Download climate change and the south african commercial forestry sector

CLIMATE CHANGE AND THE SOUTH
AFRICAN COMMERCIAL FORESTRY
SECTOR
AN INITIAL STUDY
ACRUcons Report 54
December 2006
School of Bioresources Engineering
and Environmental Hydrology
University of KwaZulu-Natal, Pietermaritzburg
CLIMATE CHANGE AND THE SOUTH AFRICAN
COMMERCIAL FORESTRY SECTOR
AN INITIAL STUDY
Michele Warburton and Roland Schulze
Report to
Forestry SA
ACRUcons Report 54
December 2006
School of Bioresources Engineering and Environmental Hydrology
University of KwaZulu-Natal, Pietermaritzburg, South Africa
Disclaimer
While every reasonable effort has been made by the authors to obtain objective and realistic results in this study,
neither the authors, the School of Bioresources Engineering and Environmental Hydrology nor the University of
KwaZulu-Natal, nor any of their employees, make any warranty, express or implied, or assume any legal liability
or responsibility for the accuracy, completeness or usefulness of any information, product or process disclosed
in this report.
CLIMATE CHANGE AND THE SOUTH AFRICAN FORESTRY SECTOR
An Initial Study
EXECUTIVE SUMMARY
SECTION 1:
SETTING THE SCENE
Concern, awareness and interest surrounding climate change is increasing. In the scientific arena, it
has become accepted that climate change is a reality, with the debate now focused on the magnitude
and timing of change, the increasing frequency and intensity of extreme events, as well as on the
potential impacts of change on natural and anthropogenic systems, including the commercial forestry
sector in South Africa. It is for the above reasons that this report was commissioned by Forestry SA.
The report is made up of nine sections:
•
•
•
•
•
•
•
•
•
Section 1 sets the scene with a general introduction to the phenomenon of climate change;
Section 2 covers the detection of climate change over South Africa as already evidenced in
analyses of climatic records for the period 1950 - 2000;
Section 3 reviews recent literature pertaining to climate change and forestry;
Section 4 assesses the Kyoto Protocol and land based carbon sinks and sequestration, from a
forestry perspective;
Section 5 presents climate scenarios for the future and the approach taken in this report on
plausible future climates;
Section 6 discusses the baselines of present climate over South Africa, and the mapping of
climatically optimum and sub-optimum areas of major commercially grown species/hybrids
under present climatic conditions as a point of departure for the next section, viz.
Section 7, the core of this report, in which the results of a sensitivity analysis on potential
impacts of climate change on major commercially grown tree species and hybrids in South
Africa are presented and interpreted;
Section 8 addresses issues on adaptation strategies to climate change; while in
Section 9, overall conclusions are drawn and recommendations for further research are made.
SECTION 2:
DETECTION OF CLIMATE CHANGE OVER SOUTHERN AFRICA
A series of analyses of trends in temperature and rainfall parameters over the period 1950 - 2000 is
presented in this Section. In almost every analysis two clear statistically significant clusters of
warming over this period emerge, viz. over the Western Cape and around the Midlands of KwaZuluNatal, along with the KwaZulu-Natal coast. The clusters show up in the analyses of annual means of
both minimum and maximum temperatures, of cold spells, as well as of heat units. Another distinct
conclusion that can be drawn is the warming over the interior when analysing frost occurrences, with
the Free State and Northern Cape provinces standing out in this regard. There appears to be a shift
towards an earlier ending of the frost season, and a shorter frost season with fewer frost occurrences.
These changes are not, however, spatially uniform. Changes in precipitation patterns were also
identified. Importantly, these precipitation changes are not always apparent in large space-time
averages; rather, they are most apparent at sub-annual scales, and in the derivative statistics of the
attributes of precipitation.
ii
SECTION 3:
CLIMATE CHANGE AND
LITERATURE REVIEW
THE
COMMERCIAL
FORESTRY
SECTOR:
A
Since, with global warming, changes in the growth rates of forests could impact significantly on forest
management and timber markets, an adjustment in forest policy and planning may be required.
Literature searches showed that, to date, there is only a limited literature worldwide on potential
impacts of climate change on commercial forestry, with the majority of literature referring to natural
forests. Uncertainties as to the responses of trees under conditions of elevated atmospheric CO2, of
changing temperatures and rainfall patterns still exist. While progress on forest related climate change
experiments is being made, a large amount of research is still needed.
SECTION 4:
THE KYOTO PROTOCOL
SEQUESTRATION
AND
LAND
BASED
CARBON
SINKS
AND
The Kyoto Protocol commits developed nations and countries in transition to achieving quantified
reductions in greenhouse gas emissions by at least 5 per cent below 1990 levels in the commitment
period 2008 – 2012. South Africa entered into acceptance of the Kyoto Protocol in 2002. As a
developing country, South Africa is not required to reduce its greenhouse gas emissions. However,
its economy is highly dependent on fossil fuels and the country is a significant emitter in terms of
emission intensity and emissions per capita.
One objective of the Kyoto Protocol is the promotion of sustainable development by implementing
policies and measures such as protection of sinks and reservoirs of greenhouse gases (e.g.
promotion of sustainable forest management practices, afforestation and reforestation), promotion of
sustainable agriculture, advancement of environmentally sound technologies, and investigations into
carbon sequestration technologies. With specific reference to the forest industry, four ways of
increasing carbon sequestration would be protection of secondary and other degraded forests in order
to allow them to regenerate, restoration of indigenous forests through assisted and natural forest
regeneration, establishment of forest plantations on currently non-forested lands, and increasing the
tree cover on agricultural and pasture lands.
In addition, opportunities exist for reducing carbon emissions from forests by conserving the existing
carbon stocks in forest vegetation and soil, for example through avoidance of deforestation, altering
the forest harvesting regimes, and controlling the anthropogenic and natural disturbances of forests
(such as fire and pest outbreaks).
Many concerns and issues surround the use of forestry as a carbon sink. Nevertheless, forestry
remains an effective, low cost method of removing carbon from the atmosphere in a sustainable
manner. Progress is being made in resolving problems, issues and concerns surrounding forestry
and carbon sinks. The science behind carbon sinks is well understood and established and many of
the issues and concerns surround technical details, with misunderstanding often existing between
scientists and non-scientists, and between various countries. There appears considerable scope for
forestry projects under the Clean Development Mechanism of the Kyoto Protocol and the additional
benefit which forestry could provide in terms of carbon credits places it in a favourable light in
comparison to that of other land uses.
As a signatory to the Kyoto Protocol, South Africa has certain obligations which have been
incorporated into the National Climate Change Response Strategy for South Africa, or NCCRS
(2004). These are highlighted from a forestry perspective in the concluding paragraphs of this section.
iii
SECTION 5:
CLIMATE SCENARIOS FOR THE FUTURE
The Intergovernmental Panel on Climate Change (IPCC) has developed different ‘storylines’ of
emission scenarios and associated climate changes for the future, these representing different
demographic, social, economic, technological and environmental developments. These scenarios,
when input into General Circulation Models (GCMs) to provide perturbations in temperature and
rainfall to represent future climates, have not been used in this study because of uncertainties which
still exist in such climate projections.
Instead, the approach taken in this study is one of a series of sensitivity analyses, where variables are
perturbed, either singularly or jointly, by small but realistic (i.e. plausible) increments from a baseline,
and the results (e.g. impacts on the forestry sector) are then compared against those from present
day climatic conditions, which are represented by a baseline (i.e. reference) climate.
Incremental climate scenarios, i.e. sensitivity analyses, allow the following:
•
•
•
•
the likely impacts on the forestry sector to be gauged,
the thresholds of change to be determined, i.e. at what level of change the impacts become
significant;
the identification of where change is likely to be more significant (i.e. identifying hotspots), and
the assessment as to which ‘driver’ of climate change is more significant than others (i.e.
temperature or rainfall, in this instance).
Eight plausible scenarios were selected, based on previous outputs from GCMs, and they were
•
•
•
temperature increases by 0.5°C, 1.0°C, 1.5°C and 2.0°C,
a temperature increase by 2.0°C in combination with a rainfall decrease by 5% and by 10%,
and
a temperature increase by 2.0°C in combination with a rainfall increase by 5% and by 10%.
SECTION 6:
BASELINE STUDIES: A POINT OF DEPARTURE FOR CLIMATE CHANGE
IMPACT STUDIES
As a point of departure for an analysis of impacts of climate change on the forestry sector, baseline
studies were undertaken. Baseline studies require detailed databases. The databases reviewed
were on gridded daily temperatures at ~ 1.7 km resolution for a 51 year base period from 1950 - 2000,
and on gridded monthly and annual rainfall, also at ~ 1.7 km resolution, both developed by scientists
from the School of Bioresources Engineering and Environmental Hydrology at the University of
KwaZulu-Natal, as well as the Institute for Commercial Forestry Research’s Forestry Productivity
Toolbox, which provides the basis for delineation of climatically optimum/sub-optimum growth areas
for various forest species. The baseline studies showed that for many species/families the areas
currently planted do not necessarily coincide with the optimum climate for that species/family,
especially in the case of Acacia mearnsii (Table ES1).
SECTION 7:
RESULTS OF CLIMATE CHANGE SENSITIVITY ANALYSES IN SOUTHERN
AFRICA ON DIFFERENT TREE SPECIES
Following on a series of analyses on potential impacts of climate change on the commercial forestry
sector in South Africa, a provincial level analysis was used to summarise findings on absolute (i.e. in
km2) changes in climatically optimum, sub-optimum and high risk areas for the plausible climate
iv
Table ES1
Areas currently (i.e. 2001) planted to Acacia mearnsii as well as to various Eucalyptus
and Pinus species/hybrids, expressed as percentages of climatic suitability classes
Climatic Suitability Class
Optimum
Moderate Risk
High Risk
Climatically Unsuitable
Totals
Percentages of Current Planted Areas
A. mearnsii
Eucalyptus
Pinus
species/hybrids
species/hybrids
36.9
82.5
75.5
43.0
4.4
17.0
18.7
6.8
1.5
1.4
6.3
6.0
100.0
100.0
100.0
scenarios used in this study. For Acacia mearnsii, the largest climatically optimum area under present
conditions occurs in KwaZulu-Natal. As temperature increases and rainfall is decreased, the area
which is climatically optimal in KwaZulu-Natal decreases significantly, with only an expansion in
suitable area occurring when both temperature and rainfall are increased. The same pattern,
although with greater fluctuations, is evident in the Eastern Cape. In Mpumalanga province, however,
the climatically optimum area for A. mearnsii increases when the temperature is increased by 1°C and
2°C, possibly due to decreased occurrences of frost. A decrease in the climatically optimum area
would occur when temperature is increased and rainfall is decreased simultaneously, but a
substantial increase in the climatically optimum area is projected to occur with both an increase in
temperature and rainfall.
The climatically optimal area for eucalypts in KwaZulu-Natal again decreases markedly with
increasing temperatures, as well as with increased temperatures combined with decreased rainfall.
The climatically optimum areas within the Eastern Cape are slightly less sensitive to increases in
temperature in comparison to areas within KwaZulu-Natal; however, they are relatively more
susceptible to decreasing rainfall. If an increase in rainfall combined with an increase in temperature
were to occur, a larger area in the Eastern Cape would become climatically optimum for the growth of
eucalypts in comparison with the area under the present climate. In Mpumalanga, the area which is
climatically optimal for the growth of eucalypts increases slightly with a 1°C increase in temperature,
and remains relatively stable for a 2°C increase in temperature when compared with the area under
present climatic conditions. An increase in temperature of 2°C together with an increase in rainfall of
10% would, however, result in a far larger proportion of Mpumalanga meeting the climatic
requirements for optimum growth of eucalypts.
In the case of Eucalyptus species/hybrids, E. nitens is highly sensitive to increases in temperature
while the Eucalyptus hybrid evaluated, viz. Eucalyptus GxU, is more robust to changes in
temperature. All the Eucalyptus species and/or hybrids are highly sensitive to changes in rainfall with
respect to their climatic optimum growth areas. With regard to changes in actual areas suitable for
eucalypts, KwaZulu-Natal is vulnerable should increases in temperature occur, while Mpumalanga
and the Eastern Cape are more robust to increases in temperature.
Overall, the Pinus species and/or hybrids are relatively robust to potentially increasing temperatures
and changing rainfall regimes. In relative terms the Pinus species were found to be more sensitive to
increasing temperatures and decreasing rainfall than the Pinus hybrid which was considered, viz.
Pinus ExC, which appears relatively robust to climate change. The climatically optimum areas within
KwaZulu-Natal are likely to decrease with increasing temperatures, while actual areas which are
climatically optimal for Pinus species/hybrids in the Eastern Cape and Mpumalanga could expand
under conditions of increasing temperatures.
v
In an overall assessment of the sensitivity of commercial forestry to climate change the following
emerged:
•
•
•
•
•
the climatic variable to which forest species is most sensitive, is rainfall;
the hybrids of both eucalypts and pines are relatively more robust than commonly grown
species to potential increases in temperature (in particular) and, to a certain degree, to
decreases in rainfall;
areas currently under plantations where the present climate is only moderately suitable will,
under conditions of increasing temperature and decreasing rainfall, most likely become high
risk areas; and thus species with large proportions at present already being planted in only
moderately suitable climates are highly vulnerable to climate change;
on a provincial basis the climatically optimal areas for plantation forestry within KwaZulu-Natal
are likely to decrease with climate change, while results indicate that areas within the Eastern
Cape and Mpumalanga may offer opportunities for expansion with increasing temperature; and
of the three families included in this study, viz. Acacia, Eucalyptus and Pinus, the Pinus family
is relatively more robust to climate change than the other two.
Given the potential impacts of climate change on the commercial forestry sector, and the fact that
changes in climate can already be detected in southern Africa, the development and implementation
of adaptation policies, measures and strategies is crucial. Concepts surrounding adaptation were
explored in Section 8.
SECTION 8:
ADAPTATION TO CLIMATE CHANGE
On the question as to whether one should wait and see what will happen to climates in the future,
protagonists of adaptation argue as follows:
•
•
•
Climate change is a reality and the evidence to support this, at both the global and regional
scales, is already immense. Although there is still uncertainty surrounding the magnitude and
timing of climate change in the future, the negative impacts of this change are likely to be
enormous. Thus, adaptation needs to occur in the immediate future. Processes need to be
implemented now, as the time scales for these processes to take effect and be adopted are
likely to be long in the case of commercial forestry.
The changes in climate which are being observed at present already are occurring at a far
faster rate than the changes in climate which have occurred in the past, and they will continue
to occur in the future if action is not taken.
Given the pressing world problems as they stand, any adaptation measure implemented now
should already address many of the present day problems of climate variability and make
allowances for the future. In implementing adaptation measures, a ‘no regrets’ approach is
required. The measures implemented under a no regrets approach will have benefits which
are equal to or exceed their cost to society, and will be of benefit regardless of climate change.
From a South African perspective, the action-orientated response strategy of the NCCRS identifies a
number of interventions that are required. These interventions are listed in Section 8. Two
interventions in the NCCRS relate specifically to the commercial forestry sector:
•
The first, on ‘Changes in forestry practices’, proposes the use and development of more heat
and drought resistant hybrids and species, while also suggesting that competition for land from
more lucrative uses may increase. It also highlights that ‘current commercial forests make
vi
extensive use of exotic species, a practice that may influence biodiversity and other climate
change sensitive factors such as excessive water use and soil properties’.
The second intervention relating to forestry places this sector in a far more positive light, by
calling for the ‘establishment and extension of forest schemes through the Department of
Water Affairs and Forestry and the forestry industry’, provided that these projects do not
compromise the environmental policy objectives in South Africa.
•
With respect to adaptation strategies it needs to be stressed that, unlike annual agricultural crops,
commercial forestry is only harvested after 10 - 30 years. Therefore, the consequences of a climatic
disturbance have long term impacts which are not corrected or reversed in the following year’s field
management decisions. For example, during the 1991/1992 drought the forestry industry in South
Africa lost approximately R450 million, and the repercussions of that drought were still being felt 10
years later when the trees were harvested.
Given the long time frame associated with management decisions made in forestry, the sensitivity of
the industry to climate, and the uncertainty of the magnitude and timing of climate change, the
commercial forestry sector will have to implement adaptation strategies. Such strategies will need to
vary with location, and with time scales. The commercial forestry sector is aware of potential threats
posed by climate change and the challenges this poses, as evidenced by the commissioning of this
report. If these challenges are taken seriously, a next stage would be to formulate an intention to
adapt. In that regard, the UK Forest Research Group in 2006 identified the following basic measures
which should be implemented to cope with climate change as part of a no regrets approach, and
which could well be adapted for South Africa:
•
•
•
Mixing of species, to provide insurance against climate change;
Matching of species to site, i.e. if the site is currently at the dry end of the species range, that
species should no longer be planted there; and
Giving consideration to climate change predictions now already when choosing the species to
be planted.
SECTION 9:
CONCLUSIONS AND RECOMMENDATIONS
Climate change is a reality, both at global and the regional scales. The magnitude of change is still
uncertain, as is the timing of the change. However, the severe nature of the impacts which climate
change could have on all sectors of South Africa’s economy is becoming clear. The physiological
response of trees to elevated atmospheric CO2 and changes in both temperature and rainfall still have
uncertainties surrounding them. It is clear that the climatic driver to which commercial forestry in
southern Africa is most susceptible to changes in, is rainfall. The analyses performed in this study
suggest that forestry hybrids will be more robust to climate change than currently grown species. In
addition, the Pinus family emerged as relatively more robust than wattle or eucalypts.
However, the forestry models used in this study were simple, in that they were based solely on coarse
climatic criteria of mean annual temperature and mean annual precipitation and took no cognisance
of, for example, numbers of frost days, of heat waves, of soil water stress days, runoff generating
events, soil properties, slope, competing land uses, or streamflow reduction activities.
As one way forward, a recommendation is made for a follow-up phase of this study to examine the
impacts of climate change on the commercial forestry sector by
•
considering some of the factors named above,
vii
•
•
•
using more physical conceptual models which would also estimate yield changes,
utilising future climate scenarios from recent (2006) Regional Climate Models (RCMs) for
southern Africa, and
assessing effects of competition from other land uses under climate change conditions.
viii
TABLE OF CONTENTS
Page
xi
xiv
LIST OF FIGURES
LIST OF TABLES
SECTION 1
SETTING THE SCENE
1
SECTION 2
DETECTION OF CLIMATE CHANGE OVER SOUTHERN AFRICA
5
2.1
2.2
2.3
SECTION 3
SECTION 4
4.1
4.2
4.3
4.4
SECTION 5
5.1
5.2
5.3
SECTION 6
6.1
Detection of Changes in Temperature Parameters over Southern Africa: Selected
Results
2.1.1 Trends Over Time in Annual Means of Temperature
2.1.2 Trends Over Time in Occurrences of Temperatures Above and Below
Selected Percentiles
2.1.3 Trends Over Time in Occurrences of Frost and the Length of the Frost
Season
2.1.4 Trends Over Time in Heat Units
Detection of Changes in Southern Africa’s Rainfall Parameters: Selected Results
A Summary of Changes Already Detected in the Temperature and Rainfall
Regimes of Southern Africa Over Time
CLIMATE CHANGE AND THE COMMERCIAL FORESTRY SECTOR:
A LITERATURE REVIEW
THE KYOTO PROTOCOL AND LAND BASED CARBON SINKS AND
SEQUESTRATION
The Kyoto Protocol - Unpacked
4.1.1 Mechanisms for the Implementation of the Kyoto Protocol
4.1.1.1 Joint Implementation
4.1.1.2 Clean Development Mechanism
4.1.1.3 Emission Trading Scheme
South Africa’s Role Under the Kyoto Protocol
Forestry and Land Based Carbon Sinks
4.3.1
What is Carbon Sequestration and How Does it Work? A Forestry
Perspective
4.3.2
Concerns and Problems Surrounding Carbon Sequestration from Forests
Concluding Remarks
CLIMATE SCENARIOS FOR THE FUTURE
IPCC Scenarios
Uncertainties in Climate Change Science
Approaches to Future Climate Scenarios Used in this Study
BASELINE STUDIES: A POINT OF DEPARTURE FOR CLIMATE CHANGE
IMPACT STUDIES
The School of BEEH Databases of Temperature and Rainfall
6.1.1 Database of Gridded Temperatures for Southern Africa
6.1.2 Database of Gridded Rainfall for Southern Africa
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5
5
6
7
9
11
12
16
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27
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29
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6.2
6.3
The Institute for Commercial Forestry (ICFR) Forestry Productivity Toolbox
Mapping Climatically Optimum and Sub-Optimum Areas for Forest Species Under
Baseline Climatic Conditions
6.3.1 Baseline Studies on Acacia mearnsii
6.3.2 Baseline Studies on Eucalyptus Species
6.3.3 Baseline Studies on Pinus Species
SECTION 7
7.1
7.2
7.3
7.4
7.5
RESULTS OF CLIMATE CHANGE SENSITIVITY ANALYSES IN SOUTHERN
AFRICA ON DIFFERENT TREE SPECIES
The Scenarios Used in this Study - Revisited
Results from Acacia mearnsii
Results from Eucalyptus Species
Results from Pinus Species
Summary of the Climate Change Sensitivity Analysis
SECTION 8
8.1
8.2
8.3
8.4
ADAPTATION TO CLIMATE CHANGE
Definitions of Terms
Should One Not Wait and See What Happens First?
