The World Bank · in collaboration with TE, UNESCO-IHE and VITO D4a: COCED analysis for Ghana...

The World Bank

Cost of Coastal Environmental Degradation, Multi Hazard Risk Assessment and Cost Benefit Analysis

D4a: Cost of Coastal Environmental Degradation for Ghana

8 December 2017 - version 2.0

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Colophon

International Marine & Dredging Consultants

Address: Van Immerseelstraat 66, 2018 Antwerp, Belgium

: + 32 3 270 92 95

: + 32 3 235 67 11

Email: [email protected]

Website: www.imdc.be

IMDC nv Cost of Coastal Environmental Degradation in collaboration with TE, UNESCO-IHE and VITO D4a: COCED analysis for Ghana

I/RA/12148/17.026/ABO 4 version 2.0 - 8/12/2017

Table of Contents

1. INTRODUCTION ............................................................................................................. 8

1.1 THE ASSIGNMENT ..................................................................................................... 8

1.2 SCOPE OF THE REPORT .......................................................................................... 9

1.3 STRUCTURE OF THE REPORT................................................................................. 9

2. SITUATION IN GHANA ................................................................................................. 10

2.1 COUNTRY LEVEL ..................................................................................................... 10

2.2 PILOT SITE LEVEL .................................................................................................... 11

2.2.1 Characteristics of the natural and human environment ..................................... 11

2.2.2 Human occupation and economic activities ...................................................... 12

2.2.3 Sources of environmental degradation in the pilot site ..................................... 13

3. METHODOLOGY .......................................................................................................... 16

3.1 METHOD TO ASSESS RISKS FOR EROSION AND FLOODING ........................... 16

3.1.1 Generic description of method for damage assessment. .................................. 17

3.1.2 Flooding: damage functions for tangible damages ........................................... 18

3.1.3 Flooding: damage functions for intangible damages ......................................... 21

3.1.4 Damage functions for erosion ............................................................................ 22

3.1.5 Impacts sea level rise on permanent inundation of wetlands............................ 23

3.1.6 Damage assessment versus carrying capacity for recovery ............................. 23

3.2 EXPOSURE ASSESSMENT ..................................................................................... 24

3.2.1 Land use categories........................................................................................... 24

3.2.2 Difference between the assessment at national level and at pilot site level ..... 27

3.2.3 Exposure assessment for future years (2050 and 2100) .................................. 27

3.2.4 Land use policies to limit future damages ......................................................... 29

3.3 DAMAGE ASSESSMENT .......................................................................................... 29

3.3.1 Values at risk for tangible damages .................................................................. 29

3.3.2 Number of people affected ................................................................................ 35

3.3.3 Valuation of ecosystems .................................................................................... 35

3.3.4 Damage functions .............................................................................................. 41

3.4 DAMAGES FROM WATER POLLUTION .................................................................. 47

4. COCED ANALYSIS FOR GHANA ................................................................................ 49

4.1 COCED ANALYSIS AT COUNTRY LEVEL .............................................................. 49

4.1.1 Erosion: which land uses are affected ............................................................... 49

4.1.2 Flooding: which land uses are affected ............................................................. 50

4.1.3 Total risks of erosion and flooding per year ...................................................... 53

4.1.4 Aggregated risks over different time horizons ................................................... 56


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4.2 COCED ANALYSIS FOR THE PILOT SITE .............................................................. 57

4.2.1 Erosion: which land uses are affected ............................................................... 57

4.2.2 Flooding: which land uses are affected ............................................................. 59

4.2.3 Total risks for erosion and coastal flooding per year ......................................... 62

4.2.4 Aggregated risks over different time horizons ................................................... 65

5. CONCLUSIONS ............................................................................................................ 66

5.1 METHODOLOGY ....................................................................................................... 66

5.2 SUSTAINABILITY INDICATORS FOR COASTAL DEVELOPMENT IN GHANA ..... 67

5.2.1 Analysis at regional level. .................................................................................. 67

5.2.2 Analysis at pilot site level ................................................................................... 67

6. REFERENCES .............................................................................................................. 69

List of Tables

TABLE 3-1: DAMAGE CATEGORIES AND AVAILABILITY OF METHODS FOR DAMAGES

ASSESSMENT ................................................................................................................... 18

TABLE 3-2: OVERVIEW OF LAND USE CATEGORIES FOR IMPACT ASSESSMENT ............................ 26

TABLE 3-3: ESTIMATION OF THE GDP/EMPLOYEE FOR 3 MAIN ECONOMIC SECTORS ................... 30

TABLE 3-4: ESTIMATION OF THE GDP/CAPITA FOR URBAN AND RURAL AREAS ............................ 31

TABLE 3-5: ESTIMATES OF THE VALUE OF DIFFERENT NON-RESIDENTIAL TYPES OF

BUILDINGS, TOGO. ........................................................................................................... 32

TABLE 3-6: ESTIMATION OF THE ECONOMIC VALUES AT RISK PER LAND USE CATEGORY

($/HA) .............................................................................................................................. 34

TABLE 3-7: OVERVIEW OF RELEVANT GOODS AND SERVICES DELIVERED BY WETLANDS AND

MANGROVES ($/HA) .......................................................................................................... 36

TABLE 3-8: SUMMARY STATISTICS FOR MANGROVE EVALUATIONS BY TYPE OF SERVICE (IN

US$/ HA.YR) .................................................................................................................... 37

TABLE 3-9: ESTIMATION OF THE ECONOMIC VALUES AT FOR WETLANDS AND MANGROVES

($/HA) .............................................................................................................................. 40

TABLE 3-10: DAMAGE FUNCTIONS FOR FLOODS, SHORT DURATION (E.G. A COUPLE OF

HOURS) ........................................................................................................................... 42

TABLE 3-11: DAMAGE FUNCTIONS FOR FLOODS, LONG DURATION (E.G. SEVERAL DAYS) ............ 43

TABLE 3-12: DAMAGE FUNCTIONS FOR EROSION ...................................................................... 45

TABLE 3-13: DAMAGE FUNCTIONS FOR EROSION OF ECOSYSTEMS ............................................ 47

TABLE 3-14: DAMAGES FROM POOR WATER SERVICES AND POLLUTION, TOGO .......................... 48

TABLE 4-1: IMPACTS FROM EROSION IN THREE TYPICAL YEARS (2015, 2050 AND 2100). ........... 49

TABLE 4-2: SHARE OF LAND USE CATEGORIES IN IMPACTS FROM EROSION (2015, 2050 AND

2100). ............................................................................................................................. 50

TABLE 4-3: IMPACTS FROM A 100 YEAR COASTAL FLOOD EVENT IN 2015, 2050 AND 2100. ........ 51

TABLE 4-4: RISKS FROM COASTAL FLOODING IN 2015, 2050 AND 2100. .................................... 52


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TABLE 4-5: SHARE OF LAND USE CATEGORIES AFFECTED BY FLOODING IN 2015, 2050 AND

2100. .............................................................................................................................. 52

TABLE 4-6: PROJECTIONS FOR GROWTH OF DEMOGRAPHY AND GDP UP TO 2100 ..................... 53

TABLE 4-7: NUMBER OF PEOPLE ANNUALLY EXPOSED TO RISK FOR EROSION OR FLOOD UP

TO 2100. ......................................................................................................................... 54

TABLE 4-8: ANNUAL RISK FOR EROSION UP TO 2100 (MILLION $/YEAR) ...................................... 54

TABLE 4-9: ANNUAL RISK FOR COASTAL FLOODING UP TO 2100 (MILLION $/YEAR) ...................... 55

TABLE 4-10: ANNUAL RISK FOR EROSION + FLOODING UP TO 2100 (MILLION $/YEAR) ................. 55

TABLE 4-11: TOTAL AGGREGATED RISKS FOR DIFFERENT TIME HORIZONS AND DISCOUNT

RATES.............................................................................................................................. 56

TABLE 4-12: EVOLUTION OF THE AREAS AT RISK FOR EROSION AND COASTAL FLOODING AT

THE PILOT SITE ................................................................................................................. 57

TABLE 4-13: IMPACTS FROM EROSION IN THREE TYPICAL YEARS (2015, 2050 AND 2100). ......... 58

TABLE 4-14: SHARE OF LAND USE CATEGORIES IN IMPACTS FROM EROSION (2015, 2050

AND 2100). ...................................................................................................................... 58

TABLE 4-15: IMPACTS FROM A 100 YEAR FLOOD EVENT IN 2015, 2050 AND 2100. ..................... 60

TABLE 4-16: RISKS FROM FLOODING FOR FLOODS WITH DIFFERENT RETURN PERIODS. .............. 61

TABLE 4-17: RISKS FROM FLOODING IN 2015, 2050 AND 2100. ................................................. 62

TABLE 4-18: SHARE OF LAND USE CATEGORIES IN FLOOD RISKS IN 2015, 2050 AND 2100. ........ 62

TABLE 4-19: NUMBER OF PEOPLE ANNUALLY EXPOSED TO RISK FOR EROSION OR FLOOD UP

TO 2100. ......................................................................................................................... 63

TABLE 4-20: AVERAGE ANNUAL RISK FOR EROSION UP TO 2100 (MILLION $/YEAR) ..................... 63

TABLE 4-21: AVERAGE ANNUAL RISK FOR COASTAL FLOODING UP TO 2100 (MILLION

$/YEAR) ........................................................................................................................... 64

TABLE 4-22: AVERAGE ANNUAL RISK FOR EROSION + FLOODING UP TO 2100 (MILLION

$/YEAR) ........................................................................................................................... 64

TABLE 4-23: TOTAL AGGREGATED RISKS FOR DIFFERENT TIME HORIZONS AND DISCOUNT

RATES.............................................................................................................................. 65

List of Figures

FIGURE 2-1: PILOT SITE AREA IN GHANA, SECTOR GH9-A NEW NINGO-LEKPOGUNO

(SOURCE: GOOGLE EARTH) .............................................................................................. 11

FIGURE 2-2: IMAGES OF ILLEGAL BEACH MINING ON THE GHANA COASTLINE (BOATENG, 2012) .............................................................................................................................. 14

FIGURE 3-1: SCHEMATIC OVERVIEW OF THE 4 STEP METHODOLOGY. ......................................... 17


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List of Acronyms and Abbreviations

CBA / ACB Cost Benefit Analysis / Analyse Coût-Bénéfice

CCA / ACC Climate Change Adaptation / Adaptation au Changement Climatique

CI / IC Coastal Index / Indicateur Côtier

CLC Corine Land Cover / Données de couverture et d’occupation des sols

Corine

COCED Cost Of Coastal zone Environmental Degradation / Coûts de la

Dégradation de l’Environnement Côtier

CRAF Coastal Risk Assessment Framework / Plan d’Evaluation des Risques

Côtiers

DRR / RRC Disaster Risk Reduction / Réduction des Risques de Catastrophe

EVI / IVE Economic Vulnerabity Index / Index de Vulnérabilité Economique

EWS Early Warning System / Système d’Alerte Précoce

GDP / PIB Gross Domestic Product / Produit Intérieur Brut

IRR / TRI Internal Rate of Return / Taux de Rendement Interne

IUCN / UICN the International Union for Conservation of Nature / Union

Internationale pour la Conservation de la Nature

NDF / FND the Nordic Development Fund / Fonds Nordique de Développement

NPV / VAN Net Present Value / Valeur Actuelle Nette

SVI / IVS Social Vulnerability Indicator / Indicateur de Vulnérabilité Sociale

TA / AT Technical Assistance / Assistance Technique

ToR / TdR Terms of Reference / Termes de Reference

WACA West Africa Coastal Areas management program / Programme de

Gestion des Zones Côtières d’Afrique de l’Ouest

WAEMU / UEMOA West African Economic and Monetary Union / Union Economique et

Monétaire Ouest-Africaine


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1. INTRODUCTION

1.1 THE ASSIGNMENT

The West African coastal area hosts big infrastructure, major industries, tourism, agriculture

and fishing activities as well as human settlements and its forerunners (e.g. communication

routes) that drive economic growth and provide the livelihoods of many people. It is one of

the most rapidly urbanising areas in the world and in many of the West African countries

the economic activities that form the backbone of national economies are located within the

coastal zone; however population pressures and increasing exploitation of coastal

resources have led to rapid coastal environmental degradation. Coastal ecosystems in

West Africa now face a range of challenges, including: coastal erosion, overexploitation of

natural resources (such as fisheries and sand/gravel mining), marine and coastal pollution,

rapid urbanization and unsustainable land use, and overall poor environmental governance

(The World Bank, 2016).

To address these challenges, the World Bank is developing a Programmatic Technical

Assistance (TA) for a West Africa Coastal Areas Management Program (WACA). The

project ‘WACA Erosion and Adaptation’ is part of the WACA Programmatic TA and aims to

promote sound coastal management practices for a selected group of countries. In the

countries covered by the present assignment, that is Benin, Côte d’Ivoire, Ghana and Togo,

the project is financed by the Nordic Development Fund (NDF), which has entrusted the

World Bank with its implementation.

As part of the project ‘WACA Erosion and Adaptation’, the main objectives of the

consultancy services for the ‘Cost of Coastal Environmental Degradation, Multi Hazard Risk

Assessment and Cost Benefit Analysis’ are:

To conduct a multi hazard and climate risk assessment of the coastal zone's

vulnerability to climate change and climate variability in Benin, Cote d'Ivoire, Ghana

and Togo, with a special focus on 4 selected sites;

To assess the Cost Of Coastal zone Environmental Degradation (COCED);

To evaluate the most efficient options to protect the populations, the natural assets,

the capital assets, the cultural assets and the activities of the selected pilot zones.

The following deliverables are expected:

D0: Inception Report;

D1a,b,c,d: Reports on the qualitative review of natural hazards and risk mapping, for

each country;

D2: Report on the definition of the pilot sites for each of the four countries and the

detailed methodology;

D3a,b,c,d: Reports for the quantitative risk assessment of coastal erosion and

flooding for each pilot site;


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D4a,b,c,d: Reports for the COCED analysis for each of the countries [present

deliverable];

D5: An Executive Comparative Report on the coastal zones management and the

COCED results;

D6a,b,c,d: Reports on the identification and justification of Disaster Risk Reduction

(DRR) and Climate Change Adaptation (CCA) measures for each pilot site;

D7a,b,c,d: Reports on the Cost Benefit Analysis of the selected DRR and CCA

options;

D8: An Executive Comparative Report on the selected DRR and CCA options;

D9a,b,c,d: PowerPoint presentations for the meeting with each communities council;

D10: Policy note COCED, policy measures and recommendations;

D11: Final project report.

1.2 SCOPE OF THE REPORT

The present describes the COCED analysis which has been performed for Ghana. It

includes the applied methodology, the results for the COCED at coastal level (at country

scale), and the results for the COCED at the selected pilot site.

The situation considered in this report is the actual situation. The effect of DRR and CCA

measures will be discussed in deliverables D6 (IMDC, 2017a) and D7 (IMDC, 2017b).

1.3 STRUCTURE OF THE REPORT

The report starts with a short description of the present situation in Ghana in chapter 2 with

respect to the human occupation and economic activities, the characteristics of the natural

environment and the risk for degradation.

Chapter 3 includes the methodology applied for the COCED analysis, and the results of this

analysis are presented in chapter 4.

Finally some the conclusions are formulated in chapter 5, and the list of references is

included in chapter 6.


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2. SITUATION IN GHANA

2.1 COUNTRY LEVEL

The summary hereafter presented is mostly based in the studies by deGraft-Johnson et al.

(2010), USAID (2014), Finlayson et al. (2000) and references cited therein, as well as

deliverable 3 of the present assignment (IMDC, 2017c).

Ghana lies along the Gulf of Guinea and has a 550 km long coastline with lagoons and

associated wetlands. Some of the lagoons are closed and others open (Boughey, 1957;

Kwei, 1977; Mensah, 1979 – cited by deGraft-Johnson et al. 2010). Ghana’s coastal zone

encompasses a land area extending to the 30 meter contour, and a coastal offshore shelf

area to either the end of the continental shelf or the 200 nm exclusive economic zone limit

(EPA/WB 1996; Armah & Amlalo, 1997 – cited by deGraft-Johnson et al. 2010). The

continental shelf is narrow, extending outwards to between 25 km and to 35 km, except off

Cape Coast to Saltpond where it reaches up to 80 km.

The coastal zone contains 21 Districts in four administrative regions, namely Western (6

Districts), Central (7 Districts), Greater Accra (5 Districts) and Volta Region (5 Districts)

(EPA/WB, 1996).

The West Coast (app. 95 km) extends from the Ghana-Côte d’Ivoire border to the Ankobra

River estuary, where there are gently sloping sandy beaches along with 49 lagoons. The

Central Coast (app. 321 km) from the Ankobra estuary to Tema has rocky headlands and

sandbars or spits enclosing coastal lagoons. The East Coast (app. 149 km) stretches from

Tema to the Ghana-Togo border where the shoreline is sandy; this area is characterised by

considerable erosion (Ly, 1980). Furthermore, low-lying areas, i.e. areas separated from

the sea by only a narrow barrier beach and/or low relief beach berm and dune system, may

be vulnerable to coastal flooding during extremes of climate.

Human use and occupation of the coastal zone is generally intense. In 2010, according to

USAID, about 1,842,000 persons live in Ghana in the coastal zone situated below an

elevation of 20 m (about half of those living below the 10 m line). This figure was expected

to increase to more than 3 million by the year 2050 (USAID, 2014). According to the same

publication, the gross domestic product realised in the same zone was worth about

922 million US Dollars or 2.4 % of the Ghana GDP. According to other sources (Finlayson

et al., 2000), the coastal zone in Ghana represents less than 7% of the total land area but

holds over 25% of the nation’s population.

The continued trends of the drift from rural to urban centres, the industrialisation of coastal

districts as well as the high population growth rate of 3%, place increasing stress on the

coastal ecosystems.

There are about 100 coastal lagoons in Ghana which make them an important feature of

the sandy coastline, especially as the lagoons provide many benefits and values to human

populations (Gordon 1987, 1996 – cited by deGraft-Johnson et al. 2010). However, it is

evident that the values and benefits provided by the lagoons are under increasing threat

from over-exploitation and degradation.


