Consultancy Services for Development of Disaster Risk and
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Technical note:
Risk Assessment Report
UNDP Ghana
30 October 2014
Draft Report
BC5721-101-100
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Document title
Technical note 1
October 2014
Document short title
Status
Date
Project name
Project number
Author(s)
Client
Reference
TN1
Draft Report
30 October 2014
Consultancy Services for Development of
Disaster Risk and Early Warning Systems in
Ghana
BC5721-101-100
Willem Kroonen
UNDP Ghana
BC5721-101-100/TN1/411750/Nijm
-iTechnical note
BC5721-101-100/TN1/411750/Nijm
30 October 2014
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Internet
CoC
CONTENTS
Page
1
INTRODUCTION
1.1
Goal
1.2
General approach
1.3
Outline
2
FLOOD HAZARD
2.1
Description of methodology
2.2
National flood hazard map current climate
2.2.1
DEM-based hazard mapping
2.3
National Flood hazard map in 2050
2.4
Pilot level map outlook
2.5
Assessment of flood hazard maps
3
3
6
6
8
12
12
3
DROUGHT HAZARD
3.1
Description of methodology
3.2
Application of rainfall deficit-method
3.3
National drought hazard map current climate
3.4
National drought hazard map in 2050
3.5
Pilot level map outlook
3.6
Assessment of drought hazard maps
13
13
14
16
19
24
24
4
VULNERABILITY
4.1
Description of methodology
4.2
Flood vulnerability – situation 2010
4.3
Drought vulnerability – situation 2010
4.4
Vulnerability for future (horizon 2050)
4.5
Vulnerability assessment
26
26
26
30
31
34
5
RISK
5.1
5.2
5.3
5.4
5.5
5.6
5.7
36
36
36
39
40
41
42
43
6
1
1
1
2
Description of methodology
Flood risk
Drought risk
Future flood risk (horizon 2050)
Future drought risk (horizon 2050)
Flood Risk assessment
Drought Risk assessment
LITERATURE
45
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LIST OF FIGURES
Figure 1: Example of DEM (left) and HAND indexes (right) from Renno et al. (2008) ......3
Figure 2: Validation of the HAND-hazard for the White Volta ............................................5
Figure 3: Validation of the high hazard zone of take Volta with the 84m elevation contour
line ......................................................................................................................................5
Figure 4: Validation for the Odaw drain..............................................................................6
Figure 5: Example of the flood hazard map for the national level including large water
bodies .................................................................................................................................7
Figure 6: Climate zones and meteorological stations ........................................................9
Figure 7: Flood hazards maps for the A1B and the A2 IPCC climate scenarios .............11
Figure 8: Examples of cumulative rainfall deficit in the Netherlands. ..............................13
Figure 9: Spatial distribution of maximum cumulative rainfall deficit for the years 19982011. .................................................................................................................................15
Figure 10: Spatial distribution of number of dry days ......................................................16
Figure 11: Drought hazard map of Ghana, based on grid data .......................................17
Figure 12: Drought hazard map of Ghana projected on the districts. ..............................18
Figure 13: Boxplot of maximum rainfall deficit within 30-years period for the 22 synoptic
stations, for current climate and the 2 climate scenarios A1B and A2. ...........................20
Figure 14: Boxplot of number of days within a year that cumulative rainfall deficit is
higher than 600 mm for 30 years period of current climate and A1B and A2 scenario. ..21
Figure 15: Relative increment of numbers of days with rainfall deficit above 600 mm
based for climate scenario A1B (left) and scenario A2 (right) .........................................23
Figure 16: Average number of days that the rainfall deficit exceeds the threshold of 600
mm for the current climate, scenario A2 and scenario A1B.............................................23
Figure 17: Drought hazard maps for the A1B (left) and the A2 (right) IPCC climate
scenarios ..........................................................................................................................24
Figure 18: Updated land use map with built up areas .....................................................26
Figure 19: Flood vulnerability for 2 alternatives ...............................................................27
Figure 20: Population density and proposed vulnerability ...............................................28
Figure 21: Overall flood vulnerability for 2 alternatives ....................................................29
Figure 22: Drought vulnerability for 2 alternatives ...........................................................30
Figure 23: Example of population growth ........................................................................32
Figure 24: Land use maps for 2010 and 2050 .................................................................32
Figure 25: Population density for 2050 ............................................................................33
Figure 26: Flood and drought vulnerability in 2050 ..........................................................34
Figure 27: Flood risk maps, based on land use alone (alternative 1 on the left, 2 on the
right)..................................................................................................................................37
Figure 28: Flood risk maps, based on land use and population density (alternative 1 on
the left, 2 on the right) ......................................................................................................38
Figure 29: Drought risk maps for 2 alternatives (alternative 1 on the left, 2 on the right)40
Figure 30: Flood risk maps for 2 climate scenarios for 2050 ...........................................41
Figure 31: Drought risk map for future climate scenarios A1B (left) and A2 (right). ........42
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- iv Technical note
LIST OF TABLES
Table 1: Physical meaning of the hazard levels ................................................................ 4
Table 2: Changes in precipitation patterns in the six climate regions of Ghana for the
current, A1B and A2 IPCC climate scenarios. ................................................................ 10
Table 3: Overview of district statistics ............................................................................. 12
Table 4: Overview of spatially distributed hazard statistics ............................................ 12
Table 5: Statistics of the spatial distribution of mean (14 years) maximum rainfall deficit
[mm]. ................................................................................................................................ 15
Table 6: Definition of drought hazard classification number of days based on the 14
years average value of number of days that cumulative rainfall deficits exceeds
threshold of 600 mm. ....................................................................................................... 16
Table 7: Number of days within a year that rainfall deficit is higher than 600 mm,
averaged over a 30-years period. For current climate and A1B and A2 scenario .......... 21
Table 8: Same as Table 7, stations averaged over climate zone ................................... 22
Table 9: Same as Table 8, with percentage values. ....................................................... 22
Table 10: Proposed classification for vulnerability based on population density ............ 27
Table 11: Proposed classification for overall flood vulnerability ..................................... 28
Table 12: Flood vulnerability in km2 per alternative, based on land use ....................... 29
Table 13: Flood vulnerability in km2 per alternative, based on population density ........ 29
Table 14: Overall flood vulnerability in km2 per alternative, combining land use and
population density ............................................................................................................ 30
Table 15: Drought vulnerability in km2 per alternative ................................................... 31
Table 16: % Area land use in 2010 and 2050 ................................................................. 33
Table 17: Changes In vulnerability between 2010 and 2050 .......................................... 34
Table 18: Value for flood vulnerability ............................................................................. 36
Table 19: Classification from Risk value to Flood Risk ................................................... 36
Table 20: Overview of % of territory of Ghana with a flood risk value for different
alternatives ...................................................................................................................... 39
Table 21: Classification from Risk value to Drought Risk ............................................... 39
Table 22: % of territory of Ghana with a drought risk value for 2 alternatives ................ 40
Table 23: Flood risk ......................................................................................................... 43
Table 24: Drought risk ..................................................................................................... 43
-vReport 1
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1
INTRODUCTION
1.1
Goal
The target of this technical report is twofold. Firstly it provides the technical background of the
applied methodologies in order to provide the necessary backgrounds to reproduce the maps.
Secondly an assessment of the maps is made. This report focusses on the hazard, vulnerability and
risk maps at national level. The maps are made for both floods and droughts. Besides the maps for
the current situation (current climate) also maps are provided for the future 2050 horizon. These
maps are based on two IPCC climate scenarios that are most likely to occur in West-Africa,
especially Ghana.
The maps are grid based but can also be aggregated to the district level. The maps are input for the
more detailed maps that will be produced for the 10 pilot districts which are selected in close
corporation with the CREW team. In this report only an outlook is given for the methodology that will
be used to make the more detailed pilot maps.
This technical note comprises the following information:

Description of the methodology

Application of the methodology on national level

Application of the methodology for future climate (horizon 2050).

Assessment of the maps
1.2
General approach
The methodology in general is already described in report 1 (reference). This report builds on report
1. It presents the final methodology and makes an assessment of the final maps.
Also here we use the following definition:
Risk = Hazard x Vulnerability / Capacity
This report deals with the mapping of hazard, vulnerability and risk on the national level. The
assessment of these parameters is done for two situations: the present one and a future one
(horizon 2050). For the hazard mapping the future climate is determined on the basis of the two
most likely IPCC climate scenarios. For the vulnerability mapping a socio-economic assessment is
used to predict population densities and land used developments in 2050. Risk can be determined
on the basis of hazard and vulnerability but should also take the available capacity into
consideration as this parameter offers the best possibilities to show risk reduction interventions.