The National Climate Change Response Strategy of South Africa
Where to Now: Implementation of Adaptation Strategies
SECTION 9
CONCLUSIONS AND RECOMMENDATIONS
33
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40
45
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48
60
70
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74
75
79
ACKNOWLEDGEMENTS
81
REFERENCES
82
x
LIST OF FIGURES
Page
Figure 1.1
Figure 1.2
Figure 2.1
Figure 2.2
Figure 2.3
Figure 2.4
Figure 2.5
Figure 2.6
Figure 2.7
Figure 2.8
Figure 2.9
Figure 2.10
Figure 2.11
Figure 2.12
Figure 2.13
Figure 2.14
Figure 4.1
Figure 5.1
Figure 5.2
Figure 5.3
Global temperature increases between 1856 and 2005 (Jones and Palutikof,
2006)
Variations in temperature over the past 1000 years, based on data from
thermometers, tree rings, corals, ice cores and historical records (IPCC, 2001)
Trends at the 95% confidence level over southern Africa in the 1950 - 2000
time series of annual means of daily maximum temperatures (Warburton et
al., 2005)
Trends at the 95% confidence level over southern Africa in the 1950 - 2000
time series of annual means of daily minimum temperatures (Warburton et al.,
2005)
Trends at the 95% confidence level over southern Africa in the 1950 - 2000
time series of the number of days per year below the 10th percentile of winter
(June, July, August) minimum temperatures (Warburton et al., 2005)
Trends at the 95% confidence level over southern Africa in the 1950 - 2000
time series of the number of days per year above the 90th percentile of
summer (December, January, February) maximum temperatures (Warburton
et al., 2005)
Trends at the 95% confidence level over southern Africa in the 1950 - 2000
time series of the number of frost occurrences (i.e. daily minimum < 0°C) per
season (Warburton et al., 2005)
Trends at the 95% confidence level over southern Africa in the 1950 - 2000
time series of the date of the first frost per season (Warburton et al., 2005)
Trends at the 95% confidence level over southern Africa in the 1950 - 2000
time series of the date of the last frost per season (Warburton et al., 2005)
Trends at the 95% confidence level over southern Africa in the 1950 - 2000
time series of heat units (base 10°C) for the summer season, i.e. October to
March (Warburton et al., 2005)
Trends at the 95% confidence level over southern Africa in the 1950 - 2000
time series of heat units (base 10°C) for the winter season, i.e. April to
September (Warburton et al., 2005)
Historical trend (1950-1999) over southern Africa of change per decade of
mean monthly precipitation totals, in mm (Hewitson et al., 2005)
Historical trend (1950-1999) over southern Africa of change per decade in
mean monthly number of raindays > 0 mm (Hewitson et al., 2005)
Historical trend (1950-1999) over southern Africa of change per decade in
mean monthly number of raindays > 2 mm (Hewitson et al., 2005)
Historical trend (1950 - 1999) over southern Africa of change per decade in
mean monthly dry spell duration, in days (Hewitson et al., 2005)
Historical trend (1950 - 1999) over southern Africa of change per decade in
mean monthly 90th percentile magnitude precipitation event, in mm (Hewitson
et al., 2005)
Registered CDM projects by host country (UNFCCC, 2006)
SRES emission scenario storylines and descriptions (IPCC, 2000)
Global CO2 emissions from energy and industry for all 40 SRES emission
scenarios for the period 1990 to 2100, expressed as an index where
emissions in 1990 are one (IPCC, 2000)
Potential temperature changes for the six SRES emission scenarios (IPCC,
2001)
xi
2
2
7
7
8
8
9
10
10
11
12
13
13
14
14
15
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28
29
Figure 5.4
Figure 6.1
Figure 6.2
Figure 6.3
Figure 6.4
Figure 6.5
Figure 6.6
Figure 6.7
Figure 6.8
Figure 6.9
Figure 6.10
Figure 6.11
Figure 6.12
Figure 6.13
Figure 6.14
Figure 6.15
Figure 6.16
Figure 7.1
Figure 7.2
Figure 7.3
Figure 7.4
Figure 7.5
Illustration of the ‘cascade of uncertainty’ in climate change science
(Hewitson, 2002)
Illustration of the 9x22 climate matrix for delineation of optimum/sub-optimum
growth areas for Eucalyptus grandis (after Kunz, 2004)
Climate matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006a) of
climatically suitable areas for the growth of Acacia mearnsii
Comparison of climatically optimum, moderate risk (MR) and high risk (HR)
areas for Acacia mearnsii with areas currently planted, as determined from
the National Land Cover image (2001)
Climaec matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006b) of
climatically suitable areas for the growth of Eucalyptus grandis
Climate matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006c) of
climatically suitable areas for the growth of Eucalyptus dunnii
Climate matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006c) of
climatically suitable areas for the growth of Eucalyptus smithii
Climate matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006c) of
climatically suitable areas for the growth of Eucalyptus nitens
Climate matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006c) of
climatically suitable areas for the growth of Eucalyptus GxU
Comparison of climatically optimum, moderate risk (MR) and high risk (HR)
areas for Eucalyptus species with areas currently planted, as determined from
the National Land Cover image (2001)
The number of Eucalyptus species or hybrids out of 13 which were analysed,
which satisfy the optimum climate criteria for a given 1’ latitude x 1’ longitude
pixel
Climate matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006d) of
climatically suitable areas for the growth of Pinus taeda
Climate matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006e) of
climatically suitable areas for the growth of Pinus patula
Climate matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006f) of
climatically suitable areas for the growth of Pinus elliottii
Climate matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006g) of
climatically suitable areas for the growth of Pinus ExC
Comparison of climatically optimum, moderate risk (MR) and high risk (HR)
areas for Pinus species/hybrids with areas currently planted, as determined
from the National Land Cover image (2001)
The number of Pinus species or hybrids out of six which were analysed, which
satisfy the optimum climate criteria for a given 1’ latitude x 1’ longitude pixel
Mapped results of the Acacia mearnsii sensitivity analyses to various climate
change scenarios
Percentage changes in the climatically optimum and sub-optimum, i.e.
moderate risk, areas for Acacia mearnsii with increases in temperature
Percentage changes in the climatically optimum plus sub-optimum, i.e.
moderate risk, areas for Acacia mearnsii for a 2°C increase in temperature in
combination with a range of negative and positive changes in rainfall
Areas (km2) which are climatically optimal for Acacia mearnsii for various
plausible scenarios of climate
Mapped results of the Eucalyptus dunnii sensitivity analyses to various
climate change scenarios
xii
30
34
36
36
37
37
38
38
39
39
40
42
42
43
43
44
44
46
47
47
49
50
Figure 7.6
Figure 7.7
Figure 7.8
Figure 7.9
Figure 7.10
Figure 7.11
Figure 7.12
Figure 7.13
Figure 7.14
Figure 7.15
Figure 7.16
Figure 7.17
Figure 7.18
Figure 7.19
Figure 7.20
Figure 7.21
Figure 7.22
Figure 7.23
Figure 8.1
Figure 8.2
Figure 8.3
Percentage changes in the climatically optimum areas for Eucalyptus
species/hybrids with increases in temperature
Percentage changes in the climatically optimum plus sub-optimum, i.e.
moderate risk, areas for Eucalyptus species/hybrids with changes in
temperature
Percentage changes in the climatically optimum areas for Eucalyptus
species/hybrids for a 2°C increase in temperature in combination with a range
of negative and positive various changes in rainfall
Percentage changes in the climatically optimum plus sub-optimum, i.e.
moderate risk, areas for Eucalyptus species/hybrids for a 2°C increase in
temperature in combination with a range of negative and positive changes in
rainfall
Mapped results of the Eucalyptus grandis sensitivity analyses to various
climate change scenarios
Mapped results of the Eucalyptus GxU sensitivity analyses to various climate
change scenarios
Mapped results of the Eucalyptus nitens sensitivity analyses to various
climate change scenarios
Mapped results of the Eucalyptus smithii sensitivity analyses to various
climate change scenarios
Areas (km2) which are climatically optimal for Eucalyptus species/hybrids for
various plausible scenarios of climate
Mapped results of the Pinus elliottii sensitivity analyses of various climate
change scenarios
Percentage changes in the climatically optimum areas for Pinus
species/hybrids with increases in temperature
Percentage changes in the climatically optimum plus sub-optimum, i.e.
moderate risk, areas for Pinus species/hybrids with changes in temperature
Percentage changes in the climatically optimum areas for Pinus
species/hybrids for a 2°C increase in temperature in combination with a range
of negative and positive changes in rainfall
Percentage changes in the climatically optimum plus sub-optimum, i.e.
moderate risk, areas for Pinus species/hybrids for a 2°C increase in
temperature in combination with a range of negative and positive changes in
rainfall
Mapped results of the Pinus ExC sensitivity analyses of various climate
change scenarios
Mapped results of the Pinus patula sensitivity analyses of various climate
change scenarios
Mapped results of the Pinus taeda sensitivity analyses to various climate
change scenarios
Areas (km2) which are climatically optimal for Pinus species/hybrids for
various plausible scenarios of climate
The adaptation process (after Arnell, 2005; as cited by Schulze, 2005b)
Limits to adaptation (after Arnell, 2005; as cited by Schulze, 2005b)
Feedbacks between adaptation strategies and climate change impacts (ideas
from IPCC, 2001; Vogel, 2003; Schulze, 2005b)
xiii
51
51
52
52
53
55
56
58
60
61
62
62
64
64
65
66
68
69
76
77
77
LIST OF TABLES
Page
Table 1.1
Table 4.1
Table 6.1
Table 6.2
Table 6.3
Table 7.1
Table 7.2
Table 7.3
Statistics of global abundances of key greenhouse gases in 2004 (WMO,
2006)
Annex I Parties included in the Kyoto Protocol and their emissions targets
(UNFCCC, 2006)
Areas currently (i.e. from the 2001 satellite imagery) planted to Acacia mearnsii
as percentages of climatic suitability classes
Areas currently (i.e. from 2001 satellite imagery) planted to Eucalyptus species
and hybrids as percentages of climatic suitability classes
Areas currently (i.e. from the 2001 satellite imagery) planted to Pinus species
and hybrids as percentages of climatic suitability classes
Distribution of areas currently planted to Acacia mearnsii in different climate
suitability classes, for a range of plausible climate scenarios
Distribution of areas currently planted to Eucalyptus species and hybrids in
different climate suitability classes, for a range of climate scenarios
Distribution of areas currently planted to Pinus species and hybrids in different
climate suitability classes, for a range of climate scenarios
xiv
3
19
40
40
41
48
59
67
1.
SETTING THE SCENE
“The evidence shows that climate change is occurring … and we cannot wait any longer to take action.”
Kofi Annan, Former UN Secretary General, 2001
Concern, awareness and interest surrounding climate change associated with global warming is
increasing. In the scientific arena, it has become accepted that climate change is a reality, with the
debate now focused on the magnitude and timing of change, as well as on the potential impacts of
change on natural and anthropogenic systems.
Over geological timescales, the Earth’s climates have changed markedly in the past. Of concern now is
not simply a change in climate, but rather the unprecedented rate and magnitude of global warming
over the past few decades. Examinations of long-term global average surface air and water
temperature records taken over land and the oceans, since approximately 1860, indicate that the global
average surface temperature has increased by 0.6°C ± 0.2°C over the twentieth century (IPCC, 2001),
as shown in Figure 1.1. Using indirect evidence from tree rings, ice cores, boreholes and other climate
sensitive indicators to reconstruct the average surface temperature for the past 1000 years, gives
further evidence that the warming which has occurred in the late twentieth century is unprecedented
(IPCC, 2001, Figure 1.2).
In 2006 the World Meteorological Organisation (WMO) declared 2005 to have been the warmest year
on record since scientific observations of temperature began some 140 years ago. Up until 2005 the
second warmest year on record had been 1998, when the average global surface temperature was
0.55°C above the annual average for 1961 - 1990 (which is the WMO’s standard period to represent
present climate), with 2002 the third warmest year on record, when surface temperatures averaged
0.48°C above the same thirty year mean. Up until 2005, 2001 had been the fourth, 2004 the fifth and
1995 the sixth warmest years on record (WMO, 2003; WMO, 2005; WMO, 2006).
Other supporting evidence for rapid recent changes in climate comes from many different regions and
types of phenomena. Evidence of alpine and continental glacier retreat in response to warming is
substantial over many locations. The Trient Glacier in southern Switzerland is a dramatic example, with
the measured retreat of the terminus of the glacier since 1986/87 being approximately 500 m. The ice
cap on Mount Kilimanjaro in Kenya has also receded, as have other tropical glaciers (Pittock, 2006).
Since the 1960s decreases of approximately 10% in snow cover have occurred, as observed by
satellites. A 10 to 15% decrease in the spring and summer sea ice in the northern hemisphere has
been observed since 1950. Between 1950 and 2000, a rise of approximately 1.8 to 1.9 mm per year in
sea level has been recorded, attributable to thermal expansion and glacier melting (Pittock, 2006).
Changes in climate are also evident through the increasing frequency and intensity of hydrologically
related extreme events (Munich Reinsurance, 2001; Kabat et al., 2003). Examples of recent extreme
events include:
•
•
•
•
•
destructive rains in Kenya in 1997,
rain-induced mudslides in Venezuela in 1999,
severe droughts in central and southwestern Asia from 1999 to 2001,
devastating floods in Mozambique in 2000 and 2001,
record setting droughts and wild fires in various parts of the USA in 2003, and
1
•
major flooding in December 2004 in Angola, China, Malaysia, Indonesia and Sri Lanka; severe
storms in France and Japan; flash flooding in Australia, Iran, South Africa and USA; tropical
storms in the Philippines and landslides in China (Glantz, 2003; WMO, 2005).
Global
Figure 1.1
Global temperature increases between 1856 and 2005 (Jones and Palutikof, 2006)
Northern Hemisphere
Figure 1.2
Variations in temperature over the past 1000 years, based on data from thermometers,
tree rings, corals, ice cores and historical records (IPCC, 2001)
2
There is increasing evidence that this rapid warming which is occurring is due mostly to human induced
changes in the atmosphere, which have been superimposed over natural variations (Pittock, 2006).
Incoming solar radiation heats the earth’s land and ocean surface, which then emits longwave radiation
into the atmosphere. Some of this radiation is absorbed by water vapour and greenhouse gases, and is
then either reflected and/or partially re-radiated back toward the earth’s surface. Thus, atmospheric
water vapour (including clouds) and greenhouse gases act similarly to a partial ‘blanket’ for longwave
radiation, thereby warming the atmosphere. This natural process, termed the greenhouse effect, has
warmed the atmosphere for centuries, thus making the earth a habitable planet (Glantz, 2003).
Through human activities the concentrations of greenhouse gases in the atmosphere have increased,
leading to an enhanced greenhouse effect, through which a larger proportion of longwave radiation than
before is reflected and re-radiated back to the earth (Pittock, 2006).
As defined by the
Intergovernmental Panel on Climate Change (IPCC, 2001), these greenhouse gases are made up
mainly of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs),
perfluorocarbons (PFCs) and sulphur hexafluoride (SF6). Statistics of the global atmospheric
abundances of three key greenhouse gases in 2004 are given in Table 1.1. Three of the main
greenhouse gases reached new highs in atmospheric concentrations in 2004, with CO2 at 377.1 parts
per million (ppm), CH4 at 1 783 parts per billion (ppb) and N2O at 318.6 ppb. These values were of the
main, respectively, 35%, 155% and 18% higher than pre-industrial levels (WMO, 2006).
Table 1.1
Statistics of global abundances of key greenhouse gases in 2004 (WMO, 2006)
Statistic
Global abundance
2004 abundance relative to year 1750
2004 relative increase (%)
Mean annual absolute increase during past 10 years
Relative contribution to anthropogenic increase in radiative forcing (%)
CO2
(ppm)
377.1
135%
0.47
1.9
62
CH4
(ppb)
1 783
255%
0.00
3.7
20
N2O
(ppb)
318.6
118%
0.22
0.8
6
Climate change is occurring, and is projected to continue occurring as a result of the ongoing and
increasing emission of greenhouse gases into the atmosphere. The impacts of climate change will be
far-reaching and complex, affecting climatic means and climate variability, and thus natural ecosystems
and human societies, both directly and indirectly.
Given the plant-to-harvest timeframes of one to several decades associated with commercial forestry, it
is crucial to assess potential impacts of climate change on the forestry sector and to consider possible
adaptation measures.
Following this ‘Setting of the Scene’ in Section 1, the remainder of the report comprises eight further
sections, viz.
•
•
•
•
Section 2, on the detection of climate change over South Africa as already evidenced in
analyses of climatic records for the period 1950 - 2000;
Section 3, on a review of the recent literature pertaining to climate change and forestry;
Section 4, on an assessment of the Kyoto Protocol and land based carbon sinks and
sequestration, from a forestry perspective;
Section 5 on climate scenarios for the future and the approach taken in this report on plausible
future climates;
3
•
•
•
•
Section 6, in which the baselines of present climate over South Africa, and the mapping of
climatically optimum and sub-optimum areas of major commercially grown species/hybrids under
present climatic conditions, are discussed as a point of departure for the next section, viz.
Section 7, the core of this report, in which the results of a sensitivity analysis on potential
impacts of climate change on major commercially grown tree species and hybrids in South Africa
are presented and interpreted;
Section 8, on adaptation strategies to climate change; and
Section 9, in which overall conclusions are drawn and recommendations for further research are
made.
4
2.
DETECTION OF CLIMATE CHANGE OVER SOUTHERN AFRICA
Although climate change at a global scale has become an accepted reality, the picture at a regional
scale is less clear. As a component of a 2-year research project commissioned by the Water Research
Commission (WRC) which was titled ‘Climate Change and Water Resources in South Africa: Potential
Impacts of Climate Change and Mitigation Strategies’ (Schulze, 2005), studies were undertaken to
investigate whether changes in climate at a regional scale over southern Africa, could already be
detected for the period 1950 - 2000. Southern Africa is defined here as the Republic of South Africa
plus Lesotho and Swaziland. Investigations into southern Africa’s temperature, rainfall and hydrological
regimes were undertaken. Changes, many of them statistically significant, were shown to have
occurred. However, these changes were often not consistent in magnitude, nor were they spatially even
within the region. Hotspots, i.e. clusters, of substantial change were detected. Summaries of results on
both temperature and rainfall changes are presented in Section 2.1. Detailed results are available in
•
•
•
2.1
Warburton, M., Schulze, R.E. and Maharaj, M. 2005. Is South Africa's Temperature Changing?
An Analysis of Trends from Daily Records, 1950 - 2000. In: Schulze, R.E. (Ed) Climate Change
and Water Resources in Southern Africa: Studies on Scenarios, Impacts, Vulnerabilities and
Adaptation. Water Research Commission, Pretoria, RSA, WRC Report 1430/1/05. Chapter 16,
275-295.
Hewitson, B.C., Tadross, M. and Jack, C. 2005. Historical Precipitation Trends over Southern
Africa: A Climatology Perspective. In: Schulze, R.E. (Ed) Climate Change and Water Resources
in Southern Africa: Studies on Scenarios, Impacts, Vulnerabilities and Adaptation. Water
Research Commission, Pretoria, RSA, WRC Report 1430/1/05. Chapter 18, 319-324.
Warburton, M. and Schulze, R.E. 2005. Historical Precipitation Trends over Southern Africa: A
Hydrology Perspective. In: Schulze, R.E. (Ed) Climate Change and Water Resources in
Southern Africa: Studies on Scenarios, Impacts, Vulnerabilities and Adaptation. Water Research
Commission, Pretoria, RSA, WRC Report 1430/1/05. Chapter 19, 325-338.
Detection of Changes in Temperature Parameters over Southern Africa:
Selected Results
Observed daily minimum and maximum temperature values for a 51 year period (1950 – 2000) were
used in this study. The data were derived from a WRC project titled ‘The Development of a Database
of Gridded Daily Temperature for Southern Africa’ (Schulze and Maharaj, 2004). Temperature records
were carefully screened, and any stations with < 20 years of daily data were omitted from the climate
change detection study, as were stations at which Fourier analysis was used as an infilling technique
where there were missing/suspect values. Records from 209 temperature stations qualified and were
used in the analysis (Warburton, Schulze and Maharaj, 2005). The Mann-Kendall test was chosen as
the statistic to analyse the temperature records for changes over time, as it is a relatively robust and
simple statistical test of trend that is not affected by non-normally distributed data.
2.1.1
Trends Over Time in Annual Means of Temperature
In each of the figures which follow, the expected outcome if there is a trend in the climatic parameter
over the 1950 - 2000 period is indicated by a triangle at a station’s location if the trend is statistically
significant at a 95% confidence level, while the opposite outcome (i.e. a significant trend, but the
reverse to what would be expected under global warming) is indicated by a square, and a circle
indicates that no statistically significant trend was detected. The shading shown in the figures has no
statistical significance; it is a by-eye grouping of stations with a significant trend in the direction
expected under climate change conditions.
5
Under conditions of climate change it is hypothesised that the annual means of daily maximum
temperatures would have increased over the past few decades. Results from the trend analysis of
annual means of daily maximum temperature are shown in Figure 2.1. As may be seen from the
shaded areas, there is a scattering of locations which show an increasing trend across the region. In
particular, a cluster of stations in the southeast of the Western Cape shows an increasing trend.
Furthermore, a large proportion of the stations in the Eastern Cape, Northern Cape and Free State
show an increasing trend over time.
Figure 2.2 displays results of the trend analysis of annual means of daily minimum temperatures.
Under climate change conditions it is hypothesised that minimum temperatures would increase over
time. Two clusters of stations in Figure 2.2, the first in the Western Cape and the second in KwaZuluNatal along the coast and into the coastal hinterland, indicate distinct areas of warming over the five
decades 1950 - 2000. Another grouping of stations with an increasing trend of annual means of daily
minima is evident in areas of Gauteng and the Free State. It is important to note that a few stations
show a cooling trend, one of these stations being a station in Lesotho with suspect data (Schulze and
Maharaj, 2004; Warburton et al., 2005).
2.1.2
Trends Over Time in Occurrences of Temperatures Above and Below Selected
Percentiles
With climate change it is hypothesised that temperatures at the extremes of distributions will change. In
order to examine this hypothesis, trends in the number of days above and below selected percentiles of
distributions of daily temperatures were analysed. Taking the common 1950 - 2000 period of daily
temperature records, values for the winter months were extracted and the 10th percentile (i.e. coldest in
10) of daily minimum temperatures was calculated for the entire record length at each of the 209
stations for the three southern hemisphere winter months of June, July and August. Following this, the
number of days per winter season with minimum temperatures below the overall 10th percentile were
calculated.
Figure 2.3 shows the results of the trend analysis of the number of days with minimum temperatures
below the 10th percentile value of winter minimum temperature. With global warming the number of
these events is expected to decrease over time. A cluster of stations in the southeast of the Western
Cape shows statistically significant decreasing trends over the 1950 - 2000 time period. A further band
of stations through the Northern Cape and Eastern Cape also show decreasing trends in the number of
days below the 10th percentile. Additionally, a number of stations in KwaZulu-Natal display a
statistically significant decreasing trend in number of days with winter minimum temperatures below the
10th percentile. What is important to note is that there are stations in the Western Cape and KwaZuluNatal that display statistically significant increasing winter month trends in minimum temperatures below
the 10th percentile. These results suggest that at a number of locations minimum temperatures below
a threshold may be reducing while at other locations minimum temperatures may be becoming more
extreme.