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There are several Ramsar sites along the coast, designated as Wetland of International

Importance:

the Muni lagoon, with a surface of 86.7 km²

the Densu Delta, with a surface of 46.2 km²

the Sakumo Lagoon with a surface of 13.4 km²

the Songor Lagoon with a surface of 287.4 km²

the Anlo-Keta Lagoon Complex, with a surface 1277,8 km²

The Songor lagoon is also part of the worldwide network of biosphere reserves (since

2011).

2.2 PILOT SITE LEVEL

2.2.1 Characteristics of the natural and human environment

The pilot site area in Ghana extends approximately 15 km between New Ningo and

Lekpoguno, on right bank of the Volta delta. Contrarily to the general alignment of the

coastline, which is following a straight line more or less west-east oriented, the coastline in-

between New Ningo and Lekpoguno is notable for the convex curvature (Figure 2-1). The

sector is, around New Ningo, largely protruding out the alignment of the adjacent areas.

Figure 2-1: Pilot site area in Ghana, sector GH9-a New Ningo-Lekpoguno

(source: Google Earth)


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The pilot site roughly covers the area between the settlements of Old Ningo and

Lekpoguno. It is situated east of New Ningo, which is situated to the east of the outskirts of

Greater Accra. The site is traversed by a coastal road, situated at a distance varying

between 100 m and 2 km from the beach.

To the east of the site is the Songor Lagoon Protected Area, a Ramsar site, and part of the

Volta delta. The wetlands associated with this lagoon extend to the area directly to the

north of Lekpoguno. Fairly extensive wetlands are also present to the West of Old Ningo.

The Songor Lagoon is a closed lagoon with inundated mudflats. Fauna in the area includes

leatherback, olive, ridley and green turtles and migratory birds. Local communities depend

on the site for fish resources, farming and salt mining.

The pilot site is a rural stretch of coast, which is scarcely supplied with sediments from the

eastward coastal drift. Because of the expected development in tourism and residential

areas along this already fragile littoral zone (i.e. a coastal lagoon behind a very narrow rim-

lido), the human stakes and settlements at threat from coastal hazards are likely to

increase in the future.

The pilot site area is an extensively low-lying area which is separated from the sea by a

narrow barrier beach and/or beach berm and dune system that largely lay at low elevations

as well. The coast is exposed to high energy swell waves and periodic storm events that

can cause erosion. During severe storms, water is pushed up and waves can attack the

shore and take sand away.

The prospect of further coastal development in conjugation with both a limited sediment

input from fluvial and coastal sources and a very flat topography at low elevations naturally

leads to a concern over an attendant increase and extent of erosion and the risk of flooding

along low-lying frontages.

The pilot site is part of the great Volta delta. Flow control by upstream dams is central to the

flooding experienced in the floodplains located downstream of the watershed. However, as

mentioned, flooding of the low-lying areas in the pilot site is not exclusively related to river

flooding but also to coastal flooding by saltwater that can occur by overflowing, breaching,

or a combination of both mechanisms. Flooding from the back by rising water-level in the

lagoon is also a factor to be considered.

2.2.2 Human occupation and economic activities

As mentioned before, the pilot site is a rural stretch of coast to the east of Greater Accra.

Apart of the town of Old Ningo and villages like Ayepita and Lekpoguno, it has no important

human settlements. Population densities are generally low in the rural zones (less than

1 person per ha) and somewhat higher (50 or more persons per ha) in the villages.

Apart from (predominantly) subsistence agriculture and husbandry, fisheries are fairly

important, and Ningo is a known landing site for catches. The marine capture fishery is an

important traditional economic activity for coastal communities and many families rely on it

for their livelihood. Aquaculture is also present, with part of the lagoon system having been

converted into ponds. The area has touristic potential, which has up till now not been

exploited.

Extraction of beach sand, rock and pebbles for use in construction material is also practiced

locally (see paragraph 2.2.3.2 below).


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2.2.3 Sources of environmental degradation in the pilot site

2.2.3.1 Land pressure and use of natural resources

At present, the pilot site is still rural and fairly sparsely populated. However, general

economic and demographic development is expected to increase land pressure, also given

the relative proximity of the greater Accra region. Tourism development is also part of the

potential increase in land pressure. At present no large scale tourism is present in the area,

but the natural qualities of the site, in combination with its vulnerability, mean that tourism is

a potential source of future environmental degradation, in terms of erosion and pollution.

Economic development can also mean that aquaculture projects, already present in the

area, may expand. This would result in a loss of natural wetlands and an increase in

pollution both of the lagoon system and of the ocean.

As far as marine fisheries are concerned, it should be noted that most of Ghana’s fishery

resources are heavily overexploited, with the sector recording a decline in production over

the past couple of years.

Economic growth can also increase the demand for construction materials and hence the

exploitation of marine sand and pebbles, contributing to the coastal erosion problem (see

paragraph 2.2.3.2 below).

2.2.3.2 Coastal erosion

The present situation along the Ghanaian coastline reflects a complex interplay between

geographical and geological conditions and natural hydrodynamic and geomorphic drivers

of coastline change, as well as varied engineering interventions and infrastructure,

including the international ports of Tema and Takoradi and the construction of the

Akosombo and Kpong dams. Overall, those human interventions cause a reduction or

interruption of the sediment supply to the coast, exacerbating erosion. Illegal sand mining

activities (Figure 2-2) are further contributing to the destruction and degradation of the

Ghanaian coast.


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Figure 2-2: Images of illegal beach mining on the Ghana coastline (Boateng, 2012)

One of the most important forces of change that occurred in recent decades in the coastal

system where the pilot site area is included was the construction of the Akosombo dam in

1964. After construction of the dam the sediment supply was cut, which implies that on a

geological time scale the delta may erode again, and may keep doing so until reaching a

new equilibrium between sediment supply and wave climate.

The control of the flow of the Volta has led to profound alterations, namely the contraction

of the coastal lagoons, the drying out and encroachment by agriculture and salt flats and

the development of villages closer to the sea on (relatively) high terraces.

Coastline change along the Ghanaian coast has been widely investigated. One the most

comprehensive studies is by (Boateng, 2012) that investigated coastline changes for the

period 1885-2002 using a variety of datasets aerial/satellite images and maps. Average

erosion rates of 4 m/year in and around the pilot site area in Ghana were reported, although

locally they can reach in excess of 8 m/year. More recent estimates seem to confirm these

higher erosion rates.


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2.2.3.3 Climate change

Climate change models (under RCP4.5) predict for the West African Coast a winter

(December-February) mean temperature increase of between 1 and 2°C for the 2036-2065

period and of between 1 and 3°C for the 2018-2100 period. In summer (June-August)

average temperature increase would be in the order of 0.5 to 1.5°C for 2036-2065 and of

1 to 3°C for 2018-2100 (IPCC, 2014).

In terms of precipitation, model results, if significant when compared to natural inter-annual

variations, indicate an increase in rainfall up to 20%, both for the October-March and for the

April-September periods. The climate in the coastal zone of Ghana will thus probably

become slightly hotter and wetter. The impact of this evolution on the coastal natural

resources is hard to predict, but it can be expected that other factors, such as economic

and demographic growth or sea level rise will have a more important impact than changes

in temperature or precipitation. Nevertheless, increase in rainfall in the watershed of the

Volta and the other rivers in the area could lead to an increased inundation risk, although

this risk is mitigated by the water level control operated by the dams on the river system.

Sea level rise (under RCP8.5) will, according to most models, be in the order of 0.85 m or

less (IPCC, 2014). Given the low level of the land this will result in an increase in the

permanently inundated land area and in the salinity of lagoons and estuaries. This will,

undoubtedly, also have an impact on ecosystems and species and on the use of natural

resources (e.g. aquaculture). Sea level rise and salt intrusion will also influence ground

water quality and its suitability for use as a source of drinking water.

Finally, acidification and warming of the ocean, combined with possible changes in marine

currents, can have an impact on fish stocks and fisheries. The economic and ecological

impact of those evolutions are not easy to predict however.


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3. METHODOLOGY

3.1 METHOD TO ASSESS RISKS FOR EROSION AND FLOODING

The risks from erosion and coastal flooding, are assessed in a four step methodology:

a) The hazard assessment: which describes flood and erosion events, with a specified

return period, for 3 periods in time (2015; 2050; 2100).

b) The exposure assessment: the people, assets, production and ecosystem services

at risk.

c) The damage assessment: the victims, restoration costs, loss of production and

services.

d) The risk assessment: aggregation of the damages for different events, type of risks,

current and future risks towards a single (or a few) risk indicator(s).

As the risk assessment needs to integrate the results of the 3 previous steps, the methods

and data need to be consistent.

Methods for hazard assessment for coastal erosion and flooding are described in

deliverable 3 of this study (IMDC, 2017d). This chapter deals with methods for the three

other steps. These methods are generic and can be applied to the 4 West African countries

studied in this project (and even outside).

As these methods are generic, we follow well established methods and terminology, as

used for the assessment of risks of (coastal) flooding and coastal erosion in OECD

countries (Ferreira and Viavattene, 2016); (Viavattene et al., 2013); (FLOODsite, 2008);

both at a general and case study level and those of more general studies of risks at global

level (Hinkel et al., 2014); (Huizinga et al., 2017), including studies looking at risks in Africa.

(Hinkel et al., 2011).

Our approach is based on an assessment of the literature and tools to maps and assess

exposure, damages and risks, local studies and information on impacts and damages from

floods and erosion (PDNA Togo, 2010); (PDNA Benin, 2011),(Kissi, 2014) and lessons

learned for application of concepts and tools during the site visit in spring 2017.

Figure 3-1 gives an overview of the different steps and data sources for the methodology.


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Figure 3-1: Schematic overview of the 4 step methodology.

3.1.1 Generic description of method for damage assessment.

We assess damages and risk per grid cell (1 ha). For a single event (e.g. one type of flood),

the damage per grid cell is calculated as follows:

Damage = (max value at risk) x (damage function)

With

Max value at risk, including the value of the assets (buildings, infrastructure, etc.)

($/ha), production values ( $/ha.year), and ecosystem services ($/ha.year)

Damage functions which are different for erosion, flooding and sea level rise.

Damage assessment methods per damage category

Table 3-1 gives an overview of the available information for damage assessment for the

different damage categories (tangible, non-tangible, direct and indirect) and the indicators

to be used to assess the damage. If damage functions and indicators for values at risk are

available, we will estimate damages in monetary terms ($ per event). A first indicator is the

area (ha) affected, differentiating between different land use categories, and the number of

people affected. For several damage categories, we will estimate the value (of the assets)

at risk and the expected damage.


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Table 3-1: damage categories and availability of methods for damages assessment

Damage category

Economy People Ecosystems Cultural goods

Floods

Tangible

Direct N° ha, $ N° ha, $** $ *

Indirect $* na $ *

Intangible

Direct Victims, people

at risk N° ha ** Na

Indirect na na Na

Erosion

Tangible

Direct N° ha,$ N° ha $ *

Indirect $ * na

Intangible

Direct N° relocated N° ha lost/$** Na

Indirect Na na Na

Sea level rise (permanent flooding)

Tangible

Direct N° ha lost

Indirect

Intangible

Direct N° ha lost/ $**

Indirect

DF: damage function available

$: damages assessed in monetary terms, using damage functions and data for Togo

$ *: accounted for (included in the damage functions for direct damage) but incomplete and uncertain

$**: based on rough estimates from literature, incomplete, uncertain N° of people or N° of ha: N° of

people or ha exposed or affected

Na: not available

3.1.2 Flooding: damage functions for tangible damages

For direct tangible damages to assets (e.g. buildings, infrastructure), damages reflect

restoration costs, and are mainly dependent on flood depth. In addition, indirect tangible

damages include losses of stocks, and losses due to interruption of production of goods or

services (e.g. transport).

Both direct and indirect tangible damages can be expressed in monetary terms ($) and

depend on the values at risk and their vulnerability. Damage functions specify the % loss of

the total value of an asset at risk, in function of flood characteristics (the flood depth, the

duration, the water speed). These characteristics are part of the hazard assessment (step

a). In this context, total value is often referred to as the maximum damager. Exposure

assessment requires to estimate the total or maximum value of the assets at risk per grid

cell. Damage functions for direct damages are more certain compared those for indirect

losses, as the latter also depend on the duration of a flood event. For some cultural goods

(historic buildings), the impact is assessed to some extent (asset value), but it does not

include additional restoration costs for this type of assets nor loss of intangible values.


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3.1.2.1 Overview of available methods and data

One can distinguish between two types of methods. Both methods apply damage functions

that estimate damage as a % loss of the total (maximum) value at risk. The detailed

methods use information on the number of houses, buildings or m² floor space at risk and

estimate their values at risk in detail, and apply different damage functions to each value

category. Second, the more generic models used for large scale analysis, use GDP/ha as a

proxy to estimate the values at risk, and apply a single damage function to these values.

The detailed method has been applied in many OECD countries to estimate flood damages

in detail. It applies different damage functions for different types of assets (residential and

non-residential buildings, building structure and its content (furniture,..), roads, vehicles,

etc.) (Huizinga et al., 2017) (Viavattene et al., 2013).

The values at risk are typically expressed in $/m² floor space, average vehicle, etc. The

most detailed assessment distinguish within these categories different types of assets

(number of floors for buildings, sectors for industry, materials,..) (Viavattene et al., 2013).

Their application requires detailed information on types of buildings at risk, their average

characteristic (m² floor space) and/or their value (value per m² floor space or total value of a

building). Even for OECD countries with a lot of information, some assumptions need to be

made (e.g. value of furniture and contents is assumed to equal 50 % of the value of the

building (Waterbouwkundig Laboratorium Borgerhout and Universiteit Gent, 2006). For non-

residential land uses (industry, commerce, ...), information on damage functions is less

available, less detailed and more uncertain. Damage functions are expressed per m² land

use or per employee, either in absolute values or % of value added for these (sub)sectors.

The more generic method builds on the results of the more detailed models, and uses

average damage functions that are applied to values at risk, expressed as $/ha, sometimes

different for the different land use categories ( residential, industry, services, agriculture,…).

These methods start from identifying the GDP per ha, which is both an indicator for the

assets at risk (buildings …) and the impact on economic activities. GDP/ha can be

estimated based on data for local GDP/capita and population density. The value of the total

assets at risk can be estimated using a generic indicator for the ratio assets/GDP from

literature (Hinkel et al., 2014) (Hallegatte et al., 2013). These methods are used in more

generic studies, e.g. at the level of a continent or worldwide.

Based on a review and meta-analysis of damage functions and information on value at

risks, (Huizinga et al., 2017) produced for different regions in the world indicator data and

damage functions. This report and data sets covers two methods, one very detailed (based

on values per m² floor space for residential, commercial and industrial buildings and

transport infrastructure) and a very rough one (one data per ha land use).

Our review of available information and damages from flooding in West-Africa indicate that

there is not enough detailed information for West Africa to apply the more detailed

approach, whereas the approach based on land use does not allow to distinguish between

areas with similar land uses.


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3.1.2.2 The method for COCED: a mixture of both approaches

Our method to define the values at risk is a combination of the more detailed methods used

in OECD countries (and that use detailed information on number of houses, buildings or m²

floor space at risk) and the very generic models used for large scale analysis, and that use

GDP/ha as a proxy.

The review of damages after the 2008 and 2010 floods in Togo and Benin indicate that the

more generic approach, based on indicators for economic activity, may be more applicable.

A first lesson from the review of the damages is that they are not dominated by the

damages to buildings (only accounting for 10 % of total damages), and that the other

damages are very diverse, with big differences between the analysis for Togo and for

Benin. Although the more generic approach is less specific, it may be more appropriate to

grasp the variety of impacts, including e.g. economic losses. Damages to buildings were

less important compared to damages to road infrastructure (Togo) or damages to irrigation

infrastructure (Benin). The major impacts on agriculture were not related to loss of crops on

the fields, but loss of crops already harvested, and loss of irrigation infrastructure. For

schools, in addition to damage to buildings and materials, loss of income for teachers was

also relevant. Finally, economic activities are less formal and land use is mostly a mixture

of different functions, although we can distinguish between the urban fabric, mixing

residential functions with private and public services and transportation, and the rural

context, mixing residential land use with agriculture and related service sectors. As these

elements are not well captured by the detailed approach that focusses on buildings or e.g.

crops, the more generic approach starting from the GDP/ha at risk is more applicable for

these countries.

On the other hand, this approach needs to account for differences within the different land

use classes. We will account for three additional elements:

First, population density is an important driver for GDP/ha.

Second, for some specific areas (ports, big industrial facilities), specific values need

to be applied.

Third, within rural and urban land uses, the presence of specific buildings (schools,

health care) and transport infrastructure is another important predictor for values at

risk.

In next sections, we will develop a consistent approach to identify these land uses, define

values at risk and define damage functions. This set includes 31 land-use categories per ha

(grouped in 4 large categories of land-uses (rural, urban, industry-services, natural area’s),

indicators for the values at risk ($/ha) and damage functions (% loss of maximum damage).

These elements are discussed in more detail in next sections.


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3.1.3 Flooding: damage functions for intangible damages

Intangible damages include risks to people (casualties, health impacts, and migration), risks

for ecosystems or cultural goods. As it is not possible or much more difficult to value these

damages in monetary terms, the exposure assessment focusses on an estimation of the

quantities at risk (number of people or ha at risk).

People

There are some damage function available for fatal risks, but they are more uncertain

compared those for tangible damages. There are no damage functions available for other

casualties or health impacts, although evidence suggests that these may be important. The

impact on fatal victims can be expressed in monetary terms using indicators for the value of

a statistical life, but these are more uncertain and controversial compared to these for

tangible goods. The first focus of COCED will be to estimate the number of people at risk,

and risks for fatal victims.

Ecosystems

Compared to buildings, there is little info on damage functions for impacts of floods on

ecosystems. For the COCED we estimate the quantity (ha) of ecosystems at risks and

apply a literature-based value per ha to them in order to obtain an estimate of the loss

involved in case of irreversible destruction. We distinguish ecosystems based on their

importance (e.g. mangroves) or based on their vulnerability (e.g. brackish and freshwater

wetlands). Note that in all cases we deal with wetland ecosystems.

