Climate Change Risk and Vulnerability
Assessment for Rural Human Settlements
Prepared by Linkdfor theDepartment of Rural Development and Land Reform: Spatial
Planning and Facilitation Directorate
July 2013
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List of Abbreviations
ARC
Agricultural Research Council
CO2
Carbon Dioxide
°C
degrees Centigrade
CCI
Climate Change Initiative
CLIVAR
Climate Research Programme’s Climate Variability and Predictability component
CRDP
Comprehensive Rural Development Programme
CSIR
Council for Scientific and Industrial Research
CSIRO
Commonwealth Scientific and Industrial Research Organisation
DAFF
Department of Agriculture Forestry and Fisheries
DEA
Department of Environmental Affairs
DRDLR
Department of Rural Development and Land Reform
DWA
Department of Water Affairs
ETCCDI
Expert Team on Climate Change Detection
FAO
Food and Agriculture Organization
GCM
Global Circulation Model
GIS
Geographical Information Systems
IIASA
Institute for Applied Systems Analysis
IPCC
International Panel on Climate Change
JCOMMPHC
Joint Technical Commission for Oceanography and Marine MeteorologyPrimary Health Care
RCP
Representative Concentration Pathway
RID
Rural Infrastructure Development
SANBI
South African National Biodiversity Institute
SARVA
South African Risk and Vulnerability Atlas
SAWS
South African Weather Services
STRIF
Social, Technical, Rural Livelihoods and Institutional Facilitation
UCTCSAG
University of Cape Town Climate Systems Analysis Group
UNDP
United Nations Development Programme
UNEP
United Nations Environmental Programme
WMO
World Meteorological Organization
WRC
Water Research Commission
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1.
Introduction .......................................................................................... 5
2.
Methodology......................................................................................... 6
2.1.
Process ................................................................................................................... 6
2.2.
Conceptual framework .......................................................................................... 6
2.3.
Climate science and uncertainty .......................................................................... 9
2.4.
Data sources and modelling ............................................................................... 10
4.Environmental risk ................................................................................. 11
2.5.
Hazard Exposure.................................................................................................. 11
2.5.1.
Changes in temperature ................................................................................................................ 12
2.5.2.
Changes in precipitation patterns .................................................................................................. 16
2.5.3.
Sea level rise, oceanic warming and ocean acidification .............................................................. 22
2.6.
Sensitivity ............................................................................................................. 23
2.6.1.
Biodiversity .................................................................................................................................... 27
2.6.2.
Invasive alien species .................................................................................................................... 28
2.6.3.
Land use and agriculture ............................................................................................................... 29
2.7.
Climate disasters and cumulative environmental impacts .............................. 30
2.7.1.
Drought .......................................................................................................................................... 31
2.7.2.
Floods and storms ......................................................................................................................... 33
2.7.3.
Veld fires .......................................................................................... Error! Bookmark not defined.
3.
Adaptive Capacity ..................................... Error! Bookmark not defined.
3.1.
Infrastructure and services ....................................... Error! Bookmark not defined.
3.1.1.
Access to basic services ................................................................. Error! Bookmark not defined.
3.1.2.
Type of dwelling ............................................................................... Error! Bookmark not defined.
3.2.
Health .......................................................................... Error! Bookmark not defined.
3.2.1.
Population age profile ...................................................................... Error! Bookmark not defined.
3.2.2.
Primary health care utilisation rate .................................................. Error! Bookmark not defined.
3.2.3.
Severe malnutrition in children under 5 years ................................. Error! Bookmark not defined.
3.3.
Economic vulnerability .............................................. Error! Bookmark not defined.
3.3.1.
Income ............................................................................................. Error! Bookmark not defined.
3.3.2.
Employment ..................................................................................... Error! Bookmark not defined.
3.3.3.
Gender ............................................................................................. Error! Bookmark not defined.
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3.3.4.
Land Tenure status .......................................................................... Error! Bookmark not defined.
3.4.
Composite Map of Adaptive Capacity ...................... Error! Bookmark not defined.
3.5.
Local vulnerability and planning .............................. Error! Bookmark not defined.
4.
Conclusion ................................................ Error! Bookmark not defined.
5.
References................................................. Error! Bookmark not defined.
Annexure I: Data Sources ................................ Error! Bookmark not defined.
The list below provides details of institutional sources of data and
online portals that can be used by climate change adaptation
practitioners and local government to inform local adaption planning
........................................................................... Error! Bookmark not defined.
Statistics South Africa (StatsSA) Digital Census Atlas Error! Bookmark not
defined.
University of Cape Town Climate Systems Analysis Group (CSAG)
Climate Information Portal ............................... Error! Bookmark not defined.
CSAG make a number of interactive maps available through their online
portal that provide access to both weather data and long term climate
projections based on downscaled GCMs and regional observation
stations. ............................................................. Error! Bookmark not defined.
South African Risk and Vulnerability Assessment (SARVA) ..............Error!
Bookmark not defined.
CSIR Geospatial Analysis Platform (GAP) ...... Error! Bookmark not defined.
Annexure II: Social vulnerability indicators and ranking ... Error! Bookmark
not defined.
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1. Introduction
There is a natural amount of carbon dioxide (CO2) in the atmosphere, and this natural amount of carbon
dioxide, together with other greenhouse gases, helps keeps the Earth at an average heat of 15°C and
ensures a stable global climate in which any change tends to happen over very long time spans. However
due to human activity, and particularly the combustion of fossil fuels, the natural balance of CO2 in the
atmosphere is being exceeded, causing the Earth to rapidly warm. This warming is resulting in changes to
the earth’s climate that include an average increase in global temperatures, rising sea levels, changes in
precipitation patterns, and an increase in the frequency and intensity of extreme weather events. These
global changes are known as climate change and threaten the way in which societies across the world relate
to and live within the natural environment.
The impacts of climate change are not evenly borne across countries, communities and households. For a
few, the net effects of climate change may be positive over certain time frames. For instance, growing
periods for crops in some areas, particularly those in the large northern hemisphere land-masses bordering
the Arctic Circle, are likely to increase. Furthermore, the ability to respond effectively to climate change is
sharply differentiated, with poor rural communities often being the least equipped to respond.
Across the world societies are preparing for the changes that climate change will bring and South Africa is no
exception. Climate change brings an even greater challenge to developing countries such as South Africa
that already experience development hurdles, such as poverty and lack of access to basic services, because
climate change will render these development hurdles even more difficult to solve. Changes in the climate
will cause resources to become scarcer as the demand for them from people who are at risk grows. There is
therefore an urgent need to put appropriate plans in place now that will make people more resilient to climate
change and enable them to not only survive climate change and keep their livelihoods intact, but also to
continue on a development path that encourages holistic human well-being.
The changes which people must make to survive climate change and protect their livelihoods are commonly
known as adaptation. In South Africa, the social and economic costs of climate change are already being
incurred and are a growing threat to the achievement of South Africa’s sustainable development goals, of
which one of the priorities is to create vibrant rural communities in line with environmental limitations. While
the development of rural communities is a priority in South Africa, these communities will most likely be the
first to feel the impacts of climate change and are most likely to be the most severely affected. It is therefore
a planning priority to identify the most critical climate change related risks for rural human settlements in
South Africa, and to lay the foundations for communities to enhance their resilience to those risks in order to
reduce their vulnerability.
The central purpose of this report is to identify and understand the factors that increase climate change risks
for rural human settlements in South Africa. As far as possible, these risk factors will be spatially mapped in
order to inform planning and assist in the development of relevant adaptation strategies at a regional and
local level.
A secondary outcome of this report is an identification of the areas in which spatial modelling of risks and
vulnerability can be improved or needs to be updated. In doing so, the report seeks to establish a conceptual
framework for spatially evaluating climate change risk and vulnerability that can be improved as better and
more up-to-date modelling and spatial data becomes available, and that can be adjusted as the actual
impacts of climate change on rural human settlements become clearer.
At the outset it is necessary to define some key concepts as they are understood and used in this risk and
vulnerability assessment:
Adaptation refers to the adjustments that human or natural systems make in response to real or projected
climate changes so as to reduce the impacts or take advantages of possible opportunities (UNDP 2010).
Adaptive Capacityrefers to the financial, physical, cultural and political ability of societies to make the
required changes needed to survive the adverse effects of climate change. Adaptive capacity is defined by
how people experience and survive the exposure to hazards.
Climate refers to the average weather over time for a specific region (FAO 2007).
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Climate change refers to any change in climate over time, whether due to natural variability or
anthropogenic forces (FAO 2007).
Climate-resilient society is one that has taken measures to adapt and respond to climate change (UNDP
2010).
Climate variability refers to variations in the mean state of the given climate for a specific region over time
(FAO 2007).
Climate change vulnerability is a result of a combination between the environmental risks that society’s
face and their abilities to cope with those risks.
Rural human settlements are places in which people live and work that lie outside of the urban edge
(DRDLR 2013)
Weather is the current atmospheric condition in a specific area. The weather includes variables such as
temperature, rainfall and wind. Weather happens currently or in the very near future (FAO 2007).
2. Methodology
The International Panel on Climate Change (IPCC)(2012) notes that selecting the right tool to evaluate risk
and vulnerability is dependent on the decision making context and that the methods taken in these
assessments vary depending on the resources and technologies available. Some approaches may make use
of global data or downscaled local climate modelling, while others may opt for more participatory avenues.
What is clear, however, is that quantitative approaches making use of climate change modelling and spatial
data need to be supplemented with qualitative inputs at the local level to inform adaptation planning.
2.1.
Process
This study draws on existing local spatial modelling of key indicators in relation to the environmental risks
and social vulnerabilities associated with climate change in order to graphically represent their comparative
spatial distribution. The data is drawn from a wide range of local sources and inevitably there are
inconsistencies in relation to, for instance, the climate change projections used in composite models of risks.
It is beyond the scope of this study to resolve these inconsistencies; however this study does draw attention
to them since progress towards consensus models within the South African research community is needed
to inform national planning.
Preliminary research results in relation to the spatial mapping were presented to the Project Steering
Committee and on the basis of feedback and engagement with stakeholders – particularly the South African
Risk and Vulnerability Atlas (SARVA) project hosted by the Council for Scientific and Industrial Research
(CSIR) and the South African Weather Service (SAWS) – revisions were made to the general approach and
data sources used.
On the basis of these revisions, presentations on risk and vulnerability were prepared and delivered in four
regional workshops, during which participants provided a level of guidance in terms of identifying and ranking
climate change risks. The outcomes from these workshops have informed the drafting of this report.
The study has since been strengthened by the outcomes from the first phase of the Long Term Adaptation
Scenario (LTAS) research process being led by the South African National Biodiversity Institute (SANBI),
which became available in August 2013.
2.2.
Conceptual framework
While spatial modelling at the national scale can provide an indication of generic climate change risks and
vulnerabilities and an indication of their comparative spatial significance, the impacts of climate change are
likely to be felt locally in very specific ways that cannot be adequately captured in national models at
sufficient resolution currently and require the development of local adaptation responses. Rural communities
and households are diverse and differ from location to location in terms of socio-economic activities and
dependencies, available resources and culture of the inhabitants. As a consequence, this assessment then
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aims to provide a broad overview of the potential climate related risks for rural human settlements which then
can be used as a guideline in more localised planning that is better able to respond contextually tothe
climate change threats that rural inhabitants face.
Projecting the impacts of climate change presents complex challenges and happens at different levels.
Because of the difficulties in projecting the impacts of climate change, there is a lively debate in the scientific
community as to which is the best method in understanding future risks. Most models for understanding
climate change threats consist of a combination of the following three elements:

