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FLOOD AND DROUGHTRISK MAPPING IN

GHANA 5 AAP PILOT DISTRICTS

Wide-ranging Flood and Drought Risk Mapping in Ghana Starting with the FiveAfrican Adaptation Programme (AAP) Pilot Districts (i.e. Aowin Suaman, Keta,West Mamprusi, Sissala East, and Fanteakwa Districts) for Community Flood andDrought Disaster Risk Reduction.

2012



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FLOOD AND DROUGHT RISK MAPPING IN GHANA-

5-AAP PILOT DISTRICTS

EPA 2012



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Research Team:

Mr. Philip Yaw Oduro Amoako

Dr. Kingsford A. Asamoah

Mr. Philip Prince Mantey

Ing. Valence Wise Ametefe

Mr. Victor Owusu Addabor

Mr. Kafui Agbleze

Other Contr ibutors:

Ms. Shoko Takemoto

Mr. Antwi-Boasiako Amoah

Mr. Winfred Nelson

Mr. Bram Miller

Ms. Kareff Rafisura

Ms. Lydia Akoi

Ms. Akua Amoa Okyere-Nyako

Mr. Elikplim D. Agbitor

Mr. Divinus Oppong-Tawiah

Mr. Frank Dankwah

Ms. Rejoyce Anum

Sponsored by:

United Nations Development Programme (UNDP)

Japan Development Official Assistance

Africa Adaptation Programme (AAP)

National Disaster Management Organization (NADMO)



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Acknowledgements

The project team is very grateful to the District Chief Executives of the various project

districts namely; Hon. Ofori Larbi, Hon. Abbass Fuseni, Hon. Alidzata Sulemanah,

Hon. Sylvester Tornyevah, Hon. Yussifu Adams and their Co-ordinators, Planning

Officers, and Assembly Members who offered their invaluable time, perspectives and

contributions. The team is indebted to Nana Asare Baffour of Begoro, Nana Ebbah Kojo

II of Enchi, Togbui James Ocloo, and Togbui Gamor II of Keta, personnel of NADMO,

and other associated organizations in the five AAP Pilot Districts for their warm

reception and keenness to share their knowledge and experiences during the risk mapping

workshops.

The team appreciates the unflinching support given by Mr. Kofi Portuphy, National

Coordinator of NADMO, Mr Ebenezer Dosoo, and Hon. Sylvester Azantilow, both

Deputy National Coordinators of NADMO, the Country Representative and staff of the

United Nations Development Programme (UNDP) and Africa Adaptation Programme

(AAP) in Accra.



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ContentsResearch Team: ............................................................................................................................... 3

Acknowledgements ......................................................................................................................... 4

List of Tables .................................................................................................................................... 8

List of Figures ................................................................................................................................... 9

Executive Summary ....................................................................................................................... 13

Abbreviations/Acronyms ............................................................................................................... 15

1.0 INTRODUCTION ................................................................................................................. 16

2.0 APPROACH AND METHODOLOGY ..................................................................................... 18

3.0 STUDY AREA ....................................................................................................................... 18

4.0 DATA .................................................................................................................................. 20

4.1 Climatic and Environmental Data .................................................................................. 20

4.2

Flood Mapping Data ...................................................................................................... 20

4.2.1 Soil texture ............................................................................................................ 20

4.2.2 Rainfall ................................................................................................................... 21

4.2.3 Altitude (Elevation) ................................................................................................ 21

4.2.4 Slope ...................................................................................................................... 22

4.2.5 Flow accumulation areas ....................................................................................... 22

4.2.6 Land use ................................................................................................................. 23

4.2.7 Proximity to Water bodies..................................................................................... 23

4.3 Drought Risk Mapping Data .......................................................................................... 24

4.3.1 Vegetation Indicator .............................................................................................. 24

4.3.2 Climatic Indicator ................................................................................................... 24

4.3.3 Soil indicator .......................................................................................................... 25

4.3.4 Drainage ................................................................................................................ 28

4.3.5 Soil texture ............................................................................................................ 29

4.3.6 Organic matter ...................................................................................................... 30

4.3.7 Land use ................................................................................................................. 31

4.3.8 Proximity to Water bodies..................................................................................... 32

5.0 GIS Analysis ........................................................................................................................ 32

5.1 Hazard and Risk Mapping .............................................................................................. 32

5.2 Data used for Flood/Drought Risk Mapping .................................................................. 33

5.3 Models and Risk Maps ................................................................................................... 34

5.4 Factors and their Weightings for Flood/Drought Risk Mapping ................................... 37

6.0 FANTEAKWA DISTRICT FLOOD AND DROUGHT RISK ANALYSIS ........................................ 39



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6.1 Fanteakwa District Location and Size ............................................................................ 39

6.2 Fanteakwa District Risk Assessment ............................................................................. 41

6.2.1 Fanteakwa Flood Risk Assessment ........................................................................ 41

6.2.2 Fanteakwa flood mitigation measures .................................................................. 49

6.2.3 Fanteakwa Flood Risk Map .................................................................................... 50

6.2.4 Fanteakwa Drought Risk Assessment .................................................................... 51

6.2.5 Fanteakwa drought mitigation measures ............................................................. 53

6.2.6 Fanteakwa Drought Risk Map ............................................................................... 54

7.0 SISSALA EAST DISTRICT FLOOD AND DROUGHT RISK ANALYSIS ....................................... 55

7.1 Sissala East District Location and Size ........................................................................... 55

7.2 Sissala East District Risk Assessment ............................................................................. 57

7.2.1 Sissala East District Flood Risk Assessment ........................................................... 57

7.2.2Sissala East District flood mitigation measures ..................................................... 64

7.2.3 Sissala East District Flood Risk Map ....................................................................... 65

7.2.4 Sissala East District Drought Risk Assessment ....................................................... 66

7.2.5 Sissala East drought mitigation measures ............................................................. 71

7.2.6 Sissala East District Drought Risk Map .................................................................. 72

8.0 WEST MAMPRUSI DISTRICT FLOOD AND DROUGHT RISK ANALYSIS ................................ 73

8.1 West Mamprusi District Location and Size .................................................................... 73

8.2 West Mamprusi District Risk Assessment ..................................................................... 75

8.2.1 West Mamprusi District Flood Risk Assessment ................................................... 75

8.2.2 West Mamprusi District Flood Mitigation Measures ............................................ 81

8.2.3 West Mamprusi District Flood Risk Map ............................................................... 82

8.2.4 West Mamprusi District Drought Risk Assessment ............................................... 83

8.2.5 West Mamprusi drought mitigation measures ..................................................... 85

8.2.6 West Mamprusi District Drought Risk Map ........................................................... 86

9.0 KETA MUNICIPALITY FLOOD AND DROUGHT RISK ANALYSIS ............................................ 87

9.1 Keta Municipal Location and Size: ................................................................................. 87

9.2 Keta Municipal Risk Assessment ................................................................................... 89

9.2.1 Keta Municipal Flood Risk Assessment ................................................................. 89

9.2.2 Keta Municipal Flood Mitigation Measures .......................................................... 98

9.2.3 Keta Municipal Flood Risk Map ............................................................................. 99

9.2.4 Keta Municipal Drought Risk Assessment ........................................................... 100

9.2.5 Keta Municipal Drought mitigation measures .................................................... 104

9.2.6 Keta Municipal Drought Risk Map ....................................................................... 105



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10.0 AOWIN SUAMAN DISTRICT FLOOD AND DROUGHT RISK ANALYSIS ............................... 106

10.1 Aowin Suaman District Location and Size: .................................................................. 106

10.2 Aowin Suaman District Risk Assessment ..................................................................... 108

10.2.1 Aowin Suaman District Flood Risk Assessment: .................................................. 108

10.2.2 Aowin Suaman District flood mitigation measures ............................................. 113

10.2.3 Aowin Suaman District Flood Risk Map ............................................................... 114

10.2.4 Aowin Suaman District Drought Risk Assessment ............................................... 115

10.2.5 Aowin Suaman District drought mitigation measures ........................................ 117

10.2.6 Aowin Suaman District Drought Risk Map .......................................................... 118

11.0 CONCLUSION ................................................................................................................... 119

12.0 BIBLIOGRAPHY ................................................................................................................. 120

13.0 ANNEX I: DATA SOURCE AND RESOLUTIONS .................................................................. 122

13.1

Flood Mapping Data .................................................................................................... 122

13.2 Drought Mapping Data ................................................................................................ 122



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List of Tables

Table 1: Basic Statistics on the 5-AAP Pilot Districts ................................................. 19

Table 2: Soil texture categories index ........................................................................... 21

Table 3: Rainfall index (flood) ....................................................................................... 21

Table 4: Altitude index (flood) ....................................................................................... 22