For freshwater wetlands, some info on damage functions is available, that can be used to

estimate the impact on ecosystems goods and services. However, compared to impacts on

the economy, this estimate is much more uncertain. Freshwater ecosystems are in any

case not very likely to be affected by coastal flooding or erosion. In the coastal zone,

freshwater and marine influences meet, resulting in a gradient from saline to brackish

conditions. Purely freshwater conditions can occur only during periods of strong river

floods, and even in that case only in the upper reaches of the coastal zone. Moreover there

are indications that the freshwater influence on the coastal ecosystem in Togo and Bénin

has diminished in recent decennia as a result of dams and flood control measures in the

watershed of (among others) the Mono River. The same applies for the Volta River in

Ghana.

For brackish ecosystems, the damage due to flooding is expected to be low in most cases,

as those ecosystems are used to functioning under conditions of fluctuating water and

salinity levels. Only extreme and long-term inundations (in the order of weeks rather than of

days) are expected to be able to cause permanent damage.

Mangrove ecosystems (or other types of woodland in the coastal zone) may incur damage

as a result of extreme wind speeds during storms and hurricanes. The Risc-Kit library

estimates that irreversible damage to mangroves only occurs at maximum wind speeds of

more than 200 km/h. The flooding and siltation that may accompany cyclones can cause

further tree mortality. But in many areas cyclones are infrequent and mangrove forests are

usually able to recover their structural integrity over a number of decades, before another

cyclone hits the same area (Mc Ivor et al., 2012).


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Generally speaking, one can expect other factors such as human pressure and climate

change (especially sea level rise) to present a much more important threat to the integrity of

coastal wetland ecosystems than flooding.

3.1.4 Damage functions for erosion

The hazard assessment indicates for a given year and event whether a cell is eroded, and

the land is lost. The approach differs (from the approach for flooding) for tangible damages

to the economy and impacts for ecosystems.

The tangible damages to the economy are assessed as follows. If a grid cell is eroded, the

total value of the assets is lost, as well as its capacity to deliver production or service

functions. These functions will be relocated towards a non-specified place. We account for

these relocation costs (assets + relocation) and we assume that for a certain period, the

level of production and services will be lower in the new location. Furthermore, the original

production and service values of the new location will be lost. We assume that in the end,

the land-use functions that are lost will be a mix of low-productivity agriculture and

ecosystems.

Intangible damages to people include migration (number of people relocated). We do not

have information or dose-response functions for impacts on victims or morbidity, but this is

unlikely to be an important impact.

The impact on ecosystems will be assessed using the size and type of ecosystems lost

(ha). Data from literature on the value of the ecosystem goods and services delivered by

wetlands and mangroves will be used as a (rough) indicator for the economic value of these

lost systems. For ecosystems, we do not assume relocation of ecosystems.

The Risc-Kit toolkit considers sediment erosion by wave action within the marshes

themselves as the main parameter determining damage to salt marshes1. For marshes in

micro-tidal areas (which is the case for the study area) Risc-Kit considers wave heights of

30 cm or more, in combination with shallow water depths (less than 1 meter) to be sufficient

to cause irreversible damage. For water depths of 3 meter or more, irreversible damage will

only occur at wave heights of more than 2 meter. Note that those figures are for “open

coast conditions”. In the case of the study area, where the lagoons and other wetlands are

situated behind sand barriers and/or within estuaries, the impact is expected to be less

severe. Permanent loss of wetlands in the study area due to erosion is thus a worst case-

assumption.

Note that for mangrove ecosystems the loss due to erosion under storm conditions can in

practice be expected to be less important still, due to the predominantly sheltered position

of the mangroves (along lagoons and river systems rather than on the coast, exposed to

the surges), their ability to break the wave energy and the fact that sediments are anchored

by the mangrove root system.

1 Mainly based on data for salt marshes in Europe. This is however the closest approximation to the West

African lagoon system to be found in the database.


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3.1.5 Impacts sea level rise on permanent inundation of wetlands

The impacts of sea level rise on flood risks and on erosion are dealt with in the methods

described above. This part focuses on a specific impact on wetlands.

Sea level rise may – in addition to (and to a larger extent than) impacts by erosion and

floods – affect wetlands due to permanent inundation and salinization. As both impacts of

inundation and salinization are difficult to assess and value, it will be limited to an

assessment of ha of ecosystems potentially affected, (and only at the pilot site level).

It should be noted that permanent loss of coastal wetlands as a result of sea level rise is, in

the flat and estuarine conditions of large parts of the coasts of Togo and Benin, a worst-

case assumption. It can indeed be supposed that the coastal ecosystems, and notably

mangroves, are able to migrate upstream along river valleys and estuaries as those evolve

into ecotopes with a more coastal signature, in line with the sea level rise. This means that

the said ecosystem types will in reality (at least partly) move upstream rather than

completely disappear. This, of course, would still be at the expense of other ecosystem

types and can only occur supposing that the land use in the upstream areas allows for such

a migration. As within the context of this project and the current state of understanding and

data, it is not possible to model these impacts and potential migration. For the pilot site, we

will estimate the area of wetlands at risk for permanent inundation, although we need to

indicate that gives the uncertainties in data, these results should be interpreted as first

rough indications of potential loss of wetlands, and excluding the potential growth of area of

wetland trough migration.

In situ adaptation and preservation of mangrove forests can only be expected if the

amounts of sediments transported from upstream allow for the bottom level in the lagoons

to rise at the same rate as the sea level. This can only occur if the sea level rise is very

gradual and if at the same time enough sediment transport can be guaranteed. Especially

the latter is not the case as dams have been and are being built on several river systems in

West Africa.

These inputs are discussed further in the following sections.

3.1.6 Damage assessment versus carrying capacity for recovery

It has to be noted that the available methods are suited to calculate the size of the total

damages from flooding or erosion (in $ per event) but do not allow to make assessments of

which groups in society are (most) affected and to which extent they are capable to recover

from the impacts and are e.g. able to pay for repair of damaged assets, to replace lost

harvest or stocks of food supply or to compensate for loss of income.

The methodology foresees to assess in detail (ha grid cells) GDP/ha, as an indicator of the

value of the assets and economy at risk. The method does not account for additional

indicators (e.g. related to poverty) to assess ability to finance restoration or recovery from

the impacts. Although indicators related to the share of poorer people are often used in

assessment of vulnerability of impacts of flooding, it is not straightforward to include this in

the methodology. Methods to assess damage reflect that damages are likely to be lower

(ceteris paribus) in areas with less or less valuable assets and the latter may also reflect

the presence of more poor and vulnerable households.


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An important element to assess to which extent an area or groups affect can recover from

impacts is how the size of the impact for an family, village, area or even nation compares to

the carrying capacity of the larger area and the different mechanisms to finance help, repair

or compensations (insurance, help and repair through local and national governments,

international help,…). In most cases, some financial and other support will be required from

the areas not affected. So, an important indicator is how the area that is affected related to

the overall size of the area and economy, and to which extent areas affected are (more)

important in terms of the value of the assets or the ability to generate revenues. A first

indicator is the comparison with the GDP of the country, in line with the current summary

data on COCED (Doumani, 2015), which helps to put the expected damages in

perspective, but which is still a poor indicator for carrying capacity, especially in the case of

low income countries (see also below, section 3.4, for an example).

The methodology will provide inputs for this assessment, but to our knowledge, there are

no hard scientific criteria to quantify to which extent an impact exceeds the carrying

capacity of a (larger) area.

3.2 EXPOSURE ASSESSMENT

3.2.1 Land use categories

Exposure assessments maps the people, assets, production and ecosystem service values

at risk. The final indicator for the exposure assessment is the number of people (for victims

at risk of dying in a flood or erosion event), the surface (ha) per type of land use and the

values at risk.

The more detailed studies in OECD countries use detailed land-use maps and other

information to map people, assets and values at risk. The more generic global studies use

more aggregated data to map exposure (GDP/km²). The proposed methodology for West-

Africa is a mixture of these approaches, combining more the generic top down info (e.g.

GDP per country) with available information on land use, population densities and other

assets and values at risk, at a detailed level (1 ha grid cell).

We distinguish 31 land use categories and classes (Table 3-2), reflecting differences in the

values at risk and vulnerabilities. Per grid cell of 1ha, we define a single land-use, based on

a combination of information on population density (based on world-pop land use maps)

and land use characteristics (open street maps; land-use maps; observations).

Rural areas (3 classes of population density between 0-1, 1-3 and 3-5 inhabitants/ha),

combine residential buildings with agriculture land uses. This classification is in line with the

PGEAT (AGAT, 2013), 2013 report that estimates the population density for the ‘rural’ area’

in the Maritime region at 1,26 inhabitants/ha ((AGAT, 2013), p. 43).

In addition, we distinguish rural grid cells containing specific high value and/or vulnerable

assets, based on objects or trajectories identified in open street maps or specific land use

maps (irrigated agriculture).


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We distinguish 2 groups of assets, depending on their impact on the value of the assets at

risk. A first set (specific assets 1) contain schools, health care, historic buildings. A second

set of these assets contain non-local roads, railways and irrigation agriculture. The value of

these additional assets are discussed in chapter 3.3.1.

Urban fabric reflects a combination of residential land use and economic activities

(services, small factories …), public functions (education, health care) and transport related

infrastructure (roads, bus stations, etc.). The values at risk depend on the population

density, and the economic productivity per capita (GDP/capita, see further, chapter 3.3).

Urban land-use is defined as the land-uses with a population density of +5 inhabitants/ha,

and we distinguish between 2 suburban and 3 urban classes. We specify different sub

classes because it is important for the damage assessment, and it is possible with available

data and methods to differentiate between classes.

As for rural areas, we distinguish (sub)urban grid cells containing specific high value and/or

vulnerable assets, such as schools, health care, historic buildings, transport infrastructure

(e.g. railway, highway), based on objects or trajectories identified in open street maps.

A third group are larger parcels with specific economic activities. These grid cells have

(in most cases) zero inhabitants (on the world-pop land use maps) and additional

information in open street maps indicates a specific activity (industry, services, ports,

transportation). These activities can be both located outside and inside the cities (e.g.

administrative centres, train stations). On the one hand, it would be preferable to have more

detail on the economic activities at risk, and e.g. to specify the presence of oil and gas

industry (be coastal storage, refinery or inland production). However, this only contributes

to more information if we can also better specify the values and assets at risk, or apply

different, sector specific damage functions, reflecting different vulnerabilities for direct or

indirect impacts. As it has to be noted that for industry information on both parameters

(location of sectors, values at risks and vulnerabilities) is very limited, we did not foresee

further subclasses for economic activities. At the pilot site level, sectors affected can be

described in more detail, but it is unlikely that data for more specific damage assessment

will be available. The fourth group are natural areas. We distinguish wetlands, mangroves,

urban parks and other areas. They are defined based on the information related to land

use, and have in general a low population density (< 1 inhabitant/ha). We further distinguish

wetlands with higher population density (< 1 inhabitant/ha). We have not enough

information to distinguish between salt, brackish and, freshwater wetlands. Note that water

bodies are listed as a specific category, as they are in general not vulnerable to flooding.

Finally, we distinguish military areas.


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Table 3-2: Overview of land use categories for impact assessment

Land use category

Ind. Description Inhabitants /

ha Economic activity

1 Rural R1 Rural 1 0-1 Agriculture

2 R2 Rural 2 1-3 Agriculture

3 R3 Rural 3 3-5 Agriculture

4 Rural + R4 Rural 4 0-1 R1 + specific assets 2

5 R5 Rural 5 1-3 R2 + specific assets 2





10 Urban U1 Sub Urban 1 5-25 services, small industry, transport

11 U2 Sub Urban 2 25-50 As U1

12 U3 Urban 3 50-75 As U1, but more dense

13 U4 Urban 4 75-125 As U1, but more dense

14 U5 Urban 5 + 125 As U1, but more dense

15 Urban + U6 Peri Urban 6 5-25 U1 + specific assets 1

16 U7 Peri Urban 7 25-50 U2 + specific assets 1

17 U8 Urban 8 50-75 U3 + specific assets 1

18 U9 Urban 9 75-125 U4 + specific assets 1

19 U10 Urban 10 + 125 U5 + specific assets 1

20 Economic E1 Quarry 0 * Mining

21 E2 Port 0 * Ports,

22 E3 Transport 0 * stations, airport

23 E4 Services 0 * Services (admin. Centres)

24 E5 Industry 0 * Industry

25 Nature N1 Other natural area 0 *

26 N2 Urban Park 0 * Park within urban area, EGS

27 N3 Wetlands < 1 EGS

28 N4 Wetlands > 1 EGS

29 N5 Mangroves 0 EGS

30 Open Water W Water 0

31 Military M /

Specific assets 1 = buildings for schools, health care, churches, monuments, etc. Specific assets 2 = non-local roads and railways or irrigated agriculture EGS Ecosystem goods and services 0 *: zero in most cases, if not, classified using the same classes as rural and urban <1 * less than 1 in most cases, if not, classified using the same classes as rural and urban


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3.2.2 Difference between the assessment at national level and at pilot site level

For the national assessment, we apply the generic rules as described above to develop a

land-use map. For the pilot sites areas, we have additionally made a visual control of the

consistency between different source maps. In some cases, combining information made it

possible to improve the land use maps.

As an example, the population density map has wrongly specified some water bodies as

areas with high population. For grid cells with no info (e.g. no inhabitants) it is checked

which land-use is the most appropriate, accounting for the information in other sources (e.g.

open street maps) and surrounding cells. In other cases, difference in land use maps

mainly illustrates the uncertainties related to this input, and we will take this into account for

the conclusions.

In addition, for the pilot sites, the interpretation of the impacts will be more detailed, and will

include some qualitative assessment.

3.2.3 Exposure assessment for future years (2050 and 2100)

Damages for future years will depend on changes in hazards and changes in the values at

risk. We assume the same damage functions (%) will apply.

First, the method will account for the impact of erosion on future land use, as areas that

are eroded are no longer at risk for flooding.

Second, the methodology will account in a simplified way for demographic and

economic growth, which affects number of people and the value of the tangible assets

at risk.

Although the area of ecosystems at risk is likely to be affected by future human impacts

and sea level rise, we will not make assumptions about these potential developments,

and use the areas today for assessment in future years.

3.2.3.1 Impacts from erosion on future land uses and risks

The methodology foresees that first the impact of erosion is calculated. For grid cells that

are eroded in 2050, we do not calculate damages for flooding in 2050. We assume that the

activities and assets from these grid cells are relocated to other grid cells, and we account

for relocation costs. We assume that locations are chosen that are no longer at risk for

future erosion (2100) or coastal flooding.

3.2.3.2 Impacts of demographic and economic growth

The values at risk along the coast are likely to increase, reflecting demographic growth and

economic growth. For both factors, future projections are available, reflecting the bandwidth

of potential future developments.

The methodology foresees to account for future growth in a simplified way. Ideally, we

would account for the impact of this growth on the spreading of population and economic

activities, and apply different growth factors for each grid cell, based on maps for future

land-uses and population densities that specify the growth of cities.


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In absence of such maps, we will use the generic method in literature, and apply a single

growth factor for each grid cell.

As demographic growth is very high in West-African coastal regions, it is important to

account for the growth of cities along the coast, especially for the potential growth in rural

and suburban areas. Future land use maps for West Africa are under development

(MAUSS project for urban population projections for Africa, as part of worldpop Africa

project), but the results are not available yet (Gilbert M., 2017). We acknowledge that the

simplified method does not adequately capture spatial variation in future growth.

The uncertainty related to both demographic and economic growth, especially at the level

of assessments per grid cell, is an important factor for overall uncertainty.

3.2.3.3 Future evolution of coastal ecosystems

For mangroves (and, to a lesser extent, also other forms of wetlands), it can be expected

that their future value (in terms of ecosystem goods and services) and/or their future extent

will be affected by a combination of population pressure, sea level rise and changes in the

watershed.

As far as the impact of population pressure is concerned, it is difficult to be certain about its

importance. Data on the evolution of the mangrove cover in West Africa during the recent

decennia are not consistent, some showing an important decrease but others a status quo

or even an increase (especially at a local level), despite an obvious increase in external

pressures. A growing acknowledgment of the value of mangroves in combination with the

protection of the most valuable areas and with concerted actions by NGO’s and

government actors may result in a slowing or even reversal of the generally expected

negative trends. A review by Oyebade et al (2010) for the period 1980-2006 found an

increase for Togo and Bénin, a moderate decline for Ghana and a severe decline for Côte

d’Ivoire.

The potential impact of sea level rise may be more important than direct human impact.

Lagoons and lower estuaries will be flooded. The impact on the mangroves will to a large

extent be determined by the capacity of the mangrove ecosystem to adapt and/or move

upstream along the main rivers in the study area. The rate of the sea level rise will be an

important factor determining this capacity. The in situ conservation of the mangrove forests

through accretion at the same rate as sea level rise is unlikely.

Another effect of climate change may be an increase in the frequency of violent storms that

may damage mangroves. On the other hand, higher carbon dioxide content in the

atmosphere generally leads to an increase in mangrove growth and biomass productivity

(PDNA Togo, 2010) (UNEP, 2007).

Changes in the watershed (damming of rivers, erosion control, changes in rainfall patterns,

etc.) will essentially determine the amounts of sediments transported towards the lagoon

and estuary systems as well as the salinity of the water in the coastal wetlands. Both are

important indicators of mangrove presence and survival, but it is hard to predict their

evolution. Too little sediment is detrimental in a situation of fast sea level rise, as the

mangroves will not be able to follow the evolution. Too much sediment, for instance as a

result of increased erosion in the watershed, can be detrimental also. As far as the salinity

is concerned, it should be noted that different mangrove species have different

requirements.


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A change in salinity within certain limits will for that reason not necessarily lead to a

disappearance of mangrove forests, but rather to a change in its species composition.

Faced with those uncertainties it seems better not to suppose a deterioration of the wetland

ecosystems in the study area between today and the years 2050/2100. In that way, the

potential future negative impacts of erosion and flooding on ecosystem values are

maximised, and no danger of underestimating the impact will exist.

It has to be noted that we will account for the impact of demographic and economic growth

on the value of the ecosystem goods and services affected. As demographic growth affects

the number of people that will profit from these goods and services, and as economic

growth will increase their value per unit, we need to account for these effects on the value

per ha (see further, value of ecosystems at risks).