Capacity can be increased through technical, institutional and social measures. However, measuring
capacity on an objective scale is very difficult and outside the scope of this project. But because we
still want to introduce the capacity parameter in the risk assessment, we have chosen to use a
relative approach and to take the present capacity as the reference value (100% or 1). This offers
the possibility to introduce the effects of capacity-increasing measures for future risk mapping efforts
and to show their effects. We intend to introduce the effects of the capacity-increasing measures
that are already foreseen in the pilot districts and hot-spot communities (training and early warning
system implementation) in the risk mapping on the district level.
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1.3
Outline
Because the methodology for hazard mapping differs significantly between flood hazard and drought
hazard they are described in two separate chapter, Chapter 2 respectively Chapter 3. Chapter 4
describes the methodology and outcome of the vulnerability mapping. The resulting risk maps are
presented and assessed in Chapter 5.
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-2Technical note
2
FLOOD HAZARD
2.1
Description of methodology
Floods are dominated by two aspects: the presence of (excessive) water and the topographical
characteristics of the area. For example, water is collected in low lying areas, flat surfaces close to a
river flood easily and rainstorms in urban areas can cause sudden floods (flash flood). It is possible
to develop 1 or 2-dimensional hydraulic models for all catchments in Ghana and calculate all kinds
of flood scenarios with them, resulting in flood hazard maps. However, this is very costly both in
terms of the data collection process as well as in model building work. Moreover, the purpose of a
flood hazard map on national level is mainly to get a general idea of which areas are more prone to
flooding than others.
Taking this into account we propose a much simpler and easier method to present similar
characteristic areas: the Hight Above Nearest Drainage(HAND)-methodology. The HAND method
describes the relative height of a certain pixel to its drainage network. Other topographical
characteristics like slope and drainage area are also taken into account. The method is described by
Renno et al. in 2008.
Figure 1 shows the application of the HAND methodology. On the left a DEM (grey) with the
drainage pattern (blue) is shown; on the right the HAND index for the same section. The cross
sections show that the actual height (upper left) is standardized to the height of the drainage cells
(upper right). The HAND index can therefore be used to classify the DEM into hazard zones
according to the topology.
Figure 1: Example of DEM (left) and HAND indexes (right) from Renno et al. (2008)
The hazard zones are related to the type of drainage, being large, medium and small. Table 1
shows the hazard description, HAND-index and hazard classification. This classification results from
the validation process. If relevant we will optimize the index, based on new validation information.
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Table 1: Physical meaning of the hazard levels
Type river
Indication of
upstream
area (km2)
Hazard description
Large
8100
Medium
Small
HAND-index
High
medium
low
The hazard is caused by large river systems (e.g.
rainy season in complete catchment, dam spills).
Therefore the high risk zone can be very high due
to the large amount of water.
<= 7
*
*
810
Medium size rivers know hazard of two types. (1)
local torrential rainfalls and (2) the high risk zone is
depending on water levels directly in a stream,
where also currents can occur. The medium risk
zone describes fiscally the transition zone where
runoff processes start dominating the hydraulic
processes.
<= 5
5 - 10
*
16
Small catchments are mainly prawn to torrential
rains as discharges come directly from the
surrounding. Drainage systems can rapidly rise, but
also the valley sides are at hazard due to runoff
processes.
<= 3
3 - 10
10-15
* Higher hand values are not used in that specific layer; the information of a lower layer is used instead
The HAND method op national level is based on the Shuttle Radar Topography Mission (SRTM)DEM.
The processing of the DEM is done in PCRaster – an environmental modelling software with raster
calculations options. However the processing steps can also be undertaken in other raster
processing packages with similar functionalities.
In order to calculate the HAND-index these steps were undertaken:
1. Derive flow direction raster
2. Classification of stream network according to the upstream area and the height of the stream
according to the DEM
3. Definition of upstream areas for each stream pixel with the same height as the downstream
stream pixel
4. HAND index is derived by the difference of step 3 and step 2
For each of the three river types shown in Table 1 the HAND index is calculated. The difference
between the river types lies in the classification of the stream. Afterwards the HAND indexes are
classified into the three hazard zones. The final hazard map is derived by the combination of the
three classified hazard maps for the different stream sizes; the order of the layers is large, medium
and finally small river systems.
To prove that the HAND method is suitable for the purpose of national flood hazard mapping we
validated the method on several typical flood areas. Figure 2, Figure 3 and Figure 4 present a
validation of the floodplains of the White Volta, the maximum water level in lake Volta and an
impression of the hazard levels around the Odaw drain (Greater Accra).
The validation shows that the hazard levels are strongly depending on the quality of the measured
elevation and the raster size. In urban areas the level of detail of the 90m DEM seems to be too
coarse, whereas the high hazard inundation areas of lake Volta and the White Volta are well
BC5721-101-100/TN1/411750/Nijm
30 October 2014
-4Technical note
represented. This is acceptable, because the relatively small Odaw drain is visible as a flood hazard
on national level, which is the purpose, and will be further corrected on pilot level.
Legend
high hazard by HAND classification
inundation recorded by Landsat
(2000-2009)
Figure 2: Validation of the HAND-hazard for the White Volta
Hazard level
Low
Medium
High
Figure 3: Validation of the high hazard zone of take Volta with the 84m elevation contour line
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Hazard level
Low
Medium
High
Figure 4: Validation for the Odaw drain
2.2
National flood hazard map current climate
2.2.1
DEM-based hazard mapping
The result of the processing of the DEM is a raster with three hazard classifications. The remaining
unclassified area is equal to the area where no inundation hazard is occurring.
Figure 5 provides an overview of the national hazard map. The map shows clearly the floodplains of
the White Volta as high hazard areas. Also the other larger rivers, lakes and downstream of lake
Volta the regions are marked as high hazard.
Smaller stream have less visible hazard classifications. When looking at a more detailed scale, it
becomes visible that the classification is clearly present (also shown in Figure 4).
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-6Technical note
Figure 5: Example of the flood hazard map for the national level including large water bodies
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2.3
National Flood hazard map in 2050
For this study we use 2 IPCC scenario’s that are most likely for Ghana, namely A1B and A2. Annex
1 provides the background information and the motivation of these two scenario’s.
The precipitation change calculated for the climate scenarios cause the flood hazard in Ghana to
change as well. The following precipitation parameters were used to determine the change in flood
hazard:
 the number of days with no rain representing the duration of the wet season,
 decrease or increase of the number of days with high precipitation events (e.g. more than 50mm
or 100mm per day),
 maximum daily rainfall,
 the relative change in volume of the annual precipitation.
We used the change in these parameters to determine the change in flood hazards for small,
medium and large scale catchments. The changes are determined for the six climate zones by using
the synoptic meteorological stations within the zones. The climate zones and meteorological stations
are presented in Figure 6.
For instance, there is one meteorological station, Navrongo, in the Savanna climate zone. For this
zone the change in annual precipitation volume for the A1B and A2 climate scenario’s is small. The
hazard on large floods will not change for both scenarios. The maximum daily rainfall intensity for
the A2 scenario will increase significantly, whereas the intensity for the A1B scenario will decrease.
For the A2 scenario it is also likely that heavy rainstorms that cause small scale floods (flash floods)
will occur more often and more severe. Therefore there is an increase in the hazard, which reflects
in an increase of the HAND index for this class. For the A1B scenario the rainfall intensities
decrease and thus a lower HAND index for the small scale flood hazard is likely. Next to that the
maximum intensities in the A1B scenario are less than in the current scenario and the number of
days with rain will increase. This reflects in a decrease of the average the rainfall intensities
throughout the rainy season. The HAND index for flood hazard of medium size catchments will
therefor decrease. For the A2 climate scenarios no major changes for the medium size catchments
are expected. Thus there is no change in HAND indexes.
Table 10 summarizes the changes for all climate zones for the flood hazard for the A1B and A2
IPCC climate scenario relative to the current HAND hazard index.
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-8Technical note
Figure 6: Climate zones and meteorological stations
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Table 2: Changes in precipitation patterns in the six climate regions of Ghana for the current, A1B and A2
IPCC climate scenarios.