Table 5: Slope gradient characteristics index (flood) .................................................. 22

Table 6: Flow accumulation areas index (flood) .......................................................... 23

Table 7: Land use index (flood) ..................................................................................... 23

Table 8: Factors and weightings for flood risk mapping ............................................ 37

Table 9: Factors and weightings for drought risk mapping ....................................... 37

Table 10: Fanteakwa Safe havens ................................................................................. 47

Table 11: Sickness during drought ................................................................................ 84



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List of Figures

Figure 1: Contributions to GDP by Sector (1998-2005) .............................................. 16

Figure 2: Map of Ghana Showing the AAP Pilot Districts ......................................... 18

Figure 3: Flood and Drought Risk Mapping Methodology Flow Chart .................... 34

Figure 4: Flood Risk Model ........................................................................................... 35

Figure 5: Drought Risk Model ....................................................................................... 36

Figure 6: Workshop Participants at Fanteakwa District-Begoro .............................. 39

Figure 7: Validation Workshop ..................................................................................... 40

Figure 8: GPS Training .................................................................................................. 40

Figure 9: Cause of flooding ............................................................................................ 41

Figure 10: Type of rainfall ............................................................................................. 42

Figure 11: Predominant soil type .................................................................................. 43

Figure 12: Colour of soil ................................................................................................. 44

Figure 13: Vulnerable Sectors ....................................................................................... 45

Figure 14: Flood prone areas ......................................................................................... 46

Figure 15: Safe Havens in locality ................................................................................. 48

Figure 16: Flood mitigation measures........................................................................... 49

Figure 17: Fanteakwa District flood risk map ............................................................. 50

Figure 18: Sell or pledge assets ...................................................................................... 51

Figure 19: Sickness during drought period .................................................................. 52

Figure 20: Drought mitigation measures ...................................................................... 53

Figure 21: Fanteakwa District drought risk map ........................................................ 54

Figure 22: Workshop Participants at Tumu ................................................................ 55



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Figure 23: Validation Workshop ................................................................................... 56

Figure 24: GPS Training ................................................................................................ 56


Figure 26: Rainfall pattern over the past 5 years ........................................................ 58


Figure 28: Colour of the soil .......................................................................................... 60

Figure 29: Building materials ........................................................................................ 61


Figure 31: Safe haven ..................................................................................................... 63


Figure 33: Sissala East District flood risk map ............................................................ 65

Figure 34: Duration of drought ..................................................................................... 66

Figure 35: Sell or pledge assets ...................................................................................... 67

Figure 36: Dispersion of family members during drought ......................................... 68

Figure 37: Sectors most vulnerable to drought ............................................................ 69

Figure 38: Sickness during drought period .................................................................. 70

Figure 39: Sissala East drought mitigation measures ................................................. 71

Figure 40: Sissala East drought risk map ..................................................................... 72

Figure 41: Workshop Participants at West Mamprusi District-Walewale ............... 73



Figure 44: Cause of flooding .......................................................................................... 75





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Figure 49: Safe havens in locality .................................................................................. 80


Figure 51: West Mamprusi District Flood Risk Map .................................................. 82

Figure 52: Frequency of drought................................................................................... 83

Figure 53: Drought mitigation ....................................................................................... 85

Figure 54: West Mamprusi District Drought Risk Map ............................................. 86

Figure 55: Workshop Participants at Keta Municipal Assembly .............................. 87



Figure 58: Cause of flooding .......................................................................................... 89


Figure 60: Duration of flood disaster ............................................................................ 91



Figure 63: Safe havens .................................................................................................... 94


Figure 65: Safe Haven .................................................................................................... 96

Figure 66: Flood Evacuation Plan ................................................................................. 97

Figure 67: Flood mitigation............................................................................................ 98

Figure 68: Keta Municipal Flood Risk Map................................................................. 99

Figure 69: Frequency of drought................................................................................. 100

Figure 70: Sell or Pledge Assets ................................................................................... 101



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Figure 71: Evacuation................................................................................................... 102

Figure 72: Sectors most vulnerable to drought .......................................................... 103

Figure 73: Keta Municipal Drought mitigation measures ........................................ 104

Figure 74: Keta Municipal Drought Risk Map .......................................................... 105

Figure 75: Workshop Participants at Aowin Suaman District-Enchi ..................... 106

Figure 76: Validation Workshop ................................................................................. 107

Figure 77: GPS Training .............................................................................................. 107

Figure 78: Cause of flooding ........................................................................................ 108

Figure 79: Predominant soil type ................................................................................ 109

Figure 80: Colour of soil ............................................................................................... 110

Figure 81: Flood prone areas ....................................................................................... 111

Figure 82: Safe havens in locality ................................................................................ 112

Figure 83: Flood mitigation measures......................................................................... 113

Figure 84: Aowin Suaman District Flood Risk Map ................................................. 114

Figure 85: Sell or pledge assets .................................................................................... 115

Figure 86: Duration of Drought................................................................................... 116

Figure 87: Drought mitigation measures .................................................................... 117

Figure 88: Aowin Suaman District Drought Risk Map ............................................. 118



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Executive Summary

In Ghana, “rain-fed agriculture” constitutes about 40% of Gross Domestic Product (GDP)

therefore flood and drought induced mostly by Climate Change and variability has a

significant impact on the economy1. This is evident from the negative impacts of flood

and drought on the socio-economic lifestyle of the citizenry in some parts of the country

particularly in the Upper East, Upper West, and the Northern Region. Since 2007, floods

in these three Northern regions of the country have been very unpredictable and severe,

resulting in many deaths, destruction to the ecology, critical infrastructure, agriculture

and other properties as well as causing disruptions to the socio-economic system. A case

in point was in August, 2007, when floods in the Northern parts of the country alone

affected about 350,000 people with 49 casualties; causing an estimated damage of over

130 million United States Dollars (US$), not including long term losses. Consequently,

the drought and flood-prone areas in Ghana have to be mapped more adequately and

systematically for a more effective disaster risk reduction and Climate Change

Adaptation.

Communities for that reason must know about flood and drought related disasters and

what capacities they have to enable them prepare, manage and adapt to the risks

associated with these hazards. A risk mapping project is therefore necessary for the

identification and assessment of the risk prone areas in the country. This initial risk

mapping project aims at equipping the five African Adaptation Project (AAP) Pilot

Districts (Aowin Suaman, Keta, West Mamprusi, Sissala East and Fanteakwa Districts),

with flood and drought risk maps as a planning tool for effective community flood and

drought Disaster Risk Reduction and Climate Change Adaptation.

The risk-mapping project will improve the capacity of communities to prepare and

respond to flood and drought hazards by identifying the high-risk areas for risk reduction.

It will also make it possible for both private and public sector to integrate disaster risk

considerations into their development policies, planning, and programming at all levels

1 (Report 1996-2000)



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with special emphasis on disaster prevention, mitigation, preparedness, and vulnerability

reduction.

The project used GIS analysis involving the application of geostatistical techniques

in the development and modelling of flood and drought risk maps through the

combination of climatic, environmental and other ancillary data layers in multi-

criteria evaluation. Ratings and classification for each factor/layer were ranked

from low to very high based on degree of vulnerability. Subsequently, every layer

was re-classified based on these ranks, multiplied by their standard weight, and

then added to other layers to obtain the output risk maps. The output risk maps for

flood and drought for the respective districts are symbolized with a green-yellow-

orange-red colour scheme indicating no-risk, low-risk, medium-risk, and high-risk

areas. The selection and weighting of different factors for hazard and risk maps

were informed by literature and expert input from the NADMO Research team.

The accuracy of hot spots in the risk maps were validated by stakeholder’s

workshop undertaken in the five (5) AAP beneficiary districts.

This study forms the basis of the NADMO/UNDP-AAP effort to implement effective

Disaster Risk Reduction strategies in order to build the resilience of the various disaster-

affected communities in Ghana and would ultimately serve as the basis for replication in

other districts. The Africa Adaptation Programme (AAP) Ghana is a programme to

develop capacity and financing options for mainstreaming climate change adaptation in

Ghana. A key element of AAP Ghana has been a series of activities that have been aimed

at developing the capacity of districts in Ghana to mainstream climate change adaptation

(CCA) and disaster risk reduction (DRR) into their District Development Planning

processes. This work has ultimately focused on five AAP pilot districts (Aowin Suaman,

Keta, Sisalla East, West Mamprusi and Fanteakwa Districts).