3.2.4 Land use policies to limit future damages

A specific component is the degree to which future land use policies account for risks to

coastal erosion and flooding, and direct growth and development towards the less

vulnerable areas. For the COCED we will assume a Business as usual development, and

apply the low and high ranges for future development to each grid cell. A scenario in which

land use is oriented to minimize damages is part of the protection measures, which are

assessed in a separate report (IMDC, 2017e).

For wetlands, although policies can play an important role in maintaining and enhancing

their value, we choose not to take them explicitly into account. This means that we will not

consider externally caused negative evolutions (as explained above), but neither will we

consider positive policy induced ones.

3.3 DAMAGE ASSESSMENT

As explained above, damage assessment requires a consistent set of information on the

assets or values at risk (total value in $/ha) and damage functions (% of total value). We

distinguish between tangible risks, risks for victims and impacts on ecosystems.

3.3.1 Values at risk for tangible damages

3.3.1.1 Methodology

For tangible damages in urban, rural and economic land uses, the values at risk include

estimates of the value of the assets (buildings, infrastructure) and the value of goods and

services produced. As explained above (section 3.1.1) the approach for COCED is a

mixture of defining values at risk, based on GDP/ha, while accounting for population

density, specific land use areas and the presence in urban and rural areas of specific

assets.


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A. Values for urban and rural land uses.

The GDP per ha reflects the value of the economic activity in that location. In absence of

such a detailed valuation map, we map GDP using information on land use, population

density and GDP per capita, accounting for differences between rural and urban areas in

employment and the sector of employment (agriculture, industry, and services).

To this purpose, we apply a method in 4 steps:

1. In a first step, we calculate the difference between value added per employee in

different sectors, based on the most recent data (2010, only available at national

level, based on UN data as they report both share in GDP and employment). The

results in Table 3-3, based on the relative share of the sectors in total value added

(GDP) (column 1) and employment (column 2) indicate that value added per

employee is much higher for industry (column 3), whereas the differences for the

service sector and agriculture are limited.

Table 3-3: Estimation of the GDP/employee for 3 main economic sectors

Sector GDP/employee (TOGO )

share in value added Share in employment GDP/employee.

(1) (2) (3)

% % $/employee

1 Agriculture 22,0% 44,7% 1.526

2 Industry 28,4% 14,4% 6.115

3 Services 49,6% 45,5% 3.379

Total 100,0% 40,9%

Average 3.100

(1) share of main sectors in value added, 2010 source: UN, data.un.org/CountryProfile. (2) share of main sectors in employment, 2010 , source UN, data.un.org/CountryProfile.

2. Next, we estimate average value added/employee for rural and urban areas

accounting for the share of the three sectors in rural and urban employment in the

maritime region. As we don’t have specific data for Ghana for this region, we use

data from Togo (AGAT, 2013) as a proxy. As the share of industry and services in

higher in urban areas, the value added/employee is 126 % higher in urban area

compared to the rural area (data for 2010) (Table 3-4, line 6).

3. We assume that the difference in value added per employee is a good proxy for the

difference in GDP/capita, and that the data for 2010 are a good proxy for the current

situation. Based on the latest data for average GDP/capita for f2016 (World Bank),

we calculate a different GDP/capita for urban, rural and suburban areas (Table 3-4,

line 9).


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Table 3-4: Estimation of the GDP/capita for urban and rural areas

A Share of sector in employment (1) Rural Urban Sub-

Urban Average

1 Agriculture 95 % 5 % n.a.

2 Industry 0,8 % 14,6 % n.a.

3 Services 4,2 % 80,8 % n.a.

4 Total 100 % 100 % n.a.

B VA/employee

5 VA/empolyee ($/employee) (2) 1.640 3.699

6 Share in population (3) 30% 70%

7 Difference urban/rural (4) 100 226 188

C GDP/capita

8 Data GDP/capita 2016 (5)($/capita) n.a. n.a. n.a. 1.432

9 Calculated GDP/capita (6)($/capita) 762 1.719 1.241 1.432

n.a. = not available (1) Share of main sectors in urban and rural employment, Maritime region, 2012 (AGAT, 2013). (2) Based on Table 3-3 and (1), in $ /employee, current $ (3) Share in rural and total population, Maritime region, 2012 (AGAT, 2013), we assume sub-urban

is included in urban. (4) Rural = 100; average calculated accounting for share in population (line (3)) (5) Average GDP /capita, current $/capita, data 2016, source: World bank (GDP and population) (6) Data for rural and urban calculated accounting for differences in urban/rural GDP/employee (line

(4) and share in population urban rural (3). Suburban calculated as average of rural and urban.

4. The average GDP/capita for the main land use categories is applied to the average

number of inhabitants for the different classes, which results in a diverse indicator for

GDP/ha, reflecting population density and main sector of activity (Table 3-6; column

(3)). It confirms that wealth and GDP are higher in urban areas and in the more

densely populated coastal area.

B. Values for specific high value assets in urban and rural land uses

In addition, we correct for the presence of high value assets (e.g. schools or transport

infrastructure) in certain grid cells (Table 3-6, column (4)). To this purpose, we estimate the

value for this type of assets, based on some generic key indicators.

1. For urban and rural grid cells with specific assets like schools, other public buildings,

etc. We estimate this value at 115.680 $, based on an average of different estimates for

this type of buildings, mainly based on specific data for Togo (PDNA Togo, 2010), and

confirmed by these for Benin and the more generic, worldwide assessment of Huizinga,

2017. Table 3-5 gives an overview of the used information to make this assessment.

It has to be noted that the average value does not cover two very high valued buildings

(hotel, cultural centre), as these are outliers for the generic evaluation. It shows

however that our approach will not be able to capture to very special, unique high value

assets. Hotel infrastructure may be of specific importance. In addition, it has to be noted

that the intangible value of e.g. cultural or historic buildings is not captured with this

value, based on standard building or restoration costs.


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Table 3-5: Estimates of the value of different non-residential types of buildings, Togo.

Type of building Value Remark Source

$ 2010 /building

1 Commercial building 56.399 200 m² floor space, incl. content (1)

2 Industrial building 56.399 200 m² floor space, incl. content (1)

3 School 61.890 Togo, incl. 20 % materials (2)

4 School 70.554 Benin, incl. 20 % materials (3)

5 Church / mosque 66.016 (1)

6 Theatre hall 12.378 (1)

7 Police station 13.203 200 m² floor space (1)

8 Sports/cultural centre 618.898 (1)

9 Hotel 1.916.058 (1)

Average value (1-7) 48.120 Value for Togo & Benin (4)

Average value (incl. 1-9) 319.088 (5)

Value applied for Ghana 115.680 Value for Ghana & Ivory Coast (6)

n.a. = not available (1) Huizinga, 2017, values converted from euro using exchange rate average 2010-2016 (2) PDNA Togo, 2011, values converted from FCA using 2010 exchange rate (3) PDNA Benin, 2010, values converted from FCA using 2010 exchange rate (4) Average of value 1 to 7, excluding the outliers 8 and 9 (5) Average of value 1 to 9, including the outliers 8 and 9, only used for sensitivity (6) Value based on line (4), adapted for difference in GDP/capita for Ghana (World Bank data)

6. For urban areas, we apply this average value of 115.680 $ for each grid cell with an

additional valuable assets. For urban grid cells U8 or U9, this adds 29 % to 20% to

the value of the total assets at risk. This is a significant, but not a dominant increase

in values at risk.

For sub-urban and rural areas we assume that the values of these additional assets

are lower (75 % and 50 % of the urban value), which reflects that e.g. schools or

churches are likely to be smaller or have less facilities. As the value at risk based on

GDP is however smaller, this add on has a larger share in total assets (+/- 50 % for

sub-urban and + 95 % for rural).

7. For rural areas, we have also distinguished a second category of special assets

(roads, irrigation) that are only relevant in rural areas (irrigation), or with a lower value

per ha, which is only worth distinguishing for rural areas (non-local roads). This value

is estimated 2.652 $/ha, based on literature and taken into account differences in

GDP/capita. The value for roads (3232 $/ha) is based on the assumption of a road of

100 meter long, 5 meter width and the average value per m² from PDNA, Togo, 2010

(3,8 $/m²) and Huizinga, 2017 (9,9 $/m²). As we do not have specific information on

the value at risk for irrigated agriculture, we use the same value as for transport. This

corresponds to about 11 times the average yearly value added per ha for agriculture.

C. Values for specific land uses

For areas that are specified on land use maps (or open street map) as port, industrial or

administrative area, we have to use another approach. As there is zero or very limited

population in these grid cells, we cannot apply an approach based on population density.

The values for these land uses is based on the literature review in Huizinga, 2017, and

cross checked with information from Togo. The data are reported in Table 3-6, column (4).


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The value per ha for industrial and services are very similar (around 370.000 $/ha.year).

These values per ha are consistent with an assumption that 1/3 of the area is built up, using

the building costs per m² in the same study from Huizinga, 2017.

As we do not have separate data for transportation sector or port activities, we apply the

same values as for industry. We neither have information for quarries, but it seems

reasonable to assume that the value of the assets is lower compared to industry, services

or urban fabric. We assume a value of 10 % of that of industry, but this estimate is highly

uncertain.

C. Estimating the value of the assets, based on information about GDP/ha.

In case detailed information on the numbers, type and value of the assets is not available,

the preferred method is to estimate the values of the total assets using the ratio

assets/GDP from literature (Hinkel et al., 2014; Hallegatte et al., 2013).

(Hallegatte et al., 2013), demonstrated that there is a strong, fixed relationship between the

two factors (value assets/ha = 2.8 x GDP/ha), for a wider range of GDP levels. In our

approach however, some part of the total assets are captured by different methods (specific

industrial areas, ports, and administrative quarters) and specific assets (schools, health

care etc.). We assume that we capture 25 % of the total value in this specific approaches.

Therefore, we use an adapter factor of 2.1 (= 2.8 x 75 %) to calculate the value of the

assets, based on the data in Table 3-6, column (3). We have to add the value of the assets

assessed with the other methods (= column (4)) to calculate total value of the assets

(column (5)).

3.3.1.2 Results for rural, urban and economic land uses

Table 3-6, line 1-24 gives an overview of the results of this approach for Ghana, applied to

rural and urban areas. For natural areas that have some population, we use the same

approach as for rural to estimate the tangible damages on buildings etc.


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Table 3-6: Estimation of the economic values at risk per land use category ($/ha)

Ind. Description pp/ha. GDP/cap GDP/ha VSA/ha factor TV ASTS/ha

N° $/capita $/ha.year $/ha $/ha

(1) (2) (3) (4) (5) (6)

1 R1 Rural 1 0,5 762 381 - 801 762

2 R2 Rural 2 2 762 1.525 - 3.202 762

3 R3 Rural 3 4 762 3.050 - 6.404 762

4 R4 Rural 4 0,5 762 381 2.457 3.257 762

5 R5 Rural 5 2 762 1.525 2.457 5.659 762

6 R6 Rural 6 4 762 3.050 2.457 8.861 762

7 R7 Rural 7 0,5 762 381 53.583 54.384 762

8 R8 Rural 8 2 762 1.525 53.583 56.785 762

9 R69 Rural 9 4 762 3.050 53.583 59.988 762

10 U1 Sub Urban 1 15 1241 18.613 39.087 1241

11 U2 Sub Urban 2 37,5 1241 46.532 97.717 1241

12 U3 Urban 3 62,5 1719 107.455 225.656 1719

13 U4 Urban 4 100 1719 171.929 361.050 1719

14 U5 Urban 5 150 1719 257.893 541.575 1719

15 U6 Peri Urban 6 15 1241 18.613 80.375 119.462 1241

16 U7 Peri Urban 7 37,5 1241 46.532 80.375 178.092 1241

17 U8 Urban 8 62,5 1719 107.455 107.167 332.823 1719

18 U9 Urban 9 100 1719 171.929 107.167 468.217 1719

19 U10 Urban 10 150 1719 257.893 107.167 648.742 1719

20 E1 Quarry O* O** 35.856 35.856

21 E2 Port O* O** 358.563 358.563

22 E3 Transport O* O** 358.563 358.563

23 E4 Services O* O** 395.487 395.487

24 E5 Industry O* O** 358.563 358.563

25 N1 Other natural

area O*

26 N2 Urban Park O*

27 N3 Wetlands < 1 1 150 150 1

28 N4 Wetlands > 1 762 300 381 801 762

29 N5 Mangroves 0 1 3.847 3.847 1

30 W2 Water 0

31 Military

(1) Pp/ha = number of persons per ha, based on the land-use category

(2) GDP /capita, different for urban, rural and suburban areas (see Table 3-4)

(3) GDP/ha = (2) x (1)

(4) VSA/ha = Value of specific assets in rural and urban areas (schools, health care, roads).

(5) Factor = factor that describes ratio assets to GDP, based on Hallegate, 2013,

(6) TV ASTS/ha = Total value of the assets per ha (6) = ((3) x (5)) + (4)


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3.3.2 Number of people affected

The number of people affected per grid cell or land-use category can be estimated from the

data in Table 3-6, column (1). This applies both to rural, urban, economic and natural land

uses, provided the worldpop database has allocated population to these locations.

3.3.3 Valuation of ecosystems

3.3.3.1 Objective and scope

Natural land uses are valued based on the literature of the value of ecosystem goods and

services delivered by the nature types.

There is a large scientific and more generic literature related to the goods and services

provided by wetlands and their importance for society. These include the benefits for local

inhabitants of terms of goods (fish, wood …) and services (e.g. flood protection) provided

by the wetlands as for the society at large (carbon sequestration, recreation). The overview

of (de Groot et al., 2012) indicates that coastal wetlands provide a wide range of highly

valued ecosystems (expressed in $/ha.year). It provides both important provisionary goods

(food, water and raw materials), regulatory services (including erosion prevention, waste

treatment etc.), habitat services (e.g. nursery functions) and cultural services (recreation).

Even if we account for all the uncertainties in the estimates, these studies indicate that the

total value for these EGS are important and that coastal wetlands and mangroves are

among - from the perspective of this indicator - the most valuable ecosystems but also with

a very wide range of values in literature.

Although there is a large number of studies worldwide to quantify and value (in monetary

terms) these goods and services, there is very little specific information for Africa in general

and no specific valuation studies of ecosystems services of wetlands for West Africa.

Hence, we have to use benefit transfer and built on valuation studies from other regions to

estimate the values for West-Africa, accounting for differences in functions, GDP…

Table 3-7 below gives an overview of the different types with examples. Natural land uses

that are most likely to be affected by erosion, coastal flooding and sea level rise are

wetlands (N3, N4) and mangroves (N5). Consequently, the focus is on the value estimates

from literature for the goods and services that are delivered by wetlands and mangroves

(Brander, 2006; Vegh, 2014, BCA 2013; Schuijt, 2002; Salem and Mercer, 2012;

Chaikumbung, 2016). The state of the art in literature and available data and resources

within this study allows us to make one general estimation for the value of wetlands and for

one mangroves at country level, but not make a location and wetland specific estimate. As

there is no need to specify the value of each good or services, the estimate in US$/ha/year

covers the whole bundle of different goods and services.

This estimate for the value for ecosystems (€/ha/year) is the equivalent for the production

value for other land uses ($/ha.year). It is used to estimate the values lost, in case the

ecosystems is fully lost due to erosion or permanent flooding (sea level rise).

It has to be noted that although we have also included other natural areas as land-use

categories (urban parks, other natural areas), first analysis indicates they the area affected

by coastal erosion or flooding is zero or negligible, and therefore we do not value these

land use categories. Lessons from valuation studies on mangroves and wetlands


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We first discuss the valuation on mangrove ecosystems. Although those represent only a

relatively minor component of the overall wetland system under study, they can be

expected to have (by far) larger ecosystem values per unit of area. Moreover, mangrove

ecosystems are well studied, and comparatively much information is available as to their

(potential) value. This is much less the case for the other types of wetlands. For one thing,

the term “wetland” covers a lot of different types of ecosystems, and applying the (often

generic) figures available in literature to the specific lagoon- and estuarine system of the

study area will yield figures whose reliability are questionable. For another, we are not

aware of any recent studies on the value of the ecosystem goods or services present in the

area, which would allow them to be taken into account in a reliable way.

Ecosystem goods and services provided by mangrove ecosystems have been well studied.

The most important ones are listed below.

Table 3-7: Overview of relevant goods and services

delivered by wetlands and mangroves ($/ha)

Category Goods or services Comments

Provisioning Timber for construction

Firewood Used on a large scale for salt production, fish smoking,

…

Charcoal

Salt

Fish Including extensive forms of aquaculture

Provision of other fauna

products Crabs, oysters, birds, …

Medicines

Soil improvement Sediment rich in organic material is applied to fields

Regulating Carbon sequestration

Not only in the standing wood, but also in the organic

sediments. Mangroves have the largest average

carbon stocks per unit area of any ecosystem.

Erosion control Applies to sediments in the lagoon system, not to the

beaches

Sediment trapping/nutrient

retention

Limits transport of (potentially polluted) sediments to

the marine environment

Supporting Nutrient cycling Fixation of nutrients and pollutants in soil and wood.

Nursery function for fish

species

Including marine species, with impact on marine

fisheries. Clear evidence exists for the relation between

presence of mangroves and size of fish catches at sea.

Maintenance of habitat and

biodiversity e.g. shelter and food for manatees

Cultural Tourism

Cultural and spiritual value

Aesthetic value

Education and research


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Note that, as mentioned by BCA (2013), there can be a trade-off between direct values

(e.g. timber harvesting) and indirect values associated with a healthy mangrove ecosystem

(e.g. nursery function). That is, consumptive uses may reduce the quantity and/or quality of

the non-consumptive values.

The above list applies to the systems in the study area and is based on a combination of

literature sources and field visits. Certain functions that are frequently mentioned in

international literature have not been included here. The main example of this is coastal

protection (also termed storm protection, flood protection, shoreline protection, ect.). This is

an important function for mangrove systems that actually line the coast, as is the case in a

number of south-east Asian countries and also in e.g. Senegal and Guinée in West Africa.

The strong wave climate of the coasts in the Gulf of Guinea however excludes this, and

mangroves in the study area are mostly confined to the relatively secluded environment of

lagoons and estuaries.