After evaluating minimum temperature trends at the ends of their distributions, a next assessment was
that on maximum daily temperatures at their “extremes” of their distributions. In this assessment the
90th percentile (i.e. hottest in 10) of summer months’ (i.e. December, January and February) maximum
daily temperatures was calculated for the entire 51 year record at each station, and trends over time in
the number of days above the 90th percentile were analysed. Figure 2.4 displays the results of the
results of the time trend analysis for the 1950 - 2000 period of the number of days with maximum
temperatures above the 90th percentile across the RSA, Lesotho and Swaziland. Under global
warming the number of these events is expected to increase. As may be seen in Figure 2.4, a large
percentage of stations in the Western Cape and Eastern Cape show a statistically significant
6
Figure 2.1
Trends at the 95% confidence level over southern Africa in the 1950 - 2000 time series
of annual means of daily maximum temperatures (Warburton et al., 2005)
Figure 2.2
Trends at the 95% confidence level over southern Africa in the 1950 - 2000 time series
of annual means of daily minimum temperatures (Warburton et al., 2005)
increasing trend in the number of days with maximum daily temperatures exceeding the 90th percentile,
as do stations in a distinct band along the KwaZulu-Natal coast.
2.1.3
Trends Over Time in Occurrences of Frost and the Length of the Frost Season
In order to evaluate whether changes in frost patterns over time could be detected in southern Africa,
three statistics were analysed, viz. the number of frost occurrences per frost season, the date of the first
frost, and the date of the last frost. A frost event was considered to occur when a day’s screen height
minimum temperature dropped below 0°C. Any station that experienced fewer than 20 individual frost
events over the 51 year period of record was considered to be in a frost-free area and its records were
not subjected to further analysis.
7
Figure 2.3
Trends at the 95% confidence level over southern Africa in the 1950 - 2000 time series
of the number of days per year below the 10th percentile of winter (June, July, August)
minimum temperatures (Warburton et al., 2005)
Figure 2.4
Trends at the 95% confidence level over southern Africa in the 1950 - 2000 time series
of the number of days per year above the 90th percentile of summer (December,
January, February) maximum temperatures (Warburton et al., 2005)
In Figure 2.5 the results of the trend analysis for the number of frost occurrences per season are
displayed spatially. Under climate change conditions the number of frost occurrences is hypothesised
to decrease. Immediately evident from Figure 2.5 is the large number of stations across the region
displaying statistically significant decreasing trends in the number of frost occurrences per season
between 1950 and 2000. In particular, the majority of stations in the Northern Cape, Free State and
Gauteng display this decreasing trend. A few stations, however, in the Western Cape, KwaZulu-Natal
and Lesotho display statistically significant increasing trends.
8
Figure 2.5
Trends at the 95% confidence level over southern Africa in the 1950 - 2000 time series
of the number of frost occurrences (i.e. daily minimum < 0°C) per season (Warburton et
al., 2005)
With decreases in the number of frost occurrences per season being evident at many locations, the
next step was to analyse whether changes in the duration of the frost season could be detected. This
was achieved by analysing whether or not trends over time in the first frost date and last frost date
could be detected. Under climate change conditions it is hypothesised that the frost season would
become shorter, i.e. later first frost dates and earlier last frost dates are assumed to occur.
Figure 2.6 displays spatially the results of the trend analysis of the date of the first frost occurrence. A
grouping of stations over the interior shows a statistically significant later first frost dates over the time
period considered. Against this hypothesis is the large number of stations showing a statistically
significant earlier first frost date. Figure 2.7 shows trends in the date of the last frost occurrence. The
majority of stations in the Northern Cape, Eastern Cape, Free State, North West and Mpumalanga
provinces display a statistically significant earlier dates of the last frost in a season, as would be
expected under global warming.
From these results it is suggested that the frost season over the interior of the region, in particular over
the Free State, is becoming shorter through a decrease in the number of frost occurrences per season
and an earlier end to the frost season. At other locations it could be surmised that the frost season is
becoming more severe, or shifting to a date earlier in the year. Further analysis is therefore required to
draw clearer conclusions regarding changes in frost patterns.
2.1.4
Trends Over Time in Heat Units
Heat units, expressed as degree days, are the accumulation of mean daily temperature values above a
certain lower temperature threshold, below which little or no active plant growth/development is
assumed to take place, and below an upper limit, above which active development similarly is assumed
to cease (Schulze, 1997).
Using a lower threshold mean daily temperature of 10°C, the trends in heat units for the summer and
winter seasons of southern Africa were analysed. The summer months, in this instance, were defined
as the period October to March, with winter months then being from April to September. Under climate
9
Figure 2.6
Trends at the 95% confidence level over southern Africa in the 1950 - 2000 time series
of the date of the first frost per season (Warburton et al., 2005)
Figure 2.7
Trends at the 95% confidence level over southern Africa in the 1950 - 2000 time series
of the date of the last frost per season (Warburton et al., 2005)
change conditions it is hypothesised that in both summer and winter there would be an increasing trend
over time in heat units.
Figure 2.8 shows the results of the trend analysis for heat units in the summer season. A large number
of stations in the Western Cape show statistically significant increasing trends in heat units, from 1950 2000, as does a band of stations along the KwaZulu-Natal coast. Another band of stations with
increasing trends are shown stretching through the Northern and Eastern Cape provinces. A few
stations in Gauteng also display an increasing trend. Only 3 stations display a statistically significant
decreasing trend and they are located in KwaZulu-Natal.
10
Figure 2.8
Trends at the 95% confidence level over southern Africa in the 1950 - 2000 time series
of heat units (base 10°C) for the summer season, i.e. October to March (Warburton et
al., 2005)
The spatial distribution of trends for heat units in the winter season are shown in Figure 2.9.
Immediately evident is the large proportion of stations over the RSA displaying a statistically significant
increasing trend over time in winter heat units. In particular, distinct clusters of stations in the Western
Cape and KwaZulu-Natal (along the coast and inland to the Pietermaritzburg area) show increasing
trends. The majority of stations in the Northern Cape similarly show increasing trends.
The trends in heat units seem to mimic the changes shown for other temperature variables, with trends
in winter occurring over larger areas than in summer and with two clear areas of warming emerging, viz.
the Western Cape and KwaZulu-Natal. Increasing trends in heat units have implications as to whether
crops can be grown profitably in certain areas and on the occurrences of crop pests. For example, the
coddling moth develops and thrives between a lower threshold of 11°C and an upper threshold of 34°C
(Schulze, 1997). With an increase in heat units, areas which previously were not affected by this crop
pest may in future be.
2.2
Detection of Changes in Southern Africa’s Rainfall Parameters: Selected
Results
The recent (20th century) historical record shows strong trends in southern Africa’s regional climate,
most notably with respect to secondary attributes of rainfall – attributes such as intensity and frequency
(New et al., 2005; Hewitson et al., 2005). Coupled with this are the strong temperature trends shown in
Section 2.1. On the hemispheric scale there are also suggestions that the El Niño - Southern
Oscillation teleconnection to southern Africa may be becoming less robust (Landman and Mason, 1999;
Sewell and Landman, 2001), with associated implications for climate predictability in the region.
The overview of analyses of historical trends of precipitation presented below are by Hewitson et al.
(2005). Their study used robust regression with an interpolated 0.1º gridded precipitation data set that
draws on over 3 000 station records across South Africa. While it is recognised that any interpolation
techniques used can introduce their own sets of problems, comparison of the trends from the
interpolations show good agreement with trends calculated on the few stations with long term records.
11
Figure 2.9
Trends at the 95% confidence level over southern Africa in the 1950 - 2000 time series
of heat units (base 10°C) for the winter season, i.e. April to September (Warburton et
al., 2005)
The trends are assessed over the 50 year period from 1950 - 1999. While trends in the annual totals
are not strong, some notable changes in the past 50 years may be identified over southern Africa when
one considers trends on sub-annual and seasonal scales. Figure 2.10 shows the seasonal changes in
mean monthly totals, expressed as change per decade. Figures 2.11 to Figure 2.14 show the trends in
derivative statistics from the daily data, and include changes in numbers of raindays per month, dry
spell durations, and the 90th percentile event magnitude. The trends show a clear, spatially cohesive
picture of historical change for the 50 year period under review. While the spatial nature of the change
contains significant local detail, some of the broad regional characteristics include:
•
•
•
•
increases in regions where orography plays a strong role,
increases in the late summer dry spell duration for much of the summer rainfall region,
arid zones, in general, receiving more raindays, and
contrasting changes in the winter rainfall region, with mountainous regions receiving more
raindays per month and increased totals, while the neighbouring coastal plain regions display the
reverse (Hewitson et al., 2005).
The changes are, in places, strong and of notable consequence for climate-related impacts. Attribution
of the change to anthropogenic forcing is uncertain at this stage. Furthermore, the role of local land use
change in forcing regional climate response is still unclear, as is the degree to which the low frequency
large scale variability of the atmospheric circulation may be contributing to the change. Nonetheless,
the changes are in line with what would be anticipated from global warming, given an atmosphere with
increased humidity and an expected increase in the atmospheric circulation.
2.3
A Summary of Changes Already Detected in the Temperature and Rainfall
Regimes of Southern Africa Over Time
In almost every analysis performed two clear clusters of warming over the 51 year period 1950 - 2000
emerge, these being a cluster of stations in the Western Cape and a cluster of stations around the
Midlands of KwaZulu-Natal, along with a band of stations along the KwaZulu-Natal coast. The clusters
show up clearly in the analyses of annual means of both minimum and maximum temperatures, in the
12
Figure 2.10
Historical trend (1950-1999) over southern Africa of change per decade of mean
monthly precipitation totals, in mm (Hewitson et al., 2005)
Figure 2.11
Historical trend (1950-1999) over southern Africa of change per decade in mean
monthly number of raindays > 0 mm (Hewitson et al., 2005)
13
Figure 2.12
Historical trend (1950-1999) over southern Africa of change per decade in mean
monthly number of raindays > 2 mm (Hewitson et al., 2005)
Figure 2.13
Historical trend (1950 - 1999) over southern Africa of change per decade in mean
monthly dry spell duration, in days (Hewitson et al., 2005)
14
Figure 2.14
Historical trend (1950 - 1999) over southern Africa of change per decade in mean
monthly 90th percentile magnitude precipitation event, in mm (Hewitson et al., 2005)
percentile analyses for cold spells, as well as in the heat unit analysis. Another distinct conclusion that
can be drawn is the warming over the interior of South Africa when analysing frost occurrence, where
the Free State and Northern Cape provinces stand out in this regard. It must be noted, however, that
according to the temperature data there appears to be a shift towards an earlier ending of the frost
season, but nevertheless a shorter frost season with fewer frost occurrences. From this analysis of
temperature records considerable evidence is emerging that certain changes in temperature
parameters are have occurred across southern Africa over the period 1950 - 2000, however, that these
changes are not spatially uniform.
The changes in precipitation patterns identified already, are notable, and of consequence to society, the
natural ecosystem as well as to the agriculture and water resources sectors. Importantly, these
precipitation changes are not always apparent in large space-time averages. Particularly, the changes
are most apparent at sub-annual scales, and in the derivative statistics of the attributes of precipitation.
15
3.
CLIMATE CHANGE AND THE COMMERCIAL FORESTRY SECTOR: A
LITERATURE REVIEW
There is, to date, only a limited literature worldwide on potential impacts of climate change on
commercial forestry, with the majority of literature referring to natural forests. In this section a brief
review of the potential impacts of climate change on forestry is presented.
It has been hypothesised that climate change may alter the productivity of forests, shift resource
management, alter the economic process of adaptation and thus change forest product harvests at
global, national and regional scales (Alig et al., 2004). Impacts on forests arise from increases in
atmospheric CO2 concentration, changes in temperature and rainfall regimes, as well as from an
increase in climate variability expressed by extreme events’ increasing in both frequency and severity.
Climate change will impact tree photosynthesis, growth rates, leaf phenology, seed development and
nutrient cycling (van der Meer et al., 2002).
A large amount of uncertainty surrounds the exact nature of the impacts of climate change on forestry.
The reasoning for this is threefold:
•
•
•
First, most impact studies have been conducted on small trees, over short durations, inside
greenhouses or in field chambers that modify the environment and do not allow for interactions
with other natural stressors (Karnosky, 2003).
Secondly, it has been shown that interactions with other factors such as soil fertility (Oren et al.,
2001), atmospheric pollutants (Isebrands et al., 2001) and soil moisture (Chaves and Pereira,
1992) can offset the ‘fertilisation’ effect of elevated atmospheric CO2 concentrations on forest
growth.
Thirdly, almost all studies on impacts of elevated CO2 concentrations on trees have either
considered a doubling of CO2 concentrations or a single large addition of CO2, and thus little is
known about the dose response or the interactive effects of varying doses of greenhouse gases
(Karnosky, 2003).
Under elevated atmospheric CO2 concentrations, photosynthesis is enhanced (Curtis, 1996; Eamus
and Ceulemans, 2001). Norby et al. (1999) estimated the average enhancement of photosynthesis to
be approximately 60% for trees exposed to elevated atmospheric CO2 concentrations. This response
will, however, vary between species (Naumberg et al., 2001), vary by nitrogen fertility level, by season
and by co-occurring pollutant concentrations (Noormets et al., 2001a; b). The effect of elevated
atmospheric CO2 concentrations on long term growth rates and productivity of trees is even less clear
(Körner, 2000), and it is to date not known whether or not positive growth rates will be maintained
throughout a tree’s life cycle. Accurate prediction of the growth response of trees in a forest stand is
simply not possible from short-term greenhouse or chamber studies (Karnosky, 2003). The free-air
CO2 enrichment (FACE) experiments have, however, shown an increase of 28% in above-ground
biomass at an elevated CO2 concentration of 550 ppm (IPCC, 2001). Stomatal conductance of forest
trees has been shown to generally decrease by approximately 21% under elevated CO2 concentrations
(Medlyn et al., 2001). This raises the question as to whether, with decreased stomatal conductance
and increased growth, trees will use more or less water?
It is believed that root growth will increase under elevated atmospheric CO2 concentrations. It is
primarily the production and mortality of fine roots produced by trees under elevated atmospheric CO2
concentrations which will increase (Matamala and Schlesinger, 2000; Pregitzer et al., 2000; King et al.,
2001; Pritchard et al., 2001).
16
Generally, it has been shown that the nitrogen levels in the foliage of trees growing under elevated
atmospheric CO2 concentrations is decreased (Lindroth et al., 2001a). This trend follows through to the
litter layer, where it has also been shown that nitrogen levels will decrease (Norby et al., 2001b).
However, it has been shown that the quantity of litter will increase by 20 - 30% under elevated CO2
concentrations (DeLucia et al., 1999). What remains uncertain is the effect of enhanced CO2 levels on
nutrient mineralization and litter decomposition (Karnosky, 2003).
In regard to wood quality and chemical composition of forest trees, very little is known to date. It is
believed that with climate change the disturbance regimes in a forest will be changed, these will include
more frequent insect and disease outbreaks (Simberloff, 2000) and/or a greater frequency of wild fires
(Flannigan et al., 2000).
Uncertainty as to the response of trees under conditions of elevated atmospheric CO2, changing
temperatures and rainfall patterns still exists. Forward progress on these issues is being made,
however, but large amount of research is still need. Research into forestry responses needs to move
from short term, small scale chamber or greenhouse experiments, to long term, large scale experiments
allowing for natural interactions to occur.
Changes in the growth of forests and disturbance regimes would impact forest management and timber
markets. Consequently, adjustments in forest policy and planning are likely to be required. In carrying
out this process an important element to consider will be the Kyoto Protocols and the mechanisms
developed to implement them, and use of land based carbon sinks and carbon sequestration. These
issues are discussed in Section 4.
17
4.
THE KYOTO PROTOCOL AND LAND BASED CARBON SINKS AND
SEQUESTRATION
4.1
The Kyoto Protocol - Unpacked
In February 1991 representatives of various countries met under the auspices of the United Nations to
draw up the global Framework Convention of Climate Change (UNFCCC), which was signed at the Rio
Earth Summit of 1992. As a so-called ‘framework’ convention it laid out broad principles and objectives,
leaving a large number of details to be negotiated later. The principal aim of the UNFCCC was the
stabilisation of atmospheric greenhouse gas concentrations in order to avoid ‘dangerous anthropogenic
interference with the climate system’ (Glantz, 2003). The UNFCCC came into force on 21 March 1994,
and to date (2006) has received instruments of ratification from 189 member countries that are parties
to the convention.
The UNFCCC set out the following basic principles:
•
•
•
•
the need to limit climate change on a basis of equity, in accordance with each country’s common
but differentiated responsibilities and respective capacities, with developed countries thus
expected to take the initiative;
the need to recognise the specific needs and special circumstances of developing countries,
especially the most vulnerable ones;
the need for precautionary measures in the absence of full scientific certainty, qualified by the
need to be cost-effective and comprehensive, by taking account of all sources and sinks of
carbon, adaptation, and all economic sectors; and
the right to sustainable development, and the need to avoid unjustified discrimination or a
disguised restriction on international trade (Pittock, 2006).
At the first meeting in 1995 of the countries which had signed the UNFCCC, known as the Conference
of Parties (COP-1), a sub-group was established to negotiate an agreement known as the ‘Berlin
Mandate’, which aimed at strengthening efforts to combat climate change. Intensive negotiations
followed, and at COP-3 in 1997 in Kyoto, Japan, delegates agreed to what is now known as the Kyoto
Protocol.
The Kyoto Protocol commits developed nations and countries in transition (e.g. former Soviet bloc
countries), termed Annex I countries, to achieve quantified reductions in greenhouse gases. Article 3 of
the Kyoto Protocol states that Annex I countries need to reduce ‘their overall emissions of such gases
by at least 5 per cent below 1990 levels in the commitment period 2008 – 2012’. The required
reduction in greenhouse gas emissions varies between countries, and these are given in Table 4.1.
The reductions in emissions are described in terms of the percentage of the Parties’ 1990s emissions.
The maximum amount of emissions that a party may emit over the commitment period in order to
comply with its emission target, is known as the party’s assigned amount. Emissions, in terms of the
Kyoto Protocol, are measured as the equivalent in carbon dioxide, by methods as defined by the Kyoto
Protocol, with the greenhouse gases being the same as those included by the IPCC (2001).
Following the agreement on the Kyoto Protocol in 1997, further negotiations took place before it was
ratified. Negotiations took place in Buenos Aires in 1998 and in The Hague in 2000, with a reconvening
of the parties in Bonn in July 2001, where a package of agreements was accepted. However, drafts on
other mechanisms much as compliance and land use, land use change and forestry were deferred to
another meeting in Marrakesh, Morocco in October – November 2001. The agreements reached at this
meeting became known as the Marrakesh Accords. Not all points of contention, however, were agreed
18
Table 4.1
Annex I Parties included in the Kyoto Protocol and their emissions targets (UNFCCC,
2006)
Target (1990 –
Country
2008/2012)
EU-15*, Bulgaria, Czech Republic, Estonia, Latvia, Liechtenstein, Lithuania,
- 8%
Monaco, Romania, Slovakia, Slovenia, Switzerland
USA**
- 7%
Canada, Hungary, Japan, Poland
- 6%
Croatia
- 5%
New Zealand, Russian Federation, Ukraine
0%
Norway
+ 1%
Australia
+ 8%
Iceland
+10%
* The EU’s 15 state members will redistribute their targets amongst themselves
** The USA has not ratified the Kyoto Protocol
upon at this meeting, thus a further meeting took place in New Delhi in late 2002 where further
agreements were reached, including the ‘Delhi Declaration on Climate Change and Sustainable
Development’, which reaffirms development and poverty eradication as the primary principles in
developing countries.
It also recognised the member countries’ common, but differentiated,
responsibilities and national development priorities and circumstances in implementing the
commitments under the UNFCCC (Pittock, 2006).
The Kyoto Protocol legally binds a party, once ratified, to its emission reduction targets. In order for the
Kyoto Protocol to enter into force ‘not less than 55 Parties to the Convention, incorporating Parties
included in Annex I which accounted in total for at least 55 per cent of the total carbon dioxide
emissions for 1990 of the Parties included in Annex I’ (Article 25.1, Kyoto Protocol 1997) need to have
ratified the Protocol. To date 150 countries have ratified the Protocol, including South Africa, and these
countries account for 61.6% of world carbon emissions. The USA and Australia are two significant
countries which have, to date, withdrawn from the Kyoto Protocol process. The USA contributes
approximately 20% of global greenhouse gas emissions (Pittock, 2006) and thus without the USA
ratifying the Protocol, with its aims and objectives, it will be difficult to achieve.
In their withdrawal from the Kyoto Protocol the USA heightened the political interest in the process. The
USA stated that it believed the Kyoto Protocol to be ‘fatally flawed’ (Glantz, 2003), ‘because it exempts
80% of the world, including major population centers such as China and India, from compliance, and
would cause serious harm to the US economy … there is clear consensus that the Kyoto Protocol is
unfair and ineffective means of addressing global climate change concerns’ (Pittock, 2006).
One objective of the Kyoto Protocol is the promotion of sustainable development. In achieving their
emission targets it thus requires Parties to implement policies and measures such as
•
•
•
•
•
•
improvements in energy efficiency,
protection of sinks and reservoirs of greenhouse gases (e.g. promotion of sustainable forest
management practices, afforestation and reforestation),
promotion of sustainable agriculture,
research and development into renewable forms of energy,
investigation into carbon sequestration technologies, and
advancement of environmentally sound technologies (Article 1.1a, Kyoto Protocol, 1997).
19
Cooperation between Parties to the Protocol is encouraged through the sharing of experiences and
exchanges of information, in order to enhance the individual and combined effectiveness of their
policies and measures adopted (Article 1.1b, Kyoto Protocol, 1997). A further stipulation of the Kyoto
Protocol is that all policies and measures implemented in order to meet emission targets need to be
undertaken in such a manner as to minimise the adverse effects on international trade, and minimise
the negative environmental, social and economic impacts on other Parties, in particular minimising the
negative impacts on developing countries (Article 1.3, Kyoto Protocol, 1997).
Provision for the review of commitments is included in the Protocol, and thus a strengthening can occur.
Negotiations for the post- 2012 targets were due to begin in 2005, by which time each country included
in Annex I should ‘have made demonstrable progress in achieving its commitments’ (Article 3.2, Kyoto
Protocol 1997). In order to achieve their targets, Annex I countries have to put into place domestic
policies and measures. In addition to these domestic policies and measures, Annex I countries may
use three mechanisms which were established to assist them in meeting their quantified emission
limitations and reduction commitments. The mechanisms are termed
•
•
•
Joint Implementation,
Clean Development Mechanism, and the
Emission Trading Scheme.