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Abbreviations/Acronyms

AAP – African Adaptation Programme

C- Clay

CERSGIS – Center for Remote Sensing and Geographic Information SystemsCl- Clay loam

CS- Coarse Sand

Csl – Coarse sandy loam

DEM- Digital Elevation Model

DI- De Martonne aridity Index

ETP- Annual Evapotranspiration

FAO-Food and Agricultural Organization

FI- FAO aridity index

FS- Fine sand

FSL – Fine Sandy Loam

GDP – Gross Domestic Product

GIS – Geographic Information System

GPS- Global Positioning System

GSS – Ghana Statistical Service

L- Loam

NADMO-National Disaster Management Organization

NDVI- Normalised Difference Vegetation Index

P- Annual Precipitation

S- Sand

SI – Silt

Sic – Silty Clay

Sil- Silty loam

SL- Sandy loam

T- Annual mean temperature

TM- Thematic Mapper



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1.0 INTRODUCTION

The Ghanaian economy is largely dependent on “rain-fed agriculture” which constitutes

approximately 40% of Gross Domestic Product (GDP).2

Figure 1: Contributions to GDP by Sector (1998-2005)

However, vulnerability and adaptation assessment carried out in the agricultural sector of

Ghana3 pointed out that Climate Change and variability adversely affects rainfall,

temperature and water availability for agricultural production thereby creating food

insecurity and making the Ghanaian economy extremely vulnerable. The report indicated

that maize production will decrease in yield by 7% by 2020, cassava and cocoyam will

decrease in yield by 43% and 53% respectively by 2080 and cocoa production will not be

possible at all in Ghana given the envisaged changes in temperature of about 4.5oC by the

year 2080. The report further stated that Climate Change and variability also contributes

to water stress and water insecurity, which increases exposure to climate disasters such as

floods and drought. In August, 2007, floods in the Northern parts of the country alone

2 (Report 1996-2000)

3 (DRR Forum 2009)

1998 1999 2000 2001 2002 2003 2004 2005

Agric 40.6 40.5 39.6 39.6 39.5 39.8 40.4 40.6

Service 32.1 31.9 32.7 33 33 33.4 32.4 27.1

Industry 27.4 27.6 27.8 27.4 27.5 27.4 27.2 32.3

0

5

10

15

20

25

30

35

40

45

P e r c e n t a g e

Contribution to GDP by Sector



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affected about 350,000 people with 49 casualties causing an estimated damage of over

130 million United States Dollars (US$), not including long term losses. Thousands of

homes, buildings, and agriculture produce, particularly, food stock, livestock and farms

were also destroyed.

To make the Ghanaian economy resilient to water and food insecurities due to Climate

Change and variability, communities must know the flood and drought affected areas in

their communities and what capacities they have to manage these disasters. The Ghana

risk-mapping project seeks to equip every district in Ghana, with access to flood and

drought risk maps showing areas at risk and safe havens as a planning tool for effective

community flood and drought Disaster Risk Reduction. Even though long-term climate

projections ensures that surveillance systems are able to detect changing patterns of this

phenomena, yet Disaster Risk Reduction, which ensures reducing vulnerabilities and

increasing capacities in general, will help populations cope with the effects of Climate

Change and variability.

The Flood and Drought Risk maps were the product of lengthy and complex analytical

processes by the research team expressing a model of risk reality. The beneficiary

communities and stakeholders expressed how well these map(s) expresses this risk reality

at validation workshops held in each of the five AAP Pilot Districts mapped.



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2.0 APPROACH AND METHODOLOGY

The methodology adopted for the study included a tabletop analysis of the five study

areas, GIS-based preliminary flood and drought risk mapping, field verification of the

developed preliminary flood and drought maps, vulnerability assessment and

stakeholders’ validation workshop. GIS layers on climate, land use, vegetation, soil and

topography were combined in a multi-criteria analysis to produce the specific flood and

drought risk maps. The risk maps were colour coded green-yellow-red indicating low-

moderate-high risk areas respectively.

3.0 STUDY AREA

This initial phase of the Ghana risk mapping project covered only the 5 AAP pilot

districts of West Mamprusi in the Northern Region, Sissala East District of the Upper

West Region, Aowin Suaman District of the Western Region, Fanteakwa in the Eastern

Region and Keta in the Volta Region (see Fig. 3 and Table 1).

Figure 2: Map of Ghana Showing the AAP Pilot Districts



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Table 1: Basic Statistics on the 5-AAP Pilot Districts4

DISTRICT/MUNICIPAL REGION CAPITAL AREA (Sq km) POPULATION

SISALA EAST UPPER WEST TUMU 4744 51,182

WEST MAMPRUSI NORTHERN WALEWALE 5013 117,821

AOWIN SUAMAN WESTERN ENCHI 2,638 119,128

FANTEAKWA EASTERN BEGORO 1150 132,488

KETA VOLTA KETA 1,086 133,661

4 (GSS 2005)



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4.0 DATA

Data for the study came from secondary sources consisting of climatic and environmental

data sets as well as ancillary and derived data sets from the processing of the data layers.

4.1 Climatic and Environmental Data

Climate related data was obtained from the Ghana Meteorological Agency, which holds

historical data sets from their weather monitoring stations from as far back as the 1960s.

Historical data on temperature, rainfall and relative humidity were of importance in

developing the risk maps.

Environmental data relating to the boundaries of the pilot districts, location of

settlements, drainage, and altitude were also obtained from CERSGIS. Derived data sets

such as slope, proximity of settlements to water bodies/watersheds and altitude for

example was obtained from GIS analysis. Additionally, data on land use information as

well as the vegetation index were derived from satellite image analysis.

4.2 F lood Mapping Data

Data used in the flood mapping included soil texture, rainfall, altitude, slope, flow

accumulation areas, land use, and proximity to water bodies.

4.2.1 Soil texture

Soil texture is related to erodibility, water retention capacity, crusting and aggregate

stability. The amount of available water is related to both texture and structure. Soils high

in silt (silt loam) tend to have higher available water holding capacity. On the contrary

sandy soils have the least available water – holding capacity. Sandy soils tend to be more

prone to drought than clayey soils because they retain less water at field capacity and the

water retained is consumed more rapidly by the growing plants. The soil textural classes

are grouped according to their water holding capacity as in Table 2.



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Table 2: Soil texture categories index

Class Description Texture Index

1 heavy textured C, SiC, 32 medium textured cSL, SL, CL, SC, SiL, SiCL, L 2

3 light textured Si, , S, fS, cS, fSL 1

The texture symbols in Table 2 mean the following: C means clay, SiC means silty clay,

cSL means coarse sandy loam, SL means sandy loam, CL means clay loam, SiL means

silty loam, L means loam, Si means silt, S means sand, fS means fine sand, cS means

coarse sand and fSL means fine sandy loam.

4.2.2 Rainfall

The rainfall pattern influences the vulnerability of areas to flooding. Places with very

high annual rainfall rates, given other underlying environmental factors, may be prone to

either slow unset, rapid unset floods or river floods.

Table 3: Rainfall index (flood)

Class Description

Annual

Rainfall Index

1 High n/a 3

2 Medium n/a 2

3 Low n/a 1

4.2.3 Al titude (Elevation)

The elevation of a place above sea level affects its susceptibility to flooding with low-

lying areas at more risk as against highland areas, which are virtually safe from the

hazard.



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Table 4: Altitude index (flood)

Class Description Elevation Index

1 very high <200 1

2 gentle 200-500 2

3 Low lying >500 3

4.2.4 Slope

Slope angle and general topography are undoubtedly important determinants of water

flow. Flooding becomes acute when slope angle is below a critical value and then

decreases logarithmically. The probability of flooding increases with increasing rainfall

for the same slope class. The slope class of zero – 4 is depicted with an index of three

showing such soils are very prone to erosion and drought hazards. Gentle slopes with the

slope percentage of 4 – 16 have the index weight of two indicating they are relatively

more prone to drought hazards compared to slopes greater than 16% that have the index

weight of one, indicating very low susceptibility.

Table 5: Slope gradient characteristics index (flood)

Class Description Slope % Index

1 very gentle to flat <4 3

2 gentle 4 – 16 2

3 steep >16 1

4.2.5 F low accumul ation areas

Flow accumulation areas are derived from the topography of an area and show the likely

areas for the accumulation of overland flow. This layer factors in the slope and the flow

direction to the lowest slopes and accounts for the slopes with the highest inflow.



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Table 6: Flow accumulation areas index (flood)

Class Description Flow Acc Index

1 high 3

2 medium 23 low 1

4.2.6 Land use

Land use characteristics influence the susceptibility of a place to the effects of drought

hazard. Land use data was derived from the processing of 2010 landsat TM image of the

country obtained from the Centre for Remote Sensing and GIS (CERSGIS) at the

University of Ghana. The different classes of broad land use categories and their

associated ranks are given in table 7.