The physical impacts of climate change consisting of changes such as increased temperature and
changes to precipitation patterns - the location specific physical impacts of climate change can be
considered to define hazard exposure.

The sensitivity of the bio-sphere to the physical impacts of climate change, such as the extent and
nature of impacts on plant and animal species resulting from decreases in surface water availability;

The social impacts of climate change as determined by the human consequences of changes to the
environment and the socio-economic consequences of climate change mitigation actions. An
example is increased malnutrition.
The ability or capacity of societies to adapt the above hazards and sensitivities is an additional dimension
that needs to be considered. The United Nations Development Programme (UNDP) summarises these
variables in their approach to understanding climate change vulnerability:
Vulnerability = exposure to climate hazards and perturbations x sensitivity – adaptive capacity
(UNDP 2010).
In the UNDP approach, the interaction between hazard exposure, based on climate change projections, and
sensitivity, based on an analysis of bio-physical characteristics, can be understood as encompassing the
environmental risks posed by climate change. Vulnerability is therefore a product of the extent to which these
risks are mitigated or exacerbated by the presence or absence of adaptive capacity.
The conceptual framework used in this study to analyse the risk and vulnerability for rural human settlements
is based on the UNDP approach and is shown in Figure 1 below. In terms of this framework the interaction
between the location-specific impacts of climate change (hazard exposure) and the sensitivity of natural
systems to these impacts can be understood as defining the environmental risks posed by climate change.
Rural communities tend to be more reliant on natural resources than urban communities and consequently
are often more directly vulnerable to the environmental risks associated with climate change.
Adaptive capacity represents the ability of communities to respond to the environmental risks. Therefore,
where adaptive capacity is high, social vulnerability is low and vice versa. As a concept, adaptive capacity
combines subjective qualities, such as levels of organisation and institutional capacity,which are difficult to
directly measure with characteristics of communities that influence social vulnerability that can and are
measured, such as income levels and access to basic services. Due to the scope of this study, it has been
possible to spatially map only those dimensions of social vulnerability that have been quantified and for
which spatially referenced data is available.
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Figure 1: Conceptual Framework
While it is theoretically possible to produce a composite spatial mapping of overall climate vulnerability based
on the above model, there are several practical limitations to this exercise:

Current assessments of climate related hazards draw from different models and climate change
scenarios, which limit the comparability of the data.

Some data related to environmental risks does not incorporate or anticipate climate change
projections, making these static snapshots difficult to compare with future projections of climate
hazard.