These mechanisms and the use of the so-called carbon ‘sinks’ in the land use, land use change and
forestry sector will be discussed in the sections below. In order for Annex I Parties to use these
mechanisms, they must comply with the methodology and reporting obligations under the Kyoto
Protocol. Additionally, the Parties must provide evidence that their use of the mechanisms is
‘supplemental to domestic action’. Domestic actions need to form a significant proportion of the Parties’
efforts to meet their commitments, thus ensuring that Annex I countries implement policies and
mechanisms within their own borders. Under the Kyoto Protocol businesses, environmental NGOs and
other ‘legal entities’ may participate in the mechanisms, provided that it is under the responsibilities of
their governments.
4.1.1
Mechanisms for the Implementation of the Kyoto Protocol
4.1.1.1 Joint Implementation
Joint implementation refers to the generation and transference of emission reductions by investment in
a project in one Annex I country by another Annex I country, with the resulting emission reduction units
counting against the investing Annex I country target.
Although the term ‘joint implementation’ does not appear, it is defined in Article 6 of the Kyoto Protocol
(1997). Given the economic status of the former Soviet bloc countries, these will be the most likely
Annex I countries that others will invest in, as these former Soviet bloc countries provide the greatest
scope for cutting emissions at the lowest cost.
A project developed under this mechanism needs to provide a ‘reduction in emissions by sources, or an
enhancement of removals by sinks, that is additional to any that would otherwise occur’ (Article 6.1b,
Kyoto Protocol 1997). A provision for the formation of an Article 6 Supervisory Committee was made.
This Committee, formed at a COP meeting, was to formulate guidelines for Joint Implementation, and
the requirements for verification and reporting. Joint Implementation was launched in 2006, with
assessment of various projects expected prior to the COP in November of 2006.
20
4.1.1.2 Clean Development Mechanism
‘The purpose of the clean development mechanism shall be to assist Parties not included in Annex I in
achieving sustainable development and in contributing to the ultimate objective of the Convention, and
to assist Parties included in Annex I in achieving compliance with their quantified emission limitation
and reduction commitments under Article 3’ (Article 12.2, Kyoto Protocol 1997).
Thus in essence, the clean development mechanism, or CDM, is similar to Joint Implementation, but
credits are generated by an Annex I country when they invest in projects in non-Annex I countries. The
credits generated under this mechanism are termed ‘certified emission reductions’ and may contribute
to the investing Annex I countries’ target. The CDM also aims at assisting non-Annex I countries in
achieving sustainable development, which is an ultimate objective of the Kyoto Protocol and
Convention.
CDM is to be supervised by an Executive Board formed at a COP. Through independent auditing and
verification of projects, transparency, efficiency and accountability will be ensured. Under the CDM, a
share of proceeds from certified project activities needs to be used to cover administrative expenses
and a proportion is contributed to the Adaptation Fund through an adaptation levy placed on projects
under the clean development mechanism. The Adaptation Fund was established by the Marrakesh
Accords. It is a fund to assist developing countries that are party to the Protocol, and which are
particularly vulnerable to the adverse effects of climate change, to meet the costs of adaptation. CDM
was put into effect at COP–7, and certified emission reductions are allowed to accrue from 2000
onwards.
Currently (2006) there are 421 registered CDM projects with 680 000 000 certified emission reduction
units expected from these project until 2012, with a further 84 projects in the review and registration
process (UNFCCC, 2006). The distribution of projects by host country is shown in Figure 4.1 below.
India has, by far, the largest number of CDM projects underway. South Africa currently (2006) has four
CDM projects are underway in, viz.
•
•
•
•
Rosslyn Brewery Fuel-Switching Project, Gauteng province
o Undertaken by the South African Breweries Ltd, this is a large scale project switching
from coal and petroleum fuels to natural gas (UNFCCC, 2006). To date no Annex I
party is involved.
PetroSA Biogas to Energy Project, Western Cape
o Undertaken by MethCap SPV1 (Pty) Ltd, an independent power producer which sells
to PetroSA, this is a small scale project involving the generation of renewable energy
connected to South Africa’s energy grid (UNFCCC, 2006). To date no Annex I party is
involved.
Lawley Fuel Switch Project,
o Undertaken by Corobrik (Pty) Ltd, in collaboration with The Netherlands, this is a large
scale project, also involving the switching from coal and petroleum fuels to natural gas
(UNFCCC, 2006).
Kuyasa Low-cost Urban Housing Energy Upgrade Project, Khayelitsha, Western Cape
o Undertaken by the City of Cape Town, this project aims at improving energy efficiency
through insulated ceilings, solar water heating installation and energy efficient lighting.
21
Chile
3%
China
8%
Others
20%
Mexico
13%
Ecuador
2%
Malaysia
3%
India
30%
Brazil
19%
Honduras
2%
Figure 4.1
Registered CDM projects by host country (UNFCCC, 2006)
4.1.1.3 Emission Trading Scheme
Emission trading (Article 17, Kyoto Protocol 1997) allows for the buying and/or selling of emission
allowances between Annex I countries. Thus an Annex I Party may transfer some of the emissions
under its assigned amount to another Annex I Party which is experiencing difficulty in meeting its
emission target. Emission reduction units, i.e. those obtained through Joint Implementation, as well as
certified emission reductions, i.e. those obtained through the clean development mechanism and
removal units, i.e. credits obtained through land based carbon sinks, may also be traded. In order to
avoid countries’ over-selling emission allowances, countries are required by the Protocol to hold a
minimum level of emission units in a commitment period reserve.
4.2
South Africa’s Role Under the Kyoto Protocol
South Africa ratified the UNFCCC in 1997 and, subsequently, entered into acceptance of the Kyoto
Protocol on the 31st of July 2002 (UNFCCC, 2006). As a developing country, South Africa is not
required to reduce its greenhouse gases. However, the South African economy is highly dependent on
fossil fuels and the country is a significant emitter in terms of emission intensity and emissions per
capita (NCCRS, 2004). As a Party to the UNFCCC (2006) and signatory to the Kyoto Protocol (1997),
South Africa has the following obligations:
•
•
•
•
Prepare and periodically update a national inventory of greenhouse gas emissions and sinks;
Implement and prepare national and regional programmes, where appropriate, to mitigate
climate change and assist adequate adaptation to climate change;
Promote and cooperate in the development, application and dissemination of technologies,
practices and processes that control, reduce or prevent anthropogenic emissions of greenhouse
gases;
Collaborate in preparing for adaptation to the impacts of climate change;
22
•
•
•
•
•
Consider climate change impacts in the relevant social, economic and environmental policies
and actions with a view to minimising adverse effects on the economy and on public health;
Encourage sustainable management, promote and cooperate in the conservation and
improvement of sinks and stores of all greenhouse gases;
Encourage scientific, technological, technical, socio-economic and other research, systematic
observation and development of data archives related to the climate system and furthering the
reduction of uncertainties;
Encourage and participate in the full, open and prompt exchange of relevant scientific,
technological, technical, socio-economic and legal information regarding the climate system and
climate change; and
Support and assist in education, training and public awareness relating to climate change.
These obligations are incorporated into the National Climate Change Response Strategy for South
Africa (NCCRS, 2004).
4.3
Forestry and Land Based Carbon Sinks
During the commitment period Annex I Parties which have ratified the Kyoto Protocol have agreed to a
reduction in their greenhouse gas emissions. In order for these Annex I Parties to meet their
commitments, the Kyoto Protocol makes provision for afforestation, reforestation and deforestation and
other agreed land uses and land use changes.
Under the Kyoto Protocol, a forest is a carbon sink, and thus a new forest or the expansion of an
existing forest will generate carbon credits for the removal of CO2 from the atmosphere (Sedjo, 2001).
At present, forest growth is one of the few known ways of removing CO2 from the atmosphere.
Additionally, many studies have found that forestry activities provide the lowest cost method of
controlling or reducing CO2 in the atmosphere (Binkley et al., 2002; Brown et al., 2002). Scientists
believe that in the future, carbon sequestration may become one of the major services that forests
provide (Pohjola and Valsta, 2006).
4.3.1
What is Carbon Sequestration and How Does it work? A Forestry Perspective
Although the fundamental science of carbon sinks is well understood (Sedjo, 2001), the process of
carbon sequestration is complex and the amount of carbon removed is difficult to measure, as quite
often growth, yield, net primary production and carbon turnover are confused with carbon sequestration
(Karnosky, 2003). Via photosynthesis, CO2 in the atmosphere is removed by plants or trees, and the
carbon is bound to the cells of the plant or tree while the oxygen is released into the atmosphere
(Sedjo, 2001; Brown et al., 2002). As the live biomass dies, the carbon is stored in the dead organic
matter, and as decomposition of the organic matter occurs, a portion of the carbon is transferred to the
soil. In the case of trees, some of the carbon is stored in the durable wood products (Brown et al.,
2002).
The amount of carbon held within a forest is dependent on the amount of dry biomass in the forest area
(Sedjo, 2001). Thus, carbon sequestration of a forest can be quantified on the basis of the forest’s net
ecosystem productivity (Jarvis, 1989; Malhi et al., 1999; Scarascia-Mugnozza et al., 2001). This is the
net primary productivity after the heterotrophic respiration caused by the decomposition of both aboveand below-ground litter has been subtracted. The net ecosystem productivity is the amount of organic
carbon which is immobilised in the forest ecosystem as living woody biomass and as soil organic matter
over a given amount of time and per unit of land surface (Scarascia-Mugnozza et al., 2001).
23
Besides the use of forests as carbon sinks in providing a relatively low cost option compared with the
costs of making changes in the energy sector to meet the greenhouse gas emission targets, there are
other associated benefits. Forests offer additional benefits such as erosion reduction, watershed
protection and the biodiversity protection of the existing indigenous forests (Sedjo, 2001). The IPCC
(2001) states that through activities such as reforestation and afforestation, agroforestry, natural and
assisted forest regeneration, and the slowing of deforestation, approximately 12 to 15% of the ‘business
as usual’ carbon emissions could be avoided or removed from the atmosphere.
According to the Kyoto Protocol, carbon credits may only accrue as a result of direct human-induced
changes in either the use and/or management of the land. Brown et al. (2002) suggest the following
four ways of increasing carbon sequestration by forests:
•
•
•
•
protection of secondary and other degraded forests, in order to allow them to regenerate,
restoration of indigenous forests through assisted and natural forest regeneration,
establishment of forest plantations on currently non-forested lands, and
increasing the tree cover on agricultural and pasture lands.
In addition, Brown et al. (2002) highlight the following opportunities to reduce carbon emissions from
forests by conserving the existing carbon stocks in forest vegetation and soil through
•
•
•
avoidance of deforestation,
altering the forest harvesting regimes, and
controlling the anthropogenic and natural disturbances of forests, such as fire and pest
outbreaks.
Another method of increasing the carbon credits generated by forests is the utilisation of the forest
biomass as a source of energy or transforming forest biomass into timber products (Vine et al., 1999;
Binkley et al., 2002). These forest bio-energy substitution projects are particularly attractive if the forest
biomass is used to replace fossil fuels, as potentially the emission offsets thus generated will be infinite
(IEA Bioenergy, 2001).
4.3.2
Concerns and Problems Surrounding Carbon Sequestration from Forests
Outside of the forest sector, objections have been raised to the use of forests as carbon sinks on the
grounds of ‘permanence’, ‘additionality’, ‘leakage’, measurability and a lack of technology transfer
(Binkley et al., 2002; Brown et al., 2002).
Permanence refers to the potential reversibility of forest carbon sequestration in the event that the
forest is harvested, burned, subjected to insect attack or to poor management practices (Harkin and
Bull, 2001). Anthropogenic events such as the non-enforcement of contracts, non-compliance with
guarantees, expropriation, uncertain property rights, policy changes, land tenure and market risks add
to the concerns about the permanence of forest sinks. Owing to these concerns, several proposals
were put forward that acknowledge that forestry carbon sinks are a temporary means for abating
emissions of greenhouse gases, and that the economic and environmental benefits of temporary
storage be assessed. In essence, the proposals suggest that forestry projects be viewed as providing a
service from nature that can be rented (Brown et al., 2002). At the COP-9 meeting the decision was
taken that forestry credits be issued for a limited time period, with the credits then having to be renewed
or replaced with other valid credits prior to the initial credits expiring (UNFCCC, 2003).
Additionality implies that actions taken to mitigate climate change must be in excess of ’business as
usual’ activities (Pollution Probe, 2002). Many projects which may now be implemented to gain carbon
24
credits may have been implemented anyway for political or commercial reasons, thus the project would
not in essence reduce greenhouse gases below the ‘business as usual’ scenario (Brown et al., 2002).
An example of this is that an exotic tree plantation for pulpwood that has high returns may have been
implemented regardless of the additional benefits of carbon credits, while a forest restoration project on
degraded land with no monetary benefit would probably not have occurred (Brown et al., 2002).
Leakage is defined as an unexpected loss of greenhouse gas reduction benefits when activities or
markets are displaced, resulting in greenhouse gas emissions elsewhere (Schlamadinger and Marland,
2000). In essence, it is an unanticipated increase or decrease in greenhouse gas benefits outside of
the project’s accounting boundary, but occurring as a result of project activities (Brown et al., 2002). An
example would be a large reforestation effort driven by the desire to sequester carbon. These trees
would also be suitable as timber, thus the investment in and planting of commercial forestry plantations
may be reconsidered because future timber prices may appear bleak. If the investment in and planting
of commercial forests were to decrease, this would cause a leakage, i.e. greenhouse gas benefits from
commercial forestry would decrease as a result of the reforestation project (Sedjo, 2001).
Measurability of all carbon credits generated is a requirement of the Kyoto Protocol, and the process
or method of measurement should be transparent and verifiable. The measuring and monitoring of
carbon in forestry projects is one of the concerns raised. At present, tools and techniques exist to
measure carbon stocks in project areas relatively precisely. For example, it is relatively easy for the
carbon within a project area to be quantified for the present and over the time span of the project (IPCC,
2000) since carbon stocks and changes over time within the project area can be estimated using a
combination of direct measurements, activity data, models based on accepted principles of statistical
analysis, forest inventories, remote sensing techniques, flux measurements, soil sampling and/or
ecological surveys. The spatial resolution may create a point of contention, and the accuracy and
precision of the measurements may need to be traded against the cost incurred. In terms of technical
capacity, Annex I Parties are assumed to have the basic capacity for measurement verification.
However, non-Annex I Parties may require technical, institutional and financial assistance in order to
build the capacity required (IPCC, 2000).
However, problems arise in assessing the climate change mitigation effects of any project. For
example, difficulties may arise in assessing the baseline, i.e. what carbon reduction or removal would
have occurred without the project (Brown et al., 2002), and on the question as to whether this baseline
remains fixed throughout the duration of a project or whether it is periodically adjusted (IPCC, 2000). A
further difficulty is the question of additionality, i.e. would the particular project have occurred without
the benefits of carbon credits? A larger concern is the one of leakage. The exact extent of the project’s
impacts are difficult to determine. In order to account for leakage, the project’s accounting and
monitoring needs to extend outside of the project area, but how far? If the leakage is likely to be
minimal, then the monitoring area can be roughly set to the project area. However, if the leakage is
likely to be larger, the monitoring area needs to be significantly larger than the project area, with
particular difficulty then arising in determining the extent of this area and tackling political issues if it
were to stretch across national boundaries.
Verification of the carbon sequestered by an independent third party could play an essential role in
ensuring unbiased monitoring, increasing the confidence in the validity of the carbon credits claimed
and in improving the transparency of any projects and the processes followed. It has been suggested
that the FSC (i.e. Forestry Stewardship Council) method of certifying sustainably grown timber may be
a starting point of the verification process (Brown et al., 2002).
One of the most important issues surrounding the carbon sinks is associated with the definitions of the
terms forests, afforestation, deforestation and reforestation. Many possible definitions of these terms
25
exist, and the choice of definition would determine how much, and which, land is included. This, in turn,
would have implications for the amount of carbon storage, and thus carbon credits claimed.
4.4
Concluding Remarks
Many concerns and issues surround the use of forestry as a carbon sink. Nevertheless, forestry
remains an effective, low cost method of removing carbon from the atmosphere in a sustainable
manner. Progress is being made on solving the problems, issues and concerns surrounding forestry
and carbon sinks. The science behind carbon sinks is well understood and established and many of
the issues and concerns surround technical details, with misunderstanding existing between scientists
and non-scientists, and between various countries.
So, where does this leave the commercial forestry sector? There appears considerable scope for
forestry projects under the Clean Development Mechanism. The additional benefit which forestry could
provide in terms of carbon credits places it in a favourable light in comparison to that of other land uses.
26
5.
CLIMATE SCENARIOS FOR THE FUTURE
“As we know, there are known knowns. There are things we know we know. We also know there are
known unknowns. That is to say, we know there are some things we do not know. But, there are also
unknown unknowns, the ones we don’t know we don’t know.”
Donald Rumsfeld, former US Secretary of Defence, news briefing 12 February 2002
If this quote is unpacked in the context of climate change science it summarises some important
aspects, which are that
•
•
•
there are a number of aspects of climate change which are far more certain than others;
there are also uncertainties and aspects which scientists are aware of, and may have been able
to quantify in terms of risk;
however, there are also aspects of climate change science which scientists do not yet know and
understand, and which may cause surprises.
In order to assess impacts of climate change, predictions of future greenhouse gas emissions and
subsequent atmospheric concentrations are required. In addition, the strength and ability of a
community to adapt to climate change and respond to it in the future will, in itself, have feedbacks on
the climate system. In this regard, the IPCC scenarios will be discussed, as well as uncertainty related
to climate change. Plausible climate scenarios used in this study will then be detailed.
5.1
IPCC Scenarios
In 2000 the IPCC published its ‘Special Report on Emission Scenarios’ (SRES). This report established
emission scenarios which were developed by a panel, with wide consultation. It was an open process of
review and comment by experts and governments. These emissions scenarios were intended to feed
into General Circulation Models (GCMs), thereby enabling projections of future climates to be simulated
and thus enabling discussions to take place on potential impacts, vulnerabilities and adaptation.
Four different ‘storylines’ were developed. These represent different demographic, social, economic,
technological and environmental developments. The relationship between these emission driving
forces and their evolution allows a context for quantification of greenhouse gases and sulphur
emissions. Within each storyline, several scenarios were developed to allow for the examination of a
range of outcomes, resulting in 40 SRES emission scenarios of accumulated emissions by the year
2100 (IPCC, 2000). Figure 5.1 depicts the four storylines and gives a description of them. In Figure
5.2 the global CO2 emissions from energy and industry for all 40 SRES emission scenarios for the
period 1990 to 2100 are illustrated.
Of the 40 SRES scenarios, six should be considered equally sound. These span a wide range of
uncertainty. Four of the six scenarios encompass combinations of demographic change, social and
economic development, and broad technological developments which correspond to the so-called A1,
A2, B1, and B2 storylines. The remaining two scenarios fall within the A1 storyline and explore
alternative energy developments while holding all other driving forces constant (IPCC, 2000). Figure
5.3 shows the modelled anticipated temperature changes related to these six scenarios. As may be
seen, the emission scenario selected has a large influence on the potential change in temperature
which may be experienced in the future.
27
More
economic
A1: A world of rapid economic growth
and rapid introductions of new and more
efficient technologies.
A1
A2: A very heterogeneous world with an
emphasis on family values and local
traditions.
B1: A world of dematerialisation and
introduction of clean technologies.
A2
- B: balanced
- FI: fossil intensive
- T: non-fossil
More
global
More
regional
B1
B2: A world with an emphasis on local
solutions
to
economic
and
environmental sustainability.
B2
More
environmental
Figure 5.1
SRES emission scenario storylines and descriptions (after IPCC, 2000)
Figure 5.2
Global CO2 emissions from energy and industry for all 40 SRES emission scenarios for
the period 1990 to 2100, expressed as an index where emissions in 1990 are one
(IPCC, 2000)
28
Figure 5.3
Potential temperature changes for the six SRES emission scenarios (IPCC, 2001)
The SRES emission scenarios are a plausible range of emission scenarios for use in impact studies.
They are not predictions of future emission scenarios, and have no probabilities of occurrence attached
to them (IPCC, 2000).
Before analysing the impacts of climate change on the forestry sector it is first necessary to discuss
some of the uncertainties in climate change science.
5.2
Uncertainties in Climate Change Science
Uncertainty exists where there is a lack of knowledge relating to the outcomes which may be important
in the decision making process. It may result from an imprecise knowledge of a risk, for example where
the probabilities of occurrence and magnitude of a hazard and its associated consequences are
uncertain (Schulze, 2006). The first rule when assessing uncertainty is that there needs to be an
understanding of what uncertainties have been taken into account and what assumptions have been
made. In climate change studies there are numerous sources of uncertainty. A large source of
uncertainty arises from the science itself, with another large source being uncertainty about future
human behaviour (Pittock, 2006).
When assessing climate change impacts a number of assumptions are made, and a number of models
are used prior to the final impacts assessment. For example,
•
•
Socio-economic models are used to determine the future greenhouse gas emissions scenarios
based on human society behaviour patterns, while
Carbon cycle models are needed to determine the concentration of the greenhouse gases in the
atmosphere.
29
•
•
•
General Circulation Models, or GCMs, determine the effects on global climate, and these are
then downscaled to determine the local and regional changes in climate (Hewitson, 2002).
Climate variability also introduces a form of uncertainty, and sometimes the natural variability of
the earth’s climate system may obscure the climate change signal (Schulze, 2006). In addition,
uncertainty surrounding the nature of climate variability in the future exists.
Lastly, models are used to determine the impacts of climate change on biophysical and socioeconomic systems, and within these models some uncertainties exist. With each level in the
chain of climate change studies there is a ‘cascade of uncertainty’ (Hewitson, 2002), with a
growth in uncertainty at each level (Figure 5.4).
Emission Scenarios
Global Climate Sensitivity
Regional Changes
Climate Variability
Biophysical Impacts
Socio-economic Impacts
Figure 5.4
Illustration of the ‘cascade of uncertainty’ in climate change science (Hewitson, 2002)
Uncertainty is not simply restricted to climate change studies. It occurs in many areas and fields, with
the reaction to uncertainty often dependent on what decisions hinge on the results of the climate
change study. If there is even a small chance of disaster it is worthwhile taking precautions (Pittock,
2006). In other areas and fields, uncertainty does not stop decisions from being made. It is surmised
that in the field of climate change, uncertainty should not stop decisions from being made. Admissions
of the uncertainty in the climate change field should not detract from the science or the seriousness of
the potential impacts, but rather encourage critical examination of assumptions and conclusions to allow
for newer and more reliable answers to be made.