Table 7: Land use index (flood)

Class Description Index

1 Water Body 1

2 Closed Forest 2

3 Open Forest 3

4 Dense Herbaceous Cover 4

5 Grassland 5

6 Built up areas/Bare Soil 6

4.2.7 Proximity to Water bodies

The proximity to water bodies within their environments is an indication of how

vulnerable places will be to river flood, as places near to rivers would experience the

proximal effects of flooding than those farther away. Table 8 details the classes and ranks

for the proximity layer as used in the model.



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Table 8: Proximity to water index (flood)

Class Description Proximity Index

1 Very Close Up to 300m 1

2 Near 300-600 23 Very Far 600-1000 3

4 Distant >1000 4

4.3 Drought Risk Mapping Data

The data used for the drought mapping include vegetation indicator, climatic indicator,

soil indicators, land use and proximity to water bodies.

4.3.1 Vegetation I ndicator

The basic data used for this indicator was an average Normalised Difference Vegetation

Index (NDVI) land cover map of Ghana for the last 9 years obtained from the analysis of

satellite images. The NDVI gives a measure of the vegetative cover on the land surface

over wide areas. Dense vegetation shows up very strongly in the imagery, and areas with

little or no vegetation are also clearly identified. NDVI also identifies water and ice.

Vegetation differs from other land surfaces because it tends to absorb strongly the red

wavelengths of sunlight and reflect in the near-infrared wavelengths. Therefore, higher

photosynthetic activity will result in lower reflectance in the red channel and higher

reflectance in the near infrared channel. The NDVI is a measure of the difference in

reflectance between these wavelength ranges. NDVI takes values between -1 and 1, with

values 0.5 indicating dense vegetation and values <0 indicating no vegetation. NDVI has

proved to have an extremely wide (and growing) range of applications. It is used to

monitor vegetation conditions and therefore provide early warning on droughts and

famines.

4.3.2 Climatic I ndicator

Modelling of climate indicators involved six data layers namely temperature, rainfall,

relative humidity, evapotranspiration. A surface grid for each of these layers was

generated using an Inverse Distance Weighting interpolation of the mean annual recorded



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data for the weather stations in the country. FAO aridity index and the De Martonne

Aridity Index, which provide a measure of the degree of dryness in an area, were derived

from these layers as input into the model.

The De Martonne aridity map was produced using the following equation.

DI = P/ (T+ 10)

Where DI is the De Martonne aridity index, P is the annual precipitation (mm) and T is

the annual mean temperature (°C).

The FAO aridity map was also produced using the following equation.

FI = P/ETP

Where FI is the FAO aridity index, P is the annual precipitation (mm) and ETP is the

annual evapotranspiration (mm).

A composite climate indicator map was produced by combining the layers on rainfall,

relative humidity, DI and FI.

4.3.3 Soil indicator

Various soil characteristics were modelled under the soil indicator such as parent

material, soil texture, soil depth, gradient, rock fragments and drainage. Map data on

these soil characteristics were entered into a GIS spatial modeller to produce maps for

each of the soil characteristics as separate layers.

4.3.1.1 Parent Materi al

Soils derived from different parent materials react differently to soil erosion, vegetation

and desertification. Table 9 shows the description of parent materials. From the table

soils that have an index of 1 as their parent materials are considered as good soils that are

less prone to erosion, have healthy vegetation growth and less prone to drought. Soils



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with an index of 2 are moderately good soils whiles areas with an index of 3 as their

parent materials are considered as poor soils meaning they are easily prone to erosion,

have poor vegetation growth and such soils are very much prone to drought hazard.

Table 9: Parent materials index (drought)

Classes Description Parent material Index

1 Good

Shale, Schist, basic, ultra basicconglomerate,

unconsolidated 1

2 Moderate

Limestone, marble, granite, gneiss, Sandstone,

Siltstone 2

3 PoorTertiary sands, coastal sand, lagoon deposit,alluvial deposit 3

4.3.2.1 Rock fragments

Rock fragments have a great but variable effect on runoff and soil erosion (Poesen et al,

1995), soil moisture conservation and biomass production. Generally, runoff and

sediment loss are greater over stony soil surfaces than stone free soils. Interior sedimentloss increases with increasing rock fragment percentage of up to about 20%. Beyond this

value, the limited space between fragments prevents the development of scour holes that

limit soil loss. For sheet and rill erosion, however, the rock fragment cover always

reduces sediment production in an exponential way. Rock fragments on the soil surface

and within 50cm from soil surfaces are defined in three classes.

Table 10: Rock fragments index (drought)

Class Rock Fragment Cover (%) Index

1 <20 1

2 20 – 60 2

3 >60 3



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4.3.3.1 Slope Gradient

Slope angle and general topography are undoubtedly important determinants of soil

erosion. Erosion becomes acute when slope angle exceeds a critical value and then

increases logarithmically. The probability of appearance of high erosion degree decreases

with increasing rainfall for the same slope class. A digital topographic slope gradient map

at a scale of 1:50,000 was used in preparing the slope gradient indicator map. The slope

class of 0 – 4 is depicted with an index of 1 showing such soils are less prone to erosion

and drought hazards. Gentle slopes with the slope percentage of 4 – 16 have the index

weight of 2 indicating they are less prone to drought hazards compared to slopes greater

than 16% that have the index weight.

Table 11: Slope gradient characteristics index (drought)

Class Description Slope % Index

1 very gentle to flat <4 1

2 gentle 4 – 16 2

3 steep >16 3

4.3.4.1 Soil Depth

Soil depth is defined as the depth of the soil profile from the soil surface to the top of the

un-weathered parent material. Dry land soils over hilly areas are particularly vulnerable

to erosion, especially when their vegetation cover has been degraded. Soils on the

Tertiary and Quaternary consolidated formations usually have a restricted effective soil

depth due to erosion and limiting sub-surface layers such as the petrocalcic horizon,

gravely and stony layer, and/or shallow bedrock. Therefore, the tolerance of these soils to

erosion is low under hot and dry climatic conditions and severe soil erosion where rain-

fed vegetation can no longer be supported leading to drought. Soil depth is grouped into

three classes as deep (>100 cm), moderate (20-100 cm) and shallow (<20 cm) as shown

in table 12.



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Table 12: Soil depth index (drought)

Class Description Depth (cm) Index

1 deep >100 1

2 moderate 20-100 23 shallow <20 3

4.3.4 Drainage

Drainage conditions are defined based on the depth of hydromorphic features such as iron

and manganese mottles or grey colours, and depth of groundwater table. The following

drainage classes are distinguished as in Table 13.

Well-drained soil: – Soils without any iron and manganese mottles or grey colours at

depths greater than 100cm from the soil surface. Such soil types are not wet enough near

the soil surface or do not remain wet during the growing period of plants.

Moderately well drained soils: – Iron, manganese or grey mottles are present in the soil at

depths between 30 and 100cm from the soil surface. Such soils are wet enough near the

soil surface or remain wet during the early growing period of plants.

Poor drained soils: – mottles of iron and manganese are present in the upper 30cm of the

soil, or reducing (grey) colours. A permanent water table usually exists in depths greater

than 75cm. In some of these soils, the groundwater reaches the soil surface during the wet

periods of the year. Water loss is slow as such the soils are wet at shallow depths for long

periods.



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Table 13: Soil drainage characteristics index (drought)


1 well drained 1

2moderately welldrained 2

3 poor/excessive 3

4.3.5 Soil texture

Soil texture is related to erodibility, water retention capacity, crusting and aggregate

stability. The amount of available water is related to both texture and structure. Soils high

in silt (silt loam) tend to have higher available water holding capacity. On the contrary

sandy soils have the least available water – holding capacity. Sandy soils tend to be more

prone to drought than clayey soils because they retain less water at field capacity and the

water retained is consumed more rapidly by the growing plants. The soil textural classes

are grouped according to their water holding capacity as in Table 14.

Table 14: Soil texture categories index (drought)

Class Description Texture Index

1 heavy textured C, SiC, 1

2 medium textured cSL, SL, CL, SC, SiL, SiCL, L 2

3 light textured Si, , S, fS, cS, fSL 3

The texture symbols in Table 14 mean the following: C means clay, SiC means silty clay,

cSL means coarse sandy loam, SL means sandy loam, CL means clay loam, SiL meanssilty loam, L means loam, Si means silt, S means sand, fS means fine sand, cS means

coarse sand and fSL means fine sandy loam.



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4.3.6 Organic matter

Organic matter affects both the chemical and physical properties of the soil and its overall

health. Properties influenced by organic matter include: change of soil colour (brown to

black); improvement of soil structure (enhance granulation); increase in moisture holding

capacity; high absorption capacity; diversity and activity of soil organisms that are both

beneficial and harmful to crop production; and nutrient availability. Soil organic matter

also influences the effects of chemical amendments, fertilizers, pesticides and herbicides.