Another example is sand mining. This is an important commercial activity in lagoon systems

that have a large and permanently open (and therefore mostly artificial) connection to the

sea, as is the case with e.g. the canal de Vridi in Abidjan or the connection to the Lac

Nokoué in Cotonou. In Togo, such conditions do not exist.

Estimates in literature of the values of the listed ecosystem goods and services differ

considerably. Overall values as low as 400 USD/ha/year and as high as several tens of

thousands USD/ha/year have been reported. The lower figure probably only takes into

account direct uses (wood, fish, salt, etc.) of the mangroves2.

The table below shows the ranges for a number of functions as reported by(Salem and

Mercer, 2012).

Table 3-8: Summary statistics for mangrove evaluations by type of service (in US$/ ha.yr)

2 Estimated direct use values are generally relatively low when compared to other elements. However,

sustainable exploitation of mangrove forests can form an important contribution to cash income and to

the provision of basic necessities to the rural population.


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As shown by different meta-analyses (e.g. Brander et al., 2006 and Salem and Mercer,

2012), these differences can be due to a number of reasons:

Goods and functions studied: it is not always clear what ecosystems goods and

functions have been included in general estimates of mangrove ecosystems values.

For instance estimates solely based on the market values of goods provided (fish,

wood, salt, …) are generally much lower than estimates that include functions such

as nursery functions for fish, coastal protection, biodiversity conservation and

tourism.

Local and regional differences in general environment: estimates differ widely

depending on the geographical location of the ecosystem studied. For instance, GDP

per capita appears to be an important variable in explaining part of the variation in

mangrove system values. We will account for this for the selection of a central value.

Methods used: the wide range of goods and services to be valued requires the use of

different methods for estimating monetary values of ecosystem services. Whereas

valuation of provisioning services can largely be based on market prices, other

services require a broader range of techniques from environmental economics,

including avoided costs (for regulating services), revealed and stated preferences (for

cultural services and non-use values), with often larger uncertainties involved. This

implies that a direct comparison with valuation of other land uses and tangible

damages is more difficult. It is not clear how this affects comparability. For instance,

stated preference methods (contingent valuation) appear to produce value estimates

that are higher than other methods (Brander et al., 2006), whereas , 2016 finds a

lower value for stated preference methods for wetlands. Market Price estimates are

upward biased if the cost of other production inputs is neglected (Salem and Mercer,

2012).

Selection of studies: as can be derived from the table above, the distribution of

results for a given variable on the basis of a number of studies is often strongly

skewed to the right, as evidenced by the mean value being considerably greater than

the median. This means that a relatively small number of studies resulting in very

high monetary values tend to inflate the average values. Using those studies as a

basis for estimating monetary values would in most cases results in a gross over-

estimation of the actual value of the ecosystem.

The meta-analysis of (Chaikumbung et al., 2016) shows that more recent studies

result in lower values, which may indicate that studies focused first on the most

valuable mangroves and wetlands that may not be representative for the case study

areas. The large differences in literature are also related to the choice of indicator,

value per ha. On average, the higher values especially apply to the smaller wetlands,

and the value per ha strongly declines as the size of the wetland declines

(Chaikumbung et al., 2016). This implies that if a size of a wetland decreases (e.g.

with 10 %), the total value of the wetland will not decrease with 10 % but rather with

4%.


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Total economic values for mangroves as reported in the meta-analysis by (Salem

and Mercer, 2012) lie in the range of $2,772 to $80,334 US$ ha/y with a mean of

$28,662 US$/ha/y and a median of $3,847 US$ ha/y. Note that the bulk of the studies

analysed by Salem and Mercer apply to South and South East Asia, and that

estimates for African mangroves appear to be consistently lower than for Asian ones.

There is mixed evidence to which extent values for Africa may differ from world

averages. The earlier overview studies and meta-analysis (e.g. Brander, 2006)

included very few studies for Africa, and suggested values may be lower. The most

recent meta-analysis of (Chaikumbung et al., 2016) (on wetlands with focus on

developing countries) builds on a wider range of literature sources (e.g. grey

literature) resulting in 379 studies and 1432 observations (case studies) of which 17 %

in Africa. This meta-analysis shows that on average wetlands in Africa are as valuable

as in other parts of the world. It has to be noted that the meta-analysis accounted for

other differences between regions, e.g. GDP/capita. Therefore, we conclude we can

apply median or average world values for Africa, and test for impact of GDP on these

values.

3.3.3.2 Conclusion: values to be used in COCED

Given the broad range of probable values and lacking other and more precise analyses, we

use the median figure from Salem and Mercer (3,847 USD/ha/y) as the basis for evaluating

the total value of the mangrove ecosystems in the study area. This value is within the range

of 2000 à 9000 USD/ha/year reported by UNEP (2006). We consider it to be a realistic3 but

conservative value in that the contribution of the non-use value in this figure is probably

underrated. On the other hand, elements such as values for tourism do at present

represent a potential rather than a real value that would need investments in infrastructure

to be realised.

For wetlands, the different overview studies also report a very broad range of values, but

lower compared to the values for mangroves. The average value in (Brander et al., 2006)

and (Chaikumbung et al., 2016) are in the same order of magnitude (2800 $/ha.year and

2000 $/ha.year)(rounded numbers). The median value in Brander et al, 2006 is 150

USD/ha.year). In line with the approach for mangroves, we use this median value for the

valuation of wetlands. It would be preferable to estimate the total value based on the value

functions of the meta-analysis in (Chaikumbung et al., 2016)but it is beyond the scope of

the current study to collect all the required information on individual wetlands to apply the

function. We use the median value as we don’t have indications that there are reasons for

these wetlands to assume the higher values in $ per ha would apply. Although GDP per

capita for Togo is much lower than the average in the studies, the impact on the value in

the meta-analysis is limited (given overall uncertainty) and is compensated by the higher

population density for the coastal wetlands in Togo.

3 A very detailed study by UNEP (2011) has estimated the value of a mangrove wetland in Kenia at 1,092

US%/ha/y, counting direct use, indirect use and existence value. This figure is considerably lower than

the one we propose, but we prefer not to consider the results of this one study as being automatically

applicable to the situation in West-Africa.


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It has to be noted that even more so than for the mangroves, this is an approximate (and a

conservative) value. It should be noted that this value will be applied to different types of

wetlands, i.c. lagoons on the one hand and lowlands that are temporarily inundated (either

by river floods or by tides and storms, or by both) on the other hand. Especially the value of

the lagoons can in reality not entirely be dissociated from the fact whether the lagoon is

fringed by mangroves or not. This reality is more or less being taken into account by the

fact that a much larger value per hectare is applied to mangrove stands than to other types

of wetlands.

Table 3-8 summarises the values to be applied for natural land uses. For wetlands with no

inhabitants, the value of 150 $/ha.year applies. In case the information on population

density suggests there is some habitation in the wetland area and mixed land-uses, and

given all the uncertainties, the value for GDP/ha.year is similar for N3 and R1. In case there

is some population in the wetland (mixed), we use the indicators for R1 to assess the value

of the assets at risk (e.g. houses) (= 800 $/ha for Ghana).

Table 3-9: Estimation of the economic values at for wetlands and mangroves ($/ha)

Ind. Description pp/ha. GDP/cap GDP/ha VSA/ha factor TV ASTS/ha

N° $/capita $/ha.year $/ha $/ha

(1) (2) (3) (4) (5) (6)

25 N1 Other natural

area O* /*

26 N2 Urban Park O* /*

27 N3 Wetlands < 1 1 150 150 1

28 N4 Wetlands > 1 762 300 381 801 762

29 N5 Mangroves 0 1 3.847 3.847 1

/*: as these ecosystems are not important in the land uses affected by coastal flooding and erosion, we

have not estimated a value.

(1) Pp/ha = number of persons per ha, based on the land-use category

(2) GDP /capita, different for urban, rural and suburban areas (see Table 3-4)

(3) GDP/ha = (2) x (1)

(4) VSA/ha = Value of specific assets in rural and urban areas (schools, health care, roads).

(5) Factor = factor that describes ratio assets to GDP, based on (Hallegatte et al., 2013)

(6) TV ASTS/ha = Total value of the assets per ha (6) = ((3) x (5)) + (4)

Note that the same data are used for the 4 countries and that we do not adapt these

estimates to account for differences in GDP/capita. Overall uncertainties are that big that

we cannot differentiate this value between the countries, and we don’t want to give the

impression that – by correction for just one of the many factors - we are confident that these

values are higher for some countries.


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3.3.4 Damage functions

3.3.4.1 Floods Tangible damages

The damage functions for floods (tangible damages) are based on the review of worldwide

literature on flood damage functions in (Huizinga et al., 2017). It has to be noted that the

information for Africa is very limited, and that the selected damage functions build on

information for other continents.

These damage functions are not country specific, and apply to the 4 countries in this study.

All damage functions are listed in Table 3-10, and specified in function of water depth, in

meter. The damage functions for urban and rural areas are based on these for residential

areas for Africa (Huizinga, 2011). It means that in case an urban area is flooded with

0.5 meter, the damage is equal to 22 % of the total value of the damages, as estimated in

Table 3-6, column (6). The table gives some reference points of a continuous damage

function, with 0 damage for 0 inundation, and with – as an example – 0,44 % of damage for

each cm of inundation in the first category (0,44 = 55 % / 50 cm). Especially at very low

inundation depths, there is uncertainty to which extent a threshold (e.g. 20 cm) should be

applied. However, the overall accuracy of information does not allow for such fine tuning of

the methodology.

We apply the same function for urban and rural areas. In both areas, the values at risk refer

to both the buildings at risk and the economic activities. Note that we do not use the

specific damage curves for agriculture and crops, because this damage is already

accounted for in the more generic formula, which is more applicable for this situation (see

general section 3.1.1). It has to be noted that the average damage curve in (Huizinga et al.,

2017) for agriculture is relatively similar to that for residential areas, especially for the first

0.5 meter.

For industry, we apply the damage curve for Africa from Huizinga, which is especially less

steep for the first meter. We also apply this function to ports, transportation areas and

quarries.

As for commerce, there is no specific damage curve for Africa, we used the one for Asia

instead (Huizinga et al., 2017). This function is more in line with that for residential areas.

These damage functions refer to floods with a short duration (e.g. a couple of hours). For

floods with a longer duration (e.g. several days), the damage function is adapted (x 1,1),

based on the differences in damage curves for short and long term floods in Risc-Kit

(Viavattene et al., 2013).


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Table 3-10: Damage functions for Floods, short duration (e.g. a couple of hours)

Ind. TV ASTS/ha Water depth

$/ha 0 0,5 1 1,5 2 3 4 5 6

1 R1 Rural 1 801 0% 22% 38% 53% 64% 82% 90% 96% 100%

2 R2 Rural 2 3.202 0% 22% 38% 53% 64% 82% 90% 96% 100%

3 R3 Rural 3 6.404 0% 22% 38% 53% 64% 82% 90% 96% 100%

4 R4 Rural 4 3.452 0% 22% 38% 53% 64% 82% 90% 96% 100%

5 R5 Rural 5 5.854 0% 22% 38% 53% 64% 82% 90% 96% 100%

6 R6 Rural 6 9.056 0% 22% 38% 53% 64% 82% 90% 96% 100%

7 R7 Rural 7 58.640 0% 22% 38% 53% 64% 82% 90% 96% 100%

8 R8 Rural 8 61.042 0% 22% 38% 53% 64% 82% 90% 96% 100%

9 R69 Rural 9 64.244 0% 22% 38% 53% 64% 82% 90% 96% 100%

10 U1 Sub Urban 1 39.087 0% 22% 38% 53% 64% 82% 90% 96% 100%

11 U2 Sub Urban 2 97.717 0% 22% 38% 53% 64% 82% 90% 96% 100%

12 U3 Urban 3 225.656 0% 22% 38% 53% 64% 82% 90% 96% 100%

13 U4 Urban 4 361.050 0% 22% 38% 53% 64% 82% 90% 96% 100%

14 U5 Urban 5 541.575 0% 22% 38% 53% 64% 82% 90% 96% 100%

15 U6 Peri Urban 6 125.847 0% 22% 38% 53% 64% 82% 90% 96% 100%

16 U7 Peri Urban 7 184.477 0% 22% 38% 53% 64% 82% 90% 96% 100%

17 U8 Urban 8 341.336 0% 22% 38% 53% 64% 82% 90% 96% 100%

18 U9 Urban 9 476.730 0% 22% 38% 53% 64% 82% 90% 96% 100%

19 U10 Urban 10 657.255 0% 22% 38% 53% 64% 82% 90% 96% 100%

20 E1 Quarry 35.856 0% 3% 13% 20% 25% 34% 46% 50% 50%

21 E2 Port 358.563 0% 6% 25% 40% 49% 68% 92% 100% 100%

22 E3 Transport 358.563 0% 36% 57% 73% 85% 100% 100% 100% 100%

23 E4 Services 395.487 0% 38% 54% 66% 76% 88% 94% 98% 100%

24 E5 Industry 358.563 0% 6% 25% 40% 49% 68% 92% 100% 100%

25 N1 Other nature -

26 N2 Urban Park -

27 N3 Wetlands 150 5% 5% 5% 5% 5% 5% 5% 5% 5%

28 N4 Wetlands 801 0% 22% 38% 53% 64% 82% 90% 96% 100%

29 N5 Mangroves 3.847 5% 5% 5% 5% 5% 5% 5% 5% 5%

30 W2 Water

31 Military


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Table 3-11: Damage functions for Floods, long duration (e.g. several days)

Ind. TV ASTS/ha Water depth

$/ha 0 0,5 1 1,5 2 3 4 5 6

1 R1 Rural 1 801 0% 24% 42% 58% 70% 90% 99% 100% 100%

2 R2 Rural 2 3.202 0% 24% 42% 58% 70% 90% 99% 100% 100%

3 R3 Rural 3 6.404 0% 24% 42% 58% 70% 90% 99% 100% 100%

4 R4 Rural 4 3.452 0% 24% 42% 58% 70% 90% 99% 100% 100%

5 R5 Rural 5 5.854 0% 24% 42% 58% 70% 90% 99% 100% 100%

6 R6 Rural 6 9.056 0% 24% 42% 58% 70% 90% 99% 100% 100%

7 R7 Rural 7 58.640 0% 24% 42% 58% 70% 90% 99% 100% 100%

8 R8 Rural 8 61.042 0% 24% 42% 58% 70% 90% 99% 100% 100%

9 R69 Rural 9 64.244 0% 24% 42% 58% 70% 90% 99% 100% 100%

10 U1 Sub Urban 1 39.087 0% 24% 42% 58% 70% 90% 99% 100% 100%

11 U2 Sub Urban 2 97.717 0% 24% 42% 58% 70% 90% 99% 100% 100%

12 U3 Urban 3 225.656 0% 24% 42% 58% 70% 90% 99% 100% 100%

13 U4 Urban 4 361.050 0% 24% 42% 58% 70% 90% 99% 100% 100%

14 U5 Urban 5 541.575 0% 24% 42% 58% 70% 90% 99% 100% 100%

15 U6 Peri Urban 6 125.847 0% 24% 42% 58% 70% 90% 99% 100% 100%

16 U7 Peri Urban 7 184.477 0% 24% 42% 58% 70% 90% 99% 100% 100%

17 U8 Urban 8 341.336 0% 24% 42% 58% 70% 90% 99% 100% 100%

18 U9 Urban 9 476.730 0% 24% 42% 58% 70% 90% 99% 100% 100%

19 U10 Urban 10 657.255 0% 24% 42% 58% 70% 90% 99% 100% 100%

20 E1 Quarry 35.856 0% 5% 19% 30% 37% 50% 50% 50% 50%

21 E2 Port 358.563 0% 9% 38% 60% 74% 100% 100% 100% 100%

22 E3 Transport 358.563 0% 54% 86% 100% 100% 100% 100% 100% 100%

23 E4 Services 395.487 0% 57% 81% 99% 100% 100% 100% 100% 100%

24 E5 Industry 358.563 0% 9% 38% 60% 74% 100% 100% 100% 100%

25 N1 Other nature -

26 N2 Urban Park -

27 N3 Wetlands 150 5% 5% 5% 5% 5% 5% 5% 5% 5%

28 N4 Wetlands 801 0% 24% 42% 58% 70% 90% 99% 100% 100%

29 N5 Mangroves 3.847 5% 5% 5% 5% 5% 5% 5% 5% 5%

30 W2 Water

31 Military


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3.3.4.2 Floods: people exposed and fatal victims

The damage functions for victims are based on the exposure of the population to floods and

the expected number of fatal victims per 1000 people exposed. That value is estimated at

0,16 fatal victims per 1000 people exposed, based on the average of the fatal victims in the

floods of 2009 and 2010 in Togo (0,25) and Benin (0,07).

This explorative damage function is used to estimate the number of victims from coastal floods, in order to assess the potential importance of this impact, compared to material damages.

In order to compare this impact with material damages, we value this impact (fatal risk) in

monetary terms, using the concept of the value of a statistical life (VSL). This figure is an

indicator how people and decision makers trade-off risks affecting material goods and fatal

risks. The value is based on a literature review (Navrud, 2010), and is adapted to the

GDP/capita for Ghana, in line with guidelines for benefit transfer (Navrud, 2009). Concepts

and data are in line with valuation of health impacts related to pollution (chapter 3.4).

It should be noted that these damage functions and valuation data are much more

uncertain than those for material risks.

3.3.4.3 Erosion: Damage functions

The Risc-Kit methodology nor the review in Huizinga provides damages functions to

quantify the damages from erosion. Therefore, we build a method tot estimate these

damages for the following damage categories, associated with relocation. The data build on

these for flood damages, estimated for different land uses.

Direct losses and costs of relocation: we assume that in case of erosion all assets

are lost. We use the value of the assets as estimated for flood losses (Table 3-6,

(column (6) as a proxy for the reconstruction costs. For some categories, this may be

an overestimation (e.g. vehicles), for other an underestimation (preparation of green

fields for buildings; administration costs, etc.).

Indirect effect 1: Loss of productivity due to relocation: we assume that productivity

will be 10 % lower in the new locations compared to the old ones, and that this loss

will continue for 10 years, until everybody is adapted to the new situation. Taking a

4 % discount rate into account, this is equal to a loss of 84 % of yearly GDP/ha. We

apply this percentage to the GDP per ha estimated in Table 3-6, (column (3)).