Sketch of yearly precipitation pattern
Change in HAND indexes wrt
current scenario
Climate Change
Savanna
Parameter
A1B
A2
Volume
±
Period
±
Type of
river
A1B
A2
+
+
Large
0
-1
Intensity
-
+
Medium
-1
0
No.events
-
±
Small
-1
+1
Parameter
A1B
A2
Volume
±
±
Type of
river
A1B
A2
Period
+
+
Large
0
0
Intensity
-
+
Medium
-1
+1
No.events
-
±
Small
-1
0
Parameter
A1B
A2
Volume
±
+
Type of
river
A1B
A2
Period
+
+
Large
0
0
Intensity
-
±
Medium
0
+1
No.events
-
-
Small
-1
-1
Parameter
A1B
A2
Volume
±
+
Type of
river
A1B
A2
Period
+
+
Large
0
+1
Intensity
-
+
Medium
-1
+1
+
Small
+1
+2
Guinea Savanna
Transitional zone
Moistures semi deciduous forest
No.events
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+
- 10 Technical note
Sketch of yearly precipitation pattern
Coastal zone
Tropical rainforest
Change in HAND indexes wrt
current scenario
Climate Change
Parameter
A1B
A2
Volume
±
+
Period
+
+
Intensity
±
+
No.events
+
±
Parameter
A1B
A2
Volume
±
+
Period
+
+
Intensity
+
+
No.events
+
±
Type of
river
A1B
A2
Large
0
0
Medium
0
+1
Small
+1
+1
Type of
river
A1B
A2
Large
0
+1
Medium
+1
+1
Small
+2
+1
An extract of the maps of the climate change flood hazard scenarios for the 2050 A1B and A2 IPCC
climate scenarios are shown in Figure 7. The extract clarifies the differences between the two
scenarios. The tributaries of the White Volta are represented by medium river type in the Guinea
Savanna climate region. They let see a decrease for the A1B scenario (left side) flood hazard and
an increase for the A2 scenario (right side) for the flood hazard. The extract of the map let also see
that the small rivers have a lower hazard classification for the A1B scenario than for the A2 scenario.
The difference is according to the tables presented in Table 10.
Figure 7: Flood hazards maps for the A1B and the A2 IPCC climate scenarios
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2.4
Pilot level map outlook
On pilot level the mapping will be carried out with the spatially distributed method. The scale of the
maps will be the pilot district themselves.
On national level the flood hazard mapping is based on a 90m DEM, which is a rather coarse scale.
If at pilot level scale more detailed DEM’s with a higher accuracy are available the mapping will be
based on these sources.
The pilot level maps will be validated during the field visits.
2.5
Assessment of flood hazard maps
In the paragraphs 2.2 and 2.3Error! Reference source not found. the results of the national flood
hazard mapping are presented. In the first paragraph the results are presented in a spatially
distributed way in the latter paragraph are the results aggregated to district level.
There are 216 districts in total (census 2010) in Ghana. About a third of the districts are classified as
high flood hazard districts; this means that more than 10% of the district area lies in a high flood
hazard zone. Another third of the districts are classified as medium hazard zone, which means that
more than 10% of the district area lies in a medium hazard zone. The last third of the districts have a
low hazard classification, which means that more than 80 % of the districts area is classified as low
or no hazard. The exact numbers of the district statistics are presented in Table 3.
Looking at the spatially distributed characteristic it becomes visible that roughly 65 % of the country
is classified with no hazard. This indicates that these surfaces are located high above the drainage
level and inundations are unlikely – however it does not mean that inundations are impossible. From
field visit it was concluded that a number of flooded areas are caused by blocked drainage or
culverts, which cannot be taken into account on national level.
Roughly 10% of the country are classified as high, medium and low hazard regions. Where the
major parts of the hazard zones are located along the White Volta and downstream of the
Akasombo Dam. The statistics for the spatially distributed flood hazard map are presented in Table
4.
Table 3: Overview of district statistics
District classification
Number of districts
Percentage
High hazard
76
35%
Medium hazard
69
32%
Low hazard
71
33%
Table 4: Overview of spatially distributed hazard statistics
Spatially distributed map
Area [km2]
Area
High hazard
28,640
12%
Medium hazard
30,753
13%
Low hazard
24,137
10%
155,413
65%
No hazard
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- 12 Technical note
3
DROUGHT HAZARD
3.1
Description of methodology
In contrast with flooding, drought is a slow process. That is the reason why this aspect is often
referred to as ‘the slow killer’. See for example World Meteorological Organization website that
explains the WMO and UNCCD work on the foundations to build national drought policies:
http://www.wmo.int/pages/mediacentre/news/nationaldroughtpolicies.html
Drought is dominantly influenced by meteorological circumstances, especially rainfall and
evapotranspiration. Whether areas suffer from drought depends on one hand on the spatial
variability of these meteorological variables but also on the physical properties of the land surface.
For example areas which have deep phreatic groundwater levels are more vulnerable than areas
where groundwater can be accessed by the roots in the unsaturated zone via capillary rise. The
same applies to areas where access to surface water bodies for irrigation is available.
For the hazard mapping of drought on the national scale it was decided to only take into account the
meteorological drought, e.g. the rainfall deficit. This means that other hydrological states like
phreatic groundwater level (as described above) are not taken into account. The advantage of this
method is that this way the interpretation of the maps is unambiguous. Moreover the effect of
climate change (e.g. the primary variables like rainfall and temperature) can be incorporated more
easily.
The method used is based on the idea that the cumulative rainfall deficit is the most important
combined variable (evapotranspiration minus rainfall). As both the absolute value of the cumulative
rainfall deficit as well as the duration of certain rainfall deficit is of importance a method is used that
takes all of these aspects into account by using a threshold value for the rainfall deficit.
Rainfall deficit [mm]
Input data is available from global datasets, this way the same data is used nation-wide which
makes the results more consistent. On district level however, local data can be incorporated.
Figure 8: Examples of cumulative rainfall deficit in the Netherlands.
(Red line indicates the driest year measured in history, green line indicates the driest 5% of the
years, the blue line indicates the median value of all the years and the black line indicates one
example year.)
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3.2
Application of rainfall deficit-method
Rainfall data comes from the Tropical Rainfall Measuring Mission (TRMM), which is a joint U.S.Japan satellite mission to monitor tropical and subtropical precipitation. The spatial coverage of
TRMM extents from 50 degrees south to 50 degrees north latitude and the data has a spatial
resolution of 0.25 degree by 0.25 degree. More specifically daily TRMM data (3B42_v7) is used for
the period 1998 to 2012 (14 years).
Evapotranspiration data is obtained from the Global Potential Evapotranspiration (Global-PET)
climate database. Average monthly and annual PET values at a spatial resolution of 30 arc-seconds
(approximately 1 km at tropics) are calculated for the 1950-2000 period using the Hargreaves
method with available data on monthly average temperature, available from WorldClim database,
and monthly extra-terrestrial radiation, calculated using a methodology presented by Allen et al
(1998). For more information about the global aridity and PET database we refer to Trabucco and
Zomer (2009).
For the national level we scaled the PET data to get the same spatial resolution as the TRMM data,
using the average value per grid cell. The monthly data was downscaled to daily data, using a
uniform distribution. With this data the yearly cumulative rainfall deficit is calculated based on daily
time steps using the open source program language R (cran.r-project.org). Figure 9 shows the
spatial distribution of the maximum value of the cumulative rainfall deficit for the years 1998-2011.
This figure shows that the years 2002, 2004, 2006 and 2011 have the highest values of maximum
rainfall deficit. The figure also shows that there is a high spatial variability within Ghana. If the
average over the 14 years is calculated for each grid cell (mean in Figure 9), it shows that mainly the
northern part and the southeast part of Ghana show the highest maximum rainfall deficit values.
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- 14 Technical note
Figure 9: Spatial distribution of maximum cumulative rainfall deficit for the years 1998-2011.
(Also the mean value over the 14 years is shown.)
The value of the maximum rainfall deficit alone is not the ideal drought indicator yet. A better
indication can be obtained from the number of days that the rainfall deficit is above a certain value
(threshold). To define the value of this threshold the statistics of the 14-years average value of
maximum cumulative rainfall deficit within Ghana is calculated (Table 5). Based on these statistics
the threshold of 600 mm is chosen, being the spatial average value.
Table 5: Statistics of the spatial distribution of mean (14 years) maximum rainfall deficit [mm].