Practices that increase soil moisture content can be categorized into three groups: (i)

those that increase water infiltration; (ii) those that manage soil evaporation; and (iii)

those that increase soil moisture storage capacities. All three are related to soil organic

matter.

Organic matter influences the physical conditions of a soil in several ways. Plant residues

that cover the soil surface protect the soil from sealing and crusting by raindrop impact,

thereby enhancing rainwater infiltration and reducing runoff. Surface infiltration depends

on a number of factors including aggregation and stability, pore continuity and stability,

the existence of cracks, and the soil surface condition. Increased organic matter

contributes indirectly to soil porosity (via increased soil faunal activity). Fresh organic

matter stimulates the activity of macro-fauna such as earthworms, which create burrows

lined with the glue-like secretion from their bodies and are intermittently filled with

worm cast material.

Increased levels of organic matter and associated soil fauna lead to greater pore spaces

with the immediate result that water infiltrates more readily and can be held in the soil

(Roth, 1985). The improved pore space is a consequence of the bio-turbating activities of

earthworms and other macro-organisms and channels left in the soil by decayed plant

roots. The Organic matter content in Ghanaian soils has been grouped into three classes

as in Table 15.



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Table 15: Organic matter categories index (drought)

Class Description Organic Mater Content Index

1 high >4.3 1

2 medium 2.2-4.3 23 low <2.2 3

On the whole it is worth noting that drought will proceed on landscapes where the soil is

not able to provide plants with rooting space and /or water and nutrients. In the semi- and

sub – humid zones, the land may become irreversibly decertified when the rootable soil

depth is not capable of sustaining a certain minimum vegetation cover. There are cases

where drought proceeds in deep soils, when the water balance is incapable of meeting the

needs of plants. In this case the phenomenon is reversible.

4.3.7 Land use

Land use characteristics influence the susceptibility of a place to the effects of droughts

hazards. Land use data was derived from the processing of 2010 landsat TM image of the

country was obtained from the Centre for Remote Sensing and GIS at the University of

Ghana. The different classes of broad land use categories and their associated ranks are

given in table 16.

Table 16: Land use index (drought)


1 Water Body 1

2 Closed Forest 2

3 Open Forest 3

4

Dense herbaceous

cover 4

5 Grassland 5

6 Built up areas/Bare Soil 6



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4.3.8 Proximity to Water bodies

The proximity to water bodies within their environments is an indication of how

vulnerable places will be to drought hazard, as places near to rivers would be less

affected by drought than those farther away. Table 17 details the classes and ranks for the

proximity layer as used in the model.

Table 17: Proximity to water index (drought)


1 Up to 300m 1

2 300-600 2

3 600-1000 3

4 >1000 4

5.0 GIS Analysis

GIS analysis involved the application of geostatistical techniques in the development and

modelling of flood and drought risk maps through the combination of climatic,

environmental and other ancillary data layers in multi-criteria evaluation.

5.1 Hazard and Risk M apping

Hazard maps, i.e. a map that highlights areas which have a potential to pose significant

threats to drought were prepared by weighting and overlaying the specific environmental

and climatic factors. Point based source data were converted into surface grid using the

inverse distance interpolation routines available in ArcGIS 9.3. The selection and

weighting of different factors for hazard and risk maps were informed by the literature

and expert input from NADMO Research team. Ratings and classes for each factor are

ranked from low to very high based on degree of vulnerability to the factor. Every layer

is then re-classified based on these ranks. Re-classified layers are multiplied by their

standard weight and then added to others for providing the output risk maps.



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Table 18: Data used for Flood/Drought Risk Mapping

Hazard Map Risk Map

Flood

Rainfall Flood hazard

Slope Land use

Altitude Proximity to active channels

Soil

Flow accumulation areas

Drought

Vegetation index drought hazard

De Martonne Aridity index Land use

FAO Aridity Index Proximity to active channels

Soil Indicator

Relative humidity

Rainfall

5.2 Data used for F lood/Drought Risk Mapping

The output hazard map then serves as an input factor in risk mapping, by combining them

with other environmental factors in producing the risk layers. The final risk maps were

then reclassified into high, moderate and low risk areas as three strata for planning

control interventions. The final output maps resulting from these classes were represented

with a green-yellow-red colour scheme indicating low-medium-high risk areas

respectively. The accuracy of hot spots in the risk maps were then assessed by

stakeholder’s workshop undertaken in the beneficiary districts. All data processing and

analysis were undertaken with the ArcGIS 9.3 GIS software and semi-automated using

the model maker utility.

All data used in health hazard/risk mapping is presented in table 18. Figure 3 presents the

methodological workflow adopted in the development of the risk maps.



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Figure 3: Flood and Drought Risk Mapping Methodology Flow Chart

5.3 Models and Risk Maps

Models developed for producing the risk maps for flood and drought in the AAP pilot

districts are presented in figures 4 and 5 respectively. The output risk maps for flood and

drought for the respective districts are symbolized with a green-yellow-orange-red colour

scheme indicating no-risk, low-risk, medium-risk, and high-risk areas.



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Figure 4: Flood Risk Model



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Figure 5: Drought Risk Model



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5.4 Factors and their Weightings for F lood/Drought Risk Mapping

Table 8: Factors and weightings for flood risk mapping

Factor Weighting Factor

Hazard mapping

Rainfall 0.1

Flow accumulation areas 0.2

Altitude 0.4

Slope 0.2

Soil 0.1

Risk mapping

Flood hazard 0.4

Land use 0.3

Distance to active

channels

0.3

Table 9: Factors and weightings for drought risk mapping

Factor Weighting Factor

Hazard mapping

Climate Indicator 0.4

Vegetation Index 0.4

Soil Indicator 0.2

Risk mapping

Drought hazard 0.4

Proximity to water 0.3

Land use 0.3



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Red areas depict areas at high risk of flooding/drought on a yearly basis. In longer return

period of flood events the likelihood of possible loss of lives, settlements and

infrastructure are very high. In the case of drought, possible effects are very high on farm

yields.

Orange areas indicate areas at moderate risk of flooding/drought. These areas are not

necessarily inundated by flood every year. In longer return period of flood events,

possible loss of agricultural land and settlements can happen.

Yellow areas are areas at low risk of flooding/drought. A low risk level represents an area

that experiences occasional flood/drought without significant loss.

Green areas indicate very low or no risk areas. These means these areas are less likely to

experience flooding or drought because of its topographic, geomorphic climatic or

environmental condition.



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6.0 FANTEAKWA DISTRICT FLOOD AND DROUGHT RISK ANALYSIS

Figure 6: Workshop Participants at Fanteakwa District-Begoro

6.1 Fanteakwa Distr ict Location and Size

The District can be located within longitudes 0°32.5’ West and 0°10’ East and latitudes

6° 15’ North and 6° 40’ North. It is bordered by the Volta Lake to the North, to the

North-West by Kwahu-South District, South-West by the East Akim Municipal, Lower

Manya Krobo District to the East and to the South East by the Yilo Krobo District. It is

located at the middle of the Eastern Region with the district capital at Begoro. The total

land area is 1150 sq.km, which constitutes 7.68% of the total land area within the Eastern

Region (i.e.18310 sq.km) of Ghana.



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Figure 7: Validation Workshop

Figure 8: GPS Training



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6.2 Fanteakwa Distr ict Risk Assessment

6.2.1 Fanteakwa F lood Risk Assessment

Figure 9: Cause of flooding

Cause of flooding

From the figure above 82.1% of respondents, consider rainfall as the major cause of

flooding in the area with 17.9% differing. Rainfall as the major cause of flooding could

be because of heavy precipitation in these areas. Other possible contributory factors to

flooding may include soil texture, altitude, slope, flow accumulation, land use, and

proximity to water bodies.



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Type of rainfall

Figure 10: Type of rainfall

Even though 6.9% said the pattern of rainfall in the locality is low, yet 31% said the

pattern of rainfall is moderate and the majority (62.1 %) supposed that the rainfall pattern

has become very severe within the district. This shows that within the past five years

there had been a change in the annual rainfall pattern with severe rainfall being the

highest, followed by moderate rainfall and low rainfall respectively.



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Predominant soil type

Figure 11: Predominant soil type

Clayey soil scored the highest of 42.9% followed by loamy soil (35.7%), laterite soil

(10.7%), sandy soil (7.1%) and rocky soil (3.6%).Clayey soil has the ability to hold water

for a very long time whereas rocky and laterite soil do not also drain very easily. Clayey

soils comprise of approximately 0-45% sand, 0-45% silt and 50-100% clay by volume. It

is typically not free draining, since water takes long time to infiltrate; therefore, it allows

virtually all the water deposit to run-off causing flooding. This suggests why majority of

the areas in the Fanteakwa District easily gets flooded. From the analysis it is evident that

majority of the areas are vulnerable to flooding mainly due to exposure to clayey soil.