The interrelationships between indicators for climate hazard, bio-physical sensitivity and adaptive
capacity are complex and dynamic, and a simple aggregation of indicators according to different
weights gives a misleading interpretation of the situation.

An aggregated model is not useful for understanding location specific climate risks that need to
inform adaptation planning

The weighting of different risks at a national scale is inevitably subjective and may not translate
meaningfully at a local scale.
These considerations mean that composite spatial mapping of overall climate vulnerability is more usefully
applied to understanding particular climate change risks, such as flooding or drought, in which the
relationships are better understood and for which there are already well developed mathematical models for
computing the interactions between variables.
Figure 2 provides a more detailed overview of the social vulnerability indicators that were consulted during
this study and the dimensions of environmental risk that were analysed.
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Figure 2: Expanded model of Climate Vulnerability
Adaptive capacity
•
•
•
•
•
•
•
•
Annual household
income
Access to services
Childhood malnutrition
Access to primary health
care
Gender of household
head
Population age profile
Land ownership
Type of dwelling
Hazard Exposure
•
•
•
Climate
vulnerability
•
•
•

Sensitivity




2.3.
Sea level rise
Climate risks



•
Extreme events
Floods
Droughts
Agriculture impacts
Heat waves
Veld-fires

Degraded land
Soil erosibility
Irrigation demand
Ecosystem
protection level
Invasive plant
density
Streamflow
Ground water
Coastal elevation
Very wet days
Consecutive dry days
Simple daily intensity
Index
Maximum daily temp.
Number of warm days
Growing season length
Mean change in summer
& winter precipitation
Climate science and uncertainty
Climate change presents a difficult challenge for policy makers as they are increasingly coming under
pressure to make decisions that may have far reaching implications but are based on uncertain information.
Climate projections are developed through the use of Global Circulation Models (GCMs) that are
continuously being updated, refined and improved. The objective of modelling climate change scenarios is
not to predict the future but rather to gain a better grasp of the uncertainties that exist; so as to develop
planning which is more suited to a variety of possibilities (IIASA 2012). Which scenarios will most closely
approximate the real world depends on future trends in term of economic growth, population growth and the
impacts of international and domestic binding treaties as well as improvements in modelling climate. The use
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of terms and concepts such as the ‘precautionary principle’ and ‘least-regret options’ in adaptation discourse
is a reflection of the accepted degree of uncertainty associated with climate science.
Uncertainty in science generally arises because there is either a lack of information or there is a certain
amount of disagreement about what is known arising from the way the science is explained, statistical
variation, measurement error, variability, approximation and subjective judgment.In relation to climate
change science both these factors apply and are magnified by the process of downscaling future climate
projections which inevitably exposes uncertainty at the local scale as the required level of resolution of
models is increased.(Schneider &Kuntz-Duriseti 2002)
An analysis of modelled climate data in relation to observed climate trends for the period 1960 – 2010 in
South Africa suggests that the climatic processes affecting South Africa are not adequately captured by
current GCMs and downscaling methodologies. While observed temperature trends for temperature more
closely match modelled trends than rainfall trends, observed increases have been more gradual than
suggested by the models. Climate models predict wetter autumns, while the observed trend has been
towards drier autumns. Models predicting a drying in spring have not been supported by observation.
Climate change models yield projections that are expressed in a range of probabilities. In some cases, for
practical reasons, only the median projections are shown in the narrative of this report.
In order to overcome uncertainty, policy makers must both seek to reduce uncertainty by supporting efforts to
improve capacity in data collection, research, modelling and simulation; and they must manage uncertainty
that is intrinsic to climate projections by integrating it into decision making. This study seeks both to define
and reduce uncertainty by summarising and collating existing data on risk and vulnerability and also to
identify opportunities for reducing uncertainty within existing research.
2.4.
Data sources and modelling
The data and analyses included in this report are drawn from a range of local research institutions and
Census 2011, but in most cases the GIS data on which the maps used in this report can be obtained from
the South African Risk and Vulnerability Assessment (SARVA). An attempt has been made to align the
climate change projections included in this study with the recently released outcomes from phase 1 of the
LTAS process, supplemented by modelling of specific indicators for climate extremes undertaken by the
South African Weather Services (SAWS). Appendix 1 provides a more extensive list of data sources for
those with an interest in the more technical aspects of mapping climate change risk and vulnerability.
Reports of climate change impacts on flooding, drought and rainfall patterns used were generated by
combining climate change projections with hydrological modelling in work undertaken for the Water
Research Commission by Dr Roland Schulze in the report: A 2011 Perspective on Climate Change and the
South African Water Sector. This workhas also been used in the LTAS.
The South African Weather Service and Dr Mxolisi Shongwe have made a significant contribution to this
report both in terms of conceptualizing the approach, and in providing up-to-date downscaled modeling of
indicators relating to climate extremes. This modeling has been based on the climate change scenarios that
form the basis of the 5th Assessment of the Intergovernmental Panel on Climate Change (IPCC), to be
published in 2013/2014, and are known as Representative Concentration Pathways (RCPs). Of the four
RCPs, RCP 4.5 was chosen by SAWS as a mid-range scenario, but it is premised on global efforts to
achieveagreed emissions reduction targets. The SAWS indicators used in this report are derived from
running this scenario through multiple GCM models and aggregating the outcomes on the basis of an equal
weighting for each model for the following time frames.

A near term timescale of 2021 to 2050.