Minimum
240
1st quartile
430
Median
621
Mean
602
3rd quartile
736
Maximum
1006
Figure 10 shows the spatial distribution of the number of days that the cumulative rainfall deficit
exceeds the threshold of 600 mm. Again the northern part of Ghana emerges as being an area
where the number of dry days is relatively high. Also the southeastern part shows up. Again, the
size of the problem differs from year to year. Notable dry years are 1998, 2002, 2006 and 2011.
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Figure 10: Spatial distribution of number of dry days
(in which the cumulative rainfall deficit exceeds the threshold of 600 mm for the years 1998-2012 as
well as the 14-years average value.)
3.3
National drought hazard map current climate
Table 6 shows the definition of the drought hazard classification. Based on this classification and
map with the 14-years average of the number of days that exceed the threshold of 600 mm, the final
drought hazard maps are produced. Figure 11 shows the hazard map on the grid level. Figure 12
shows the drought hazard map projected to the districts within Ghana (216 – census 2010).
Table 6: Definition of drought hazard classification number of days based on the 14 years average value of
number of days that cumulative rainfall deficits exceeds threshold of 600 mm.
NO (0)
0-5
LOW (1)
5-20
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MEDIUM (2)
20-70
HIGH (3)
>70
- 16 Technical note
Figure 11: Drought hazard map of Ghana, based on grid data
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Figure 12: Drought hazard map of Ghana projected on the districts.
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3.4
National drought hazard map in 2050
For this study we use 2 IPCC scenario’s that are most likely for Ghana, namely A1B and A2. Annex
1 provides the background information and the motivation of these two scenario’s. More information,
as well as detailed output maps of the 2 climate scenario’s can be found in the separate report
(Obuobie, 2014).
For drought both the changes in rainfall as in potential evapotranspiration are of interest as these
two variables determine the rainfall deficit. The evaporation was calculated using the Hargreaves
method. Although the Penman-Monteith method is often preferred (Allen et al, 1998) to use as daily
estimate for ET0 the big advantage of the Hargreaves method is that less variables are needed
(only daily minimum and maximum temperature and global position to estimate the incoming global
radiation). Kra (2013), from university of Ghana, presents a method for the modified Hargreaves
method that produces the essentially the same data as Penman-Monteith. For this study the
HG1234 method was chosen. Input of the daily data (rainfall, minimum temperature and maximum
temperature) for the current climate and the two IPCC climate scenario’s were used to calculate the
yearly rainfall deficit for each synoptic station.
Figure 13 shows for all the synoptic stations the maximum yearly cumulative rainfall deficit within the
30-years period of current climate and the 2 IPCC scenario’s in a boxplot. In the boxplots (box-andwhisker plots) the black dot denotes the median, solid boxes range from the lower to the upper
quartile, and dashed whiskers show the data range. Data that are further than 1.5 times the
interquartile range from the nearest quartile are shown as open bullets. Overall the maximum yearly
rainfall deficit increases slightly for most synoptic stations for the two scenario’s. For scenario A1B
the variation of maximum yearly cumulative rainfall deficit within the 30-years period increases in
comparison to the current climate and scenario A2.
As explained in paragraph 3.3 we chose a threshold of 600 mm for the cumulative rainfall deficit and
use the number of days above this threshold to indicate the hazard level. Figure 14 shows in a
boxplot for each synoptic station the number of days that the cumulative rainfall deficit exceeds the
threshold of 600 mm. For stations that do exceed this threshold it can be seen that in general the
climate scenario’s show an increase of the number of days above this threshold. And again the
variation in scenario A1B is higher in comparison to the current climate and scenario A2.
Table 7 shows for each synoptic station the number of days within a year that the rainfall deficit
exceeds the threshold of 600 mm, averaged over the 30 years period. As we used 6 climate zones
within Ghana, the outcomes of the synoptic stations within one climate zone are averaged. Table 8
shows the same information as Table 7 averaged for the 6 climate zones.Table 9 shows this
information as percentages. Figure 15 shows the spatial distribution of the relative increment of
number of days with rainfall deficit above the threshold value of 600 mm for respectively scenario
A1B and A2.
Figure 16 shows the average number of days that the cumulative rainfall deficit exceeds the
threshold of 600 mm for the current climate and the 2 climate scenario’s. Based on these results the
drought hazard classification was applied (Table 6). The resulting drought hazard maps for the
climate scenarios A1B and A2 are given in Figure 17.
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Figure 13: Boxplot of maximum rainfall deficit within 30-years period for the 22 synoptic stations, for current
climate and the 2 climate scenarios A1B and A2.
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Figure 14: Boxplot of number of days within a year that cumulative rainfall deficit is higher than 600 mm for
30 years period of current climate and A1B and A2 scenario.
Table 7: Number of days within a year that rainfall deficit is higher than 600 mm, averaged over a 30-years
period. For current climate and A1B and A2 scenario
current A1B
A2
station
76
104
61 Accra
17
14
13 Saltpond
41
19
37 Ada
53
47
64 Tema
104
143
131 Akatsi
156
190
153 Tamale
127
162
123 Yendi
79
183
85 Bole
149
205
195 Wa
13
22
8 Takoradi
3
35
1 Akim Oda
21
35
6 Koforidua
0
9
0 Abetifi
5
54
26 Kumasi
climzone
COASTAL SAVANNA
COASTAL SAVANNA
COASTAL SAVANNA
COASTAL SAVANNA
COASTAL SAVANNA
GUINEA SAVANNA
GUINEA SAVANNA
GUINEA SAVANNA
GUINEA SAVANNA
MOIST SEMI DECIDUOUS FOREST
MOIST SEMI DECIDUOUS FOREST
MOIST SEMI DECIDUOUS FOREST
MOIST SEMI DECIDUOUS FOREST
MOIST SEMI DECIDUOUS FOREST
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2
89
24
23
228
22
34
120
67
69
235
52
25
0
55
0
19
127
22
18
267
30
Sefwi
Akuse
Ho
Sunyani
Navrongo
Kete
Krachi
31 Wenchi
0 Axim
MOIST SEMI DECIDUOUS FOREST
MOIST SEMI DECIDUOUS FOREST
MOIST SEMI DECIDUOUS FOREST
MOIST SEMI DECIDUOUS FOREST
SAVANNA
TRANSITIONAL ZONE
TRANSITIONAL ZONE
TROPICAL RAIN FOREST
Table 8: Same as Table 7, stations averaged over climate zone
climzone
COASTAL SAVANNA
GUINEA SAVANNA
MOIST SEMI DECIDUOUS FOREST
SAVANNA
TRANSITIONAL ZONE
TROPICAL RAIN FOREST
current A1B
A2
58
65
61
128
185
139
20
49
25
228
235
267
24
54
31
0
0
0
Table 9: Same as Table 8, with percentage values.
climzone
COASTAL SAVANNA
GUINEA SAVANNA
MOIST SEMI DECIDUOUS FOREST
SAVANNA
TRANSITIONAL ZONE
TROPICAL RAIN FOREST
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current A1B
A2
100%
112%
105%
100%
145%
109%
100%
247%
126%
100%
103%
117%
100%
228%
130%
100%
100%
100%
- 22 Technical note
Figure 15: Relative increment of numbers of days with rainfall deficit above 600 mm based for climate
scenario A1B (left) and scenario A2 (right)
Figure 16: Average number of days that the rainfall deficit exceeds the threshold of 600 mm for the current
climate, scenario A2 and scenario A1B.
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Figure 17: Drought hazard maps for the A1B (left) and the A2 (right) IPCC climate scenarios
3.5
Pilot level map outlook
For the pilot districts we surge for a more detailed estimation of the drought hazards. Instead of the
TRMM resolution, the original resolution of the Pet data will be used.
3.6
Assessment of drought hazard maps
As described in the methodology we define drought as a meteorological drought. This means the
availability of water, either surface water or ground water, is not taken into account. Also the soil and
vegetation index are not taken into account, something that was done by the EPA studie (EPA,
2012).
The resulting drought map shows a clear spatial distribution within Ghana. The northern part of
Ghana shows high hazard, the eastern and southeastern part show both low and medium hazard.
The southwest part of Ghana however shows no hazard for drought.
One should notice that gives a general overview for the whole nation of Ghana based on an 14years average. This means that also in areas indicated as no hazard, there is the possibility that
within this area one could suffer from drought during single years. The opposite counts for areas
with high hazard.
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We made an attempt to verify the drought hazard map with the disaster database. However, we
doubt whether this disaster database represents the whole country (concentration in the south) and
whether it can be related to our definition of drought disaster.