Uncertainty in estimating the impacts of climate change on the forestry sector stems from the abovementioned elements, with additional uncertainty being introduced by the inadequate knowledge of how
trees will respond under climate change conditions. The simplicity of the forest models used in impact
studies further propagates this uncertainty.
5.3
Approaches to Future Climate Scenarios Used in this Study
The approach taken in this study is one of a sensitivity analysis, where variables are perturbed, either
singularly or jointly, by small but realistic (i.e. plausible) increments from a baseline and the results (e.g.
impacts on the forestry sector) are then compared against those from present day climatic conditions,
which are represented by a baseline (i.e. reference) climate.
30
As an initial study a sensitivity analysis is an appropriate point of departure owing to the variation of
outputs which would be obtained from different GCMs, which would depend also on which of the large
number of SRES emission scenarios were used. No single GCM scenario represents a ‘more likely
future’ than that from any other GCM, or a ‘best guess’ (IPCC, 2000). No two GCMs represent the
atmospheric processes identically, and no GCM is perfect; thus to examine output from a single GCM
model would be to look at but one scenario. Across southern Africa the various GCMs are all
corresponding similarly as to the direction of temperature change, in that they are all predicting
increases, but they are not yet corresponding identically with respect to the magnitude of temperature
change at different locations. With respect to rainfall, they are corresponding broadly in regard to the
direction of change, viz. a drying in the central/western areas and a possible wetting in the eastern
escarpment areas, but also not yet in the magnitude of rainfall change.
Incremental climate scenarios, i.e. sensitivity analysis, will allow the following (Schulze, 2006):
•
•
•
•
the likely impacts on the forestry sector to be gauged,
the thresholds of change to be determined, as to when impacts become significant;
the identification of where change is likely to be more significant (i.e. identifying hotspots), and
the assessment as to which ‘driver’ of climate change is the more significant (i.e. temperature or
rainfall, in this instance).
The plausible scenarios used in this study were based on previous outputs from GCMs for southern
Africa (e.g. those in IPCC, 2001 or from Engelbrecht, 2005), and are
•
•
•
•
•
•
•
•
a temperature increase by 0.5°C,
a temperature increase by 1.0°C,
a temperature increase by 1.5°C,
a temperature increase by 2.0°C,
a temperature increase by 2.0°C in combination with a rainfall decrease by 10%,
a temperature increase by 2.0°C in combination with a rainfall decreased by 5%,
a temperature increase by 2.0°C in combination with a rainfall increased by 5%, and
a temperature increase by 2.0°C in combination with a rainfall increased by 10%.
A range of plausible temperature increases is considered as the magnitudes of future changes are, as
yet, uncertain across southern Africa (e.g. Engelbrecht, 2005), and by considering small incremental
changes threshold points of change can be identified. Both increases and decreases in rainfall,
combined with a plausible 2°C increase in temperature, are considered, as the uncertainty surrounding
the nature of precipitation change predicted by various GCMs in the future is still high. For that reason
both a 10% decrease and increase in precipitation are considered to be plausible scenarios of future
climates over southern Africa.
31
6.
BASELINE STUDIES: A POINT OF DEPARTURE FOR CLIMATE CHANGE
IMPACT STUDIES
When using the characteristics of the fibre produced, plantations in southern Africa are classified into
two major categories, viz.
•
•
hardwoods, predominately eucalypts and wattle, and
softwoods, almost exclusively pine species.
The softwoods and hardwoods found in South Africa are either grown on
•
•
short rotations, with softwoods for pulping purposes having 15 - 20 year rotations and hardwoods
for the same purpose grown in 7 - 12 year cycles; and
long rotations, with softwoods for sawlogs grown in 25 - 30 year cycles and hardwoods having a
20 - 25 year rotation (Schulze, 2006).
The different tree species grown in southern Africa have different optimum growth areas, and different
climatic constraints determined largely by mean annual precipitation (MAP) and mean annual
temperature (MAT). Eucalyptus grandis, for example, has been found to be sensitive to frost/snow
damage at locations where MATs are lower than 18°C, while Eucalyptus nitens is not (Kunz, 2004).
Other climate constraints include drought damage, as well as pest and disease damage.
Given the relatively lengthy rotation of commercially grown forest species and time frame for which
decisions need to be made, an understanding of the impacts of climate change on the forestry sector is
crucial. As a point of departure for an analysis of impacts of climate change on the forestry sector,
baseline studies were therefore undertaken. In this section the databases which were used are
reviewed and results of the baseline studies are presented.
6.1
The School of BEEH Databases of Temperature and Rainfall
6.1.1
Database of Gridded Temperatures for Southern Africa
The values of MAT used in this study were from a Water Research Commission (WRC) study
undertaken in the School of Bioresources Engineering and Environmental Hydrology by Schulze and
Maharaj (2004), titled ‘Development of a Database of Gridded Daily Temperatures for Southern Africa’.
Schulze and Maharaj (2004) developed procedures to infill missing daily maximum and minimum
temperature values and to extend station temperature records to a common 51 year (1950 - 2000)
base period for the > 970 stations which qualified, based on the quality of their observed records. As
part of their study, a procedure was developed to generate a 51 year daily time series of maximum and
minimum temperatures at a spatial resolution of 1’ latitude x 1’ longitude, i.e. ~ 1.7 x 1.7 km.
Missing daily maximum and minimum temperature records were infilled using the Difference in
Standard Deviation Method, in which up to 9 target (or patching) stations were chosen for a control
station, based on the difference in altitude and distance between the control and target stations. These
target stations were then used to infill/extend the missing temperature records at the control station. If
there existed any further missing values after exhausting data from the 9 target stations, or the record
period still needed to be extended to the 51 year base period, the control station’s values were
infilled/extended using Fourier Analysis. Once the temperature records for the > 970 qualifying stations
had been infilled/extended, daily temperatures at the 1’ latitude x 1’ longitude grid could be generated
32
using lapse rates from 12 lapse rate regions which had been delineated for South Africa (Schulze and
Maharaj, 2004). From these 51 years of daily values the MAT was then calculated at each grid point.
6.1.2
Database of Gridded Rainfall for Southern Africa
The MAP raster (grid) surfaces used in this study were derived from a WRC project undertaken in the
School of BEEH and titled ‘Development of a Database of Annual, Monthly and Daily Rainfall for South
Africa’ (Lynch, 2004). After stringent quality controls had been applied to the daily rainfall records of all
stations covering southern Africa, the missing values were infilled using a hierarchy of statistical
techniques which included the Expectation Maximisation Algorithm, Inverse Distance Weighting,
patching from monthly values and, lastly, stochastic infilling techniques (Lynch, 2004). Following this, a
1’ x 1’ (i.e. ~ 1.7 x 1.7 km) raster surface of MAP was generated using the Geographically Weighted
Regression technique together with interpolation of residuals (Lynch, 2004).
6.2
The Institute for Commercial Forestry (ICFR) Forestry Productivity Toolbox
The Forestry Productivity Toolbox (Kunz, 2004) was developed by the ICFR as a tool which could
provide a wide range of users with data, information and knowledge on forestry management options.
In essence, the Forestry Productivity Toolbox can provide the following information:
•
•
•
•
a site classification which is based on climatic, edaphic and topographic variables,
optimum and sub-optimum growth areas of various tree species, based on climatic criteria,
estimations of potential productivity for selected commercial hardwood species, and
an assessment of the risk of site damage potentially occurring from a harvesting operation
(Kunz, 2004).
Of relevance for this study were the climatic criteria for optimum and sub-optimum growth. In order to
establish the climatic criteria for optimum growth for a particular species, Smith et al. (2005) followed
the methodology given below:
•
•
•
•
Information regarding the growth requirements for each major commercial species was collated
from existing literature.
Consistency was then sought between the various sources.
Once the consistent optimum growth criteria had been determined from the various sources, a
climatic range was linked to classes in the forest site classification of Smith et al. (2005) and the
Toolbox was populated with this information.
At a workshop of the Forest Site Classification Working Group, the results of this approach were
presented and the climatic criteria were ratified.
As a basis for mapping in this project, information on the suitable macro-climatic conditions was
extracted from the ICFR Forestry Productivity Toolbox for each species which was analysed. The
climatic criteria are presented in the form of a matrix of
•
•
Mean annual temperature (MAT) in nine categories, viz.
o
< 14°C,
o
14°C to 21°C at 1°C intervals, and
o
> 21°C; and
Mean annual precipitation (MAP) in 22 categories, viz.
o
< 700 mm
o
700 mm to 1 200 mm at intervals of 25 mm, and
33
> 1 200 mm (Schulze, 2006).
o
Figure 6.1 illustrates this 9 x 22 matrix for Eucalyptus grandis. The differently coloured boxes delineate
climatically optimum growth areas (in green), as well as sub-optimum growth areas associated with
moderate risks (bracketed), and high risk areas which were deemed unsuitable for the production of a
given species. The moderate risk sub-optimum areas are those in which the species can be grown, but
its growth cycle may experience occasional climate-driven setbacks/constraints. For example, an area
may have a moderate risk of mortality as a result of occasional drought or frost. High risk areas are
those areas where certain climate drivers occur with a frequency and/or severity that causes the area to
be unsuitable for that specific species. Areas may be high risk due to the occurrence of pests, diseases,
snow and/or frost, and the area may also be too dry for growth (Kunz, 2004). A further delineation is a
slow growth rate. These areas are unsuitable for a specific species because the growth rate would fall
below the commercially viable threshold (Kunz, 2004).
Mean Annual
Precipitation
(mm)
< 700
700 - 725
725 - 750
750 - 775
775 - 800
800 - 825
825 - 850
850 - 875
875 - 900
900 - 925
925 - 950
950 - 975
975 - 1000
1000 - 1025
1025 - 1050
1050 - 1075
1075 - 1100
1100 - 1125
1125 - 1150
1150 - 1175
1175 - 1200
> 1200
Figure 6.1
< 14
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
14 - 15
Too dry
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
15 - 16
Too dry
Too dry
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Mean Annual Temperature
(o C)
16 - 17
17 - 18
18 - 19
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
(Drought)
Too dry
Too dry
(Drought)
(Drought)
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
(Drought)
(Drought)
Optimum
Optimum
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
19 - 20
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
20 - 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
> 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Illustration of the 9x22 climate matrix for delineation of optimum/sub-optimum growth
areas for Eucalyptus grandis (after Kunz, 2004)
Within the RSA, Lesotho and Swaziland, the growth areas for each forestry species, as defined by their
respective climate criteria, were mapped at a spatial scale of 1’ latitude by 1’ longitude (i.e. a raster grid
of ~ 1.7 km x 1.7 km). Areas which fell below the minimum threshold MAP of 700 mm and outside the
MAT bounds of 13°C and 22°C were considered climatically unsuitable areas (Schulze, 2006). The
MAP grid values used in the mapping of climatically suitable areas for the various forestry species were
generated by Lynch (2004) using techniques which were summarised in Section 6.1.2. The MAT grid
values were computed by Schulze and Maharaj (2004) with the methods outlined in Section 6.1.1.
6.3
Mapping Climatically Optimum and Sub-Optimum Areas for Forest Species
Under Baseline Climatic Conditions
For the purposes of this climate change impact study Acacia mearnsii, five Eucalyptus species/hybrids
and four Pinus species/hybrids were selected for analysis. The Eucalyptus species were Eucalyptus
grandis, Eucalyptus dunnii, Eucalyptus smithii and Eucalyptus nitens, and the hybrid was Eucalyptus
GxU. The Pinus species selected were Pinus taeda, Pinus patula and Pinus elliottii, and the hybrid was
Pinus ExC. Shown in the sub-sections which follow are the 9x22 climate matrixes for each of the
species/hybrids named above, as well as their mapped climatically optimum and sub-optimum areas.
Note that the mapped suitability areas are based on macro-climatic conditions of MAP and MAT alone,
34
as this was the only level of model available, and therefore taking no cognisance of soil properties,
slope, geology, micro-climate conditions, other competing land uses or socio-political considerations.
6.3.1
Baseline Studies on Acacia mearnsii
Figure 6.2 on the climatically optimum growth areas for Acacia mearnsii shows the main potential areas
to be in a strip along the coast of the Eastern Cape, parts of the midlands and northwest of KwaZuluNatal, western Swaziland and a strip in Mpumalanga, with smaller patches of climatically optimum
areas found elsewhere (Schulze and Maharaj, 2006a).
As a verification procedure, the areas currently planted to Acacia mearnsii, as determined from the
National Land Cover (2001) satellite images, were superimposed over the mapped climatic suitability
classes, i.e. the optimum areas, as well as the moderate and high risk areas (Figure 6.3). Table 6.1
gives the distribution of the currently planted areas (i.e. 2001) as percentages of the various climatic
suitability classes. Only approximately 37% of the current area under A. mearnsii is found in climatically
optimum areas, with 43% in climates posing moderate risks and nearly 19% of the current area under
A. mearnsii being grown in high risk climatic areas.
6.3.2
Baseline Studies on Eucalyptus Species
Figure 6.4 shows the climatically optimum growth areas as well as areas of moderate and high
climatically related risk for Eucalyptus grandis. The dominant growth areas are along the north coast of
the Eastern Cape, coastal and inland areas within KwaZulu-Natal, the western highveld of Swaziland
and high rainfall regions of Mpumalanga and Limpopo (Schulze and Maharaj, 2006b). The climatic
matrices and maps of climatically optimum, moderate and high risk areas of the remaining Eucalyptus
species are shown in Figure 6.5 to Figure 6.8.
In South Africa a number of species of eucalypts are planted, each with varying climatic conditions for
optimum growth. For example, Eucalyptus GxU is suited to relatively warmer conditions (Figure 6.8)
while Eucalyptus smithii is better suited to cooler conditions (Figure 6.6). However, owing to the
spectral similarities of the various Eucalyptus species/hybrids when analysed from satellite imagery, the
National Land Cover image does not distinguish between these species/hybrids. Therefore, for
purposes of verification against the National Land Cover image (2001), areas of the climatically
optimum, moderate and high risk areas for the five selected Eucalyptus species/hybrids were grouped
together. Thus, if a 1’ latitude x 1’ longitude pixel was climatically optimal for any one species it was
considered generally optimal for eucalypts. Areas were considered in a hierarchy of optimum, then
moderate risk and lastly high risk, i.e. if a pixel’s climate was high risk for one Eucalyptus species, yet
optimal for another, it was considered optimal in the lumped coverage.
As may be seen from Figure 6.9, the majority (~ 83%) of areas currently planted to eucalypts fall within
the climatically optimum areas, with only small percentages of areas presently planted to Eucalyptus
species falling in the moderate and high risk climate areas (Table 6.2).
A further baseline analysis undertaken was determining the number of Eucalyptus species/hybrids
which satisfy the climatically optimum requirements for each 1’ x 1’ pixel.
The results from this
analysis, undertaken with the 13 Eucalyptus species/hybrids specified in Schulze (2006), are shown in
Figure 6.10. The greater the number of species satisfying optimum conditions for a given 1’ x 1’ pixel,
the greater the climatic robustness of that pixel to be planted to eucalypts. As may be seen from
Figure 6.10, the resilient Eucalyptus areas are in the midlands of KwaZulu-Natal and in an area on the
Mpumalanga/Swaziland border, where seven or more out of the 13 Eucalyptus species/hybrids satisfied
climatically optimum criteria.
35
Mean Annual
Precipitation
(mm)
< 700
700 - 725
725 - 750
750 - 775
775 - 800
800 - 825
825 - 850
850 - 875
875 - 900
900 - 925
925 - 950
950 - 975
975 - 1000
1000 - 1025
1025 - 1050
1050 - 1075
1075 - 1100
1100 - 1125
1125 - 1150
1150 - 1175
1175 - 1200
> 1200
Figure 6.2
< 14
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
14 - 15
Too dry
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
15 - 16
Too dry
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Disease
Mean Annual Temperature
o
( C)
17 - 18
18 - 19
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
(Drought)
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Disease
Disease
16 - 17
Too dry
Too dry
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Disease
19 - 20
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Disease
20 - 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
> 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Climatic matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006a) of climatically
suitable areas for the growth of Acacia mearnsii
Acacia mearnsii
Mapped Climatic Areas vs.
Current Growth Areas
Figure 6.3
Comparison of climatically optimum, moderate risk (MR) and high risk (HR) areas for
Acacia mearnsii with areas currently planted, as determined from the National Land
Cover image (2001)
36
Mean Annual
Precipitation
(mm)
< 700
700 - 725
725 - 750
750 - 775
775 - 800
800 - 825
825 - 850
850 - 875
875 - 900
900 - 925
925 - 950
950 - 975
975 - 1000
1000 - 1025
1025 - 1050
1050 - 1075
1075 - 1100
1100 - 1125
1125 - 1150
1150 - 1175
1175 - 1200
> 1200
Figure 6.4
14 - 15
Too dry
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
15 - 16
Too dry
Too dry
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Mean Annual Temperature
(o C)
17 - 18
18 - 19
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
(Drought)
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
16 - 17
Too dry
Too dry
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
19 - 20
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
20 - 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
> 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Climatic matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006b) of climatically
suitable areas for the growth of Eucalyptus grandis
M ean Annual
Precipitation
(m m )
< 700
700 - 725
725 - 750
750 - 775
775 - 800
800 - 825
825 - 850
850 - 875
875 - 900
900 - 925
925 - 950
950 - 975
975 - 1000
1000 - 1025
1025 - 1050
1050 - 1075
1075 - 1100
1100 - 1125
1125 - 1150
1150 - 1175
1175 - 1200
> 1200
Figure 6.5
< 14
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
< 14
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
14 - 15
Too dry
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
15 - 16
Too dry
Too dry
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
M ean Annual Tem perature
o
( C)
17 - 18
18 - 19
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
O ptim um
Too dry
O ptim um
Optim um
O ptim um
Optim um
O ptim um
Optim um
O ptim um
Optim um
O ptim um
Optim um
O ptim um
Optim um
O ptim um
Optim um
O ptim um
Optim um
O ptim um
Optim um
O ptim um
Optim um
O ptim um
Optim um
O ptim um
Optim um
O ptim um
Optim um
O ptim um
Optim um
O ptim um
Optim um
O ptim um
Optim um
O ptim um
Optim um
16 - 17
Too dry
Too dry
Too dry
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
Optim um
19 - 20
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
20 - 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
> 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Climatic matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006c) of climatically
suitable areas for the growth of Eucalyptus dunnii
37
Mean Annual
Precipitation
(mm)
< 700
700 - 725
725 - 750
750 - 775
775 - 800
800 - 825
825 - 850
850 - 875
875 - 900
900 - 925
925 - 950
950 - 975
975 - 1000
1000 - 1025
1025 - 1050
1050 - 1075
1075 - 1100
1100 - 1125
1125 - 1150
1150 - 1175
1175 - 1200
> 1200
Figure 6.6
14 - 15
Too dry
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
15 - 16
Too dry
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Mean Annual Temperature
(oC)
17 - 18
18 - 19
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Sub-optimum Too dry
Sub-optimum Disease
Sub-optimum Disease
Sub-optimum Disease
Sub-optimum Disease
Sub-optimum Disease
Sub-optimum Disease
Sub-optimum Disease
Sub-optimum Disease
Sub-optimum Disease
Sub-optimum Disease
Sub-optimum Disease
Sub-optimum Disease
Sub-optimum Disease
Sub-optimum Disease
Sub-optimum Disease
Sub-optimum Disease
Sub-optimum Disease
16 - 17
Too dry
Too dry
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
19 - 20
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
20 - 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
> 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Climatic matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006c) of climatically
suitable areas for the growth of Eucalyptus smithii
Mean Annual
Precipitation
(mm)
< 700
700 - 725
725 - 750
750 - 775
775 - 800
800 - 825
825 - 850
850 - 875
875 - 900
900 - 925
925 - 950
950 - 975
975 - 1000
1000 - 1025
1025 - 1050
1050 - 1075
1075 - 1100
1100 - 1125
1125 - 1150
1150 - 1175
1175 - 1200
> 1200
Figure 6.7
< 14
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
< 14
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
14 - 15
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
15 - 16
Too dry
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Sub-optimum
Mean Annual Temperature
(oC)
17 - 18
18 - 19
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Growth rate
Too dry
Growth rate
Disease
Growth rate
Disease
Growth rate
Disease
Growth rate
Disease
Growth rate
Disease
Growth rate
Disease
Growth rate
Disease
Growth rate
Disease
Growth rate
Disease
Growth rate
Disease
Growth rate
Disease
Growth rate
Disease
Growth rate
Disease
Growth rate
Disease
Growth rate
Disease
Disease
Growth rate
Growth rate
Disease
16 - 17
Too dry
Too dry
Too dry
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
19 - 20
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
20 - 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
> 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Climatic matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006c) of climatically
suitable areas for the growth of Eucalyptus nitens
38
Mean Annual
Precipitation
(mm)
< 700
700 - 725
725 - 750
750 - 775
775 - 800
800 - 825
825 - 850
850 - 875
875 - 900
900 - 925
925 - 950
950 - 975
975 - 1000
1000 - 1025
1025 - 1050
1050 - 1075
1075 - 1100
1100 - 1125
1125 - 1150
1150 - 1175
1175 - 1200
> 1200
Figure 6.8
< 14
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
14 - 15
Too dry
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
15 - 16
Too dry
Too dry
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Snow/Frost
Mean Annual Temperature
o
( C)
17 - 18
18 - 19
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Optimum
Too dry
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
16 - 17
Too dry
Too dry
Too dry
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
19 - 20
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
20 - 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
> 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Climatic matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006c) of climatically
suitable areas for the growth of Eucalyptus GxU
Eucalyptus
Mapped Climatic Areas vs.
Current Growth Areas
Figure 6.9
Comparison of climatically optimum, moderate risk (MR) and high risk (HR) areas for
Eucalyptus species with areas currently planted, as determined from the National Land
Cover image (2001)
39
Table 6.1
Areas currently (i.e. from 2001 satellite imagery) planted to Acacia mearnsii as
percentages of climatic suitability classes
Climatic Suitability Class
Optimum
Moderate Risk
High Risk
Unsuitable Climatically
100.0
Total
Table 6.2
Areas currently (i.e. from 2001 satellite imagery) planted to Eucalyptus species and
hybrids as percentages of climatic suitability classes
Climatic Suitability Class
Optimum
Moderate Risk
High Risk
Unsuitable Climatically
6.3.3
% of Current Areas
82.5
4.4
6.8
6.3
100.0
Total
Figure 6.10
% of Current Areas
36.9
43.0
18.7
1.4
The number of Eucalyptus species or hybrids out of 13 which were analysed, which
satisfy the optimum climate criteria for a given 1’ latitude x 1’ longitude pixel
Baseline Studies on Pinus Species
Figure 6.11 shows the climatically optimum growth areas of Pinus taeda to be along the north coast of
the Eastern Cape, in significant tracts of the midlands of KwaZulu-Natal, in parts of Mpumalanga and in
western parts of Swaziland. Major climatic growth constraints are drought related (Schulze and
Maharaj, 2006d).