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Colour of soil

Figure 12: Colour of soil

Brown colour soil recorded a percentage of 70.4% followed by rusty and black colour

soil with 14.8%. Depending on the chemical composition, clayey soil can be any shade of

yellow to brown to red depending on the dominant mineral. Since the soil colour

determines the soil type, the respondents have confirmed, by their overwhelming choice

of soil colour brown that, clayey soil is the predominant soil type in Fanteakwa District

by their estimation.



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Flood prone areas

Figure 14: Flood prone areas

Fig. 14 shows some of the flood prone areas in the Fanteakwa district base on the

knowledge of the respondents. Dansor in Begoro Township tops the list. Other areas

include Zion-Begoro, Nsutam, Zongo-Begoro, Adakope Odortom, Ahomahomaso,

Akrumuso, Nayinfong, Nkankanma, Zongo community, Zion, and Bosuso.

02468

101214

1618

Flood Prone Areas in Fanteakwa

Flood Prone Areas



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Safe havens

Table 10: Fanteakwa Safe havens

Frequency Percent

Valid

Percent

Cumulative

Percent

Valid Dansor (school) 4 13.3 13.3 13.3

Zion (church) 2 6.7 6.7 20.0

Bosuso (church) 5 16.7 16.7 36.7

Ahomahomaso (school) 5 16.7 16.7 53.3

Manee, Timily (church) 3 10.0 10.0 63.3

Akrumuso (church) 3 10.0 10.0 73.3

Nsutam (church) 3 10.0 10.0 83.3

Nkankanma (church) 3 10.0 10.0 93.3

Adakope Odortom

(school)2 6.7 6.7 100.0

Total 30 100.0 100.0



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Safe Havens

Figure 15: Safe Havens in locality

From the figure above the respondents selected either a nearby school or church as a safe

haven. 16.7% of the respondents choose a church in Bosuso and a school at

Ahomahomaso. We recommend further research into mapping these safe havens and

drawing evacuation plans to build community resilience to flooding.



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6.2.2 Fanteakwa fl ood mitigation measures

Figure 16: Flood mitigation measures

Fig. 16 shows that 23.3% of the respondents believe that increase forecasting and early

warning system should be the major flood mitigation measure in the district. 20% prefer

improved building standards, 16.3% favour not building in flood prone areas, and the rest

(13.3%) of the respondents like better either flood retention, insurance schemes or any

technical adaptation (e.g. water reservoirs, water transfer, water desalinization etc.)

measure as means of building community flood resilience.



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6.2.3 Fanteakwa Flood Risk M ap

Figure 17: Fanteakwa District flood risk map



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6.2.4 Fanteakwa Drought Risk Assessment

Sell or pledge assets

Figure 18: Sell or pledge assets

Fifty-two percent (52%) said they either sell or pledge their asset and 48% said they do

not sell their assets. This suggests that majority of the people find it very difficult to

survive during drought.



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6.2.5 Fanteakwa drought mi tigation measures

Figure 20: Drought mitigation measures

37.9% of respondents are of the view that landscape planning measures to improve water

balance (e.g. change of land use, reforestation etc.) is the key to building community

resilience to drought. Meanwhile, 23.3% of the respondents prefer construction of dams,

16.7% favour leakage reduction or increasing efficiency of water use (e.g. leakage

reduction, efficient irrigation etc.), 10% desire resistant seeds, 6.7% have a preference for

increase in water supply, and 3.3% choose construction of bore holes as drought

mitigation measures.



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6.2.6 Fanteakwa Drought Risk Map

Figure 21: Fanteakwa District drought risk map



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7.0 SISSALA EAST DISTRICT FLOOD AND DROUGHT RISK ANALYSIS

Figure 22: Workshop Participants at Tumu

7.1 Sissala East Distr ict Location and Size

The Sissala East District is located in the North- Eastern part of the Upper West region of

Ghana. The district capital is Tumu. It falls between Longitudes. 1.30° W and Latitude.

10° N and 11° N. The district has a total land size of 4,744 sq km - representing 26% of

the total land mass of the Upper West Region. It shares boundary on the North with

Burikina Faso, on the East with Kassena Nankana and Builsa Districts, to the South East

with West Mamprusi District, South West with Wa East and Nadowli Districts and to the

West by Sissala West District.



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7.2 Sissala East Distr ict Risk Assessment

7.2.1 Sissala East Distr ict F lood Risk Assessment

Type of rainfall


44.8% said precipitation in the district is moderate, 3.4% said it is low, and 51.7% said it

is very severe. From the views expressed, moderate to severe precipitation is the major

cause of flooding in the district. Moderate to severe rainfall over a prolonged period has

the tendency to overwhelm the ability of soil to hold water thereby causing flooding.



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Rainfall Pattern over the Past Five Years (2007-2012)

Figure 26: Rainfall pattern over the past 5 years

37.9% said the pattern of rainfall in the locality is low, 13.8% said it is moderate, and

48.3% said it has become very severe within the past five years. The long-term severe

shift in precipitation in the Sissala East district is an indication of climate change and

variability.



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Fig. 27 shows that clayey soil has the lowest of 3.4% followed by sandy soil (27.6%),

while laterite soil and loamy soil rank highest at 34.5%. Sandy soil typically comprise

approximately 80-100% sand, and 0-10% clay by volume, which makes it light and

typically very free draining. Loamy soil, on the other hand, comprise of approximately

25-50% sand, 30-50% silt, and 10-30% clay by volume, which tends to make it

somewhat heavier and fairly free draining. Sissala East District should be free draining,

given the high ranking of sandy, laterite, and loamy soils; however, due to moderate to

severe rainfall among other factors, the districts is vulnerable to flooding.



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Colour of the soil

Figure 28: Colour of the soil

Fig. 28 shows that brown colour recorded a percentage of 48.3% followed by black

(44.8%) and rusty (6.9%)



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Building materials

Figure 29: Building materials

From the figure, structures built with either bricks or blocks represent 20.7%, those built

with mud is 72.4% and mixed structures account for 6.9%. This shows that majority of

the structures in the Sissala East district is built with mud. Structures built with mud or

clay are very vulnerable during prolong periods of precipitation or during heavy rainfall

since they easily collapse.



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Flood prone areas


The figure shows that the flood prone areas are Wellembelle, Zongo, Tumu, Taffiasi,

Pina, Wahabu, Santie, Nuarijan, Kong, Nmanduamu, Kulfuo, Zongo, Gwosi, Dimajan,

Bugubelle, Banu, Dimbenno-Toridan being the flood prone areas with Tumi recording

the highest of 20.7% followed by Dimijan recording 10.3% with the rest recording 6.9%

and 3.4% respectively.



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Safe haven

Figure 31: Safe haven

The respondents chose schools, churches, mosques and a town centre in some strategic

locations as safe havens during floods. Some of the locations mention are Nmanduanu,

Wellebelle, Basissau, Banu, Bayeviella, Bugubelle and Gwosi.



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7.2.2 Sissala East Distri ct fl ood mitigation measures


From the figure, 27.6% of the respondents believe that construction of dams in strategic

flood locations is the best means of building community resilience against floods in

Sissala East District. 20.7% suggested improved forecasting and early warning system,

17.2% recommended not building in waterways, 10.3% support legislation and

enforcement of building standards, and 6.9% opted for technical adaptation measures

(e.g. raise dyke-gutter, enlarge reservoirs, and upgrade drainage systems) as a means of

flood mitigation.



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7.2.3 Sissala East Di str ict F lood Risk Map

Figure 33: Sissala East District flood risk map



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7.2.4 Sissala East Distr ict Drought Risk Assessment

Duration of drought

Figure 34: Duration of drought

From the figure, 65.5% said drought lasts for 1-2 months and 34.5% said it lasts for 3-6

months. This shows that it takes the community relatively shorter duration to recoverfrom drought.

.



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Dispersion of family members during drought

Figure 36: Dispersion of family members during drought

Fig. 36 above shows that 69% of the respondents disperse their family in situations of

drought; whereas, 31% of the respondents will not. This implies that most of families in

Sissala East District adopt dispersion of family members as coping mechanism to help

ease the effect of drought on their households.



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Sectors most vulnerable to drought

Figure 37: Sectors most vulnerable to drought

The figure shows that 89.7% of respondents understand that the agriculture sector is the

most vulnerable to drought with only 10.3% representing the health sector.



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Sickness during drought period

Figure 38: Sickness during drought period

From the diagram 69% said they get sick during drought and 31% said, they do not get

sick. From many views expressed at the workshop, during drought most people rely on

untreated water and sometimes share the same water with animals for survival, exposing

them to water-borne diseases.