For the future climate both IPCC scenario’s show an increase in rainfall deficit but this is spatially
distributed. Both scenario’s show mainly in the mid an mid-south part of Ghana an increase in the
number of days that the cumulative rainfall deficit exceeds the threshold of 600 mm. Scenario A1B
shows more variation than A2 and shows on average an higher increase.
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4
VULNERABILITY
4.1
Description of methodology
The basis for the vulnerability maps is the FAO land use map (gha_gc_adg.shp). Comparing this
map with e.g. Google or OpenStreetMap, it appeared that a lot of urban areas were missing.
Therefore, we updated the map with residential areas as follows:
 Data was downloaded as a polygon shapefile from the website www.weogeo.com.
 The residential areas were selected by querying the map: [Landuse] = "residential"
 Finally the projection was changed to from Mercator Web to UTM30N.
With the function “union” in ARCGIS, we combined these two datasets. In case the FAO land use
map did not show an urban area, but the OSM data did, this area was changed to residential.
The following two figures show the impact of this step. On the left hand side is the FAO land use
map. It is clearly visible that only the larger urban areas are included. The map on the right hand
side shows the map including the build up areas from the OpenStreetMap. This clearly reflects the
urban areas in the whole of Ghana a lot better. The built up area based on FAO data alone is 733
km2, whereas if we include the OpenStreetMap data it become 3253 km2.
Figure 18: Updated land use map with built up areas
4.2
Flood vulnerability – situation 2010
Next, we assessed the vulnerability for different `land use classes´ in case of a flood hazard. We
looked at 2 alternative classifications (see table in annex 2 to look at the differences between the
two alternatives). Results for both alternatives are shown hereunder, alternative 1 on the left, 2 on
the right.
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Figure 19: Flood vulnerability for 2 alternatives
The map above is now only taking into account the land use. It is also needed to establish the
vulnerability for flooding in relation to the population density, for which we uses the 2010 census.
Ghana is completely covered by the 2010 census, and for that purpose divided into 173 districts.
The population is provided in rural as well as urban areas for each district. We merged the landuse
map we prepared with the map with 173 districts.
Then, per district, we allocated the amount of people living in urban areas to the urban areas, and
the once living in rural areas to the remaining types (excluding ´water bodies´). This resulted in an
average population density for rural and urban areas within a district.
We use the following table to define the vulnerability for flooding based on population density alone:
Table 10: Proposed classification for vulnerability based on population density
Pop density
(persons/km2)
< 30
30 – 250
250 – 500
> 500
Vulnerability for flooding
NO
LOW
MEDIUM
HIGH
The results are shown in the following figures. On the left hand side, you see the population density
for Ghana. On the right hand the vulnerability based on population density.
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Figure 20: Population density and proposed vulnerability
To determine the overall flood vulnerability, we need to combine the land use vulnerability map with
the population density based vulnerability maps.
The values in the resulting map is minimal 0, maximum 6.. Classification is done as follows
Table 11: Proposed classification for overall flood vulnerability
Sum of vulnerability
0
1,2
3,4
5,6
Overall flood vulnerability
No
Low
Medium
High
Results are presented in the following two figures. (alternative 1 on the left, 2 on the right.)
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Figure 21: Overall flood vulnerability for 2 alternatives
The following 3 tables present the vulnerable areas (in km2) in case (1) we only look at land use, (2)
we only look at population density and (3) in case we look at the overall flood vulnerability.
Table 12: Flood vulnerability in km2 per alternative, based on land use
Vulnerability
No
Low
Medium
High
Alternative 1
7844
196438
30370
4212
Alternative 2
168384
35898
30370
4212
Table 13: Flood vulnerability in km2 per alternative, based on population density
Vulnerability
No (incl 7286 water bodies)
Low
Medium
High
92546
142170
1990
3084
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Table 14: Overall flood vulnerability in km2 per alternative, combining land use and population density
Vulnerability
No (incl 7286 water bodies)
Low
Medium
High
Alternative 1
7318
200377
28008
3159
Alternative 2
86551
121665
27487
3159
The results show that for alternative 1, the population density has not a significant impact on the
overall flood vulnerability (the areas for the vulnerability type are in the same order). In case of
alternative 2, the population density has an impact, especially the area of ; low vulnerability
increases significantly.
4.3
Drought vulnerability – situation 2010
We assessed what the vulnerability is for different `land use classes´ for the drought hazard as well.
Again, we looked at 2 alternatives (see table in annex 2 for the differences between the two
alternatives). Results are shown hereunder, alternative 1 on the left, 2 on the right.
Figure 22: Drought vulnerability for 2 alternatives
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Table 15: Drought vulnerability in km2 per alternative
No
Low
Medium
High
Alternative 1
7553
160002
70351
958
Alternative 2
168384
3254
66268
958
The alternatives differ quite a lot. Overall the vulnerability for alternative 2 is much lower that for 1.
4.4
Vulnerability for future (horizon 2050)
Changes in vulnerability between the current situation and 2050 is due to projected changes in land
use as well as population growth. In a separate study carried out by WRI (Preparation of
vulnerability maps (2050 flood and drought scenarios due to socio-economic development, Oktober
2014), it was concluded that:
- Human residential and commercial purposes built up areas are expected to grow with 6.56
% on a yearly basis. (Ghana Statistical Service, 2013. 2010 Population & Housing Census:
Summary Report of Final Result)
- Agricultural lands increases with a rate of 1.9 % per annum (Ghana Statistical Service, 2013.
2010 Population & Housing Census: Summary Report of Final Result)
- Population growth rates are based on the available district growth rates from the 2010
Population Census report.
Please note that
- for the changes in land use, only national figures are available, while population growth
figures are on district level.
- No indication or plans are available where and how the land use changes will take place.
To incorporate the land use changes we did the following:
- For each district we determined what the built up area is in 2050, in case 6.56 % annual
growth
- For each district we determined what the agricultural area is in 2050, in case of 1.9 % growth
- We only assume increase or decrease from existing urban or agricultural lands.
- In case the total area in a district appears not to be enough to accommodate these changes,
we consider that the urban built up area will take prevalence over the rural areas.
The growth of population (as well as built-up area and agriculutural area) is year-on-year. That
implies that if for a district with a population of 1000 the population growth is 3 %, the increase in
absolute numbers is 30 (between 2010 and 2011) and about 95 (between 2049 and 2050). It also
means that the number of inhabitants in 2050 is about 3 times more than it was in 2010. See also
the following figure as an example
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Figure 23: Example of population growth
The forecast changes in land use maps, results in the following two maps for 2010 and 2050.
Figure 24: Land use maps for 2010 and 2050
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The impact of the 2050 scenario is considerable, as the following tables underlines
Table 16: % Area land use in 2010 and 2050
Built up area
Agricultural
Other
2010
2050
2
16
28
31
70
52
The built up area will be 8 times higher than in 2010. The increase of agricultural area is rather
limited, mainly due to the fact that a lot of districts are not capable of accommodating the forecasted
growth of urban area as well as agricultural areas.
The population data resulted in the following population density map for 2050.
Figure 25: Population density for 2050
In overall numbers, the population increases to 64 million in 2050 (based on the growth rates from
the 2010 Population Census report), from 25 million in 2010.
The increased population density map, together with the new land use dataset results in an updated
vulnerability map for droughts and floods. The vulnerability is updated for scenario 2 only. These are
shown in the following two figures. On the left hand side, the vulnerability for flooding in 2050, on the
right hand side for drought.
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The drought vulnerability increases significantly, mainly due to the fact that all agricultural land is
deemed to be highly vulnerable.
Figure 26: Flood and drought vulnerability in 2050
4.5
Vulnerability assessment
The following table shows the changes in vulnerability for floods as well as for droughts.
Table 17: Changes In vulnerability between 2010 and 2050
No
Low
Medium
High
Flood 2010
36
51
12
1
Flood 2050
15
37
30
18
Drought 2010
70
1
28
1
Drought 2050
53
16
0
31
For both events, the vulnerability changes significantly the following 40 years. Where in the current
situation about 1 % of the country’s area is highly vulnerable to flood, this changes to almost 20 % in
2050. The driving force behind this is the continuous increase in built up areas, together with the
increase of population density.
The vulnerability for drought increases mainly due to the increase in land to be used for agricultural
purposes. The area not vulnerable at all decreases to about half of the countries size, while almost
1/3 will become highly vulnerable.