This reasoning however is an underestimate for the loss of quarries. We assume that

in that case this indirect loss will be bigger, and assuming a 20 % loss over a period

of 20 years, the damage equals 2.8 times the yearly value added /ha for quarries.

This estimate is very uncertain, as is the estimate of the Value added, but it is

unlikely to be an important land use that will be affected.

Indirect effect 2. We need to account for the losses of the economic activities that will

be pushed out by the relocation. We assume that in most cases this will be

agriculture. Therefore, we account for the loss of the assets associated with

agriculture and the value added per ha for agriculture. The value added per ha is

based on Huizinga, 2017 (483 €/ha), the assets are based on the ratio from Hallegate

(2) , we use a social discount rate of 4 % and account for losses of value added over

a period of 25 years.


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For relocation of port activities however, we assume that this will also affect other,

more valuable land uses. We apply a similar reasoning, but assume that the

relocation will be in urban areas (urban 3). Consequently, the indirect losses will be

bigger, and we use the estimate of loss of urban land (for urban 3).

Table 3-12: Damage functions for Erosion

Land use Direct Indirect 1 Indirect 2 Total

N° Descript. Relocation GDP/ha factor Ind. Cost 1s Ind. Cost 12

$/ha $/ha.y $/ha $/ha.year $/ha

(1) (2) (3) (4) (5) (6)

1 R1 Rural 1 528 381 84% 320 8.432 9.281

2 R2 Rural 2 2.113 1.525 84% 1.281 8.432 11.827

3 R3 Rural 3 4.227 3.050 84% 2.562 8.432 15.221

4 R4 Rural 4 2.279 381 84% 320 8.432 11.031

5 R5 Rural 5 3.864 1.525 84% 1.281 8.432 13.577

6 R6 Rural 6 5.977 3.050 84% 2.562 8.432 16.971

7 R7 Rural 7 38.703 381 84% 320 8.432 47.455

8 R8 Rural 8 40.288 1.525 84% 1.281 8.432 50.001

9 R69 Rural 9 42.401 3.050 84% 2.562 8.432 53.395

10 U1 Sub Urban 1 25.797 18.613 84% 15.635 8.432 49.864

11 U2 Sub Urban 2 64.493 46.532 84% 39.087 8.432 112.013

12 U3 Urban 3 148.933 107.455 84% 90.263 8.432 247.628

13 U4 Urban 4 238.293 171.929 84% 144.420 8.432 391.146

14 U5 Urban 5 357.440 257.893 84% 216.630 8.432 582.502

15 U6 Peri Urban 6 83.059 18.613 84% 15.635 8.432 107.126

16 U7 Peri Urban 7 121.755 46.532 84% 39.087 8.432 169.274

17 U8 Urban 8 225.282 107.455 84% 90.263 8.432 323.977

18 U9 Urban 9 314.642 171.929 84% 144.420 8.432 467.494

19 U10 Urban 10 433.788 257.893 84% 216.630 8.432 658.851

20 E1 Quarry 14.343 17.928 284% 50.737 8.432 73.511

21 E2 Port 143.425 179.282 84% 150.597 357.590 651.611

22 E3 Transport 143.425 179.282 84% 150.597 8.432 302.454

23 E4 Services 158.195 197.743 84% 166.105 8.432 332.732

24 E5 Industry 143.425 179.282 84% 150.597 8.432 302.454

25 N1 Other natural area

26 N2 Urban Park

27 N3 Wetlands 150 3.539 3.539

28 N4 Wetlands 801 381 84% - 4.216 6.410

29 N5 Mangroves - 3847 13.616 8.432 13.616

30 W2 Water

31 Military

(1) Direct costs of relocation = TV ASTS/ha = Total value of the assets per ha (see Table 3-4)

(2) GDP /ha.year (see Table 3-4)

(3) Indirect losses (productivity losses) as % of GDP.year /ha

(4) Indirect costs 1 = (2) x (3).

(5) Indirect costs 2 = loss of values in relocated area

(6) Total damages due to erosion


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3.3.4.4 Impacts on ecosystems

Earlier in this report, we described the factors and processes that determine damages

caused to ecosystems as a result of both inundation and erosion (see § 3.1.3).

It appeared that, as a result of the important potentially affected ecosystems consisting

mainly of mangroves or other brackish wetland types, damage to ecosystems from

inundation would be minor and/or recoverable in most cases. We concluded that other

pressures, such as human-induced degradation and climate change (especially sea level

rise) probably forms a more important threat to the ecosystems than damages through

inundation. Only extreme wind speeds and inundations lasting weeks rather than days may

cause permanent damage. Risc-Kit indicates that the former would only occur at wind

speeds of 200 km or more. The same source gives no damage function for the inundation

or erosion of mangrove forests.

The provision of ecosystem services would be temporarily halted during the inundations,

and may be somewhat reduced for some time after the event (e.g. impact on fish

communities in the wetlands). We do believe the ecosystems to be resilient though, and

propose to suppose a loss of 20 % of the value during one month after an extreme event.

For the erosion of other types of “salt marshes” (the closest approximation to the lagoon

and estuarine system of the West African Coast) Risc-Kit suggests (for micro tidal habitats,

which is the case here) irreversible damage starting from a wave height of 30 cm to 2 m

(depending on the water depth) for open coast situation. We propose to use a water depth

of 2 to 3 m and to not take into account the mitigating influence of the fact that the wetlands

are separated from the coast by sand bars and inlets. Under those circumstances damages

would start at a wave height of 30 cm and would become irreversible from a wave height of

1 meter. Serious or irreversible damage to the wetlands as a result of erosion forces can as

such thus not be discounted. Damage from erosion to mangrove forests seems much less

likely but can be assumed under a worst-case scenario.

In summary, we propose to approach damage to wetland ecosystems as a result of

flooding resp. erosion as follows:

Type of hazard Impact on mangroves Impact on other brackish

wetland types

Inundation 5 % /year * 5 % /year *

Erosion 15 % loss Total loss

Sea level rise 50 % loss 75 % loss

Value ($/ha.year) 3847 150 (N3)

* based on assumption of 20 % loss of ecosystem functions value during 3 months after the event

As indicated, those are conservative assumptions (real impact will probably be lower). To estimate the impact of erosion on ecosystems in line with other land-uses for which we

estimated impacts in $/ha, we have to make additional assumptions. Contrary to other land

uses, we do not assume that the wetlands eroded will be relocated, neither by natural or

man-made mechanisms. It has been discussed earlier that the likely development of

wetlands and mangroves in the baseline scenario for future decades in very uncertain, and

influenced by many natural and manmade pressures.


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Given these overall uncertainties, we apply simple assumptions to estimate the value of

future losses due to erosion. We apply a constant value ($/ha.year) over a period of

100 years at a 4 % social discount rate.

Table 3-12 summarises these results and we’ve added the values for rural 1 for

comparison. It shows that the total loss for wetlands with no population (N3, 3750 $/ha) are

lower than those for agriculture with low population, as in the latter case we account for loss

of assets and relocation costs. As N4 is a mixed land use, with some habitation, we apply

the average of the values for R1 and N3.

For mangroves, we first apply the 15 % loss to the value per ha (resulting in 577 $/ha.year).

Using the same assumptions and accounting rules as for wetlands, the current value of

these future losses amounts to 13.616 $/ha.

Table 3-13: Damage functions for erosion of ecosystems

Land use Direct Indirect 1 Indirect 2 Total

N° Description Relocation GDP/ha factor Ind. Cost 1s Ind. Cost 12

$/ha $/ha.y $/ha $/ha.year $/ha

(1) (2) (3) (4) (5) (6)

1 R1 Rural 1 528 381 84% 320 8.432 9.281

25 N1 Other natural

area

26 N2 Urban Park

27 N3 Wetlands 150 3.539 3.539

28 N4 Wetlands (7) 801 381 84% - 4.216 6.410

29 N5 Mangroves - 3847 13.616 13.616

30 W2 Water

(1) Direct costs of relocation = TV ASTS/ha = Total value of the assets per ha

(2) GDP /ha.year (see Table 3-4)

(3) Indirect losses (productivity losses) as % of GDP.year /ha

(4) Indirect costs 1 = (2) x (3).

(5) Indirect costs 2 = loss of values in relocated area

(6) Total damages due to erosion

(7) Average of estimate for N3 and Rural 1.

3.4 DAMAGES FROM WATER POLLUTION

The impact of pollution has already been indicated as an important treat for sustainable

development of the coastal region in section 2.4. There is only limited information to

quantify and value the impact of pollution.

Doumani (2015), estimates the impacts and damages from different water quality and

pollution issues for Togo, as shown in the table below. The most important impact

categories relate to public health impacts (mortality and morbidity) from water-borne

diseases associated with poor water and sanitation provision as well as behaviour

practices. These are quantified in Daly’s (disability adjusted life years lost) based on dose

response functions for diarrheal prevalence and water/sanitation access, and impacts are

valued based on human capital approach and info on GDP.


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The costs for surface and ground water quality is associated with the defensive spending to

bring water bodies to acceptable water quality standards and restored ecosystem services.

The costs of water scarcity was not valued due to the lack of data. In total, The damages

related to water augment to 1,1 % of GDP.

Table 3-14: Damages from poor water services and pollution, Togo

Damage category Health impacts (1) Damages

Mortality Morbidity Million $/year % GDP

Drinking Water 33,306 1,413 33.4 0.26%

Sanitation 46,494 1,973 30.8 0.24%

Drinking Water and Sanitation 73,056 3,101 48.5 0.38%

Water Quality 33.4 0.26%

Total 146 1,1 %

(1) Expressed in Daly’s (disability adjusted life years)

We have no information to assess to which extent these data are applicable for Ghana. As

GDP/capita is higher in Ghana, these impacts may be a lower bound estimate. However,

this is just one of the many factors affecting these estimates.


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4. COCED ANALYSIS FOR GHANA

4.1 COCED ANALYSIS AT COUNTRY LEVEL

4.1.1 Erosion: which land uses are affected

Table 4-1 and Table 4-2 show the land-use categories affected by erosion in 2015, 2050

and 2100, and impacts on people affected and damages. The impacts for 2050 and 2100

are both compared to the baseline (2014) and are modelled, as described above.

We assume that the erosion in 2015 is 2.8 % (1/36) of the impact in 2050. People affected

and damages in e.g. 2050 are calculated as the product of the number of ha eroded per

land use category and the damage per ha (see Table 3-12).

Damages in 2050 have to be interpreted as the damages due to the total erosion of

15000 ha between 2015 and 2050, and are assessed without accounting for demographic

or economic growth, or discounting. In the next section, we will account for demographic

and economic growth.

The table below indicates that erosion will increase significantly during this century. In

2100, 26000 ha or 2.6 % of the area of the coastal zone (defined as a zone of 20 km wide

along the coast of Ghana) will be affected by erosion. In addition, the affected area is more

densely populated than the average for the coastal zone, and has a higher share of

economic activities (industry and services). The erosion risk is twice as high in the first

period up to 2050 (+400 ha/year) then in the period after 2050 (200 ha/year).

Table 4-1: Impacts from erosion in three typical years (2015, 2050 and 2100).

Erosion Year Rural Urban Economic Natural Total

ha 2015 297 93 0 29 420

2050 10.702 3.351 15 1.059 15.127

2100 17.670 5.968 44 2.337 26.019

People 2015 254 6.152 - 0,1 6.406

2050 9.153 221.464 - 2,6 230.620

2100 16.463 379.516 - 9,8 395.990

Damage 2015 3,0 23,4 0,1 0,1 26,6

Million $ 2050 107,8 841,3 4,4 3,4 956,9

2100 180,5 1.437,7 13,2 7,7 1.639,2

People/ha 2015 0,9 66,1 - 0,0 15,2

2050 0,9 66,1 - 0,0 15,2

2100 0,9 63,6 - 0,0 15,2

Damage/ha 2015 10.070 251.036 304.235 3.191 63.259

$/ha 2050 10.070 251.036 304.235 3.191 63.259

2100 10.216 240.918 303.048 3.299 63.000

Year: 2050 = total impact of erosion in 2050 compared to reference (2014) Land uses Urban (includes suburban)


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Urban areas only account for 23% of ha eroded but have by far the largest share in number

of people affected and damages. After 2050, the impacted area for all land use categories

increases while the number of people per ha and the damage per ha remains more or less

constant.

Table 4-2: Share of land use categories in impacts from erosion (2015, 2050 and 2100).

Erosion Year Rural Urban Economic Natural Total

ha 2015 71% 22,2% 0,1% 7,0% 100%

2050 71% 22,2% 0,1% 7,0% 100%

2100 68% 22,9% 0,2% 9,0% 100%

People 2015 4% 96% 0,0% 0,0% 100%

2050 4% 96% 0,0% 0,0% 100%

2100 4% 96% 0,0% 0,0% 100%

Damage 2015 11% 88% 0,5% 0,4% 100%

1000$ 2050 11% 88% 0,5% 0,4% 100%

2100 11% 88% 0,8% 0,5% 100%

Share of land use categories in the coastal zone (20 km from coast, 998.000 ha)

2015 83% 9% 0,1% 7% 100%

Year: 2050 = total impact of erosion in 2050 compared to the reference (2014) Land uses Urban (includes suburban)

4.1.2 Flooding: which land uses are affected

First, we discuss impacts of a 100 year flood (event with return period of 100 years, T100)

in 2015, 2050 and 2100, based on current land uses and without demographic or economic

growth (frozen world). Next we present these results presented as risks, accounting for the

probability of the floods. Finally, we assess the impact of demographic or economic growth

on these risks.

4.1.2.1 Impacts from a 100 year flood

Table 4-3 shows the land-use categories affected by a 100 year flood in 2015, 2050 and

2100, and the related impacts on people affected and damages. In 2015, 42000 ha or 4.2%

of the coastal zone is at risk for coastal flooding from a 100 year flood. As the area affected

is especially rural, overall population density and economic activity is lower compared to the

area affected by erosion or the average of the coastal zone. Urban areas account for a

minor share in the flooded area but account for 60% of the people flooded and more than

70% of the damages.

Due to sea level rise, it is expected that in 2050 and in 2100 a T100 flood will affect a much

larger area. On the other hand, as part of the flooded area is eroded by 2050, the size of

the area affected could decrease. The figures show that differences between 2015 and

2050 are limited. After 2050, the size of flooded areas increase significantly, for all land

uses, and especially the number of people affected and damages increase.

As indicated above in chapter 3.3.4, there are no good damage functions to assess the

impacts of floods on human health. We test the potential impacts of one human health

impact category – risk on fatalities – using a simplified damage function. The expected

number of fatalities due to coastal flooding increases from 12 to 17 in 2100.


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If we monetize this impact using the value of a statistical life for Ghana (101834$/VSL), it

adds 1,3% to the material damages. For further analysis, we do not include these figures,

as they are uncertain and incomplete, and are relatively small compared to material

damages.

In summary, impacts of a 100 year flood are dominated by material damages to

infrastructure and the economy in urban areas.

Table 4-3: Impacts from a 100 year coastal flood event in 2015, 2050 and 2100.

Flood T100 Year Rural Urban Economic Natural Total

Ha 2015 33.412 1.383 - 7.941 42.736

(T100) 2050 32.610 1.282 - 7.588 41.481

2100 47.956 2.197 - 9.344 59.498

People 2015 26.849 47.122 - 27 73.997

(T100) 2050 26.414 39.248 - 27 65.688

2100 41.535 62.154 - 68 103.758

Damage 2015 24,50 74,09 - 1,24 99,8

Million $ 2050 24,18 62,02 - 1,24 87,4

2100 37,32 93,11 - 1,91 132,4

People/ha 2015 0,8 34,1 - 0,0 1,7

2050 0,8 30,6 - 0,0 1,6

2100 0,9 28,3 - 0,0 1,7

Damage/ha 2015 733 53.560 - 156 2.336

$/ha 2050 742 48.356 - 163 2.108

2100 778 42.377 - 205 2.224

Ha 2015 78% 3% 0,0% 19% 100%

(T100) 2050 79% 3% 0,0% 18% 100%

2100 81% 4% 0,0% 16% 100%

People 2015 36% 64% 0,0% 0,0% 100%

(T100) 2050 40% 60% 0,0% 0,0% 100%

2100 40% 60% 0,0% 0,1% 100%

Damages (1) 2015 25% 74% 0,0% 1,2% 100%

T100 2050 28% 71% 0,0% 1,4% 100%

million $ 2100 28% 70% 0,0% 1,4% 100%

Victims

Number* 2015 4,3 7,6 - 0,2 12,1

2050 4,2 6,3 - 0,2 10,8

2100 6,7 10,0 - 0,5 17,1

Monetized 2015 0,4 0,8 - 0,0 1,2

(2) ($ ) 2050 0,4 0,6 - 0,0 1,1

2100 0,7 1,0 - 0,0 1,7

(1) Damages include material damages for all land uses and loss of ecosystem services for natural land uses, but excluding victims

(2) Risk to victims of flooding using the value of a statistical life, adapted for Ghana (101.834$ per VSL)


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4.1.2.2 Risks from floods in 2015, 2050 and 2100

Table 4-5 and Table 4-5 show the total risks of floods, accounting for the damage and

probability of different flood events, as discussed in the methodology section. The risk

indicators accounts for the full range of possible flood events.

Risk = expected damage = damage from the flood x probability of the flood

At the pilot site level, a more detailed approach was used, and three floods with return

period of 10, 50 and 100 years were assessed.

For the country analysis, a simplified approach for modelling floods events was used, which

can be interpreted as a 100 yearly flood.

To define the weighting factors we used both information from theory and damage

assessments of coastal floods in Europe, and lessons learned from the relative size of the

damages in the flood events in the pilot site for Ghana (see further tables 4-15 and chapter

4.2.3). To calculate the total risk, damages of the T100 flood event were weighted with a

factor of 0,20. This factor reflects that in the pilot sites the damages of a more frequent

flood event (T10) were only 50% lower compared to a T100 flood event.

Table 4-4: Risks from coastal flooding in 2015, 2050 and 2100.