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7.2.5 Sissala East drought mitigation measures

Figure 39: Sissala East drought mitigation measures

The figure shows that 24.1% of the respondents prefer the construction of boreholes as a

means of drought mitigation, with 13.8% suggesting the use of both drought resistant

seeds, and improved early warning as a means of mitigating drought. The rest suggested

increase in water supply and reservoirs, distillation, water leakage reduction, construction

of dam, landscape planning, insurance schemes and legislation as a means of drought

mitigation.



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7.2.6 Sissala East D istrict D rought Risk Map

Figure 40: Sissala East drought risk map



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8.0 WEST MAMPRUSI DISTRICT FLOOD AND DROUGHT RISK

ANALYSIS

Figure 41: Workshop Participants at West Mamprusi District-Walewale

8.1 West Mamprusi District Location and Size

West Mamprusi District is in the Northern Region of Ghana. The district capital is

Walewale. The district is bordered to the north by Builsa, Kassena-Nankana and

Bolgatanga districts, in the Upper East Region; to the south west by Gonja, Tolon-

Kumbungu and Savelugu district in the Northern Region; to the west by the Sissala and

Wa districts; and to the east by East Mamprusi and Gushiegu-Karaga Districts.




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8.2 West Mamprusi Distr ict Ri sk Assessment

8.2.1 West M amprusi Di str ict F lood Risk Assessment

Cause of flooding


From the figure majority of the respondents believe that rainfall is the major cause of

flooding in the West Mamprusi district. Only a few attribute flooding to other causes

aside of rainfall.



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Type of rainfall


Forty-eight (48%) said the pattern of rainfall in the district is moderate, 16% said it is

low, while 36% said it is severe. This shows that the district experiences mostly moderate

to severe rainfall, which makes the district vulnerable to flooding.



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Fig. 46 depicts the types of soil in the West Mamprusi district. The Figure shows that, the

type of soil that is most prevalent in the district is sandy soil representing 64%, followed

by clayey soil representing 20%. The area is also dotted with laterite and loamy soilrepresenting 4%, whereas, rocky soil represent 8% of valid respondents.



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Colour of Soil


The figure above shows that 44% of soils in the area have black colour as well as brown

colour followed by 12%, which represent the rusty colour of soil. It should be borne inmind that the soil colour determines the soil type. Loamy soil, which ranks high on the

list, is black in colour.



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Flood Prone Areas


The flood prone areas within the West Mamprusi district are Kubuori, Nayinfong,

Walewale, and Yagaba. Both Kubuori and Walewale recorded the highest percentage of

thirty-two followed by Yagaba and Nayinfong respectively.



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Safe havens in locality

Figure 49: Safe havens in locality

Respondents believed that in situations of flood, schools located in Walewale, Nayinfong,

Yagaba and Kubuori are the unequalled locations for safe haven.



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8.2.2 West Mamprusi D istrict Flood Miti gation M easures


Fig. 50 shows that 40% of respondents prefer improved forecasting and early warning

system as a priority measure of flood mitigation. Eight percent (8%) suggest technical

adaptation, enforcement of building standards, or insurance scheme, and 24%

recommend construction of dams as means of flood mitigation in the district.



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8.2.3 West Mamprusi D istrict Flood Risk Map

Figure 51: West Mamprusi District Flood Risk Map



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8.2.4 West Mamprusi Di str ict Dr ought Ri sk Assessment

Frequency of drought

Figure 52: Frequency of drought

From Fig. 52, 27.3% of the respondents said drought occurs every 0-1 year, 45.5%

claimed it occurs every 1-5 years, whilst 27.3% said it occurs every 5-10 years. From the

response, drought occurs, at least, every 1-5 years in the West Mamprusi District.



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Table 11: Sickness during drought

Frequency Percent

Valid

Percent

Cumulative

Percent

Valid yes 15 60.0 60.0 60.0

no 10 40.0 40.0 100.0

Total 25 100.0 100.0

Sickness during Drought

From Table 11, 60% of respondents said they get sick during drought and 40% said they

do not get sick. During drought, most water sources in the district are dried and the

residents rely on sparse and untreated water and sometimes share the same water with

animals for their survival.



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8.2.5 West Mamprusi drought mi tigation measures

Figure 53: Drought mitigation

From the figure, twelve percent (12%) of the respondents choose construction of dams,thirty-two percent (32%) want the construction of boreholes, forty percent (40%) prefer

increased use of drought resistant seeds, eight percent (8%) choose increase in water

supply (reservoir volumes, water transfer, desalinization-purify or distil), and 4% opted

for landscape planning and leakages reduction.



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8.2.6 West Mamprusi D istrict Drought Risk M ap

Figure 54: West Mamprusi District Drought Risk Map



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9.0 KETA MUNICIPALITY FLOOD AND DROUGHT RISK ANALYSIS

Figure 55: Workshop Participants at Keta Municipal Assembly

9.1 Keta Municipal Location and Size:

Keta Municipal is one of the 18 administrative districts of the Volta Region with Keta as

the capital. The Municipality lies within Longitudes 0.30 E and 1.05 E and Latitudes 5.45

N and 6.005 N. It is located east of the Volta estuary, about 160 km to the east of Accra,

off the Accra-Aflao main road. It shares common borders with Akatsi district to the

north, Ketu district to the east, South Tongu district to the west and the Gulf of Guinea to

the south. Out of the total surface area of 1,086 sq km, water bodies cover approximately

362 sq km (about 30 per cent). The largest of these is Keta Lagoon, which is about 12 km

at its widest section and 32km long. The remaining land area is only 724 sq km.



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9.2 Keta Mun icipal Risk Assessment

9.2.1 Keta Municipal F lood Risk Assessment

Cause of Flooding


From Fig. 58, 78.3% of the respondents said rainfall is the major cause of flooding in the

Keta Municipality while 21.7% said no.



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Type of Rainfall


Thirteen (13%) said the pattern of rainfall in the Keta Municipality is low, 69% said it is

moderate and 17.4% said it is severe. This explains that the familiar type of rainfall in

Keta Municipality is moderate with intermittent low to severe precipitation.



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Predominant Soil Type


Sandy soil recorded the highest score of 65.2% followed by clayey soil (21.7%), andlaterite soil (13%). Sandy soil has the biggest particles among the three choices; and the

bigger the soil particles the better aeration and drainage of soil. It usually comprise of

approximately 80-100% sand, 0-10% silt, and 0-10% clay volume with poor water

holding capacity due to very low organic content.



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Colour of Soil


Brown colour has a percentage of 43.5% followed by rusty colour with 30.45 and black

colour of the soil with 26.1%.The colour of soil can say a lot about the conditions in thesoil as well as the presence of water and other elements. Brown colour soil is typically

loose with poor water holding capacity; whereas, rusty soil colour is compact and tends

to prevent water from draining easily.



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Choice of Safe Havens

Figure 63: Safe havens

Fifty two percent (52%) of respondents prefer school locations as safe haven, followed by

mosque (20%), church and park respectively.



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Flood Prone Areas


Respondents scored highest for Horvi and Agorbledokui as flood prone areas in Keta

Municipality. Other areas are Keta, Horvi, Fiaxor, Trekume, Vodza, Kedzi, Dzita

Anyanui, Woe, Dzelukope, Blemazado, Akplorwutorkor, Agorbledokui, Sakome,

Atorkor and Abutiakorkope.



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Safe Haven in Keta Municipality

Figure 65: Safe Haven

The respondents choose the riverbank in horvi as safe haven. They also choose schoollocations in Sakome, Keta and Trekume. They recommended that residents in Kedzi,

Vodza, Nyikutor, Tregui, and those in Agorbledokui should relocate to safer locations.



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Flood Evacuation Planning

Figure 66: Flood Evacuation Plan

Fig. 66 shows that majority (66.7%) of the respondents were of the view that flood

evacuation is necessary but not planned.



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9.2.2 Keta Muni cipal F lood M itigation Measures

Figure 67: Flood mitigation

Thirty-two percent (32%) of the respondents opted for flood retention as priority

mitigation measure in the Keta Municipality. 20% prefer not to build in risky areas, 16%

favour increase forecasting and early warning, and 12% want legislation and enforcement

of building standards.



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9.2.4 Keta Municipal Drought Risk Assessment

Figure 69: Frequency of drought

Fig. 69 shows that 39.1% of the respondents believed that the frequency of drought is 1-5

years, 34.8% said it is 0-1 year, 21.7% said it is 5-10 years, and 4.3% said it is 15-20

years. This shows that there is the probability of drought in every five-year period.