A long-term timescale of 2071 to 2100.
GCMs are built by making use of a grid of data points that expands the entire globe and this limits the
resolution at which projections can be generated. (SARVA 2011) Statistical downscaling is needed to
accomplish higher resolution projections for more regional locations.
The LTAS serves as the national reference point for adaptation planning, and the climate scenarios included
in the draft outcomes from Phase 1 of the process usea variety of RCP’s and A2 and A4 scenarios.
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Professor Guy Midgeley and Petra De Abreau from SANBI have been helpful in communicating and sharing
the technical reports from the Phase 1 process. This study includes some projections from the LTAS that use
the RCP 8.5 scenario that would be likely to result from low take-up of global mitigation efforts.
Although these projections help to provide perspective in terms of trends and the range of probability for
future precipitation patterns and temperature there is a clear policy need for a national framework for
incorporating up-to-date modelling of this nature from SAWS and other sources such as the Climate Systems
Analysis Group (CSAG) into models that incorporate sensitivity data, such as the WRC models of flood and
drought risk, in a transparent manner that represents the current consensus of the scientific community and
can be updated on a regular basis as modelling capacity improves.
4.Environmental risk
Due to its geographical location, complex topography and its position at the confluence of major ocean
currents (the warm Agulhas current along the East coast and the cold Benguela current along the West
coast) South Africa experiences an unusually wide range of weather conditions and a high degree of natural
variability (DEA 2011).
Climate change refers to changes in the long-term average of weather conditions (SARVA2011). It is globally
accepted that carbon dioxide concentrations, average surface temperatures and sea levels will rise in the
future. GCMs project that the average surface temperature of the earth, for example, could increase between
a range of 1.4 °C and 5.8 °C between 1990 and 2100. This rise is two to ten times greater than the previous
century and signifies long term climate change rather than short term weather variability (FAO 2013).
At the same time, a growing body of research indicates that increases in the variability of weather linked to
climate change, including an increase in the frequency and intensity of extreme weather events and extreme
climate events, represent an immediate challenge in relation to disaster risk management (IPCC 2012).
Extreme weather events refer to extremes in atmospheric conditions such as temperature, rainfall and wind
experienced over a day or a few weeks. Extreme weather events may have extreme impacts on human
settlements as a result of, for instance, heat waves, flooding, or wind-related cyclone damage. Extreme
climate events can be thought of as long term deviations from mean weather patterns or an accumulation of
extreme weather events over a period of years or decades. An increase in multi-year droughts, for instance,
would be considered an extreme climate event. It should be noted that risks of precipitation extremes such
as drought and flooding are not mutually exclusive and can occur in both wetter and drier scenarios. (LTAS,
2013) Collectively, extreme weather and extreme climate events are referred to as climate extremes (IPCC
2012).
Climate extremes can have cumulative impacts. For instance, a combination of below average rainfall and
above average temperature can result in an elevated risk of veld-fires. South African rural human
settlements are at particular risk from climate extremes such as floods or droughts due to a variety of social
vulnerabilities, such as poor infrastructure and services. An example of this is the difficulty in providing relief
services to dispersed settlements where access by road is poor.
2.5.
Hazard Exposure
In the context of climate change, hazard exposure can be understood as the extent to which the changes in
atmospheric conditions resulting from the global increase in GHG concentration are experienced in a
particular location. These changes includeaverage increases in temperature over time, increases in the
frequency and intensity of storms, and changes in precipitation patterns, and seal level rise. Hazard
exposure can result in both gradual impacts such as declines in crop yields over many years or sudden
impacts resulting from an increased exposure to extreme weather events such as floods, droughts and
storms. Hazard exposure is not experienced the same way everywhere. Sealevel rise obviously has no direct
impact on inland rural communities, but is relevant to coastal human settlements where assets may be
vulnerable to beach erosion caused by storm surges.
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The indicators used to analyse hazard exposure in this assessment were selected in collaboration with the
South African Weather Services (SAWS) and form a part of the extreme temperature and precipitation
indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), a
collaboration between the World Climate Research Programme’s Climate Variability and Predictability
component (CLIVAR), the European Space Agency’s Climate Change Initiative (CCI) and the Joint Technical
Commission for Oceanography and Marine Meteorology (JCOMM).
2.5.1.
Changes in temperature
In South Africa, temperatures are largely affected by the topography of any given location and its distance
from the sea. The inland areas are highly elevated and they experience a warm summer with daily
temperatures reaching 26-28 °C. These high areas also experience cool winters with mean daily
temperatures of around 0-2°C which also may be accompanied by frost. The temperature of the east coast is
determined by the warm Mozambique current and the areas between East London and Mozambique are
therefore warmer throughout the year. The northern parts of the coast are sub-tropical and experience a
warm winter with daily minimums of around 9-10°C and a hot summer with a maximum of 32°C. The interior
which contains the Nama-karoo biome has more of an extreme climate than the rest of the country with daily
maximum highs in summer reaching 34°C and minimums in winter at around 6°C. The temperatures of the
West Coast are influenced by the Benguela current and therefore the area experiences daily highs in the
summer of around 32°C and daily winter minimum temperatures of around 6°C with no frost (SARVA 2012).
In the future the temperature for South Africa, as for the rest of Africa and the world, is expected to rise. Due
to the moderating influence of the ocean, temperature is likely to increase less over the coastal regions than
the interior. Temperature maximums will however, increase and new record temperatures can be expected.
Map 1shows the projected average change in mean annual temperature for the intermediate period of 20462065 compared to the baseline records of 1971-1990., suggesting thattemperature increases in the interior
regions will be more severe than on the coast.
12
provides projections of the seasonal changes in maximum temperatures for period 2015-2035 relative to
1971-2005. The 90th percentile (upper panels), median (middle panels) and 10th percentile (lower panels)
are shown for an ensemble of downscalings of ten CGCM projections, for each of the seasons. The
downscalings were generated using the Climate Systems Analysis Group’s (CSAG) statistical downscaling
procedure. All the CGCM projections contributed to CMIP5 and AR5 of the IPCC, and are for RCP8.5. Map 3
provides the same projection for the period 2075 – 2095, showing the stronger signal of climate change in
the more distant future. (LTAS 2013)
Table 1 describes a range of possible impacts of increased temperature on rural human settlements.
Table 1: Impacts of changes in temperature
Temperature change
Increased number of warm and
very hot days and increased
maximum daily temperatures
Impact

Increased evaporation impacting on the availability of surface water

Soil degradation due to increased acidity, nutrient depletion, declining
microbiological diversity, lower water retention and increased runoff.

Positive or negative impacts on crops and growing season length
depending on local topography, precipitation and crop types. Some crops,
particularly deciduous fruits, require a chill factor during winter to be
productive.

Increased incidence of heat waves and associated risks for human and
livestock health from heat stress, particularly for the very old and young,
and those already suffering from illness.

Increase in the concentration and range of pests and pathogens that
comprise human and livestock disease vectors, such as malaria and ticks.