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- 34 Technical note
Especially the areas that are currently built up will change significantly. Built up areas along the
coast will expand, resulting in built up area along almost the whole coastal stretch. Relative smaller
built up areas in 2010 will be almost 10 times bigger and all existing cities will have spread out to
accommodate the growing population. Parts that are not built up in 2050 will become agricultural,
especially in the southern part of Ghana, all area will be used either to live, or as agriculture.
The western part remains relatively empty, this is also due to the fact that current activities are
limited as well and the presence of natural parks.
Remarks:
 Note that a high vulnerability does not necessary mean a high risk. The next paragraph will
present this in more detail.
 The best national level land cover map currently available was used. In the near future new and
more accurate land cover maps will become available and should be used to update the maps
we provided.
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5
RISK
5.1
Description of methodology
The risk is assessed by multiplying the vulnerability with the hazard. The value for flood vulnerability
is presented in the following table:
Table 18: Value for flood vulnerability
Vulnerability
No
Low
Medium
High
Value
0
1
2
3
The hazard map is on a scale of 0 – 6, with 0 no flooding, 6 highly frequent flooding.
Accordingly, the minimum value for risk is 0 (either the land use is such, that flooding does not result
in any problem or it never floods) , the maximum is 18 (valuable land use, frequently flooded)
5.2
Flood risk
We propose to use the following classification to convert the risk value to `high, medium, low`.
Table 19: Classification from Risk value to Flood Risk
Risk value
0
1–4
5 – 10
> 10
Risk
No risk
Low risk
Medium Risk
High Risk
The following two maps are the flood risk maps, based on land use alone..
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Figure 27: Flood risk maps, based on land use alone (alternative 1 on the left, 2 on the right)
The following 2 maps are also flood risk maps, based on land use & population density (so including
the overall flood vulnerability).
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Figure 28: Flood risk maps, based on land use and population density (alternative 1 on the left, 2 on the
right)
The following table shows the percentage of the territory of Ghana that falls within a risk value, for
land use only, as well as for ´land use and population density`, also for both alternatives.
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Table 20: Overview of % of territory of Ghana with a flood risk value for different alternatives
Only land use
Risk value alternative 1 (%) alternative 2 (%)
0
1
2
3
4
5
6
8
9
10
12
15
18
5.3
68
0
0
9
0
11
9
0
0
1
1
0
0
92
0
0
1
0
2
2
0
0
1
1
0
0
Land use and pop density
alternative 1
alternative 2
(%)
(%)
67
80
0
0
0
0
9
5
0
0
12
6
9
6
0
0
0
0
1
1
1
1
0
0
0
0
Drought risk
The risk is assessed by multiplying the vulnerability for drought with the hazard. The hazard value
for drought is of a scale of 0 – 3 (0: no hazard, 3: high hazard). The resulting map has values
between 0 – 9.
Proposed Classification is presented in the following table
Table 21: Classification from Risk value to Drought Risk
Risk value
0
1-3
4-6
6-9
Risk
No
Low
Medium
High
The following two maps are the resulting drought risk maps. Alternative 1 on the left hand side,
alternative 2 on the right hand side.
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Figure 29: Drought risk maps for 2 alternatives (alternative 1 on the left, 2 on the right)
The following table shows the percentage of the territory of Ghana that falls within a drought risk
value for both alternatives.
Table 22: % of territory of Ghana with a drought risk value for 2 alternatives
Risk value
alternative 1 (%)
0
1
2
3
4
6
9
5.4
alternative 2 (%)
42
9
24
16
3
5
0
90
0
3
1
2
4
0
Future flood risk (horizon 2050)
To determine the future flood risk, the following information is required:
- Vulnerability for 2050, as described in paragraph 4.4, this is only been done for the land use
scenario 2
- Flood hazard for climate scenario A1B, as describe in paragraph 2.3
- Flood hazard for climate scenario A2, as describe in paragraph 2.3
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Figure 30: Flood risk maps for 2 climate scenarios for 2050
5.5
Future drought risk (horizon 2050)
The future drought risk maps are produced by multiplying the future hazard maps (A1B and A2) with
the vulnerability map for future climate.
Figure 31: Drought risk map for future climate scenarios A1B (left) and A2 (right).Figure 31 shows
the drought risk maps for future climate scenario’s A1B (left) and A2 (right). Both use the alternative
2.
In the northern part of Ghana in increase in clearly visible. This is mainly due to future land use
change and thus increase of drought vulnerability. In the southern part, downstream of lake Volta
the high risk is mainly due to an increase of the drought hazard.
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Figure 31: Drought risk map for future climate scenarios A1B (left) and A2 (right).
5.6
Flood Risk assessment
The following table shows the result of the flood risk assessment for the whole of Ghana. The area
with low risk was 5 % in 2010, and will be similar in 2050 taking land use changes and climate
change into account. The medium hazard is about 12 % in the current situation, and increases to
almost 20 % in 2050, for both climate scenarios. This means that, without any measures of actions,
the areas with medium flood risk increases with 50 %.
Looking at the high risk area of Ghana, we can see from the table that it increases from about 1% to
4 – 5 % in 2050. This means that the area with high risk is 4 to 5 times as much as it is now.
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Table 23: Flood risk
Risk
value
0
1
2
3
4
5
6
8
9
10
12
15
18
5.7
Land use alternative 2
current
80
0
0
5
0
6
6
0
0
1
1
0
0
A1B
72
0
0
4
0
6
7
0
2
4
2
2
2
A2
70
0
0
4
0
6
7
0
2
4
3
2
2
Drought Risk assessment
Table 25 shows the result of the drought risk assessment for the whole of Ghana. Overall the
drought risk increases in the 2050 scenario’s. The area with no risk decreases from 90% in the
current situation (for climate reference the period of 1976-2005 is taken) to 80% in 2050 (for climate
the period 2036-2065 is taken). The area with low risk is 3 % in the current situation, and will
increase to 10% in 2050 taking both land use changes (vulnerability) and climate change (hazard)
into account. The medium hazard is about 5 % in the current situation, and decreases to almost 2 %
in 2050. Looking at the high risk area within Ghana we see that for the current situation there is no
area indicated as high risk and for the future 2050 scenario’s this area will be 6-7%.
Although the figures for both scenario’s look the same, averaged over Ghana, the scenario’s show a
clear spatial variation. For the a2 scenario the drought risks arises mainly in the northern part of
Ghana whereas in the a1b scenario besides the northern region also the area in the southeast
(downstream of lake Volta) crops up.
Table 24: Drought risk
Risk
value
0
1
2
3
4
6
9
current
91
0
3
0
1
4
0
a2
82
2
3
5
0
2
6
a1b
80
2
3
6
0
2
7
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- 44 Technical note
6
LITERATURE
Allen R.G., Pereira L.S., Raes D. & Smith M. 1998. Crop evapotranspiration: Guidelines for
computing crop requirements. Irrigation and Drainage Paper No. 56, FAO, Rome, Italy.
EPA, 2012. Flood and drought risk mapping in Ghana – 5 AAP Pilot districts. Wide-ranging Flood
and Drought Risk Mapping in Ghana Starting with the Five African Adaptation Programme (AAP)
Pilot Districts (i.e. Aowin Suaman, Keta, West Mamprusi, Sissala East, and Fanteakwa Districts) for
Community Flood and Drought Disaster Risk Reduction.
Kra, E.Y. 2013. Hargreaves Equation as an All-Season Simulator of Daily FAO-56 Penman-Monteith
Eto. Agricultural Science Volume 1, Issue 2 (2013), 43-52, http://todayscience.org/AS/v12/AS.2291-4471.2013.0102005.pdf
Obuobie, E. Climate Change Scenarios for Ghana, A Consultancy Study on Climate Change
Scenarios for Ghana. October 2014, draft
Renno et Al (2008)
Trabucco, A., and Zomer, R.J. 2009. Global Aridity Index (Global-Aridity) and Global Potential
Evapo-Transpiration (Global-PET) Geospatial Database. CGIAR Consortium for Spatial
Information. Published online, available from the CGIAR-CSI GeoPortal at:
http://www.csi.cgiar.org.
http://www.cgiar-csi.org/data/global-aridity-and-pet-database
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Annex 1: background of climate change
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Introduction
Future climate change is highly important to incorporate in the national flood and drought risk
mapping. Therefor 2 IPCC scenario’s that are most likely to occur in Ghana are selected. This work
was executed by local climate expert dr Emmanuel Obuobie, in close corporation with the team
members of Royal HaskoningDHV and HKV.
This annex provides the motivation for the selection of the 2 IPCC scenario’s used in our study.