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Sell or Pledge Assets during Drought

Figure 70: Sell or Pledge Assets

56.5% of the respondents are of the view that residents of Keta Municipality do not sell

or pledge their assets during drought; whilst, 43.5% of the respondents think they do.



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Evacuation

Figure 71: Evacuation

Fig. 71 shows that respondents do not have a clear-cut opinion on evacuation. 21.7% of

the respondents are not sure whether the people are likely, moderately, or less likely to

evacuate during disaster; whereas, 7.4% were divided on whether people are most likely

or not prepared to evacuate. We recommend research study on “social vulnerability: - risk

perception of evacuation in Keta Municipality.”



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Sectors Most Vulnerable to Drought

Figure 72: Sectors most vulnerable to drought

Agriculture is the most vulnerable sector in the municipality represented by 52.2%

followed by the transport sector representing 26.1%, water supply representing 17.4%,

and the health sector being the least vulnerable pegged at 4.3%



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9.2.5 Keta Muni cipal Drought miti gation measures

Figure 73: Keta Municipal Drought mitigation measures

The respondents are of the view that construction of boreholes should be the primary

means to mitigate drought in the Municipality. Other secondary means may include

enforcement of building legislation, construction of dams, increased water supply, and

awareness creation.



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9.2.6 Keta Municipal Drought Risk M ap

Figure 74: Keta Municipal Drought Risk Map



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10.0 AOWIN SUAMAN DISTRICT FLOOD AND DROUGHT RISK ANALYSIS

Figure 75: Workshop Participants at Aowin Suaman District-Enchi

10.1 Aowin Suaman Distri ct Location and Size:

The District is located in the mid-western part of the Western Region of Ghana between

latitude five degrees twenty-five minutes and six degrees fourteen minutes North (5° 25’

N and 6° 14’ N) and longitude 2° 30’W and 3° 05’W. It shares boundaries with Jomoro

District to the South,Wasa Amenfi to the East, Juabeso-Bia and Sefwi-Wiawso to the

North and the Republic of La Cote D’lvoire to the West. The land size of Aowin Suaman

is 2,717 square kilometers which constitutes about 12% of the total land size of the

Western Region (i.e. 23,921 square kilometers). The district capital is Enchi.



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10.2 Aowin Suaman Distr ict Risk Assessment

10.2.1 Aowin Suaman Distr ict F lood Risk Assessment:


Fig. 78 shows that 95.7% of the valid respondents said that rainfall is the major cause of

flooding in the area; whereas, 4.3% said no.



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What is the predominant soil type in your locality/area?


Clayey soil (47.8%) is highest, followed by sandy soil, (26.1 %), loamy soil (21.7%), and

laterite soil (4.3%)



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Colour of soil


Brown colour had a percentage of 39.1% followed by rusty and black colour of the soil

with 26.1%.



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Flood prone areas


Fig. 81 shows the flooded prone areas in Aowin Suaman district. The list include

Yiwabra, Omnte, Old Yakase, Ohiamaadwen, Nyankaman, Nakaba, Karlo, Jensui,

Damoahkor, Akyemfu, Aduom, Adonikrom, Abotare, with Enchi and Sewum being the

areas frequently flooded.



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Safe havens in locality

Figure 82: Safe havens in locality

Fig. 82 shows that 20.8% of respondents selected school locations, and church premisesas safe havens. Some of the areas chosen include Enchi, Jensu, Sewum, Nakaba, Boinso,

Denkyira, Ohiamaadwen, Old Yakaso, and Adonikrom



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10.2.2 Aowin Suaman District fl ood mi tigation measures


From Fig. 83 above 47.8% of respondents are of the view that not building in flood prone

areas is the most important mitigation measure. 21.7% choose increase forecasting,

17.4% choose enforcement of building standards, and 8.7% opted for technical flood

protections e.g. raise dyke-gutter, enlarge reservoirs, and upgrade drainage systems.



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10.2.3 Aowin Suaman Di stri ct Flood Risk Map

Figure 84: Aowin Suaman District Flood Risk Map



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10.2.4 Aowin Suaman Distr ict Dr ought Risk Assessment

Sell or Pledge Assets

Figure 85: Sell or pledge assets

77.8% said residents of Aowin Suaman do not have to either sell or pledge their assets

during drought.



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10.2.6 Aowin Suaman Distri ct Drought Risk Map

Figure 88: Aowin Suaman District Drought Risk Map



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11.0 CONCLUSION

Improving community capacity to prepare and respond to climate-induced phenomena

through this risk mapping, will allow them to target the high-risk areas for risk

mitigation. This Flood and drought risk mapping also makes it possible for government

and non-governmental organizations to identify the risk factors and allocate resources,

build infrastructure, and ensure that early warning systems are put in place to guarantee

community disaster resilience.



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12.0 BIBLIOGRAPHY

Benjamin A. Gyampoh and Winston A. Asante. “Mapping and Documenting

Indigenous Knowledge in Climate Change Adaptation in Ghana .” 2011.

A Bryman- Recherche. Quantitative data analysis with SPSS release 8 for

windows . 1999.

Amoako, P. Y. O. and S. T. Ampofo. Hazard Mapping in Ghana, UN

DP/NADMO, . Accra, 2007.

Andrew Shepherd, Charles Jebuni, Ramatu Al-Hassan, Andy McKay, Colin

Poulton, Ann Whitehead, and Jonathan Kydd. “Economic Growth in Northern

Ghana. DFID.” Revised Report for DFID Ghana, 2005.

Anim-Kwapong, and Frimpong. “Vulnerability of Agriculture to climate - impact

of climate change on cocoa production. Vulnerability and Adaptation Assessment

under the Netherlands Climate Change Studies Assistance Programme Phase 2 .”

2006.

Antonius, R. Interpreting quantitative data with SPSS . 2003.

C Acton, RL Miller, D Fullerton, J Maltby . SPSS for social scientists. . 2009.

Consult, Sync. Capacity Assessment, Disaster Preparedness of NADMO. Accra,

2008.

FAO. Climate change will have major impact on fishing industry, says UN

agency. . 2008. UN News Centre.

http://www.un.org/apps/news/story.asp?NewsID=27330 (accessed August 23 ,

2011).

GA Morgan, NL Leech , GW Gloeckner, KC Barrett . SPSS for introductory

statistics . 2004.

Ghana, Environmental Protection Agency (EPA) of. “First National

Communication to the United Nations Framework Convention on ClimateChange (UNFCCC). EPA of Ghana, Ministry of Environment, Science and

Technology .” Accra, 2000.



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Gyampoh, B. A., S. Amisah, M. Idinoba, and J. Nkem. “Using traditional

knowledge to cope with climate change in rural Ghana. Unasylva No. 231/232,

Vol 60. FAO ftp://ftp.fao.org/docrep/fao/011/i0670e/i0670e14.pdf.” 2009.

JJ Meulman, WJ Heiser . SPSS categories 11 . 2001.

Norusis, Author Marija. SPSS 16. Guide to data analysis. 2008.

Nyong, A., F. Adesina, and B. Osman Elasha. “The value of indigenous

knowledge inclimate change mitigation and adaptation strategies in the African

Sahel. Mitigation and Adaptation Strategies for Global Change 12:787-97. .”

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Report, Presidential. “Ghana Vision 2020 document.” 1996-2000.

Stott PA, and Kettleborough JA. “Origins and estimates of uncertainty in

Predictions of twenty-first century temperature rise. Nature 416: 723-726.

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in Ghana 2011.” 2002.

UN. “Disaster Risk Reduction: Risk and Poverty in a Changing Climate: Invest

today for a safer tomorrow.” Global Assessment Report, 2009.

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Economies in the Limpopo and Volta Basins.” Accra, Ghana, 2008.



13.0 ANNEX I: DATA SOURCE AND RESOLUTIONS

13.1 F lood Mapping Data

Feature Data Source/Year Resolution/Scale

Soil Soil Research Institute 1:250,000

Rainfall GMet (annual average) District level

Altitude/Elevation DEM 1:50,000

Slope DEM 1:50,000

Flow accumulation area DEM 1:50,000

Land use 2010 Landsat TM Image 30m

Proximity to waterbodies

Derived from Spatial Analysis 1:50,000

13.2 Drought Mapping Data

Feature Data Source/Year Resolution/Scale

Vegetation Indicator Average NDVI land cover mapfrom 2001 – 2010

30m

Climatic Indicator

Temperature GMet (annual average) District level

Rainfall GMet (annual average) District level

Relative humidity GMet (annual average) District level

Evapotranspiration GMet (annual average) District level

Soil IndicatorParent material Soil Research Institute 1:250,000

Soil Texture Soil Research Institute 1:250,000

S il d th S il R h I tit t 1 250 000

aap risk mapping in ghana

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