Increased risk of wild fires and associated damage to crops, property and
infrastructure.
Map 1: Average change in mean annual temperature
Source: Agricultural Research Commission
13
Map 2: Projected change in average seasonal maximum temperatures for 2015 – 2035 relative to 1971 - 2005
14
Map 3: Projected change in average seasonal maximum temperatures for 2075 – 2095 relative to 1971 - 2005
2.5.2.
Changes in precipitation patterns
Currently South Africa has a mean annual rainfall of around 450mm and is therefore regarded as semi-arid.
However, the country experiences marked regional differences in rainfall patternsin terms of the timing,
intensity and quantity of rainfall and these differences are projected to increase in the near and in the long
term. The west of the country is drier than the east. Areas which border Namibia (the Richtersveld) may only
receive less than 50 mm of rainfall while the mountains of the south west Cape can receive more than 600
mm of rainfall. Annual potential evapo-transpiration may exceed annual precipitation by ratios of up to 20:1
(Palmer & Ainslee 2013).There are three major rainfall zones in South Africa: the winter rainfall region of the
western, south western and southern Cape; the bimodal rainfall region of the Eastern Cape, and the summer
rainfall region of the Highveld and KwaZulu Natal (ibid).
Downscaled climate change modelling of the change in the annual number of consecutive dry days
undertaken by SAWS as shown in Map 4suggest that in the near term (2021 – 2050) there will be an
increased drying and associated risk of drought in the western and north eastern parts of the country,
becoming more pronounced in the long term (2071 – 2100). The SAWS projections also indicate the range of
probabilities, which is significant, and suggests that in some parts of the country (particularly the southern
and eastern cape) the direction of change is not certain, and may change over the near term and long term.
Map 4: Percentage change in consecutive dry days
2021 – 2050 (q25%)
2021 – 2050 (q50%)
2021 – 2050 (q75%)
2071-2100 (q25%)
2071-2100 (q50%)
2071-2100 (q75%)
Source: (SAWS 2013)
16
Note on interpretation of projections
The SAWS maps used in this assessment provide an indication of the range of probabilities for particular
indicators, divided into quartiles. The middle map is the second quartile and represents the median
projection – i.e. 50% of the probability lies on either side of this projection. The first and the third quartiles
(q25% and q75%) frame the mid 50% of probability. In other words, there is a 50% likelihood that
observed change will fall between these two projections.
The maps drawn from the LTAS generally provide the median projection, and the 90 th Percentile and 10th
Percentile projections. In other words, there is an 80% likelihood that observed change will fall between
these two projections.
17
Map 5shows projected change in the average seasonal rainfall (mm) over South Africa for Dec-Jan-Feb
(Summer), Mar-Apr-May (Autumn), Jun-Jul-Aug (Winter) and Sep-Oct-Nov (Spring), for the period 20152035 relative to 1971-2005. The 90th percentile (upper panels), median (middle panels) and 10th percentile
(lower panels) are shown for an ensemble of downscalings of ten CGCM projections, for each of the
seasons. The downscalings were generated using the CSAG statistical downscaling procedure. All the
CGCM projections contributed to CMIP5 and AR5 of the IPCC, and are for RCP8.5.
For comparative purposes, the Map 6 shows the same seasonal rainfall projections, also for RCP 8.5, using
a dynamically downscaled variable resolution model called the conformal-cubic atmospheric model (CCAM)
that can be used for climate change projections as well as near term seasonal forecasts. The projections
using this model that have been incorporated into the LTAS Phase 1 report were performed as a
collaboration between South Africa’s Centre for Scientific and Industrial Research (CSIR) and the
Commonwealth Scientific and Industrial Research Organisation (CSIRO) in Australia. The persontile
projections in the maps shown here are arranged vertically rather than horizontally, as is the case with the
CSAG statistically downscaled projections.
These modelling exercises show that there are wide range of plausible rainfall futures for South Africa. In
general though, the seasonal projections suggest little overall change in the near term future (2015-2035) for
summer rainfall, but drier autumns. The South-Western Cape and Limpopo regions seems particularly at risk
to an overall decrease in rainfall, particularly in the longer term.
18
Map 5: Percentage change in seasonal rainfall for the period 2015 – 2035, relative to 1971-2005, CSAG statistical downscaling, RCP 8.5.
Source (LTAS 2013)
19
Map 6: Percentage change in seasonal rainfall for the period 2015 – 2035, relative to 1971-2005,
CSIR/CSIROdynamic downscaling, RCP 8.5.
20
As has already been noted, an important aspect of climate change is the increased risk of extreme weather
events, and greater variability in weather. An increase in the projected number of dry days for any
particularregion is not incompatible, therefore, with an increase in the projected number of consecutive wet
days but rather indicates a future in which rainfall is more erratic. Note that the blue areas in these maps
denote a decrease in the percentage of consecutive wet days, while the yellow areas represent an increase.
Map 7provides SAWS projections of the change in the increase in the number of consecutive wet days.
These projections confirm the overall trend in the SAWS projections of drying in the west of the country, with
a possibility of an increase in consecutive wet days shown in the third (wetter) quartile for the east of the
country. Note that the blue areas in these maps denote a decrease in the percentage of consecutive wet
days, while the yellow areas represent an increase.
Map 7: Percentage change in consecutive wet days
2021 – 2050 (q25%)
2021 – 2050 (q50%)
2021 – 2050 (q75%)
2071 – 2100 (q25%)
2071 – 2100 (q50%)
2071 – 2100 (q75%)
Source: (SAWS 2013)
Climate change modelling undertaken for the Water Research Commission (WRC) suggests that there will
be an overall increase in the annual variability of rainfall, and an increased risk of rainfall arriving in the form
of intense precipitation events.
Map 8, which is drawn from work commissionedby the WRC, maps projected changes in the annual
variability of precipitation for the period 2046 to 2065 (as indicated by the ratio of change to the standard
deviation, which in this case measures the average amount rainfall measurements differ from the median
value) in comparison to catchment management data from 1971 to 1990. As can be seen from the map, in
almost all catchments there is an increase in projected variability(Schulze 2011).
21
Map 8: Changes in rainfall variability
Source: (Schulze 2011)
Map 9: Change in extreme precipitation events
Source: (Schulze 2011)
Map 7, drawn from the same WRC report, maps the ratio changes in the projected number of days per
annum in which precipitation exceeds 25mm for the period 2046 to 2065 in comparison to catchment
management data from 1971 to 1990. As can be seen from the map, in almost all catchments except some
22
in the Northern and Western Cape, there is an increase in the projected number of extreme precipitation
events (WRC 2011).
Table 2 describes a range of impacts that changes in precipitation patterns may have on rural human
settlements.
Table 2: Impacts of changes in precipitation patterns
Change in precipitation pattern
Increased number of
consecutive dry days
Potential Impacts




Increase in number of
consecutive wet days and/or
increase in extreme
precipitation events