Furthermore the main outcomes are presented for the 6 climate regions in Ghana. For a more
detailed description about the used datasets, the downscaling method and literature references we
refer to the report ‘Climate Change Scenarios for Ghana’ (Obuobie, 2014). This report also shows
maps about climate variables throughout the year.
Motivation for selection of 2 IPCC scenarios A1B and A2
Two sets of daily climate scenario data downscaled from a General Circulation Model (GCM) and
Regional Climate Model (RCM) were derived under this consultancy and used for describing
changes in climate (rainfall and evapotranspiration) in the 6 climate regions of Ghana. The scenario
data comprised rainfall, minimum and maximum temperatures. The temperature data were used in
the Hargreaves method (Allen et al, 1998 and Kra, 2014) to compute the evapotranspiration data
described in this study. The scenario data are based on the Intergovernmental Panel on Climate
Change (IPCC) Special Report Emissions Scenarios (SRES) A1B and A2 (IPCC, 2007). The two
scenarios were chosen from 6 IPCC SRE scenarios because of their high potential in relating to the
future development of Ghana, compared to the others. The A1B was selected as a likely scenario
because it represents “business as usual” and lies between the extremes produced by other
scenarios (IPCC, 2007). As such, it is a relatively conservative, but not overly cautious, scenario.
Considering that a shift from the current energy mix towards a mix in favour of renewable energy is
expensive and Ghana is still struggling to build her economy, it does not appear that the ratio of
Ghana’s energy mix will change any significantly in the 21st century. This makes the A1B scenario
plausible for Ghana. The A2 scenario, which paints a picture of a very heterogeneous world with
emphasis on family values and local tradition, was selected as the other likely scenario of interest
because it depicts extensive fossil use and a very slow technological change. This is one of the
extreme (high greenhouse gas emission) scenarios that can be expected. Currently, Ghana drills oil
and very likely to intensify her use of fossil fuel, which may tend to slow technological changes in the
near future. This situation makes the A2 scenario a plausible one for Ghana.
Nearly all the GCMs used in the IPCC AR4 are consistent in projecting rise in temperature over
West African though the magnitude of change varies among models. The same cannot be said for
precipitation. The projections for precipitation show significant uncertainty about the magnitude and
direction of change. The uncertainty in the projections can be attributed to uncertainty in both future
emissions of greenhouse gases (e.g. carbon dioxide), and in the representation of key processes
within models. As a result, estimates of climatic risk are best made through the integrations of
models in which the uncertainties are explicitly incorporated by using different models and exploring
different emissions scenarios. This is often referred to as multi-model ensemble experiment (Diallo
et al., 2012). In this way an “ensemble” of results is produced which can be used to quantify the
uncertainty in the climate projections using statistical techniques. However, for this study, climate
scenario outputs (temperature and rainfall) from the GCM and RCM that best quantifies the current
climate were derived and used for the analysis and mapping of climate change in the 6 climate
regions of Ghana. Past studies (EPA-Ghana, 2008; Diallo et al., 2012; McCartney et al., 2012; and
Obuobie and Asante-Sasu, 2013) have indicated that the ECHAM5 model (Roeckner et al., 2003)
together with older versions of ECHAM is one of the few GCMs that realistically simulate most of the
features of the West Africa Monsoon and best quantifies the current climate of the wider West Africa
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region including Ghana. Therefore, climate change scenario data from ECHAM5 and ECHAM5driven RCM only were considered in this study.
Main outcomes – projected on the 6 climate regions of Ghana
Changes in the future climate relative to the current were analyzed and mapped on the basis of
the 6 climatic zones identified in Ghana, namely, Sudanno savannah, Guinea savannah,
Transition zone, Deciduous forest, Coastal savannah, and Rain forest (Figure 1). The 22
synoptic stations were grouped into the 6 climate regions (Table 1) and data for the current and
future climates for stations within each climate zone were averaged to obtain climate output for
the zone. Basic statistics of mean annual and monthly rainfall and evapotranspiration
(calculated from the minimum and maximum temperature data) values were computed for each
of the 6 zones for the current climate, A1B-driven future climate and the A2-driven future
climate. Changes in the future rainfall and evapotranspiration, relative to the current, were
determined using equation (1) below modified from Salack et al. (2011):
 P future  P current 
 100 …………………………………….(1)
RPDccs = 
P current


where RPDccs is the relative change in rainfall (%) or evapotranspiration (%), P future is the mean
annual or monthly rainfall or evapotranspiration for the future climate, and Pcurrent is the mean
annual or monthly rainfall or evapotranspiration for the current climate.
The resulting changes in mean annual and monthly rainfall and evapotranspiration for the 6
climate zones were mapped in ArcView GIS. The maps were prepared for changes in future
climate under the A1B and A2 scenarios.
Report 1
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Figure 1: Map of Ghana showing the 6 climate regions of the country
Table 1: Groupings of Synoptic stations by climate regions of Ghana
Climate region
Synoptic stations
Sudanno savannah
Navrongo
Guinea savannah
Bole, Tamale, Yendi, Wa
Transition zone
Kete Krachi, Wenchi
Moist Semi Deciduous forest
Abetifi, Akim Oda, Akuse, Ho, Koforifua,
Kumasi, Sefwi Bekwai, Sunyani, Takoradi
Coastal savannah
Accra, Ada, Akatsi, Saltpong, Tema
Rain forest
Axim
Changes in Rainfall
Rainfall changes projected by REMO for the A1B scenario and ECHAM5 for the A2 scenario
are graphically depicted in figures 2 and 3, respectively, as well as in table 2. Under the A1B
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scenario, the mean annual rainfall is projected to decrease of about 2% - 6% in the 2050,
relative to the current values in the 3 southern climate regions (Rain forest, coastal savannah
and moist semi deciduous forest) and increase of about 1% to 8% in the 3 northern climate
regions (Transition, Guinea savannah and Sudanno savannah) (Figure 2). The projected
changes in mean annual rainfall in the 6 climate regions for the 2050 under the A2 scenario are
increases of about 3% to 17%, over the current values (Figure 3). The highest increase is
expected to occur in the Guinea savannah climate region (16.8%), while the Sudanno savannah
climate region is expected to experience the least increase (3.1%) in the annual rainfall. These
results are consistent with analysis of climate change for the larger West Africa region (Sylla et
al., 2012; Paeth, et al., 2011).
Seasonally, the rainfall projections for the 2050 tend towards increases (range of about 3% 47%) in mean values in December - February (DJF), and decreases (about 7% - 23%) in March
- May (MAM) for all 6 climate regions under the A1B scenario. The mean seasonal rainfall
values in June – August (JJA) show increases (1% - 22%) in four climate regions (Sudanno
savannah, Guinea savannah, Transition zone and Moist semi deciduous forest), a decrease of
about 6% in the Coastal savannah climate region, and no significant change in the Rain forest
climate region. For the same A1B scenario, the rainfall values in September – November (SON)
depicts increases of about 4% - 23%, over the current values, for the three northern climate
regions (Sudanno savannah, Guinea savannah, Transition zone), and decreases of about 2% 11% in the 3 southern climate regions (Rain forest, Coastal savannah and Moist semi
deciduous forest. For the figures we refer to Obuobie (2014).
Analysis of the A2 scenario projections for the 2050 reveals positive changes of about 16% 42% in the DJF mean rainfall, relative to the current values, in 4 climate regions (Sudanno
savannah, Guinea savannah, Transition zone, and Moist semi deciduous), a slight decrease of
about 1% for the Coastal savannah region and no significant change for the Rain forest climate
region. The JJA seasonal mean rainfall projections show increases for all the 6 climate regions,
with a range of about 5% - 35%. The SON rainfall also shows increases (about 12% - 49%) for
the entire climate regions except for the Rain forest where the projection shows a decrease of
about 9% in the mean seasonal rainfall. For the MAM season, the 2050 rainfall is expected to
decrease in the range of 3% - 6% in for the Sudanno savannah, Transition zone, Coastal
savannah, and Tropical Rain forest climate regions, increase by about 22% in the Guinea
savannah, and remain significantly unchanged in the Moist semi deciduous climate region. For
the figures we refer to Obuobie (2014).
Figures 4 and 5 show the current and projected (the 2050) rainfall at the monthly time step for
the entire climate regions for the A1B and A2 scenarios, respectively. The mono-modal rainfall
pattern observed in the current climate in the Sudanno savannah and Guinea savannah climate
regions and the bi-modal pattern in the moist semi-deciduous, coastal savannah, and tropical
rain forest are very well preserved in the 2050 climate. In the Transitional zone, the bi-modal
pattern observed in the current climate is preserved but weakly in the 2050 climate, with the first
peak occurring in March as opposed to June in the current climate. Unlike the A1B scenario,
the rainfall patterns and peak months in all the climate regions are likely to remain unchanged in
the 2050 under the A2 scenario.