Climatically optimum growth areas of Pinus patula are shown in Figure 6.12. These areas occur in an
arc inland of the coast from the northeastern areas of the Eastern Cape, through KwaZulu-Natal and
40
including a strip along this province’s border with Lesotho and the Free State, the western third of
Swaziland and into Mpumalanga. The major constraint on the inland side of this arc is a lack of rainfall,
while on the coastal side of the arc temperatures tend to be too high (Schulze and Maharaj, 2006e).
The climatically optimum growth areas of Pinus elliottii, as well as those areas deemed to be at
moderate risk for successful growth of this species, are shown in Figure 6.13. Large tracts along the
coastal and inland areas of the northeastern Eastern Cape province and KwaZulu-Natal, as well as the
western half of Swaziland, and significant parts of Mpumalanga are shown to be climatically suitable for
Pinus elliottii production (Schulze and Maharaj, 2006f). Climatically optimum, moderate risk and high
risk areas for Pinus ExC are shown in Figure 6.14.
From the above it is evident that numerous Pinus species/hybrids are grown throughout South Africa.
As in the case of Eucalyptus species, the National Land Cover satellite derived image (2001) cannot
distinguish between the various species/hybrids of pines. Therefore, the four climatically optimum
growth areas of the chosen Pinus species and hybrids, viz. Pinus elliottii, Pinus ExC, P. patula and P.
taeda, were superimposed over one another. A hierarchy was established, viz. if a 1’ x 1’ (~1.7 km x
1.7 km) pixel was defined as being climatically optimum for any one of the four Pinus species/hybrids, it
was deemed to be climatically optimal for the combined Pinus coverage.
This combined coverage of climatically optimum, moderate and high risk climate areas was compared
to the areas currently planted to pines, as defined by the National Land Cover image (2005). As may
be seen from Figure 6.15, the majority of the currently grown Pinus species occur within the areas
mapped as climatically optimal, with the remainder of the currently planted Pinus species occurring
within moderate risk climate areas. In Table 6.3 the percentage distributions are presented of the areas
currently planted to Pinus species/hybrids between the climatically optimum, moderate risk and high
risk climate areas.
Table 6.3
Areas currently (i.e. from 2001 satellite imagery) planted to Pinus species and hybrids
as percentages of climatic suitability classes
Climatic Suitability Class
Optimum
Moderate Risk
High Risk
Unsuitable Climatically
% of Current Areas
75.5
17.0
1.5
6.0
100.0
Total
A final baseline analysis undertaken for the three Pinus species and the one hybrid selected was the
determination of the number of the species/hybrids which satisfy the climatically optimum requirements
per pixel. The results from this analysis are shown in Figure 6.16. The greater the number of species
or hybrids satisfying the climatically optimum conditions per 1’ x 1’ pixel, the greater the climatic
robustness of that pixel. As may be seen from Figure 6.16, the robust Pinus areas are the midlands of
KwaZulu-Natal and an area on the Mpumalanga/Swaziland border, where five out of the six Pinus
species/hybrids (i.e. the four already analysed plus Pinus greggii N and Pinus greggii S) which are
grown in South Africa satisfy the climatically optimum criteria.
41
Mean Annual
Precipitation
(mm)
< 700
700 - 725
725 - 750
750 - 775
775 - 800
800 - 825
825 - 850
850 - 875
875 - 900
900 - 925
925 - 950
950 - 975
975 - 1000
1000 - 1025
1025 - 1050
1050 - 1075
1075 - 1100
1100 - 1125
1125 - 1150
1150 - 1175
1175 - 1200
> 1200
Figure 6.11
14 - 15
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
15 - 16
Too dry
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Mean Annual Temperature
(oC)
17 - 18
18 - 19
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
(Drought)
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
16 - 17
Too dry
Too dry
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
19 - 20
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
20 - 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
> 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Growth rate
Climate matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006d) of climatically
suitable areas for the growth of Pinus taeda
Mean Annual
Precipitation
(mm)
< 700
700 - 725
725 - 750
750 - 775
775 - 800
800 - 825
825 - 850
850 - 875
875 - 900
900 - 925
925 - 950
950 - 975
975 - 1000
1000 - 1025
1025 - 1050
1050 - 1075
1075 - 1100
1100 - 1125
1125 - 1150
1150 - 1175
1175 - 1200
> 1200
Figure 6.12
< 14
(Drought)
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
< 14
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
14 - 15
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
15 - 16
Too dry
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Mean Annual Temperature
(oC)
17 - 18
18 - 19
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
(Drought)
Too dry
(Drought)
Sub-optimum
(Drought)
Sub-optimum
(Drought)
Sub-optimum
Optimum
Sub-optimum
Optimum
Sub-optimum
Optimum
Sub-optimum
Optimum
Sub-optimum
Optimum
Sub-optimum
Optimum
Sub-optimum
Optimum
Sub-optimum
Optimum
Sub-optimum
Optimum
Sub-optimum
Optimum
Sub-optimum
Optimum
Sub-optimum
Optimum
Sub-optimum
Optimum
Sub-optimum
Sub-optimum
Optimum
16 - 17
Too dry
Too dry
Too dry
(Drought)
(Drought)
(Drought)
(Drought)
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
Optimum
19 - 20
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
20 - 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
> 21
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Too dry
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Disease
Climate matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006e) of climatically
suitable areas for the growth of Pinus patula
42
M ean A nnual
Precip itation
(m m )
< 700
700 - 725
725 - 750
750 - 775
775 - 800
800 - 825
825 - 850
850 - 875
875 - 900
900 - 925
925 - 950
950 - 975
975 - 1000
1000 - 1025
1025 - 1050
1050 - 1075
1075 - 1100
1100 - 1125
1125 - 1150
1150 - 1175
1175 - 1200
> 1200
Figure 6.13
14 - 15
(D rought)
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
15 - 16
(D rought)
(D rought)
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
M ean Annual Tem peratu re
o
( C)
17 - 18
18 - 19
(D rought) (D rought)
(D rought) (D rought)
(D rought) (D rought)
(D rought) (D rought)
O ptim um
(D rought)
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
16 - 17
(D rought)
(D rought)
(D rought)
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
19 - 20
(D rought)
(D rought)
(D rought)
(D rought)
(D rought)
(D rought)
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
20 - 21
(D rought)
(D rought)
(D rought)
(D rought)
(D rought)
(D rought)
(D rought)
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
> 21
(D rought)
(D rought)
(D rought)
(D rought)
(D rought)
(D rought)
(D rought)
(D rought)
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
O ptim um
Climate matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006f) of climatically
suitable areas for the growth of Pinus elliottii
M ean Annual
P re c ip ita tio n
(m m )
< 700
700 - 725
725 - 750
750 - 775
775 - 800
800 - 825
825 - 850
850 - 875
875 - 900
900 - 925
925 - 950
950 - 975
975 - 1000
1000 - 1025
1025 - 1050
1050 - 1075
1075 - 1100
1100 - 1125
1125 - 1150
1150 - 1175
1175 - 1200
> 1200
Figure 6.14
< 14
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
< 14
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
14 - 15
T o o d ry
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
15 - 16
T o o d ry
T o o d ry
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
Snow
M e a n A n n u a l T e m p e ra tu re
(o C )
17 - 18
18 - 19
T o o d ry
T o o d ry
T o o d ry
T o o d ry
T o o d ry
T o o d ry
T o o d ry
T o o d ry
O p tim u m
T o o d ry
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
16 - 17
T o o d ry
T o o d ry
T o o d ry
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
19 - 20
T o o d ry
T o o d ry
T o o d ry
T o o d ry
T o o d ry
T o o d ry
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
20 - 21
T o o d ry
T o o d ry
T o o d ry
T o o d ry
T o o d ry
T o o d ry
T o o d ry
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
> 21
To o d ry
To o d ry
To o d ry
To o d ry
To o d ry
To o d ry
To o d ry
To o d ry
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
O p tim u m
Climate matrix (after Kunz, 2004) and map (Schulze and Maharaj, 2006g) of climatically
suitable areas for the growth of Pinus ExC
43
Pinus
Mapped Climatic Areas vs.
Current Growth Areas
Figure 6.15
Comparison of climatically optimum, moderate risk (MR) and high risk (HR) areas for
Pinus species/hybrids with areas currently planted, as determined from the National
Land Cover image (2001)
Figure 6.16
The number of Pinus species or hybrids out of six which were analysed, which satisfy
the optimum climate criteria for a given 1’ latitude x 1’ longitude pixel
The baseline study presented in this Section forms the foundation upon which the climate change
sensitivity study which now follows, is based.
44
7.
7.1
RESULTS OF CLIMATE CHANGE SENSITIVITY ANALYSES IN SOUTHERN
AFRICA ON DIFFERENT TREE SPECIES
The Scenarios Used in this Study - Revisited
Following on from the baseline studies in Section 6, sensitivity analyses for a range of plausible climate
change scenarios were undertaken. As stated previously in Section 5.3, the scenarios used in this
study are:
•
•
•
•
•
•
•
•
a temperature increase of 0.5°C,
a temperature increase of 1.0°C,
a temperature increase of 1.5°C,
a temperature increase of 2.0°C,
a temperature increase of 2.0°C in combination with a rainfall decrease of 10%,
a temperature increase of 2.0°C in combination with a rainfall decrease of 5%,
a temperature increase of 2.0°C in combination with a rainfall increase of 5%, and
a temperature increase of 2.0°C in combination with a rainfall increase of 10%.
A subset of the scenarios is shown in the mapped results, while all scenarios are illustrated in the
graphed results.
7.2
Results from Acacia mearnsii
Acacia mearnsii has historically been proven to be the most drought resistant of the commercially
produced hardwood species which are currently grown in southern Africa (Herbert, 1993). Climatically
the optimum growth areas of A. mearnsii occur where the MAP is in the range of 750 - 1 200 mm for
cooler areas and 850 - 1 200 mm for warmer areas. If the MAP is higher, the maturing of the bark is
delayed, the incidence of bagworm increases and diseases occur more frequently, with gummosis
disease occurring if the A. mearnsii is been grown in humic soils (Smith, 1998). With respect to MAT,
the ideal range is 16 - 19°C, with mid-summer temperatures > 22°C. Acacia mearnsii is sensitive to
frost, thus the daily means of winter temperature should not drop below 2°C (Smith, 1998).
Mapped results of the climate change sensitivity analysis for Acacia mearnsii are illustrated in Figure
7.1 while in Figure 7.2 graphs are displayed of the percentage changes in climatically optimum and
moderate risk areas for A. mearnsii with changes in temperature and Figure 7.3 shows graphs of the
relative percentage changes in climatically optimum and moderate risk areas of A. mearnsii for varying
changes in rainfall assuming a base scenario of a 2°C temperature increase and present day rainfall.
As may be seen from the above-mentioned figures, A. mearnsii is relatively robust to future changes in
temperature. For example, for a temperature increase of 1°C the climatically optimum area of A.
mearnsii over southern Africa decreases by only ~ 13%, while for a 2°C increase in temperature the
climatically optimum area of A. mearnsii decreases by approximately 30%.
Although relatively robust to changes in temperature, Acacia mearnsii is highly sensitive to changes in
rainfall, as may be seen from Figure 7.1 and Figure 7.3. For example, a 10% decrease in rainfall
combined with a 2°C increase in temperature would result in the climatically optimum areas of A.
mearnsii decreasing by approximately 40%. Equally, A. mearnsii is highly sensitive to increases in
rainfall, and the climatically optimum area is simulated to increase by ~ 40% for a mere 5% increase in
rainfall combined with a 2°C temperature increase, while for a 10% increase in rainfall the climatically
optimum area would expand by approximately 90% if the climatic criteria for this species and the simple
model based on MAT and MAP are accepted as realistic.
45
Present
T + 2°C &
PPT -10%
Figure 7.1
T + 1°C
T + 2°C
T + 2°C &
PPT +10%
Mapped results of the Acacia mearnsii sensitivity analyses to various climate change scenarios
5
0
0
0.5
1
1.5
2
%
Change in
rea
%
Change
inA
Area
-5
-10
-15
-20
-25
Optimum
Sub-Optimum
-30
Increases
in Temperature
(°C)
Changes
in Temperature
Figure 7.2
Percentage changes in the climatically optimum and sub-optimum, i.e. moderate risk,
areas for Acacia mearnsii with increases in temperature
120
Optimum
Sub-Optimum
%Change
% Change
in Area
80
40
0
-10
-5
0
5
10
-40
-80
Rainfall
Changes
(%)
Changes
in Rainfall
Figure 7.3
Percentage changes in the climatically optimum plus sub-optimum, i.e. moderate risk,
areas for Acacia mearnsii for a 2°C increase in temperature in combination with a
range of negative and positive changes in rainfall
To place the values presented above into perspective, the current areas of Acacia mearnsii identified by
the National Land Cover image (2001) were compared against the climatically optimum, moderate risk
and high risk areas under the various climate scenarios used. The results are shown in Table 7.1.
When plausible increases of only temperature are considered, the distribution of current areas between
47
Table 7.1
Distribution of areas currently planted to Acacia mearnsii in different climate suitability
classes, for a range of plausible climate scenarios
Climate Suitability Class
Optimum
Moderate Risk
High Risk
Climatically Unsuitable Areas
% of Areas Currently Planted to Acacia mearnsii
Present
T + 1°C
T + 2°C
T + 2°C,
T + 2°C,
PPT - 10%
PPT + 10%
36.9
49.0
45.4
23.0
65.3
43.0
30.1
29.8
27.7
23.3
18.7
19.5
23.4
42.4
10.6
1.4
1.4
1.4
6.9
0.8
100.0
Total
T = Temperature; PPT = Precipitation
100.0
100.0
100.0
100.0
climatically optimum, moderate and high risk areas does not alter greatly. However, with an increase in
temperature plus a 10% decrease in rainfall, 42% of the current areas of A. mearnsii would fall into a
high risk climate area. With an increase in temperature and a 10% increase in rainfall a greater
proportion of the currently grown A. mearnsii would fall into a climatically optimum region, with
percentage increasing from 37% to 65%.
Under climate change conditions it is hypothesised that shifts in other land uses are also likely to occur,
and areas currently suitable for forestry may longer be suitable in the future. However, other areas
which are climatically unsuitable for forestry under present climatic conditions may become suitable in
the future. In order to asses whether changes in the distribution of climatically suitable forestry areas
would change in the future, an analysis of the distribution of climatically optimum areas for Acacia
mearnsii for the scenarios used in this study was undertaken on a provincial basis, with Swaziland also
included.
The results are presented in Figure 7.4. Under present climate conditions, the greatest climatically
optimum area for Acacia mearnsii occurs in KwaZulu-Natal. As temperature increases and rainfall is
decreased the area which is climatically optimum in KwaZulu-Natal decreases significantly, with only an
expansion in suitable area occurring when both temperature and rainfall are increased. The same
pattern, although with greater fluctuations, is evident in the Eastern Cape. In Mpumalanga province,
however, the climatically optimum area for A. mearnsii increases when the temperature is increased by
1°C and 2°C, possibly due to decreased occurrences of frost. A decrease in the climatically optimum
area would occur when temperature is increased and rainfall is decreased simultaneously, but a
substantial increase in the climatically optimum area is projected to occur with both an increase in
temperature and rainfall.
7.3
Results from Eucalyptus Species
Eucalyptus dunnii is a species native to northeastern New South Wales and southeastern Queensland
(Lee et al., 2005). It is a fast-growing tough, very dense and coarse grained hardwood species (Lee et
al., 2005). Eucalyptus dunnii grows optimally with a MAP in excess of 800 mm and an MAT in the
range 15 - 19°C (Kunz, 2004). However, at higher MAPs E. dunnii will grow at higher temperatures
(Herbert, 2000). At a MAT above 19°C, E. dunnii is prone to disease (Kunz, 2004).
In Figure 7.5 the results of the climate change sensitivity analyses for Eucalyptus dunnii are mapped,
while Figure 7.6 illustrates percentage changes in the climatically optimum areas of the various
Eucalyptus species/hybrids with increases in temperature from present climate, and Figure 7.7 gives
percentage changes in the climatically optimum plus sub-optimum areas (i.e. the moderate risk areas)
48
1600
1400
Area (km2)
Area
(km2)
1200
1000
800
600
400
T + 2C, PPT +10%
T + 2C, PPT -10%
200
T + 2C
T + 1C
0
Present
KwaZulu-Natal Mpumalanga
Figure 7.4
Swaziland
Eastern Cape
Areas (km2) which are climatically optimal for Acacia mearnsii for various plausible
scenarios of climate
of Eucalyptus species with increases in temperature. Figure 7.8 displays graphs of the percentage
changes in the climatically optimum areas for the various Eucalyptus species/hybrids for varying
negative and positive changes in rainfall from a base of a 2°C increase in temperature and present
rainfall, while in Figure 7.9 sub-optimum (i.e. moderate risk) areas have been included.
Results show Eucalyptus dunnii to be highly robust to changes in temperature, with relatively small
changes in the climatically optimum areas occurring with global warming. A 2°C increase in
temperature only decreases the optimum area by ~ 25% (Figure 7.6). Results are similar when both
climatically optimum and sub-optimum (i.e. moderate risk) areas are considered (Figure 7.7). With
changes in rainfall, E. dunnii is the least sensitive (i.e. most robust) of the selected Eucalyptus
species/hybrids. A 50% decrease in climatically optimum area is experienced with a 10% decrease in
rainfall in association with a 2oC increase in temperature, with the inverse occurring for an increase in
rainfall, where a 10% increase in rainfall results in a 50% increase in the climatically optimum area of E.
dunnii (Figure 7.8). Again, results are similar when both climatically optimum and moderate risk areas
are considered (Figure 7.9).
Eucalyptus grandis, when grown commercially in South Africa in cooler areas, requires a MAP of
approximately 900 mm and a MAT of around 16°C, while in slightly warmer areas with a MAT of
approximately 22°C a MAP of ~ 1 000 mm is required (Smith, 1998). Eucalyptus grandis is sensitive to
both frost and snow, and therefore monthly means of daily minimum temperatures in the winter months
should ideally exceed 4°C (Schulze and Maharaj, 2006b).
49
Present
T + 1°C
T + 2°C &
PPT -10%
T + 2°C &
PPT +10%
Figure 7.5
T + 2°C
Mapped results of the Eucalyptus dunnii sensitivity analyses to various climate change scenarios
20
0
%Change
ChangeininArea
Ar
%
0
1
2
-20
-40
-60
-80
E dunnii
E grandis
E GxU
E nitens
E smithii
-100
Temperature
Changes (°C)
Increases
in Temperature
Figure 7.6
Percentage changes in the climatically optimum areas for Eucalyptus species/hybrids
with increases in temperature
20
Change
Ar
%%
Change
in in
Area
0
0
1
2
-20
-40
-60
E dunnii
E grandis
E GxU
E nitens
E smithii
-80
Increases
in Temperature
Temperature
Changes(°C)
Figure 7.7
Percentage changes in the climatically optimum plus sub-optimum, i.e. moderate risk,
areas for Eucalyptus species/hybrids with changes in temperature
51
120
% Change
in Area
%Change
in Ar
80
E dunnii
E grandis
E GxU
E nitens
E smithii
40
0
-10
-5
0
5
10
-40
-80
RainfallChanges
Changes(%)
Rainfall
Figure 7.8
Percentage changes in the climatically optimum areas for Eucalyptus species/hybrids
for a 2°C increase in temperature in combination with a range of negative and positive
various changes in rainfall
80
%
%Change
ChangeininArea
Ar
E dunnii
E grandis
E GxU
E nitens
E smithii
40
0
-10
-5
0
5
10
-40
-80
Rainfall
Changes
Rainfall
Changes
(%)
Figure 7.9
Percentage changes in the climatically optimum plus sub-optimum, i.e. moderate risk,
areas for Eucalyptus species/hybrids for a 2°C increase in temperature in combination
with a range of negative and positive changes in rainfall
52
Present
T + 2°C &
PPT -10%
Figure 7.10
T + 1°C
T + 2°C
T + 2°C &
PPT +10%
Mapped results of the Eucalyptus grandis sensitivity analyses to various climate change scenarios
In Figure 7.10 the results of the climate change sensitivity analyses for Eucalyptus grandis are
mapped. Eucalyptus grandis is relatively robust to changes in temperature, with a 1°C increase in
temperature resulting in a 10% reduction in the climatically optimum area, while with a 2°C increase in
temperature the climatically optimum area of E. grandis is reduced by 30% (Figure 7.6). When
considering both climatically optimum and sub-optimum (i.e. moderate risk) areas, there is little change
in the suitable areas from the present day scenario when only temperatures are assumed to increase
(Figure 7.7). Eucalyptus grandis is, however, highly sensitive to changes in rainfall. A rainfall decrease
of 10% from the present in combination with a 2°C increase in temperature, is projected to result in a
60% decrease in the climatically optimum area for E. grandis (Figure 7.8). On the other hand, a 10%
increase in rainfall from the present would increase the climatically optimum area of E. grandis by 85%
when temperatures are 2oC higher than now. Some buffering is provided when both climatically
optimum and sub-optimum (i.e. moderate risk) areas are included, as smaller changes occur from the
base scenario (Figure 7.9). Interestingly, E. grandis and Acacia mearnsii have very similar climatic
criteria for optimum growth, and it is for that reason that their responses to the climate change
sensitivity scenarios are very similar.
Eucalytus grandis x urophylla (GxU) is a hybrid which thrives in warm climates where the MAT exceeds
17°C, but it requires a MAP in excess of 950 mm. With a MAT less than 17°C Eucalyptus GxU may
experience frost damage, and with a MAT below 16°C snow damage may occur to this hybrid (Kunz,
2004).
In Figure 7.11 the results of the suitable areas for Eucalytus GxU are mapped for the various selected
plausible climate scenarios for the future, with graphed results of percentage changes in areas shown in
Figures 7.6 - 7.9. As may be seen from the above-mentioned figures, Eucalytus GxU is particularly
robust to changes in temperature. Even for a 2°C increase in temperature the percentage of optimum
area does not alter from the present (Figure 7.6), and when both climatically optimum and sub-optimum
(i.e. moderate risk) areas are considered, there is even a slight increase in the percentage area where
this hybrid can be grown with a 2°C increase in temperature (Figure 7.7). Thus, Eucalytus GxU is far
more robust to changes in temperature then E. grandis, from which this hybrid is derived.