Changes in the variability and
timing of precipitation


2.5.3.
Decreases in runoff and stream flow and an increased risk of
drought, affecting crop production, food security and rural
livelihoods.
Reduced stream flow is a particular threat for rural communities
that are directly dependant on surface water resources.
Loss of soil moisture affecting crops and increasing the risk of soil
erosion due to wind.
Increased risk of veld-fires and resultant damage to property,
grazing, and crops.
Increased risk of floods, with consequent risks of damage to
crops, property and loss of life.
Water logging of soil affecting crops.
Increased risk from water borne diseases such as cholera.
Damage to bulk water infrastructure, irrigation systems and
water reticulation.
Damage to property and crops from winds associated with
violent storms.
Extreme precipitation events are often preceded by lightening,
which is responsible for a significant number of fatalities in rural
areas every year
Farmers rely on predictable rains for timing the planting of crops,
and subsistence farmers practicing rain-fed agriculture are
particularly at risk.
Increased variability and unpredictable timing of rainfall impacts
directly on the management of catchments and bulk water
infrastructure, threatening the availability of water.
Sea level rise, oceanic warming and ocean acidification
Climate change related rise in sea levels stems from both the thermal expansion of water and the melting of
glaciers and land-based ice sheets at the poles. Thermal absorption by the oceans has significantly mitigated
the effects of increased CO2levels on atmospheric temperatures, but as the rate of atmospheric warming
increases it is expected that the melting of land-based ice will contribute more to sea-level rise than thermal
expansion does currently.
The increased atmospheric emissions of CO 2 from anthropogenic sources are partially mitigated by an
increase in oceanic absorption of CO2; however this results in an increase in the acidity of ocean water that
is likely to be detrimental to many marine species, particularly those relying on calcification processes to
develop skeletons or shells, such as molluscs, corals and plankton.
The current rate of sea-level rise shows some regional differences across the South African coastline, with
the west coast rising 1.87 mm per year, the south coast by 1.47 mm per year and the east coast by about
2.74mm per year. These differences are due to the impact of the Benguela and Agulhas ocean currents and
tectonic movements. The rate of sea level rise is very likely to increase in future, but there is currently still a
high degree of uncertainty over the time scales and extent of change (DEA 2011).
23
Increases in sea surface temperatures have already been observed in South African waters and are
expected to continue. Increased temperatures in our coastal and estuarine waters impact on the ranges of
marine and estuary species, and an increasing southwards penetration of tropical fish species has been
observed.
Both sea level rise and ocean acidification are gradual and incremental climate change phenomena. The
impact of sea level rise, combined with more violent storm surges, is a particular risk for urban infrastructure
that is close to current coastal setback lines. Although there may be threats to particular rural households
and settlements from sea-level rise, the primary threat for coastal rural settlements from changes to the
marine and estuarine environment is in terms of the consequences of loss of biodiversity for rural livelihoods.
The main risks are reflected in Table 3 below.
Table 3: Impacts of changes in oceanic systems
Changes in oceanic systems
Sea level rise
Oceanic warming
Ocean acidification
2.5.4.
Potential Impacts
 Salinisation of water sources provided by
coastal aquifers on which some coastal
communities depend
 Damages to infrastructure and property
located in coastal areas with a low
elevation, aggravated by storm surges
associated with extreme weather.
 Changes to the distribution and ranges of
estuarine and marine species important to
livelihoods in rural fishing communities.
 Impacts on the development and
reproduction of estuarine and marine
species important to livelihoods in rural
fishing communities.
Sub-regional implications of climate modelling for hazard exposure
The LTAS is embarking on a detailed analysis of the economic and resource implications for adaptation
responses of downscaled climate modelling at a sub-regional scale. Due to the importance of catchment
management for South Africa as a semi-arid country, the LTAS is basing its subregional analysis on the six
water management regions used in the National Water Strategy process. These are shown in Map 10 below.
Map 10: Major water management regions
24
The LTAS Climate Technical Report provides key messages for each of these zones in relation to the
statistical and dynamic downscaling of climate projections that inform the adaptation process, and these are
summarised in the tables below for each zone. The figures shown here are from the CSAG statistical
downscaling scenarios. The CCAM dynamic downscaling tends to report slightly higher anomalies.
Zone 1 (Limpopo)
Near term (2015 – 2035)
Mid term (2040 – 2060)
Long term (2080 – 2100)
Temperature
Mostly within the range
of current variability, but
showing
an
annual
anomaly (increase) of
about 2 °C towards the
end of the period under
RCP8.5
Average
annual
increases of between 1
and 3 °C
3 – 6°C average annual
increase in temperature
under
RCP8.5,
substantially exceeding
historical variability.
Rainfall
Increased drying under
RCP
4.5
scenarios
strengthening over time,
but within the range of
current
climate
variability. No significant
drying under RCP 8.5
Increased drying under
RCP
4.5
scenarios
strengthening over time,
but within the range of
current
climate
variability. No significant
drying under RCP 8.5
Increased drying under
RCP
4.5
scenarios
strengthening over time,
but within the range of
historical
climate
variability. No significant
drying under RCP 8.5
Zone 2 (KZN)
Near term (2015 – 2035)
Mid term (2040 – 2060)
Long term (2080 – 2100)
Temperature
1 – 2°C average annual
increase in temperature
under
RCP8.5,
exceeding
historical
climate variability.
Average
annual
increases of between 1
and 3 °C under RCP8.5
3 – 5°C average annual
increase in temperature
under
RCP8.5,
substantially exceeding
historical variability.
Under RCP4.5, within
the range of historical
climate variability.
Rainfall
Increased drying under
RCP
4.5
scenarios
strengthening over time,
but within the range of
current
climate
variability. No significant
drying under RCP 8.5
2 to 3 °C under RCP4.5
Under RCP4.5, within
the range of historical
climate variability.
Increased drying under
RCP
4.5
scenarios
strengthening over time,
but within the range of
current
climate
variability. No significant
drying under RCP 8.5
1 to 3 °C under RCP4.5
Increased drying under
RCP
4.5
scenarios
strengthening over time,
but within the range of
historical
climate
variability.
However
drying under RCP 8.5
significantly
exceeds
historical variability
25
Zone 3 (North West)
Near term (2015 – 2035)
Mid term (2040 – 2060)
Long term (2080 – 2100)
Temperature
1 – 2.5°C average
annual
increase
in
temperature
under
RCP8.5,
exceeding
historical
climate
variability.
Average
annual
increases of between 1
and 3 °C under RCP8.5
3 – 6.5°C average
annual
increase
in
temperature
under
RCP8.5,
substantially
exceeding
historical
variability.
Under RCP4.5, within
the range of historical
climate variability.
Under RCP4.5, within
the range of historical
climate variability.
Below4°C
under RCP4.5
increase
Rainfall
Slight drying under RCP
4.5 and 8.5 scenarios,
but within the range of
current
climate
variability.
Slight drying under RCP
4.5 and 8.5 scenarios,
but within the range of
current
climate
variability.
Slight drying under RCP
4.5 and 8.5 scenarios,
but within the range of
current
climate
variability.
Zone 4 (Northern Cape)
Near term (2015 – 2035)
Mid term (2040 – 2060)
Long term (2080 – 2100)
Temperature
1 – 2.5°C average
annual
increase
in
temperature
under
RCP8.5,
exceeding
historical
climate
variability.
Average
annual
increases of between 1
and 3 °C under RCP8.5
3 – 5.5°C average
annual
increase
in
temperature
under
RCP8.5,
substantially
exceeding
historical
variability.
Under RCP4.5, within
the range of historical
climate variability.
Under RCP4.5, within
the range of historical
climate variability.
Rainfall
Slight drying under RCP
4.5 and 8.5 scenarios,
but within the range of
current
climate
variability.
Below
4°C
under RCP4.5
Slight drying under RCP
4.5 and 8.5 scenarios,
but within the range of
current
climate
variability.
increase
Slight drying under RCP
4.5 and 8.5 scenarios,
but within the range of
current
climate
variability.
26
Zone 5 (Eastern Cape)
Near term (2015 – 2035)
Mid term (2040 – 2060)
Long term (2080 – 2100)
Temperature
Already reaching and
exceeding 2°C historical
climate variability under
RCP 8.5.
Average
annual
increases of between 1
and 2 °C under RCP8.5,
exceeding
historical
climate variability
2 – 5°C average annual
increase in temperature
under
RCP8.5,
substantially exceeding
historical variability.
Under RCP4.5, within
the range of historical
climate variability.
Below
3°C
under
exceeding
variability.
Under RCP4.5, within
the range of historical
climate variability.
increase
RCP4.5,
historical
Rainfall
Drying under RCP 4.5
and 8.5 scenarios, well
outside the range of
current
climate
variability.
Drying under RCP 4.5
and 8.5 scenarios, well
outside the range of
current
climate
variability.
Drying under RCP 4.5
and 8.5 scenarios, well
outside the range of
current
climate
variability.
Zone 6 (Southwestern
Cape)
Near term (2015 – 2035)
Mid term (2040 – 2060)
Long term (2080 – 2100)
Temperature
Already reaching 1.5°C
anomaly in temperature
increase,
exceeding
historical
climate
variability under RCP
8.5.
Average
annual
increases of between 1
and 2 °C under RCP8.5,
exceeding
historical
climate variability
2 – 4°C average annual
increase in temperature
under
RCP8.5,
substantially exceeding
historical variability.
Under RCP4.5, within
the range of historical
climate variability.
Below
3°C
under
exceeding
variability.
Drying under RCP 4.5
and 8.5 scenarios, well
outside the range of
current
climate
variability.
Drying under RCP 4.5
and 8.5 scenarios, well
outside the range of
current
climate
variability.
Under RCP4.5, within
the range of historical
climate variability.
Rainfall
2.6.
Drying under RCP 4.5
and 8.5 scenarios, well
outside the range of
current
climate
variability.
increase
RCP4.5,
historical
Sensitivity
In the context of this study, sensitivity refers to the reactions of ecological systems to exposure to the
changes in atmospheric conditions associated with climate change. Sensitivity is measured by the reactions
of a unit of analysis to the impacts of climate change. For instance, a 2°C increase in temperature may result
in a quantifiable increase or decrease in the incidence of a plant species in a particular ecosystem. Equally, it
may affect the geographical extent of a particular ecosystem (such as savannah). Although ecological
systems are complex, and their sensitivity to climate change is imperfectly understood, this is becoming an
increasingly important focus of adaptation research.
27
2.6.1.
Biodiversity
Biodiversity is a key dimension of sensitivity to climate change. South Africa has a rich natural heritage of
biodiversity in terms indigenous and endemic species. The IPCC 4th Assessment Report concluded that
climate change will have, and is already having, significant impacts on biodiversity in terms of the distribution
and incidence of species and therefore on the spatial extent of ecosystems, and this has been confirmed by
empirical observations.
Many indigenous species have intrinsic commercial value (such as rooibos), cultural value and medicinal
value and biodiversity itself forms an important aspect of the countries value proposition as a tourist
destination. As a consequence, biodiversity contributes directly to rural livelihoods and the adaptive capacity
of rural communities.
Examples of the impacts of climate change on ecosystems include:

Bush encroachment on grasslands due to elevated CO2 levels favouring woody plant species.

Changes in the composition dominant plant and animal species as a result of differences in the
resilience of species to increases in temperature, changes in rainfall, and frequency of veld-fires –
these often favour pioneer species (weeds) and invasive aliens.
Studies of indigenous plant and animal species cited in South Africa’s 2 nd National Communication to the
UNFCCC estimate that the area of land currently optimal for supporting the countries existing biomes could
be reduced by between 38 and 55% by 2050 as a result of climate change. The most substantial losses are
likely to be incurred in the western, central and northern regions of the country and include negative impacts
on commercially significant species such as the rooibos plant.
Changes to ecosystems as a result of climate change are initially most marked at the boundaries between
different biomes and the nature of the changes will depend on the resilience of particular species to changes
in temperature and precipitation and the ability and speed at which both plant and animal species are able to
migrate (Campbell, A., et al. 2009). Map 11 below indicates the current spatial extent of the main South
African biomes.
Map 11: Current spatial distribution of South African biomes
Source: SANBI BGIS
28
While biodiversity is threatened by climate change, the concept of ecosystem-based adaptation strategies
has become increasingly important and is particularly relevant to rural human settlements. Measures to
conserve and protect biodiversity serve to improve the resilience of the environment to climate change by
securing the integrity of critical ecosystem services. Examples of such services include the role wetlands and
mangroves play in mitigating the impact of flooding, sequestrating carbon and in enhancing water quality.
Map 12 below provides an indication of the level of protection of indigenous biodiversity.
Map 12: Levels of protection of biodiversity
Source: SANBI BGIS
2.6.2.
Invasive alien species
Invasion by alien plant species has a significant impact on the sensitivity of ecosystems, posing a significant
threat to indigenous biodiversity. Alien invasive plantsare considered to have already contributed to the loss
of at least 58 fynbos plant species in the Western Cape. In a country characterised by water scarcity, an
estimated 7% of mean annual runoff is taken up by invasive plants and the economic impact of alien plant
and insect species on grazing potential and crop losses is estimated at approximately US$ 3.5 billion per
year. (DEA 2011). Map 13 indicates the current spatial distribution of infestations of invasive alien plant
species.
The impact of climate change on alien invasive species will vary from species to species, but may include the
expansion of the range of some pathogens and pests. It is likely that woody alien plants will benefit from
climate related bush encroachment, altering ecosystem functioning in relation to stream flow, nutrient cycling,
fire regimes, and incidence and behaviour of animal species amongst others. These changes almost
invariably negatively impact on the ability of ecosystems to deliver goods and services that are important to
rural communities.
29
Map 13: Average density of alien plant species
Source: SANBI BGIS
2.6.3.
Land use and agriculture
Human activities resulting in land use changes as a result of increased human population densities and
increases in land under cultivation or grazing have a significant influence on the sensitivity of the
environment to climate change. Apart from increases in human population density being directly correlated
with declines in biodiversity, land use changes contribute indirectly to carbon dioxide levels through loss of
sequestration potential and directly through carbon emissions associated with agricultural production. Land
use changes also affect stream flow characteristics, exacerbating the impact of climate extremes such as
flooding and drought.
Both traditional and commercial agricultural practices with respect to crops and livestock can contribute to
amplifying the impact of climate change on desertification and land degradation. For this reason, the
promotion of agricultural techniques that promote soil and water conservation is a key thrust of the
agricultural sectors response to climate change. South Africa has fragile soils and large areas of the country
are susceptible to soil erosion as a consequence of semi arid climate conditions, high rainfall intensity, and
limited or degraded land cover. (Schulze, R.E. 2010).Soil susceptibility to erosion is spatially represented in
Map 14 and the mean annual demand for irrigation (measured in mm water) to support crop cultivation
across the country is modelled in Map 19.
High sediment load in streamflow as a result of soil erosion in itself represents a threat to the storage
capacity and lifespan of water infrastructure such as dams and irrigation systems. It also has negative
consequences for water quality whichcan negatively impact rural communities that rely on natural water
sources.
At the same time, agriculture is of great importance for the climate resilience of the country in general and
rural human settlements in particular as a source of employment, livelihoods, and food security and as a
sector is particularly vulnerable to the impacts of climate change. As has already been noted in the
discussion of hazard exposure to the atmospheric changes associated with climate change, the impacts of
climate change on agriculture are direct and specific to particular crops and agricultural techniques. It is
beyond the scope of this study to replicate the growing body of work by institutions such as the Agricultural
Research Council on projections of climate change impacts on particular agricultural crops and animals and
made available for use by local and regional adaptation planners at a variety of online data portals and
30
published in the Atlas of Climate Change for the South African Agricultural Sector: A 2010 Perspective.
(Schulze 2010).
It is worth noting, however, that the projected changes in the variability of rainfall, coupled with projections in
the timing of rainfall, are of great significance to farmers, particularly subsistence farmers relying on rain-fed
agriculture, as the timing of planting of crops is often determined by expectations in terms of rain. Modelling
of rainfall seasonality indicates that the timing of rain in the summer rainfall regions, which tends to fall later
towards the west, will in general be delayed as a result of climate change. There is considerable uncertainty
about the modelling on rainfall at the boundaries between the summer and winter rainfall regions, and a
heightened risk of increased variability in these areas, implying both very wet and very dry periods (Schulze
2010).
Map 14: Rainfall erosivity
Source: (Schulze 2011)
2.7.
Climate disasters and cumulative environmental impacts
The relationship between disaster risk management and climate change has become an increasingly
important focus for climate change as it has become clear that increases in the frequency and intensity of
extreme weather events constitute an immediate and damaging impact of climate change. Globally, there is
strong evidence of an increase ineconomic losses from disasters related to climate extremes although there
is large regional and inter-annual variability in these impacts.
In the Special Report of the IPCC on managing the Risks of Extreme Events and Disasters to Advance
Climate Change Adaptation disasters are defined as:
Severe alterations in the normal functioning of a community or a society due to hazardous physical
events interacting with vulnerable social conditions, leading to widespread adverse human, material,
economic, or environmental effects that require immediate emergency response to satisfy critical
human needs and that may require external support for recovery.(IPCC 2012)
The contribution of climate change to the frequency and intensity of hazardous physical events in the above
definition can be understood in terms of the model of hazard exposure and sensitivity to climate extremes, to
which particular rural communities will have specific risk exposure based on their location. The level of
environmental risk and social vulnerability of rural human settlements may vary in relation to the specific
nature of the climate related events to which they are exposed. Climate related events which can assume
disastrous proportions and are of particular relevance to South African rural human settlements include:

Drought
31

Storms and Flooding

Veldfires
Climate related disasters can have either a sudden impact, as in the case of flash floods, or can have a more
gradual onset that is the result of an incremental accumulation of environmental impacts, as is often the case
with drought.
2.7.1.
Drought
Droughts are defined in South Africa as a season’s rainfall of 70% less than normal (Bruwer 1990), and are
considered progressive or ‘slow onset’ disasters. They are a temporary feature, and are typically more
widespread than localised. Droughts caused damage estimated at R1 150 million between 2000 and 2009
(DEA 2011).
During stakeholder engagement workshops drought was consistently ranked as a major concern, despite the
fact that the risk of drought may actually decrease for much of the country, with the exception of the west and
northern interior which is projected to be subject to drying. This trend is illustrated in Map 15, which spatially
represents trends in relation to the percentage change in consecutive dry days. While drought is always the
incremental result of persistent lack of rain, its impacts on rural human settlements can manifest quite
suddenly. For instance, some rural communities in the arid west of the country depend on boreholes that
may experience relatively sudden changes in water availability and quality as a result of long duration
impacts on groundwater recharge rates.
Drought should not only be thought of as a meteorological phenomenon relating to rainfall but needs also to
be considered as a hydrological phenomenon reflected in changes to streamflow which is sensitive tofactors
such as evaporation rates, groundwater availability and recharge rates, geology, soil characteristics and land
cover. Even when a meteorological drought is technically broken as a result of rainfall, it is possible for the
amount of rainfall to have been insufficient to break a hydrological drought.
Map 16 illustrates a projection of changes in the spatial distribution of moderate hydrological droughts for the
period 2046 – 2065 in comparison to a baseline period of 1971 – 1990. Although this projection is based on
a different (and smaller) set of downscaled GCMs than those used by SAWS to project the number of dry
days, the overall spatial distribution of drying trends is similar.
Map 16: Changes in the incidence of hydrological drought of moderate intensity
Source: (Schulze 2011)
32
Error! Not a valid bookmark self-reference. uses a comparable set of GCMs and timeframes for
meteorological drought and projects a decrease in drought across virtually the entire country, thereby
illustrating the importance of hydrological sensitivity in determining the possible impacts of climate
change.(Schulze 2011)
Map 17: Changes in meteorological drought of moderate or more severe intensity
Source: (Schulze 2011)
The modelling of drought by the WRC is undertaken by comparing probabilities forannual rainfall or
streamflow in any particularyear in the future period to the average annual rainfall or streamflow for the
current period. When the more distant future, i.e. the period 2071 to 2100 is compared to the intermediate
future, i.e. the period 2046 – 2065, a long term drying trend in the western and north eastern regions of the
country with potentially significant socio-economic implications becomes more apparent from the
meteorological data, as illustrated in Map 18.
Map 18: Changes in the incidence of meteorological drought in the distant future.
Source: (Schulze 2011)
33
This also illustrates the effect of future amplification of climate change impacts which can result in changes to
the direction of trends in precipitation patterns.
Much of South Africa is already arid and requires irrigation for the cultivation of crops. The demand for
irrigation is expected to increase in the future as rainfall becomes less predictable. Map 19 shows the current
mean annual irrigation demand for South Africa, hence highlighting areas already sensitive to drought.
Map 19: Mean annual requirement for irrigated water to support crops
Source (Schulze 2011)
2.7.2.
Floods and storms
Messaging about the impacts of climate change tends to focus on droughts as an impact of climate change.
Historical data indicates that floods are responsible for a greater number of human fatalities and cause
greater damage to assets than droughts. In South Africa between 2000 and 2009 floods, associated with
high and often concentrated rainfall events, have caused damage estimated at R4 700 million, and 140
deaths have resulted. Storms are associated with heavy precipitation, high winds, and flash floods, and often
with coastal and landslide damage. Each component has the ability to cause extensive damage. Difficult to
dissociate from floods, storms are most often considered sudden events that can come in a number of forms,
most commonly associated with severe thunderstorms and cold fronts. Storms have cost South Africa R395
million, and have resulted in six reported deathsand it is expected that the frequency of intense storms is
likely to increase as a result of climate change (DEA 2011).
Although floods typically have a sudden impact, they can also have a gradual onset resulting from an
accumulation of rainfall over several days or weeks. In these cases, flooding is typically preceded by water
logging, in which soil becomes saturated and is unable to absorb additional rainfall. Water logging can cause
extensive crop losses and the sensitivity of particular locations is determined by soil types and depth, as well
as the geological sub-strata. Water logging is not typically a problem in the arid Northern Cape, but can be a
problem in the eastern third of the country (Schulze2010).
Map 7 indicates shows the SAWS projections for consecutive wet days, with a trend towards an increase in
the eastern parts of the country, providing an indication of exposure to precipitation patterns responsible for
flood. Error! Reference source not found. (Schulze 2011) shows at catchment level the change in the
number of days per annum in which stream flow exceeds 2mm when combining precipitation projections for
the period 2046 – 2065 and comparing them to a baseline model of stream flow for the period 1971 – 1990.
An increase in the number of days where stream flow exceeds 2mm is associated with a risk of regional and
local flooding, and as can be seen in the map, this is projected to increase for much of the country, with the
exception of the south west and some catchment in the north east, where there is expected to be a decrease
in the number of days per annum with high stream flow.
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