Report 1
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Figure 2: Changes in annual rainfall in climate regions of Ghana under IPCC A1B scenario
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Figure 3: Changes in annual rainfall in climate regions of Ghana under IPCC A2 scenario
Table 2: Summary of annual rainfall data and projected changes
Climate region
Current Climate
A1B Scenario
A2 Scenario
(1976-2005)
(2036-2065)
(2036-2065)
Mean rainfall
(mm)
Projected
Mean
rainfall
(mm)
Projected
changes
(%)
Projected
Mean
rainfall
(mm)
Projected
changes
(%)
Sudanno savannah
882.2
956.2
+8.4
909.8
+3.1
Guinea savannah
1016.7
1068.4
+5.1
1188.0
+16.8
Transition zone
1234.4
1245.7
+0.9
1388.0
+12.4
Moist semi deciduous
forest
1228.0
1166.6
-5.0
1327.8
+8.1
Coastal savannah
763.2
717.1
-6.0
805.3
+5.5
Rain forest
1937.9
1891.7
-2.4
2068.3
+6.7
Report 1
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600
Monthly total rainfall (mm)
A1B scenario
500
400
300
200
100
0
Jan
Feb
Mar
SS (1976-2005)
TZ (1976-2005)
CS (1976-2005)
Apr
May
Jun
Jul
SS (2036-2065)
TZ (2036-2065)
CS (2036-2065)
Aug
Sep
Oct
GS (1976-2005)
MSD (1976-2005)
TRF (1976-2005)
Nov
Dec
GS (2036-2065)
MSD (2036-2065)
TRF (2036-2065)
Figure 4: Current (1976-2005) and Future (3036-2065) mean monthly rainfall for the 6 climate regions of Ghana
(Sudanno Savannah - SS, Guinea Savannah - GS, Transition Zone - TZ, Moist Semi Deciduous – MSD, Coastal
Savannah - CS, and Tropical Rain Forest – TRF) under the IPCC A1B scenario
Monthly total rainfall (mm)
600
A2 scenario
500
400
300
200
100
0
Jan
Feb
Mar
SS (1976-2005)
TZ (1976-2005)
CS (1976-2005)
Apr
May
Jun
SS (2036-2065)
TZ (2036-2065)
CS (2036-2065)
Jul
Aug
Sep
Oct
GS (1976-2005)
MSD (1976-2005)
TRF (1976-2005)
Nov
Dec
GS (2036-2065)
MSD (2036-2065)
TRF (2036-2065)
Figure 5: Current (1976-2005) and Future (3036-2065) mean monthly rainfall for the 6 climate regions of Ghana
(Sudanno Savannah - SS, Guinea Savannah - GS, Transition Zone - TZ, Moist Semi Deciduous – MSD, Coastal
Savannah - CS, and Tropical Rain Forest – TRF) under the IPCC A2 scenario
Changes in reference evapotranspiration
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Figures 6 and 7 depict projected changes in evapotranspiration (ETo) in the 6 climate regions of
Ghana for the 2050 for the A1B and A2 scenarios, respectively. Under the A1B scenario, the
ETo is projected to increase by about 1% - 9% in 4 of the climate zones (Sudanno Savannah,
Transition Zone, Moist Semi Deciduous, and Coastal Savannah), decrease by about 12% in the
Tropical Rain Forest, and remain significantly unchanged in the Guinea Savannah region.
Under the A2 scenario, the ETo is projected to increase by about 5% - 6% across all the climate
regions.
Figure 6: Changes in annual ETo in climate regions of Ghana under IPCC A1B scenario
Report 1
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Figure 7: Changes in annual ETo in climate regions of Ghana under IPCC A2 scenario
Figures 8 and 9 show the mean monthly evapotranspiration (ETo) for the current period and the
2050 in all the 6 climate regions of Ghana under the A1B and A2 Scenarios, respectively.
Relative to the current period, the 2050 projected ETo, under the A1B scenario, were higher in
all the 6 climate regions for most part of the year (October – June) except for the Tropical rain
forest where the projected ETo was at all time of the year lower than the values of the current
period. For the A2 scenario, the projected ETo values were higher than the values of the current
period at all times of the year in all 6 climate regions.
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8
A1B scenario
Evapotranspiration (mm)
7
6
5
4
3
2
1
0
Jan
Feb
Mar
Apr
SS (1976-2005)
TZ (1976-2005)
CS (1976-2005)
May
Jun
Jul
Aug
SS (2036-2065)
TZ (2036-2065)
CS (2036-2065)
Sep
Oct
GS (1976-2005)
MSD (1976-2005)
TRF 91976-2005)
Nov
Dec
GS 92036-2065)
MSD (2036-2065)
TRF (2036-2065)
Figure 8: Current (1976-2005) and Future (3036-2065) mean monthly evapotranspiration for the 6 climate regions of
Ghana (Sudanno Savannah - SS, Guinea Savannah - GS, Transition Zone - TZ, Moist Semi Deciduous – MSD,
Coastal Savannah - CS, and Tropical Rain Forest – TRF) under the IPCC A1B scenario
Evapotranspiration (mm)
8
A2 scenario
7
6
5
4
3
2
1
0
Jan
Feb
Mar
SS (1976-2005)
TZ (1976-2005)
CS (1976-2005)
Apr
May
Jun
SS (2036-2065)
TZ (2036-2065)
CS 92036-2065)
Jul
Aug
Sep
GS (1976-2005)
MSD (1976-2005)
TRF (1976-2005)
Oct
Nov
Dec
GS (2036-2065)
MSD (2036-2065)
TRF (2036-2065)
Figure 9: Current (1976-2005) and Future (3036-2065) mean monthly evapotranspiration for the 6 climate regions of
Ghana (Sudanno Savannah - SS, Guinea Savannah - GS, Transition Zone - TZ, Moist Semi Deciduous – MSD,
Coastal Savannah - CS, and Tropical Rain Forest – TRF) under the IPCC A2 scenario
Report 1
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Annex 2: Alternatives for vulnerability classification for land
use
Label
Rainfed croplands
Mosaic cropland (50-70%) / vegetation (grassland/shrubland/forest) (20-50%)
Mosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland (20-50%)
Mosaic forest (50-70%) / cropland (20-50%)
Closed to open (>15%) broadleaved evergreen or semi-deciduous forest (>5m)
Closed (>40%) broadleaved evergreen and/or semi-deciduous forest (>5m)
Open (15-40%) broadleaved deciduous forest/woodland (>5m)
Mosaic forest or shrubland (50-70%) / grassland (20-50%)
Mosaic grassland (50-70%) / forest or shrubland (20-50%)
Closed to open (>15%) (broadleaved or needleleaved, evergreen or deciduous) shrubland (<5m)
Closed to open (>15%) broadleaved deciduous shrubland (<5m)
Closed to open (>15%) herbaceous vegetation (grassland, savannas or lichens/mosses)
Closed (>40%) grassland
Grassland
Sparse (<15%) vegetation
Sparse (<15%) grassland
Closed to open (>15%) broadleaved forest regularly flooded (semi-permanently or temporarily) - Fresh or
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Flood
Alternative
1
HIGH
MEDIUM
MEDIUM
LOW
LOW
LOW
LOW
LOW
LOW
LOW
LOW
LOW
LOW
LOW
LOW
LOW
NO
Alternative 2
HIGH
MEDIUM
MEDIUM
LOW
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
Droughts
Alternative
1
HIGH
MEDIUM
MEDIUM
MEDIUM
LOW
LOW
LOW
LOW
MEDIUM
LOW
LOW
LOW
LOW
LOW
NO
NO
LOW
Alternative
2
HIGH
MEDIUM
MEDIUM
MEDIUM
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
brackish water
Closed (>40%) broadleaved forest or shrubland permanently flooded - Saline or brackish water
Artificial surfaces and associated areas (Urban areas >50%)
Bare areas
Non-consolidated bare areas (sandy desert)
Water bodies
Residential
NO
HIGH
NO
NO
NO
HIGH
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NO
HIGH
NO
NO
NO
HIGH
LOW
LOW
NO
NO
NO
LOW
NO
LOW
NO
NO
NO
LOW