Eucalytus GxU is, however, highly sensitive to changes in rainfall, with its response being similar to that
of E. grandis. For a decrease of 10% in rainfall in combination with a 2oC temperature increase, a 60%
decrease in the climatically optimum area for Eucalytus GxU would occur when compared with present
climatic conditions, while for a 10% increase in rainfall the climatically suitable optimum area is
projected to increase by 85% (Figure 7.8). When both climatically optimum and moderate risk areas
are considered, Eucalytus GxU becomes slightly more robust to changes in rainfall than when only
optimum areas are considered (Figure 7.9).
Eucalyptus nitens is a rapidly growing and medium sized Eucalyptus species. It requires a MAP > 850
mm, increasing by ~ 17 mm for each °C increase in MAT above 13°C (Herbert, 1993; Schulze, 1997).
Ideally E. nitens requires a MAT in the range of 14 - 15°C (Kunz, 2004). Eucalyptus nitens is a cold
tolerant species, and it becomes vulnerable to droughts and disease when the MAT is higher than 16°C
(Herbert, 1993; Kunz, 2004).
In Figure 7.12 results of suitable areas for Eucalyptus nitens are mapped for the various plausible
climate scenarios for the future which were considered, with graphed results of percentage changes in
areas shown in Figures 7.6 - 7.9. Eucalyptus nitens is very sensitive to changes in temperature, as
may be seen from Figure 7.12 where, with a plausible future climate scenario of a 2°C increase in
temperature there are virtually no climatically optimum E. nitens areas left in southern Africa. The
graphed results in Figure 7.6 show that for a 2°C increase in temperature there is an 80% reduction in
the climatically optimum area of E. nitens, with a decrease of 50% of climatically optimum area already
54
Present
T + 1°C
T + 2°C &
PPT -10%
T + 2°C &
PPT +10%
Figure 7.11
T + 2°C
Mapped results of the Eucalyptus GxU sensitivity analyses to various climate change scenarios
Present
T + 1°C
T + 2°C &
PPT -10%
T + 2°C &
PPT +10%
Figure 7.12
T + 2°C
Mapped results of the Eucalyptus nitens sensitivity analyses to various climate change scenarios
occurring when the increase in temperature is only 1°C. When both optimum and sub-optimum (i.e.
moderate risk) climate areas are considered, E. nitens becomes only very slightly more robust (Figure
7.7). This sensitivity of E. nitens to temperature is attributable to the narrow ideal MAT range of 14 15°C (as stated above) required by E. nitens, since outside of this range it becomes susceptible to
disease and drought. By increasing temperature there are only few areas in southern Africa which
meet both the MAT and MAP requirements of this species, with most areas becoming high risk drought
and disease prone areas for this species.
Eucalyptus nitens is also sensitive to changes in rainfall. A 10% decrease in rainfall from the present in
association with a 2°C increase in temperature, would result in a 60% decrease in the climatically
optimum area for E. nitens (Figure 7.8). A 10% increase in rainfall together with a 2oC temperature
rise, on the other hand, is projected to result in a 100% increase in the climatically optimum area for E.
nitens. This is the highest percentage increase in climatically optimum area of all Eucalyptus
species/hybrids when increases in rainfall are assumed to occur in a warmer climate. When both
climatically optimum and sub-optimum (i.e. moderate risk) areas are considered, E. nitens, becomes
somewhat more robust to changes in rainfall (Figure 7.9). However, as the changes in rainfall assume
a 2°C increase in temperature, the area suitable for its growth is small already and thus the changes
are negligible.
Eucalyptus smithii is a fast growing hardwood species, but it is water demanding, requiring a MAP >
850 mm which has to be increased by ~ 25 mm per year for every 1°C increase in temperature over a
base MAT of 15°C (Herbert, 1993; Schulze, 1997). Optimum growth rates of E. smithii are achieved for
MAT between 15 and 17°C (Kunz, 2004), with moderate growth rates occurring up to 19°C (Herbert,
1993). If the MAT is < 15°C, E. smithii is susceptible to damage by frost, and with a MAT > 18°C this
species is prone to disease (Kunz, 2004).
In Figure 7.13 the mapped results of the suitable areas for Eucalyptus smithii under the various
plausible climate scenarios for the future are shown, with graphed results of percentage changes again
shown in Figures 7.6 - 7.9. Eucalyptus smithii is sensitive to changes in temperature, and for a 2°C
increase in temperature the climatically optimum area over southern Africa is projected to decrease by
approximately 60% (Figure 7.6). However, for a 1°C increase in temperature the reduction in the
climatically optimum area would be only 25%. Thus, as the change in temperature becomes greater, E.
smithii would become relatively more sensitive to climate change.
Eucalyptus smithii is, however, highly sensitive to changes in rainfall. When analysing percentage
changes in area due to changes in rainfall occurring in association with a 2oC increase in temperature,
a 60% decrease in the climatically optimum area would result from a 10% decrease in rainfall (Figure
7.6). An 85% increase in the climatically optimum area would, however, occur if the rainfall were to be
increased by 10% in conjunction with temperatures 2°C higher than present ones. This sensitivity to
rainfall in a warmer climate is similar to the responses of Eucalyptus grandis and Eucalyptus GxU.
Currently, 83% of the area under Eucalyptus species (as identified by the National Land Cover image,
2001) falls within the areas mapped as climatically optimum. However, it is hypothesised that under
climate change conditions, shifts in other land uses and their optimum growing conditions will also
occur. Thus, areas currently under Eucalyptus species which fall within the climatically optimum
criteria, may no longer fall within these criteria in a future climate. In order to determine the possible
impacts of climate change on the areas currently under eucalypts, the distribution of the current areas
within climatically optimum, sub-optimum (i.e. moderate risk) and high risk areas were determined for
the plausible climate scenarios used in this study.
57
Present
T + 1°C
T + 2°C &
PPT -10%
T + 2°C &
PPT +10%
Figure 7.13
T + 2°C
Mapped results of the Eucalyptus smithii sensitivity analyses to various climate change scenarios
Table 7.2 quantifies the results of the above analysis. With an assumed 2°C increase in temperature,
67% of the areas currently planted to eucalypts would fall within climatically optimum areas. Therefore,
although the specific species/hybrid of Eucalyptus may need to be changed from where they are grown
at present, the majority of the areas where eucalypts are currently grown would remain suitable with a
2°C rise in temperature. However, Eucalyptus species/hybrids are highly sensitive to changes in
rainfall, in particular to decreases in rainfall. If the temperature were to increase by 2°C and the rainfall
were at the same time to have decreased by 10%, only approximately 40% of the areas currently under
Eucalyptus would fall within either a climatically optimum or moderate risk area, and 43% of the
currently grown Eucalyptus would be in high risk areas. If, however, rainfall were to increase by at least
10% with an associated increase in temperature of 2°C, there would be little change in southern Africa
in the climate suitability of the currently grown Eucalyptus.
Table 7.2
Distribution of areas currently planted to Eucalyptus species and hybrids in different
climate suitability classes, for a range of climate scenarios
Climate Suitability Class
Optimum
Moderate Risk
High Risk
Climatically Unsuitable Areas
% of Areas Currently Planted to Eucalyptus Species
Present
T + 1°C
T + 2°C
T + 2°C,
T + 2°C,
PPT - 10%
PPT + 10%
82.4
75.9
66.7
36.7
79.6
4.4
2.7
5.3
4.8
4.0
6.9
10.6
15.4
42.9
4.4
6.3
10.8
12.6
15.6
12.0
100.0
Total
T = Temperature; PPT = Precipitation
100.0
100.0
100.0
100.0
Although not all Eucalyptus species/hybrids currently grown in southern Africa are included in this
study, it has been shown that, in general, eucalypts are moderately sensitive to changes in
temperature, but highly sensitive to changes in rainfall. The hybrid Eucalyptus GxU appears far more
robust to changes in temperature then than the Eucalyptus species per se.
The final analysis undertaken on Eucalyptus species/hybrids was a determination of absolute changes
(i.e. in km2) in the climatically optimum, sub-optimum and high risk areas per province for the plausible
climate scenarios used in this study. The results shown in Figure 7.14 are for the changes in
climatically optimum areas for the various scenarios. The climatically optimal area for eucalypts in
KwaZulu-Natal decreases markedly with increasing temperatures, as well as with increased
temperatures combined with decreased rainfall. The climatically optimum areas within the Eastern
Cape are slightly less sensitive to increases in temperature in comparison to areas within KwaZuluNatal; however, they are relatively more susceptible to decreasing rainfall. If an increase in rainfall
combined with an increase in temperature were to occur, a greater area in the Eastern Cape would
become climatically optimal for the growth of eucalypts in comparison with the area suitable under the
present climate. In Mpumalanga, the area which is climatically optimal for the growth of eucalypts
increases slightly with a 1°C increase in temperature, and remains relatively stable for a 2°C increase in
temperature when compared with the area under present climatic conditions. An increase in
temperature of 2°C together with an increase in rainfall of 10% would, however, result in a far larger
proportion of Mpumalanga meeting the climatic requirements for optimum growth of eucalypts.
Potentially, if temperature and rainfall were therefore to increase in the future, new areas for the growth
of Eucalyptus species could therefore be sourced in the Eastern Cape and Mpumalanga. However, if
rainfall were to decrease by approximately 10% in a warmer climate, the consequences on
commercially grown eucalypts could be significant.
59
4000
3500
Area
(km2)
Area (km2)
3000
2500
2000
1500
1000
T + 2C, PPT +10%
T + 2C, PPT -10%
T + 2C
500
T + 1C
0
KwaZuluNatal
Figure 7.14
7.4
Present
Swaziland
Western
Cape
Mpuma
langa
Eastern
Cape
Areas (km2) which are climatically optimal for Eucalyptus species/hybrids for various
plausible scenarios of climate
Results from Pinus Species
Pinus elliottii is a hardy pine species, and it is the most resistant of the commercially grown species in
South Africa to attacks of Sphaeropsis sapinea after hail damage (Smith, 1998). Ideally it requires a
MAP of 850 mm or more. With regard to MAT, for optimum growth P. elliottii requires a MAT > 14°C,
with midwinter daily means exceeding 10°C. Thus, P. elliottii is suited to the warmer, more coastal
areas of southern Africa (Smith, 1998).
In Figure 7.15 mapped results of the climate change sensitivity analyses for Pinus elliottii are shown,
while in Figure 7.16 percentage changes in the climatically optimum areas of the various Pinus
species/hybrids are illustrated for increases in temperature from present climate, and in Figure 7.17
graphs are displayed of the percentage changes in the climatically optimum plus sub-optimum (i.e.
moderate risk) areas for the various Pinus species/hybrids for the plausible future scenarios of a 1°C
and 2°C increase in temperature. Figure 7.18 shows percentage changes in the optimum and Figure
7.19 shows changes in the optimum plus sub-optimum (i.e. moderate risk) areas for varying changes in
rainfall in combination with a 2oC temperature increase.
As seen in Figure 7.15, Pinus elliottii is relatively robust to changes in temperature, with little change in
the climatically optimum and sub-optimum areas occurring when temperatures are increased. The
graphed results in Figure 7.16 show that for an increase of 1°C in temperature the climatically optimal
areas of P. elliottii are projected to decrease by 15%, while for a 2°C increase in temperature a 30%
decrease in the climatically optimum area would result. Thus, as temperature increases by up to 2° the
corresponding decreases in climatically optimum areas are simulated to be linear (Figure 7.16).
60
Present
T + 2°C &
PPT - 10%
Figure 7.15
T + 1°C
T + 2°C
T + 2°C &
PPT + 10%
Mapped results of the Pinus elliottii sensitivity analyses of various climate change scenarios
20
10
%
inin
Area
%Change
Changes
Ar
0
0
1
2
-10
-20
-30
-40
-50
Pinus elliottii
Pinus ExC
Pinus patula
Pinus taeda
-60
Increases
in Temperature
(°C)
Temperature
Changes
Figure 7.16
Percentage changes in the climatically optimum areas for Pinus species/hybrids with
increases in temperature
10
0
Changes
Ar
%%Change
in in
Area
0
1
2
-10
-20
-30
-40
Pinus elliottii
Pinus ExC
Pinus patula
Pinus taeda
-50
Increases
in Temperature
Temperature
Changes(°C)
Figure 7.17
Percentage changes in the climatically optimum plus sub-optimum, i.e. moderate risk,
areas for Pinus species/hybrids with changes in temperature
If both climatically optimum and sub-optimum (i.e. moderate risk) areas are considered, Pinus elliottii is
shown to be robust to changes in temperature (Figure 7.17).
Pinus elliottii is, however, highly sensitive to changes in rainfall, and climatically optimum areas are
significantly reduced if rainfall decreases simultaneously with a temperature increase (Figure 7.18).
The percentage decrease in climatically optimum areas for P. elliottii for a 10% reduction in rainfall in
combination with a 2°C increase in temperature is 60%. An increase of 10% in rainfall would result in
62
an increase of the climatically optimum area of P. elliottii increasing by 90%. If both climatically
optimum and sub-optimum (i.e. moderate risk) areas are considered P. elliottii is, relative to only
optimum areas, more robust to changes in rainfall (Figure 7.19).
The increased robustness when both climatically optimum and moderate risk areas are considered is
attributable to Pinus elliottii not having a large proportion of high risk areas in South Africa, with most
areas being prone to medium risk drought only.
Pinus elliottii x caribaea (ExC) is a Pinus hybrid which grows optimally in areas with high temperatures.
It thus thrives in the coastal areas of the Eastern Cape, eastern KwaZulu-Natal, western Swaziland and
eastern Mpumalanga, wherever the MAT is greater than 16°C (Schulze and Maharaj, 2006g).
In Figure 7.20 the mapped results of the suitable areas for Pinus ExC under the various plausible
climate scenarios for the future are shown, with graphed results of percentage changes in areas shown
in Figures 7.16 to 7.19. As Pinus ExC thrives in warmer regions, it is highly robust to changes in
temperature, with slight increases in the climatically optimum areas of Pinus ExC even projected to
occur when temperature is increased (Figure 7.16).
Pinus ExC also proves to be relatively more robust to decreases in rainfall than the other three Pinus
species included in this study. For a 10% decrease in rainfall in combination with a 2oC temperature
increase, a 50% reduction in the climatically optimum area is shown in Figure 7.18. An increase of 10%
in rainfall in a warmer climate results in an increase of 60% in climatically optimum areas for Pinus ExC.
This increase is less than that experienced by the other Pinus species for the same climate scenario.
When compared with P. elliottii and P. taeda, Pinus patula is the climatically least tolerant of the Pinus
species evaluated. For optimal growth a MAP of > 950 mm is required and MAT should be in the range
13 - 18°C. However, the mid-summer means of daily temperature should not exceed 22°C (Smith,
1998). Pinus patula is highly susceptible to Sphaeropsis sapinea after hail damage (Smith, 1998).
In Figure 7.21 the mapped results of the climatically suitable areas for Pinus patula are shown for the
various plausible future climate scenarios, with graphed results of percentage changes in areas shown
in Figures 7.16 to 7.19. Pinus patula is highly sensitive to temperature changes, as may be seen from
Figure 7.21. The climatically optimum areas of P. patula decrease markedly with an increase of 2°C,
with only small areas remaining in KwaZulu-Natal and Mpumalanga. With respect to percentage
changes in area, Figure 7.16 shows that a 2°C increase in temperature is projected to decrease the
climatically optimum area for P. patula by 50%, while a 1°C increase in temperature would result in a
25% reduction in area. Pinus patula thus displays a linearly decreasing response to increasing
temperatures, certainly to up a 2°C threshold.
The projected responses of Pinus patula to changes in rainfall are similar to both the responses of P.
elliottii and P. taeda. Assuming a 2°C increase in temperature with a simultaneous 10% decrease in
rainfall would result in a 60% decrease in the climatically optimum area of P. patula, while a 10%
increase in rainfall in association with a 2°C temperature rise, on the other hand, would increase the
climatically optimum area of P. patula by an estimated 90% (Figure 7.18). The highly sensitive nature
of P. patula to changes both in temperature and rainfall corresponds with its being the least tolerant of
three Pinus species included in this study.
Pinus taeda is the most demanding, in terms of climate and soils, of the three Pinus species included in
this study. Pinus taeda requires a MAP in excess of 950 mm and a MAT of 13°C and higher, but may
be grown at lower rainfalls if the temperatures are also relatively lower (Smith, 1998). Pinus taeda is
sensitive to drought, and is prone to snow damage (Smith, 1998).
63
120
Pinus elliottii
Pinus ExC
Pinus patula
Pinus taeda
%%Change
Changein
inArea
Ar
80
40
0
-10
-5
0
5
10
-40
-80
Changes
Temperature
RainfallinChanges
(%)
Figure 7.18
Percentage changes in the climatically optimum areas for Pinus species/hybrids for a
2°C increase in temperature in combination with a range of negative and positive
changes in rainfall
80
%%Change
ChangeininArea
Ar
Pinus elliottii
Pinus patula
Pinus taeda
40
0
-10
-5
0
5
10
-40
-80
Changes
Temperature
Rainfall in
Changes
(%)
Figure 7.19
Percentage changes in the climatically optimum plus sub-optimum, i.e. moderate risk,
areas for Pinus species/hybrids for a 2°C increase in temperature in combination with a
range of negative and positive changes in rainfall
64
Present
T + 1°C
T + 2°C &
PPT -10%
T + 2°C &
PPT +10%
Figure 7.20
Mapped results of the Pinus ExC sensitivity analyses of various climate change scenarios
T + 2°C
Present
T + 1°C
T + 2°C &
PPT -10%
T + 2°C &
PPT +10%
Figure 7.21
Mapped results of the Pinus patula sensitivity analyses of various climate change scenarios
T + 2°C
Figure 7.22 displays the mapped results of the areas suitable for cultivation of Pinus taeda under the
various plausible climate scenarios for the future, with graphed results of percentage changes in area
shown in Figures 7.16 to 7.19. The responses of P. taeda mimic those of Pinus elliottii to changes in
temperature and rainfall. Pinus taeda is relatively robust to changes in temperature, as may be seen
from Figure 7.22, and when expressed in percentages, a 1°C increase in temperature would reduce
the climatically optimum area of P. taeda by 15% while a 2°C increase in temperature would result in a
30% decrease in climatically optimum areas (Figure 7.16).
With changes in rainfall associated with a 2°C increase in temperature, Pinus taeda is highly sensitive
to both increases and decreases in rainfall. For a decrease of 10% in rainfall, the climatically optimum
area of P. taeda is projected to decrease by 60%. An increase of 10% in rainfall in conjunction with a
global warming of 2°C would, however, result in an increase in the climatically optimum area of P. taeda
of 90% (Figure 7.18).
From the analyses conducted above, the Pinus hybrid appears the most robust to changes in
temperature and rainfall, with Pinus patula being the most sensitive of the three Pinus species.
With the high overall sensitivity of the Pinus species to changes in climate, an analysis was performed
to determine how the current areas under Pinus species (as identified by the National Land Cover
image, 2001) would be distributed between the climatically optimum, sub-optimum (i.e. moderate risk)
and high risk areas for the five plausible climate scenarios used in this analysis. The climatically
optimum areas for all the Pinus species for each plausible scenario were combined and compared
against the current areas under pines. Although not all Pinus species were included in the study, the
combined climatically optimum areas of the four Pinus species/hybrids included in this study covers a
wide range of temperature and rainfall regimes.
In Table 7.3 the percentage distributions of the areas currently planted to Pinus species between
climatically optimum, sub-optimum (i.e. moderate risk) and high risk areas are given. Currently, 76% of
the Pinus species/hybrids cultivated in southern Africa fall within the climatically optimum area as
defined from the ICFR Toolbox (Kunz, 2004). As may be seen, overall the Pinus species are robust to
temperature changes, and 77% of the current area under Pinus species/hybrids could still fall within the
climatically optimum areas for Pinus with an increase of 2°C in temperature. The species or hybrid of
Pinus planted in a particular location may thus need to be changed, but of the total area which is
currently planted, over 3/4s would remain climatically optimum for at least one of the species/hybrid of
Pinus.
Table 7.3
Distribution of areas currently planted to Pinus species and hybrids in different climate
suitability classes, for a range of climate scenarios
Climate Suitability Class
Optimum
Moderate Risk
High Risk
Climatically Unsuitable Areas
% of Areas Currently Planted to Pinus Species
Present
T + 1°C
T + 2°C
T + 2°C,
T + 2°C,
PPT -10%
PPT +10%
75.5
78.4
77.1
53.9
85.3
17.0
13.9
15.3
30.7
10.2
1.5
0.3
0.0
0.0
0.0
6.0
7.4
7.6
15.4
4.5
100.0
Total
T = Temperature; PPT = Precipitation
100.0
67
100.0
100.0
100.0
Present
T + 1°C
T + 2°C &
PPT -10%
T + 2°C &
PPT +10%
Figure 7.22
Mapped results of the Pinus taeda sensitivity analyses to various climate change scenarios
T + 2°C
With a 2°C increase in temperature in combination with a 10% decrease in rainfall, 54% of the current
area under Pinus would remain in climatically optimum areas, with 30% falling within moderate climate
risk areas. Thus, even with an increase in temperature plus a decrease in rainfall Pinus will, in general,
remain a robust genus to plant, and the area currently under Pinus will be able to remain planted, albeit
possibly under a different species or hybrid, but still within the Pinus family.
For an increase in rainfall combined with a 2°C increase in temperature, approximately 85% of the
current areas of Pinus fall within climatically optimum areas, with 10% falling within moderate risk
climate areas. To reiterate, therefore, although the actual species or hybrid that is planted may need to
change, much of the current area will still remain climatically suitable for the Pinus genus.
The final analysis undertaken for Pinus species was a determination of absolute changes (i.e. in km2) in
climatically optimum, sub-optimum (i.e. moderate risk) and high risk areas per province, for the various
plausible climate scenarios used in this study. The results are shown in Figure 7.23. The climatically
suitable areas for Pinus species/hybrids in KwaZulu-Natal decreases markedly with increasing
temperatures, as well as with increased temperatures in combination with decreased rainfall. The
climatically optimum areas within the Eastern Cape are robust to increases in temperature, with slight
increases in optimal growth areas occurring for a 1°C increase in temperature, and no discernible
changes occurring for a 2°C increase in temperature. With both an increase in temperature and rainfall,
a larger proportion of the Eastern Cape than at present is projected to become climatically suitable for
the growth of Pinus species/hybrids.