Flood risk Year Rural Urban Economic Natural Total

Risk/year 2015 4.900 14.818 - 248 19.966

1000$ 2050 4.837 12.403 - 248 17.487

2100 7.465 18.623 - 383 26.470

Risk/ha.year 2015 733 53.560 156 2.336

$/ha affected 2050 742 48.356 163 2.108

2100 778 42.377 205 2.224


Table 4-5: Share of land use categories affected by flooding in 2015, 2050 and 2100.


Damage 2015 37% 61% 0,0% 1,9% 100%

2050 95% 0% 0,0% 4,9% 100%

2100 46% 49% 0,0% 4,7% 100%



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4.1.3 Total risks of erosion and flooding per year

4.1.3.1 Methods and indicators

In this section, we calculate the yearly risk for erosion and flooding for the period 2015 up to

2100, and account for demographic and economic growth. We calculate yearly average

risks ($/year) for 5 periods, starting with the period 2015-2020. As risks for erosion and

floods are expressed in a common indicator, they can be added and compared.

Demographic growth is based on the projections for Ghana (at national level) of the UN,

estimated for every 5 year period up to 2100.

Economic growth (growth of GDP/capita) is based on projections for economic growth for

the period up to 2022 from IMF, and accounting for demographic growth as indicated

above. For the longer term (2030-2100) we use the average growth rate of the past

35 years in the region (the average for Benin, Togo, Côte d’Ivoire and Ghana).

Table 4-6: Projections for growth of demography and GDP up to 2100

Demography GDP/capita GDP

2015-2020 2,16% 3,52% 5,68%

2020-2030 1,94% 1,83% 3,77%

2030-2050 1,59% 0,43% 2,02%

2050-2075 1,05% 0,43% 1,48%

2075-2100 0,51% 0,43% 0,94%

Sources: UN, IMF and own assumptions

To illustrate the importance of demographic and economic growth, we present data on

future impact first assuming a ‘frozen world’, without growth. Second we present the impact

of demographic growth on number of people affected and consequently the impact of

economic growth per capita on damages.

As future damages and risks are valued lower than current risks and damages, we

calculate future damages using 4 discount rates (2%, 4%, 6%, and 8%) that cover the

range of different views and practices related to discounting. In addition, we show the basic

figures of the calculations, with and without accounting for demographic and economic

growth.

As we cannot model the impact of demographic and economic growth on land-use in detail,

we apply generic indicators for these growth factors to the sum of damages of different land

uses. We recognize that this is a very approximate approach, but it is the only one feasible

within this project. On the one hand, we may underestimate the impact of growth, as we

use projections for the national level and apply them to the coastal area, which is likely to

grow faster. On the other hand, we may overestimate its impact, as our approach assumes

that the number of people and economic activity will grow as fast in affected areas than in

areas not affected. In the reference scenario we thus assume that people will not adapt

their investment behavior and account for risks of erosion or coastal flooding.


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4.1.3.2 Risks for erosion and flooding 2015 to 2100

The tables below indicate how the risks per year for erosion and flooding are likely to

evolve over time due to demographic and economic growth. In addition, they show the

range of the current value of these future risks for different discounting rates.

Table 4-7 shows that more than in 2015 20.000 people are exposed to risks for erosion or

flooding, and this number will triple by 2100. Before accounting for demographic growth, the

total number of people exposed to risk of erosion remains constant until 2050 and will halve

by 2100. The number of people exposed to risk of coastal flooding will slightly decrease by

2050 and will increase after 2050. Due to impact of demographic growth, the number of

people exposed to these risks will increase.

Table 4-7: Number of people annually exposed to risk for erosion or flood up to 2100.

Erosion (1) Coastal Flood (2) Total (3)

Demog growth Frozen

(4) DeGr incl

(5) Frozen

(4) DeGr incl

(5) Frozen

(4) DeGr incl

(5)

2015 6.589 6.589 14.799 14.799 21.389 21.389

2020 6.401 7.107 14.557 16.163 20.958 23.270

2030 6.401 8.579 14.078 18.870 20.479 27.449

2050 6.401 11.705 13.138 24.024 19.538 35.729

2075 3.314 7.831 16.468 38.910 19.782 46.741

2100 3.307 8.880 20.752 55.717 24.059 64.597 (1) Number of people affected by erosion in a certain year, in addition to people affected in earlier years (for example,

people in 2020 are in addition to the people affected in the period 2015-2020). (2) Number of people affected by flood risk in a certain year (cumulative; people affected in e.g. 2030 may have also

been at risk in 2020. (3) = (1) + (2) (4) Frozen world = demography and economy as in 2015, sea level rise as in IPCC projections (5) DeGr incl = demographic growth = assessment of number of people, including the impact of demographic growth,

based on data from table 4-6.

Without economic or demographic growth, the risk per year for erosion will remain rather

constant up to 2050 and decline afterwards. Demographic and economic growth will result

in a stepwise increase of the risk for erosion up to 2100. Using a 6% or 4% discount rate

the current value of the risk per year for erosion will decrease over time.

Table 4-8: Annual risk for erosion up to 2100 (million $/year)

Erosion Impact DeEc growth (1) Impact of discount rates

Not Discounted Discount rate

Frozen (2) DeEcGr incl (3) 2% 4% 6% 8%

2015 26,6 26,6 26,6 26,6 26,6 26,6

2020 26,6 34,4 31,1 28,1 25,3 22,7

2030 26,6 49,0 36,2 26,5 19,4 14,0

2050 26,6 72,7 35,8 17,4 8,3 3,9

2075 13,7 53,9 16,0 4,7 1,3 0,4

2100 13,6 67,8 12,2 2,1 0,4 0,1 (1) Impact of demographic and economic growth (2) Frozen world = demography and economy as in 2015, sea level rise as in IPCC projections (3) DeECGr incl = demographic and economic growth = assessment of damages and risks, including the impact of demographic and economic growth, based on data from table 4-6


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Before accounting for economic or demographic growth and discounting (column (2) in the

table below), the annual risk for coastal flooding will decline with around 10% towards 2050

and will increase after 2050 (x 1.3 in 2100). Demographic and economic growth will first

compensate for the decline and will enforce the rise in risks after 2050 (x 6). Using a 6% or

4% discount rate the current value of the risk per year for erosion will decrease over time.

Table 4-9: Annual risk for coastal flooding up to 2100 (million $/year)

Coastal Impact DeEc growth (1) Impact of discount rates

Flooding Not Discounted Discount rate


2015 20,0 20,0 20,0 20,0 20,0 20,0

2020 19,6 25,4 22,9 20,7 18,6 16,7

2030 18,9 34,8 25,7 18,9 13,8 10,0

2050 17,5 47,8 23,6 11,5 5,5 2,6

2075 21,6 85,0 25,3 7,3 2,1 0,6


The table below indicates that the total annual risk for the coastal area is estimated at 47

million $ in 2015, which corresponds to 0.8% of the estimated GDP of the coastal zone

(and 0.2% of GDP for the whole country). Coastal erosion accounts for 57% of these

impacts. Over time risks will increase. Before accounting for demographic and economic

growth, risks would decrease because the size of the area affected by erosion decreases

after 2050. However, this is compensated by economic growth.

The current value of future risks per year decreases for all discount rates.

Table 4-10: Annual risk for erosion + flooding up to 2100 (million $/year)

Erosion + Impact DeEc growth (1) Impact of discount rates



2015 46,5 46,5 46,5 46,5 46,5 46,5

2020 46,2 59,8 54,0 48,7 43,9 39,4

2030 45,5 83,8 61,9 45,4 33,1 24,0

2050 44,1 120,5 59,4 28,9 13,8 6,5

2075 35,3 138,9 41,3 12,0 3,4 0,9



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4.1.4 Aggregated risks over different time horizons

Table 4-11 below indicates the aggregated risks for erosion and flooding over different time

horizons, and for different discount rates.

At 6% discount rate, the total cumulative damage between 2015 and 2030 (15 years)

amounts to 620 million $.

Table 4-11: Total aggregated risks for different time horizons and discount rates

GHANA country analysis Time horizon

Discount rate million $/year

Erosion 2% 4% 6% 8%

2015-2020 (1) 143 137 131 126

2015-2030 479 413 358 311

2015-2050 1.201 851 625 475

2015-2075 1.652 1.027 694 502

2015-2100 2.004 1.109 713 507

Flooding 2% 4% 6% 8%

2015-2020 (1) 106 102 98 94

2015-2030 350 302 262 229

2015-2050 845 604 447 342

2015-2075 1.457 838 536 376

2015-2100 2.069 978 568 384

Erosion + Flooding 2% 4% 6% 8%

2015-2020 (1) 249 239 229 220

2015-2030 828 715 620 540

2015-2050 2.046 1.455 1.072 817

2015-2075 3.109 1.865 1.230 879

2015-2100 4.074 2.088 1.281 890

(1) Total aggregated risk over full period 2015-2020 (in million $)


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4.2 COCED ANALYSIS FOR THE PILOT SITE

In this section we give the same indicators for the pilot site as those used for analysis at the

country level. We do not repeat the methodological remarks with each table.

For the comparison of results at the regional and pilot site level, one should take into

account some differences in the approach. First, the analysis uses a finer grid for the

assessment of land use classes and impacts and the analysis of coastal flooding build on a

detailed modelling of three different coastal floods, with a return period of 10, 50 and

100 years (T10, T50 and T100). The assumptions for demographic and economic growth

are the same as for the analysis at regional level.

The total area at risk for erosion and coastal flooding increases from 840 ha in 2015 to

1250 ha in 2100, which corresponds to 21% and 32% of the pilot study area (3900 ha). In

the next sections, we will further detail and analysis affected land uses and related impacts.

Table 4-12: Evolution of the areas at risk for erosion and coastal flooding at the pilot site

Ghana Pilot site

Year Erosion Coastal flooding TOTAL

ha 2015 11 827 838

2050 400 554 954

2100 759 494 1253

% of pilot site 2015 0,3% 21% 21%

2050 10,2% 14% 24%

2100 19% 13% 32%

4.2.1 Erosion: which land uses are affected

Table 4-13 below shows the land-use categories affected by erosion in 2015, 2050 and

2100, all compared to the baseline (2014).

Damages in 2050 have to be interpreted as the damages due to the total erosion of 400 ha

between 2015 and 2050, and assessed without accounting for demographic or economic

growth, or discounting. In the next section, we will account for demographic and economic

growth.

Erosion at the pilot site accounts for 3% of erosion at country level.


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Table 4-13: Impacts from erosion in three typical years (2015, 2050 and 2100).

Erosion GHANA

Pilot Year Rural Urban Economic Natural Total

ha 2015 10 1,7 0,0 0,0 11

2050 341 58 0 0 400

2100 587 173 0 0 759

People 2015 7 60 - - 67

2050 241 2.116 - - 2.357

2100 553 7.187 - - 7.740

Damage 2015 94 227 - - 321

1000$ 2050 3.289 7.935 - - 11.224

2100 5.886 27.320 - - 33.207

People/ha 2015 0,7 36 - - 5,9

2050 0,7 36 - - 5,9

2100 0,9 42 - - 10,2

Damage/ha 2015 9.632 136.520 - - 28.088

$/ha 2050 9.632 136.520 - - 28.088

2100 10.032 158.274 - - 43.730


The following table indicates the dominant share of rural land use in area affected, in line

with the share of land use categories for the total pilot site area. Urban areas account for

90% of people affected and 70% or more of the damages. Towards 2100, the increase in

area affected will include more urban land uses.

Table 4-14: Share of land use categories in impacts from erosion (2015, 2050 and 2100).

Erosion GHANA

Pilot year Rural Urban Economic Natural Total

ha 2015 85% 14,5% 0,00% 0,00% 100%

2050 85% 14,5% 0,00% 0,00% 100%

2100 77% 22,7% 0,00% 0,00% 100%

People 2015 10% 90% 0,00% 0,00% 100%

2050 10% 90% 0,00% 0,00% 100%

2100 7% 93% 0,00% 0,00% 100%

Damage 2015 29% 71% 0,00% 0,00% 100%

1000$ 2050 29% 71% 0,00% 0,00% 100%

2100 18% 82% 0,00% 0,00% 100%

Share of land use categories for the pilot site (3914 ha)

2015 80% 19% 0% 1% 100%



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4.2.2 Flooding: which land uses are affected

First, we discuss impacts of a 100 year flood (event with return period 100 years, T100) in

2015, 2050 and 2100, based on current land uses and without demographic or economic

growth (frozen world). Next we present these results presented as risks, accounting for the

probability of the floods. Finally, we assess the impact of demographic or economic growth

on these risks.

4.2.2.1 Impacts from a 100 year flood

At the pilot site, flood events with return period of 10, 50 and 100 year have been modelled.

We discuss impacts on ha, people and related damages of a T100 flood. Although impacts

from a T50 and T10 flood event are somewhat smaller (see further), the T100 flood also

gives a good idea of the impact of the more frequent floods.

Table 4-16 shows the land-use categories affected by a 100 year flood in 2015, 2050 and

2100, and related impacts on people affected and damages. Due to sea level rise, it is

expected that in 2050 a T100 flood will affect a much larger area. However, as part of the

flooded area is eroded by 2050, the size of the area declines. In the pilot site, the net effect

is that the area and number of people flooded declines over time.

Urban areas account for 13 to 25% in the flooded area but account for +75% of the people

affected and related damages.

As indicated above in chapter 3.3.4, there are no good damage functions to assess the

impacts of floods on human health. We test the potential impacts of one human health

impact category – risk on fatalities – using a simplified damage function. As for the analysis

at national level, the number of expected victims of a T100 coastal flood event is limited,

and if we monetize this impact using the value of a statistical life for Ghana (101834$/VSL),

it adds 2,7% (2015) to 4,2% (2100) to the material damages. For further analysis, we do not

include these figures, as they are uncertain and incomplete, and are relatively small

compared to material damages.


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Table 4-15: Impacts from a 100 year flood event in 2015, 2050 and 2100.

Flood T100 GHANA Pilot

year Rural Urban Economic Natural Total

Ha (T100) 2015 683 145 - - 827

(T100) 2050 439 115 - - 554

2100 427 66 - - 494

People 2015 523 3.007 - - 3.530

(T100) 2050 355 2.047 - - 2.402

2100 333 1.008 - - 1.341

Damages(1) 2015 0,26 1,88 - - 2,1

T100 2050 0,15 0,89 - - 1,0

million $ 2100 0,13 0,39 - - 0,5

People/ha 2015 0,8 20,8 4,3

2050 0,8 17,8 4,3

2100 0,8 15,2 2,7

Damage/ha 2015 383 13.009 2.589

$/ha 2050 338 7.731 1.870

2100 306 5.852 1.052

Ha 2015 83% 17% 0,0% 0% 100%

(T100) 2050 79% 21% 0,0% 0% 100%

2100 87% 13% 0,0% 0% 100%

People 2015 15% 85% 0,0% 0,0% 100%

(T100) 2050 15% 85% 0,0% 0,0% 100%

2100 25% 75% 0,0% 0,0% 100%

Damages(1) 2015 12% 88% 0,0% 0,0% 100%

T100 2050 14% 86% 0,0% 0,0% 100%

million $ 2100 25% 75% 0,0% 0,0% 100%

Victims

Number 2015 0,1 0,5 - - 0,6

2050 0,1 0,3 - - 0,4

2100 0,1 0,2 - - 0,2

Monetized 2015 0,01 0,05 - - 0,06

(2)($ ) 2050 0,01 0,03 - - 0,04

2100 0,01 0,02 - - 0,02

(1) Damages include material damages for all land uses and loss of ecosystem services for natural land uses, but excluding victims

(2) Risk to victims of flooding using the value of a statistical life, adapted for Ghana (101.834$ per VSL)


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4.2.2.2 Risks from coastal flooding in 2015, 2050 and 2100

This section discusses the total risks of coastal flooding, accounting for the damage and

probability of different flood events, as discussed in the methodology section. The risk

indicators account for the full range of possible flood events.

Risk = expected damage = damage from the flood x probability of the flood

At the pilot site level, a more detailed approach was used, and three floods with return

period of 10, 50 and 100 years were assessed. Total risk is the weighted sum of the

damages of the three events.

Table 4-17 indicates that for the Ghana pilot site the damages of a flood with a return

period of 10, 50 or 100 years are very similar. This indicates that the indicators described

for a T100 flood event in the previous section are also a good proxy for more frequent

events.

The 10 year total storm event has the largest share (+75%) in the total risk.

Table 4-16: Risks from flooding for floods with different return periods.

Flood risk GHANA Pilot

year T10 T50 T100 Weighted average

Damages/event.year

1000$ 2015 6.251 6.615 6.830

2050 5.220 4.572 6.863

2100 2.300 1.990 8.573

Weight of event in total risk 0,272 0,031 0,014

Risk/year 2015 1,85 2,06 2,14 0,59

Million $ 2050 0,84 0,97 1,04 0,27

2100 0,45 0,49 0,52 0,14

Share in weighted average

2015 84% 11% 5% 100%

2050 84% 11% 5% 100%

2100 85% 10% 5% 100%

The tables below indicate the relative importance of different land uses in the total risks for

2015, 2050 and 2100. Between 2015 and 2050, total area and its distribution among land

use categories do not differ much. After 2050, flooding in urban areas decrease, which has

a big impact on number of people affected and damages, both in terms of damages per ha

and total damages.


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Table 4-17: Risks from flooding in 2015, 2050 and 2100.

Flood risk year Rural Urban Economic Natural TOTAL

ha 2015 187 43 0,0 0 230

2050 106 33 0,0 0 139

2100 99 20 0,0 0 120

Damage /year 2015 72 523 - - 595

1000$ 2050 39 233 - - 272

2100 31 114 - - 145

Risk/ha.year 2015 382 12.211 2.582

$/ha affected 2050 369 7.088 1.954

2100 313 5.574 1.211

All indicators are risks and are the weighted average for three flood events. Land uses Urban (includes suburban)

Table 4-18: Share of land use categories in flood risks in 2015, 2050 and 2100.


Damage 2015 12% 88% 0% 0,0% 100%

2050 14% 86% 0% 0,0% 100%

2100 21% 79% 0% 0,0% 100%


4.2.3 Total risks for erosion and coastal flooding per year

4.2.3.1 Methods and indicators

As we do not have specific projections for demographic or economic growth in the pilot site,

we use the national data to assess these impacts.

