number of people impacted under 2 feet sea level scenario. Ketu South district on the eastern coast of the country have population moderately impacted and Jomoro, Ellembelle, and Nzema East have the least impacted number of people.

Figure 7: Population impacted by 2 feet sea level rise

Figures 8 and 9 show the same pattern with increasing number of potential populations impacted. The most impact on the population of the coastal districts will be felt at 10 feet sea level rise scenario (figure 9).

Figure 8: Population impacted by 6 feet sea level rise

Figure 9: Population impacted by 10 feet sea level rise

Conclusion

The main objective of this study was to assess and analyze the impact of sea level rise on the coastal communities of Ghana. Several studies related to the use of GIS and sea level rise impact assessment were explored. In order to analyze the sea level rise impact and assess the damage, a model of inundated areas was created using an SRTM DEM in ArcMap following the bathtub model. This model in the form of three different sea level rise scenarios was then overlaid with the shapefile of the study area making sure to use a projection that preserves area.

The Area inundated by each sea level rise scenario was determined for the various coastal districts. Population data was in a non-spatial form and was joined to the shapefile of the study area in order to estimate the proportion and percentage of population inundated under the 3 sea level scenarios.

Through analyses, 10 ft sea level rise scenario will have the most impact on a range of different areas and population. Beaches, estuaries, and creeks will most likely be affected, followed by floodplains and tidal areas. Additionally, valuable infrastructure such as road network, residential and non-residential buildings, and utilities will also be damaged. As a result of an impact on infrastructure, many people will be displaced or forced to move out to other safe locations.

Although there are few limitations in the study, the results can nevertheless, be used by the local government and stakeholders for further research. Additionally, the findings can be valuable for the local and regional government to effectively develop mitigation and adaptation measures. The major limitation of these study was the inadequate availability of data. Storm data and tidal data obtained were incomplete and could not be verified so it was omitted from the analysis.

In future studies on flood hazard assessment of the coast of Ghana, it is recommended that, land use changes and infrastructure such as roads and buildings be included to determine the economic cost of potential inundations.

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