4500
4000
3500
Area
(km2)
Area (km2)
3000
2500
2000
1500
1000
T + 2C, PPT +10%
T + 2C, PPT -10%
500
T + 2C
0
T + 1C
KwaZuluNatal
Figure 7.23
Mpuma
langa
Swaziland
Present
Northern
Province
Eastern
Cape
Western
Cape
Areas (km2) which are climatically optimal for Pinus species/hybrids for various
plausible scenarios of climate
In Mpumalanga, the area which is climatically optimal for the growth of Pinus species increases slightly
with both a 1°C and a 2°C increase in temperature, compared against the area suitable under present
69
climatic conditions. An increase in temperature of 2°C in association with an increase in rainfall of 10%
would result in a far larger proportion of Mpumalanga meeting the climatic requirements for optimum
growth of Pinus species/hybrids. Thus, with anticipated changes in climate in the future, if an increase
in rainfall were to occur, there would potentially be more areas in Mpumalanga and the Eastern Cape
which could become climatically suitable for the growth of Pinus species/hybrids.
7.5
Summary of the Climate Change Sensitivity Analysis
A large volume of results has been presented in this Section. Before summarising, a reminder needs to
be sounded that the scenarios considered are plausible climate change scenarios, with only changes in
climatically suitable areas considered. It furthermore needs reiteration that the climatic requirements for
optimum and sub-optimum growth are expressed by very broad indices only, viz. MAP and MAT. No
cognisance has therefore been taken of possible effects of soils, geology, market forces, management
and local scale climates.
A large portion of the area currently planted to Acacia mearnsii is climatically only of moderate
suitability, and thus the current areas under wattle are at the outset already considered sensitive to
changes in climate, as only small decreases in rainfall and increases in temperature would shift the
current areas from climatically moderate to climatically high risk areas.
In the case of Eucalyptus species and/or hybrids, E. nitens is highly sensitive to increases in
temperature while the Eucalyptus hybrid evaluated, viz. Eucalyptus GxU, is more robust to changes in
temperature. All the Eucalyptus species and/or hybrids are highly sensitive to changes in rainfall with
respect to their climatically optimum growth areas. With regard to changes in actual areas suitable for
eucalypts, KwaZulu-Natal is vulnerable should increases in temperature occur, while Mpumalanga and
the Eastern Cape are more robust to increases in temperature.
Overall, the Pinus species and/or hybrids are relatively robust to potentially increasing temperatures
and changing rainfall regimes. In relative terms the Pinus species were found to be more sensitive to
increasing temperatures and decreasing rainfall than the Pinus hybrid which was considered, viz. Pinus
ExC, which appears relatively robust to climate change. The climatically optimal areas within KwaZuluNatal are likely to decrease with increasing temperatures, while actual areas climatically optimal for
Pinus species/hybrids in the Eastern Cape and Mpumalanga could expand under conditions of
increasing temperatures.
In assessing the sensitivity of commercial forestry to climate change the following emerged:
•
•
•
•
•
the one climatic driver to which the forest species are most sensitive to, is rainfall;
the selected hybrids of both eucalypts and pines are relatively more robust than the commonly
grown species to potential increases in temperature (in particular) and to a certain degree to
decreases in rainfall;
areas currently under plantations where the climate is only moderately suitable will, under
conditions of increasing temperature and decreasing rainfall, most likely become high risk areas;
and thus species such as Acacia mearnsii, with large proportions at present already being
planted in only moderately suitable climates, are highly vulnerable to climate change;
on a provincial basis the climatically optimal areas for plantation forestry within KwaZulu-Natal
are likely to decrease with climate change, while it appears that areas within the Eastern Cape
and Mpumalanga may offer opportunities for expansion with increasing temperature; and
of the three families included in this study, viz. Acacia, Eucalyptus and Pinus, the Pinus family is
relatively more robust to climate change than the other two.
70
Given the potential impacts of climate change on the commercial forestry sector, and the fact that
changes in climate can already be detected at not only a global scale, but also in southern Africa, the
development and implementation of adaptation policies, measures and strategies is crucial. In the
following Section concepts surrounding adaptation will be explored.
71
8.
ADAPTATION TO CLIMATE CHANGE
One thing we must be aware of is that even if the Kyoto Protocol is implemented in full, the emission of
greenhouse gases will result in our having to contend with the effects of climate change for decades to
come in the form of more frequent and more intensive natural catastrophes.
Munich Reinsurance Group, Annual Review: Natural Catastrophes 2001, 2002.
The above quotation confirms the need to adapt to changes in climatic regimes. Prior to discussing
adaptation strategies, to reviewing the National Climate Change Response Strategy for South Africa
with particular reference to the commercial forestry sector, and exploring the question of how to go
about the process of adaptation, a number of definitions of terms used are given below.
8.1
Definitions of Terms
Adaptation is the adjustment, in response to actual or expected climatic stimuli and their impacts, of
ecological, social or economic systems. It is the change in processes, practices or structures in order to
minimise the potential damages, or to take advantage of opportunities associated with climate change
(IPCC, 2001). These responses may be either planned or be autonomous. In addition, adaptation may
be reactive (i.e. if it occurs after the impact has taken place) or anticipatory (i.e. if it occurs before the
impact takes place; IPCC, 1995). Adaptation thus implies a long term process with an array of
measures which may be employed to reduce the sensitivity and vulnerability to a hazard or a stress.
Mitigation is the reduction of the magnitude of human-induced climate change by reducing greenhouse
gas emissions. Mitigation is needed in addition to adaptation, as the projected changes in climate in the
21st century are large, and thus the potential impacts will require costly adaptation measures. In some
instances the anticipated changes will be outside of societies’ capacity to adapt (Pittock, 2006).
Adaptive capacity is the ability or potential of a system, region or community to adjust to the actual or
expected climate stresses or to cope with the consequences of the stresses (IPCC, 2001; O’Brien et al.,
2004). Adaptive capacity is a function of a region’s or community’s wealth, technology, level of
education, information, skills, infrastructure, access to resources, stability and management capabilities
(O’Brien et al., 2004). Thus the adaptive capacity of a system reflects its resilience, stability, robustness
and flexibility (Rayner and Malone, 1998; Smit et al., 1999; Smit et al., 2000).
Vulnerability of a system, region or community to climate change may be viewed as the extent to
which changes in climate and climate variability may cause damage or harm (Kasperson and
Kasperson, 2001). In other words, the vulnerability of a system can be viewed as its ability to absorb
the shock of an event, cope with it and recover from it (Schulze, 2003).
8.2
Should One Not Wait and See What Happens First?
Two views are presented below, viz. those of sceptics of climate change and, in a form of rebuttal,
views of protagonists of adaptation.
The sceptics view …
•
There are still uncertainties surrounding climate change science, and sceptics often argue that
given the range of uncertainty, we should adopt a wait and see approach, or delay responses
until further progress can be made in narrowing the uncertainty of climate change (Pittock, 2006).
72
•
•
•
They argue that large natural changes in climate have occurred in the past, which were not
caused by humans, so why should the changes now occurring be due to human influence?
If life survived changes in the past, will it not survive similar changes in the future?
Are other world issues, such as hunger and water supplies not more important to address, rather
than focussing so much on climate change?
As a rebuttal, the views of protagonists of adaptation . . .
•
•
•
•
Changes in climate are a reality and the evidence to support this, at both the global and regional
scales, is already immense (cf. Chapters 1 and 2). Although there is still uncertainty surrounding
the magnitude and timing of climate changes in the future, the negative impacts of these
changes are likely to be enormous. Thus, adaptation needs to occur in the immediate future.
Processes need to be implemented now, as the time scales for these processes to take effect
and be adopted are likely to be long.
The changes in climate which are being observed at present already are occurring at a far faster
rate than the changes in climate which have occurred in the past (IPCC, 2001), and they will
continue to occur at this fast rate in the future if action is not taken.
Given the pressing world problems as they stand, any climate change adaptation measures
implemented now should already address many of the present day problems of climate variability
and thereby already make allowances for the future. In implementing adaptation measures, a
‘no regrets’ approach needs to be taken. The measures implemented under a no regrets
approach will have benefits which are equal to, or exceed, their cost to society and will be of
benefit regardless of whether further climate change occurs or not.
These reasons to adapt, and others as listed by the IPCC (2001), are presented in Box 8.1.
Box 8.1
The IPCC’s (2001) six reasons to adapt to climate change now
1. Climate change cannot be totally avoided.
2. Anticipatory and precautionary adaptation is
more effective and less costly than forced, lastminute emergency adaptation or retrofitting.
3. Climate change may be more rapid and more
pronounced than current estimates suggest.
Unexpected events are possible.
4. Immediate benefits can be gained from better
adaptation to climate variability and extreme
atmospheric events.
5. Immediate benefits also can be gained by
removing maladaptive policies and practices.
6. Climate change brings opportunities as well as
threats. Future benefits can result from climate
change.
In conclusion to this section, Article 3 of the UNFCCC expresses the so-called ‘precautionary principle’,
which was included in the Rio Declaration at the Earth Summit (June, 1992), as follows:
“The parties should take precautionary measures to anticipate, prevent or minimise the causes of
climate change and mitigate against its adverse affects. Where there are threats of serious or
irreversible damage, lack of full certainty should not be used as a reason for postponing such
measures, taking into account that policies and measures to deal with climate change should be cost
effective so as to ensure global benefits at the lowest possible costs.”
Similar strategies, in a broader context, are used in hydrological design, engineering design and in the
insurance industry. Uncertainty should not be seen as a reason for inaction on climate change, but
rather as a reason to proceed cautiously, with a readiness to adapt policies to changing insights and
circumstances, as further research on climate change is undertaken and new results emerge.
73
Adaptation needs to be a risk management strategy which takes into account the probabilities
associated with climate change, as well as costs and benefits of the adaptations (Pittock, 2006).
8.3
The National Climate Change Response Strategy of South Africa
In the preparations for South Africa’s ratification of the UNFCCC in 1997, the urgent need for a national
climate change policy was highlighted. The National Committee for Climate Change (NCCC), a nonstatutory stakeholder body set up in 1994 to advise the Minister on climate change issues, and chaired
by the Department of Environmental Affairs and Tourism (DEAT), was tasked with developing such a
policy. Following the drawing up of a discussion document and holding of stakeholder workshops it was
identified that the climate change response strategy needed to
•
•
•
be an action-orientated response strategy,
promote integration between the programmes of the various government departments involved in
order to maximise the benefits to the country as a whole while minimising negative impacts, and
act as a significant factor in boosting sustainable social and economic development, by
supporting government objectives such as poverty alleviation and job creation (NCCRS, 2004).
As an action-orientated response strategy the NCCRS (2004) identifies a number of interventions that
are required. These interventions are listed below:
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Include climate change issues in sustainable development policies, and develop indicators and
criteria for South Africa which are consistent with those that may be required by the Designated
National Authority (DNA) for CDM projects;
Ensure that the capacity of relevant national government directorates and sub-directorates is
adequate in order for them to carry out their assigned functions regarding climate change,
including CDM activities;
Establish procedures for CDM projects;
Extend health protection and services, and promote measures to improve health;
Implement water management plans and contingency plans;
Implement adaptation strategies into rangeland practices;
Introduce adaptation measures into agricultural management practices;
Implement changes in forestry management practices;
Protect plant biodiversity, animal biodiversity and marine biodiversity;
Devise actions that will minimise the economic vulnerability of South Africa to climate change
response measures, i.e. our high dependence on fossil fuels;
Formulate a national greenhouse gas mitigation plan that promotes sustainable development,
technology transfer, foreign investment and capacity building, including
o Improvements to energy efficiency,
o Control of exhaust emissions from road-going vehicles,
o Developing and implementing a coal mining mitigation programme,
o Reducing greenhouse gas emissions from agriculture,
o Facilitating the establishment and extension of forest schemes, and
o Optimising waste management practices;
Continue to attend UNFCCC and related meetings;
Consolidate the government’s position on climate change through the Government Committee
for Climate Change;
Ensure that climate change issues are included in South African legislation, and utilise the
ongoing law reform process;
Capacitate the government and other sectors to deal with climate change, by accelerating
education, training, awareness and capacity building;
74
•
•
•
•
•
Develop a comprehensive database for South and southern Africa of climate change related
research and development;
Put into place a national information handling system to incorporate greenhouse gas data with air
pollution data, and include greenhouse gas emissions in air quality legislation;
Attract developed countries to invest in climate change related projects in South Africa, by
creating an investment welcoming impression;
Coordinate all donor funding for climate change related projects in South Africa; and
Involve both financial institutions linked to the government and the private sector.
Two specific interventions in the NCCRS (2004) relate to the commercial forestry sector.
•
•
The first intervention, on ‘Changes in forestry practices,’ proposes the use and development of
more heat and drought resistant hybrids and species, while also suggesting that competition for
land from more lucrative uses may increase. It also highlights that ‘current commercial forests
make extensive use of exotic species, a practice that may influence biodiversity and other
climate change sensitive factors such as excessive water use and soil properties.’
The second intervention relating to forestry places forestry in a far more positive light, by calling
for the ‘establishment and extension of forest schemes through the Department of Water Affairs
and Forestry and the forestry industry’, provided that these projects do not compromise the
environmental policy objectives in South Africa.
Although the NCCRS (2004) is targeted primarily at government and related institutions, it recognises
that without private and non-government sector involvement it will not succeed. It also recognises the
substantial financial and other resources which will be needed to implement the strategy. The NCCRS
(2004) provides a basis for devising strategies and measures to cope with, and adapt to, the impacts of
climate change. It is by no means as yet a comprehensive document which should be adhered to
rigidly, and it may be modified to include emerging ideas, research and development in climate change
related studies.
8.4
Where to Now? Implementation of Adaptation Strategies
Many agricultural crops cover their entire growth cycle within one season, and thus a climatic
disturbance will have a short term impact, as the new season’s crop will start afresh (Schulze, 2003).
Commercial forestry, on the other hand, is only harvested after 10 - 30 years (depending on the species
and site). Therefore, the consequences of a climatic disturbance have long term impacts which are not
corrected or reversed in the following year’s field management decisions (Roberts, 2003). For example,
during the 1991/1992 drought the forestry industry in South Africa lost approximately R450 million, and
the repercussions of that drought were still being felt 10 years later. For many trees which were young
at the time of that drought, and thus more sensitive to vagaries of climate than older trees, the impact of
that drought was still evident on the trees when they were harvested (Roberts, 2003).
Given the long time frame associated with management decisions made in forestry, the sensitivity of the
industry to climate, and the uncertainty of the magnitude and timing of climate change, the commercial
forestry sector needs to implement adaptation measures. Such adaptation measures or strategies will
have to vary with location, and with time scales. Arnell (2005, as cited by Schulze, 2005b) views the
adaptation process in three stages, viz.
•
•
•
awareness of the threat,
intention to adapt, and
active adaptation (Figure 8.1).
75
• Access to information
• Experiences of threat
• Sensitivity to threat
AWARENESS
OF THREAT
• External influences of regulation / wealth
- Government
- State of economy
INTENTION
TO ADAPT
• Internal characteristics
- Institutional capacity/will
ACTIVE
ADAPTATION
Figure 8.1
• Range of Options
- Demand
- Supply
- Culture
- Expectations
The adaptation process (after Arnell, 2005; as cited by Schulze, 2005b)
The commercial forestry sector is aware of potential threats posed by climate change and the
challenges this poses, as evidenced by the commissioning of this report. If these challenges are taken
seriously, a next stage would be the formulation of an intention to adapt.
In all sectors certain limitations to adaptation exist and these need to be appreciated (Schulze, 2005b;
Figure 8.2), as it is these limits which need to define globally the required reductions in greenhouse
gases. Physical limits of natural resources are set by nature. For example, there is a finite supply of
water which restricts the activities in the catchment, and climatically there are limits as to where forestry
can be grown, thus limiting adaptation strategies. Although growing forests may be physically feasible
in a particular area, there may be political, social and environmental pressures which place limits on the
adaptation process, such as SFRAs, land reform policies, and competition from other land uses.
Adaptation may also be limited by financial constraints, or by the capacity of the various players
involved in the commercial forestry sector.
Adaptation strategies will require foresight, and an investment of both capital and time. The adaptation
measures will need to be flexible and take on a no regrets approach, because of the uncertainties of
how various tree species will respond to climate change. Flexibility in adaptation strategies is also
needed as a result of the feedback effect that adaptation policies may have on the impacts of climate
change (Figure 8.3). In response to climate change and associated impacts, both planned and
autonomous adaptation will occur and these adaptation strategies will, in turn, affect the nature of the
climate change impact. A simple example explains this: Expansion of commercial forestry areas will
act as carbon sinks. This expansion would increase the pressure placed on water, thus further adding
to the potential negative impacts of climate change on water availability. In addition to climate change,
climate change impacts will be aggravated or alleviated by non-climatic changes such as land reform.
These changes would have their own responses which would, in turn, alter the planned adaptation
responses and ultimately feed back on the impact of climate change.
76
PHYSICAL
LIMITS
FINANCIAL
LIMITS
ADAPTATION
FEASIBILITY
LIMITS
i.e. Adjustments to
altered circumstances
CAPACITY
LIMITS
Figure 8.2
• Pressures on water
• Climatic limits
• Physically feasible but
politically, socially or
environmentally difficult
• Capacity of organizations
• Capacity of individuals
Limits to adaptation (after Arnell, 2005; as cited by Schulze, 2005b)
CLIMATE
CHANGE
AUTONOMOUS
ADAPTATION
PLANNED
ADAPTATION
• Environmental
responses
• Incremental & ad hoc
adjustments
• Constraints: economic,
social, technological,
institutional, and political
• Institutional responses
• Policy responses
• Conscious human
intervention
• Various space & time
scale considerations
CLIMATE CHANGE
IMPACTS
RESPONSES TO
NON-CLIMATIC
CHANGE
NON- CLIMATIC
CHANGE
Figure 8.3
• Institutional
• Policy
Feedbacks between adaptation strategies and climate change impacts (ideas from
IPCC, 2001; Vogel, 2003; Schulze, 2005b)
77
There may be further barriers to the successful implementation of adaptation strategies to impacts of
climate change and climate variability, and Schulze (2003) identified the following as barriers in South
Africa:
•
sectoralism between government departments at all levels, as well as within certain government
departments, and also between scientists and legislators, scientists and managers, and
legislators and managers;
a lack of clearly defined objectives on adaptation; and
a lack of sustained research support for centres with recognised experience or expertise in the
fields of climate change.
•
•
It is argued that the best way to ensure adaptability in the forest sector is to increase resilience to, and
the capacity to cope with, year-to-year climate variability such as floods and droughts. However, there
will come a point where climate change is likely to lead to extremes outside of the natural climate
variability tolerated by many tree species. In this regard, the UK Forest Research Group (2006)
identified the following basic measures which should be implemented to cope with climate change as
part of a no regrets approach:
•
•
•
Mixing of species, to provide insurance against climate change;
Matching of species to site, i.e. if the site is currently at the dry end of the species range, that
species should not be planted there; and
Giving consideration to climate change predictions when choosing the species to be planted.
78
9.
CONCLUSIONS AND RECOMMENDATIONS
Climate change is a reality, both at the global scale and the regional scale. The magnitude of change is
still uncertain, as is the timing of the change. However, the severe nature of the impacts which climate
change could have on all sectors of South Africa’s economy is becoming clear. The physiological
response of trees to elevated atmospheric CO2 and to changes in both temperature and rainfall still
have uncertainties surrounding them. It is clear that the climatic driver to which commercial forestry in
southern Africa is most susceptible to changes in, is rainfall. The analyses performed in this study
suggest that forestry hybrids are likely to be more robust to changes in climate than currently grown
species. Importantly, the Pinus family emerged as relatively more robust than wattle or eucalypts.
Development of adaptation strategies and measures are essential, and from the analyses performed in
this study the following points of departure are recommended:
•
•
Site-species matching should be improved, i.e. matching the species or hybrid specifically to the
climatic conditions at the site in question, particularly because areas which currently experience
sub-optimum climates for particular species and already experience moderate risk are likely to
become high risk areas under future climatic regimes.
There should be more investment in research and development of temperature tolerant, drought
resistant hybrids. The current hybrids are already showing promise, as they are currently more
robust to plausible changes in climate than the forestry pure species.
It is important to stress that the forestry models used in this study were simple, in that they are based
solely on two coarse climatic criteria of mean annual temperature and mean annual precipitation and
take no cognisance of, for example,
•
•
•
•
•
•
•
•
numbers of frost days,
numbers of heat waves,
numbers of soil water stress days,
actual rainfall infiltrability on a monthly or seasonal basis, and associated runoff generating
events,
soil properties,
slope,
competing land uses, or
streamflow reduction activities.
As one way forward, a recommendation is made for a follow-up to this initial study to examine the
impacts of climate change on the commercial forestry sector by
•
•
•
•
considering some of the factors bulleted above,
using more complex and physically based models which would also estimate yield changes,
utilising future climate scenarios from recent (2006) Regional Climate Models (RCMs) for
southern Africa, and
assessing competition from other land uses under climate change conditions.
Regarding more physically based models, two suggested ones are 3PG and the ACRU Eucalyptus
grandis model (Schulze, 1995), as these models would allow not only the impacts on the climatically
suitable areas for forestry under climate change conditions to be assessed, but also the impacts of such
changes on productivity and yield.
79
With respect to scenarios from RCMs, the School of BEEH at UKZN at present (December 2006) has
GCM/RCM data from three models from the 3rd IPCC and a further three from the 4th IPCC
Assessment, and the data obtained have been downscaled from a GCM resolution to a RCM spatial
resolution of 1/4 degree latitude/longitude (i.e. ~ 25 km) using empirical downscaling techniques. The
data obtained are for a present period (1960 - 1990), a future period (2080 - 2100), and with the data
from the 4th IPCC Assessment models also containing an intermediate period (2045 - 2065).
Finally, it should be borne in mind that areas at present under commercial forest plantations, but which
may be suitable for other land uses, often remain forested because the economic viability of the
competing land uses may at this point in time be minimal. However, under conditions of climate change
the other land uses may become relatively more competitive. An assessment of the competition which
commercial forestry could face from other land uses would therefore enable improved strategic
decisions to be made by the forest industry.
80
ACKNOWLEDGEMENTS
This research project was funded by Forestry SA, and their support and encouragement is
acknowledged gratefully. The authors also wish to thank Mrs Manju Maharaj for data preparation and
computer programming and Mrs Cynthia O’Mahoney for her assistance with the preparation of this
document.
81
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