4.2.3.2 Risks for erosion and flooding 2015 to 2100

The tables below indicate how the risks per year for erosion and flooding are likely to

evolve over time due to demographic and economic growth. In addition, they show the

range of the current value of these future risks for different discounting rates.

Without accounting for demographic growth, the number of people affected by erosion will

remain more or less stable until 2050 and almost double by 2100, whereas for flooding, it

will decrease over time. Before accounting for demographic growth, total number of people

affected will decrease with 60 % up to 2100. After accounting for demographic growth, total

number of people will increase (x 1,3) up to 2100.


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Table 4-19: Number of people annually exposed to risk for erosion or flood up to 2100.

Erosion (1) Coastal Flood (2) Total (3)

Demog growth Frozen

(4) DeGr incl

(5) Frozen

(4) DeGr incl

(5) Frozen

(4) DeGr incl

(5)

2015 67 67 1.007 1.007 1.074 1.074

2020 65 73 954 1.059 1.020 1.132

2030 65 88 847 1.136 913 1.224

2050 65 120 628 1.148 693 1.268

2075 75 177 543 1.283 618 1.460

2100 108 289 377 1.011 484 1.300 (1) Number of people affected by erosion in a certain year, in addition to people affected in earlier years (for example,

people in 2020 are in addition to the people affected in the period 2015-2020). (2) Number of people affected by flood risk in a certain year (cumulative; people affected in e.g. 2030 may have also

been at risk in 2020. (3) = (1) + (2) (4) Frozen world = demography and economy as in 2015, sea level rise as in IPCC projections (5) DeGr incl = demographic growth = assessment of number of people, including the impact of demographic growth,

based on data from table 4-6.

Column (2) in the table below shows how risks will evolve over time, before accounting for

economic or demographic growth and discounting. Without economic or demographic

growth, the risk per year for erosion will remain more or less constant up to 2075 and

increase over the full period. Demographic and economic growth will increase these

damages. However, the current value of future risk will decrease with higher discount rates.

At 2% discount rate, future risk almost double over time. If we apply a discount rate of 4%

or more, the current value of future risks per year decreases over time.

Table 4-20: Average annual risk for erosion up to 2100 (million $/year)

Erosion Impact DeEc growth (1) Impact of discount rates

Not Discounted Discount rate


2015 0,31 0,31 0,31 0,31 0,31 0,31

2020 0,31 0,40 0,36 0,33 0,30 0,27

2030 0,31 0,57 0,42 0,31 0,23 0,16

2050 0,31 0,85 0,42 0,20 0,10 0,05

2075 0,32 1,26 0,38 0,11 0,03 0,01


As total area flooded declines over time, so do risks. This is compensated by the

demographic and economic growth. The current value of future risks per year decreases for

all discount rates.


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Table 4-21: Average annual risk for coastal flooding up to 2100 (million $/year)

Coastal Impact DeEc growth (1) Impact of discount rates



2015 0,59 0,59 0,59 0,59 0,59 0,59

2020 0,54 0,70 0,63 0,57 0,51 0,46

2030 0,44 0,82 0,60 0,44 0,32 0,23

2050 0,27 0,74 0,37 0,18 0,09 0,04

2075 0,23 0,91 0,27 0,08 0,02 0,01


The table below indicates that the total annual risk for the pilot site is estimated at

0,9 million $/year for 2015, which corresponds to 3,7% of the estimated GDP of the pilot

site. The flood risks account for two thirds of these risks. Over time, areas that are at high

risk of coastal flooding will erode, and this will lower flood risks. As this decrease is bigger

than the increase in damages from erosion, total risks would decrease over time, but this is

more than compensated by the increase due to demographic and economic growth.


Table 4-22: Average annual risk for erosion + flooding up to 2100 (million $/year)

Erosion + Impact DeEc growth (1) Impact of discount rates



2015 0,91 0,91 0,91 0,91 0,91 0,91

2020 0,85 1,10 1,00 0,90 0,81 0,73

2030 0,75 1,39 1,03 0,75 0,55 0,40

2050 0,58 1,60 0,79 0,38 0,18 0,09

2075 0,55 2,17 0,64 0,19 0,05 0,01



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4.2.4 Aggregated risks over different time horizons

Tables below shows the aggregated risks for erosion and coastal flooding over different

time horizons, and for different discount rates. At 6% discount rate, the total damage

between 2015 and 2030 (15 years) amounts to 4,2 million $ for erosion and – in addition –

7,1 million $ for flooding. Total risks are dominated by those for flooding.

Table 4-23: Total aggregated risks for different time horizons and discount rates

Ghana pilot site Time horizon

Discount rate million $ /period

Erosion 2% 4% 6% 8%

2015-2020 (1) 1,7 1,6 1,5 1,5

2015-2030 5,6 4,8 4,2 3,7

2015-2050 14,1 10,0 7,3 5,6

2015-2075 22,4 13,1 8,5 6,0

2015-2100 32,0 15,3 9,0 6,1

Flooding 2% 4% 6% 8%

2015-2020 (1) 3,1 2,9 2,8 2,7

2015-2030 9,3 8,1 7,1 6,2

2015-2050 19,1 14,1 10,8 8,5

2015-2075 27,2 17,3 12,0 9,0

2015-2100 32,2 18,4 12,3 9,1

Erosion + Flooding 2% 4% 6% 8%

2015-2020 (1) 4,7 4,6 4,4 4,2

2015-2030 14,9 12,9 11,3 9,9

2015-2050 33,1 24,1 18,1 14,1

2015-2075 49,5 30,4 20,6 15,0

2015-2100 64,2 33,8 21,3 15,2

(1) Total aggregated risk over full period 2015-2020 (in million $ / period)


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5. CONCLUSIONS

5.1 METHODOLOGY

In addition to the qualitative description and analysis of the drivers for environmental

degradation and threats to the sustainability of the coastal zone, a methodology has been

developed to evaluate, quantify and monetize (assess in monetary terms; $/year) the risks

of erosion and coastal flooding.

This methodology integrates methods and data from different disciplines and scientific

approaches. It builds on the damage functions that have been developed around the world

for a more detailed assessment of flood risks and extends them to coastal erosion. To

identify and quantify the economic values at risk, the methods draws on a mix of national

and global GDP data for different sectors and detailed, but generally available, maps of

(estimated) population density, urban and economic activities and transport infrastructure.

This information is combined to assess the values and assets at risk, and expressed in $

per year and per ha. This info is combined with damage functions for flooding and erosion.

The information and data available make it possible to implement this methodology for the

analysis of material damage to buildings and urban infrastructure and - to a lesser extent -

economic activities (industry, services, port and agriculture) and transport. Much less

information is available on the impacts of floods on human health and impacts on natural

areas. A methodology has been developed to test to what extent these impacts are likely to

be significant, using simplified functions and methods of damage and experts, data from the

health economics and the environmental economics. It was not possible to develop a

method or collect data to assess impacts on cultural values (e.g. historic buildings).

The method identifies areas prone to coastal erosion and flood risk, estimates the number

of people at risk, and assesses expected damages and risks. It assesses the evolution of

the expected impacts and distinguishes the impact of sea level rise, population growth and

economic growth. The method provides for the integration of information on future land use

development, but as no data or maps were available, the analysis in this report assumes

builds on a simplified assumption of evenly spread growth. As this information becomes

available and projects are underway, this could be more integrated.

The method can be implemented at different levels of detail and at different geographic

scales, as shown in this report with an assessment at regional and pilot site level, and

complements more qualitative local assessments. It bridges the gap between more general

top-down modeling approaches that assess risks at the level of a larger region or continent,

and bottom-up local analysis based on local circumstances, land uses, and land use.


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5.2 SUSTAINABILITY INDICATORS FOR COASTAL DEVELOPMENT IN GHANA

The qualitative analysis has identified and described multiple mechanisms and threats that

lead to coastal degradation, including pressures from different land uses, human

occupation and economic activities, lack of adequate management of these human

activities, pollution, over-exploitation of natural resources, coastal erosion, climate change

and sea level rise.

5.2.1 Analysis at regional level.

The analysis shows that erosion will increase significantly during this century. In 2100,

26.000 ha or 2,6 % of the area of the coastal zone (defined as a zone of 20km wide along

the coast of Ghana) will be affected by erosion. In addition, the affected area is more

densely populated than the average for the coastal zone, and has a higher share of

economic activities (industry and services). The erosion risk is twice as high in the first

period up to 2050 (+400 ha/year) then in the period after 2050 (200 ha/year).

In addition, 42000 ha or 4,2 % of the coastal zone is in 2015 at risk for coastal flooding from

a 100 year flood. As the area affected is especially rural, overall population density and

economic activity is lower compared to the area affected by erosion or the average of the

coastal zone. Urban areas account for a minor share in the flooded area but account for

60% of the people flooded and more than 70% of the damages.

The analysis indicates that the total annual risk for the coastal area is estimated at

47 million $ in 2015, which corresponds to 0,8 % of the estimated GDP of the coastal zone.

Coastal erosion accounts for 57% of these impacts. Over time risks will increase. Before

accounting for demographic and economic growth, risks would decrease because the size

of the area affected by erosion decreases after 2050. However, this is compensated by

economic growth.

The current value of future risks per year decreases for all discount rates. At 6% discount

rate, the total cumulative damage between 2015 and 2030 (15 years) amounts to

620 million $.

In addition, there is some risk for human health, but this is too uncertain to quantify and

monetize these risks. A first rough attempt, looking at fatalities from flooding, suggests that

this may at least add a few %.

5.2.2 Analysis at pilot site level

At the pilot site level, a finer, more detailed grid for the assessment of land use classes and

impacts has been used. The total area at risk for erosion and coastal flooding increases

from 840ha in 2015 to 1250 ha in 2100, which corresponds to 21% and 32% of the pilot

study area (3900ha). In 2015, the flood risk is most important (21% of the area) but in 2100,

erosion is the most important threat (19% of land area).


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The dominant share of land use affected is rural areas, in line with the share of land use

categories for the total pilot site area. Urban areas account for 90% of people affected and

70% or more of the damages. Towards 2100, the increase in area affected will include

more urban land uses.

For erosion, population density and damage per ha is lower for the pilot site, compared to

the regional analysis. For flooding, however, population densities and damages per ha are

higher.

The analysis of coastal flooding builds on a detailed modelling of three different coastal

floods, with a return period of 10, 50 and 100 years (T10, T50 and T100). The 10 year total

storm event has the largest share (+75%) in total risks. This analysis shows that the coast

is at risk for frequent coastal flooding.

The analysis shows that the total annual risk for the pilot site is estimated at 0,9 million

$/year for 2015, which corresponds to 3,7% of the estimated GDP of the pilot site. The

flood risks account for two thirds of these risks.

The assumptions for demographic and economic growth are the same as for the analysis at

regional level. Over time, areas that are at a high risk of coastal flooding will erode, and this

will lower flood risks. As this decrease is bigger than the increase in damages from erosion,

total risks would decrease over time, but this is more than compensated by the increase

due to demographic and economic growth.


At 6% discount rate, the total damage between 2015 and 2030 (15 years) amounts to 11 million $.


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6. REFERENCES

AGAT, 2013. Apercu General Agriculture Togolaise, 4ieme recensement de l’agriculture.

Ministère de l’agriculture, de l’elevage et de la peche.

Boateng, I., 2012. An application of GIS and coastal geomorphology for large scale

assessment of coastal erosion and management: a case study of Ghana. J Coast Conserv

16, 383–397. https://doi.org/10.1007/s11852-012-0209-0

Brander, L., Florax, R., Vermaat, J., 2006. The empirics of wetland valuation: a

comprehensive summary and a meta-analysis of the literature. Environ. Resour. Econ. 33,

223–250.

Chaikumbung, M., Doucouliagos, H., Scarborough, H., 2016. The economic value of

wetlands in developing countries: a meta-regression analysis. Ecol. Econ. 24, 164–174.

deGraft-Johnson, K.A.A., Blay, J., Nunoo, F.K.E., Amankwah, C.C., 2010. Biodiversity

Threats Assessment of the Western Region of Ghana, The Integrated Coastal and

Fisheries Governance (ICFG) Initiative Ghana (and others).

de Groot, R.S., Brander, L., van der Ploeg, S., Costanza, R., Bernard, F., Braat, L.,

Christie, M., Crossman, N., Ghermandi, A., Hein, L., Hussain, S., Kumar, P., McVittie, A.,

Portela, R., Rodriguez, L.C., ten Brink, P., van Beukering, P., 2012. Global estimates of the

value of ecosystems and their services in monetary units. Ecosystem Services 1, 50–61.

https://doi.org/10.1016/j.ecoser.2012.07.005

Ferreira, O., Viavattene, C., 2016. Resilience-Increasing Strategies for Coasts – Toolkit.

Deliverable 5.1 – CRAF application for all study sites – Phase 2.

Finlayson, C.M., Gordon, C., Ntiamoa-Baidu, Y., Tumbulto, J., Storrs, M., 2000. The

hydrobiology of Keta and Songor Lagoons: impications for coastal wetland management in

Ghana, Supervising Scientist Report 152, Supervising Scientist, Darwin.

FLOODsite, 2008. Guidelines on Coastal Flood Hazard Mapping. HR Wallingford.

Gilbert M., 2017. personnel communication, calculations will only start in 2018.

Hallegatte, S., Green, C., Nicholls, R.J., Corfee-Morlot, J., 2013. Future flood losses in

major coastal cities, 3(9), 802–806, doi:10.1038/nclimate1979. Nat. Clim. Chang.

Hinkel, J., Brown, S., Exner, L., Nicholls, R.J., Vafeidis, A.T., Kebede, A.S., 2011. Sea-level

rise impacts on Africa and the effects of mitigation and adaptation: an application of DIVA.

Reg. Environ. Chang.

Hinkel, J., Lincke, D., Perrette, M., Nicholls, R.J., Tol, S., Mazeion, B., Fettweis, X.,

Ionescu, C., Levermann, A., 2014. Coastal flood damage and adaptation costs under 21st

century sea-level rise, Proc. Natl. Acad. Sci. USA, 111(9), 3292–3297,

doi:10.1073/pnas.1222469111.

Huizinga, J., Moel, H., Szewczyk, W., 2017. Global flood depth-damage functions.

Methodology and the database with guidelines, additional information provided in excell

worksheet.


I/RA/12148/17.026/ABO 70 version 2.0 - 8/12/2017

IMDC, 2017a. D6a: DRM and ACC measures Ghana (No. I/RA/12148/17.111/LDN), Cost of

Coastal Environmental Degradation, Multi Hazard Risk Assessment and Cost Benefit

Analysis.

IMDC, 2017b. D7a: Cost Benefit Analysis of the selected DRM and ACC options for the

pilot site in Ghana (No. I/RA/12148/17.115/ABO/), Cost of Coastal Environmental

Degradation, Multi Hazard Risk Assessment and Cost Benefit Analysis.

IMDC, 2017c. D3a: Quantitative risk assessment of coastal erosion and flooding for Ghana

(No. I/RA/12148/17.022/FBR), Cost of Coastal Environmental Degradation, Multi Hazard

Risk Assessment and Cost Benefit Analysis.

IMDC, 2017d. D3b: Evaluation quantitative des risques d’érosion côtière et d’inondation

pour le Togo (No. I/RA/12148/17.023/FBR), Cost of Coastal Environmental Degradation,

Multi Hazard Risk Assessment and Cost Benefit Analysis.

IMDC, 2017e. D6b: Les mesures DRM et ACC pour le site pilote au Togo (No.

I/RA/12148/17.112/LDN/), Cost of Coastal Environmental Degradation, Multi Hazard Risk

Assessment and Cost Benefit Analysis.

IPCC, 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II

and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change

[Core Writing Team, edited by Pachauri, R.K., and Meyer, L.A.], IPCC, Geneva,

Switzerland, 151 pp. in IPCC AR5 Synthesis Report website: http://ar5-syr.ipcc.ch/ [WWW

Document]. URL (accessed 6.2.16).

Kissi, 2014. Flood vulerabilty in togo.

Ly, C., 1980. The role of the Akosombo Dam on the Volta River in causing coastal erosion

in Central and Eastern Ghana (West Africa). Marine Geology 323–332.

Mc Ivor, A.L., Möller, I., Spencer, T., Spalding, M., 2012. Reduction of wind and swell

waves by mangroves, Natural coastal protection series.

PDNA Benin, 2011. Post disaster needs assessment, Inondations au benin, rapport

d’évalution des besoins post catastrophe, rapport par le gouvernement de la republique de

Benin avec l’appui de la banque mondiale et du système des Nations Unie , rapport final,

2011.

PDNA Togo, 2010. Post disaster needs assessment, Evaluation des dommages, pertes et

besoins de reconstruction post catastrophes des inondations de 2010 au togo, Elaboré par

le Gouvernement Togolais avec l’appui de la Banque Mondiale et du Programme des

Nations Unies pour le Développement , Rapport final.

Salem, M.E., Mercer, D.E., 2012. The economic value of mangroves: a meta-analysis.

Sustainibility 4, 359–383.

The World Bank, 2016. Terms of Reference of the consultancy services for the “Cost of

Coastal Environmental Degradation, Multi Hazard Risk Assessment and Cost Benefit

Analysis”, West Africa Coastal Areas Technical Assistance Program, Adaptation to Climate

Change in West Africa Coastal Areas Project.

UNEP, 2007. Mangroves of Western and Central Africa, UNEP-Regional Seas

Programme/UNEP-WCMC.


I/RA/12148/17.026/ABO 71 version 2.0 - 8/12/2017

USAID, 2014. Mapping the exposure of socioeconomic and natural systems of West Africa

to coastal climate stressors (Full report), African and Latin American resilience to climate

change (ARCC).

Viavattene, C., Micou, A.P., Owen, D., Priest, S., Parker, D.J., 2013. Library of Coastal

Vulnerability Indicators Guidance Document.

Waterbouwkundig Laboratorium Borgerhout, Universiteit Gent, 2006. Impact op mens en

economie t.g.v. overstromingen bekeken in het licht van wijzigende hydraulische condities,

omgevingsfactoren en klimatologische omstandigheden, MIRA/2006/02.

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