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Technical Assistance to the GCCA Climate Support Facility
under the 10th EDF Intra-ACP Financial Framework
Work Order 17
CLIMATE CHANGE ADAPTATION IN LAKE VICTORIA BASIN
Mission Report for ACP Secretariat and European Commission
Field Mission from 11th March, 2013 to 10th April, 2013
Prof. Chris Shisanya
Quality control : Manuel Harchies
Consortium SAFEGE-Prospect-ADETEF-Eco – Gulledelle 92, 1200 Brussels, BELGIUM
Climate Support Facility – WO17 – Mission Report
Mission data
Country
KENYA
Period
11TH MARCH, 2013 TO 10TH APRIL, 2013
Local coordinator
PROF. ERIC ODADA
Acronyms
ACCESS
Objectives
As specified in the ToR:


General objective: To increase adaptive capacity to adverse climate change impacts in the
Lake Victoria Basin
Specific objective: To strengthen ACCESS’ capacity in addressing adaptation needs and
CC-induced disaster reduction in the Lake Victoria Basin, their working area
Activities
As specified in the ToR:
Task 1: Review and compile work done on climate change adaptation in mountain areas. Focus
should be on: risks and needs specific to mountain ecosystems, existing CC adaptation policies, best
practices in adaptation measures and risk reduction in areas with similar conditions. Advice on climate
change measures to be taken with limit to Mt. Elgon water towers only.(see ANNEX 1)
Description of how it was carried out:
Relevant documents pertaining to climate change (CC) in mountain areas and specifically Mt. Elgon
were accessed using internet search engines. Where actual documents could not be accessed, the
Consultant used already established networks in academic circles to gain access. These documents
were reviewed and synthesized as appropriate. This task was successfully completed
Task 2: Prepare a technical briefing paper and power point presentation to be used during a training
workshop (see ANNEX 1)
Description of how it was carried out:
The technical briefing paper has been prepared following the review in Task 1. The two power point
presentations made for the Kisumu workshop surfice for this Task 1. See attached two powepoint
presentations(Ballatore_Shisanya_2013).
Task 3: Assist in downscaling – global to regional and local level, i.e. Mt Elgon area. Consultant will
be working with local experts from Kenya and Uganda and will collect and collate data for generating
RCM outputs through downscaling in the basin, the consultant will also Review previous work on
climate downscaling. Including changes in rainfall and flow regimes. ET based IFPRI (2008)
hydromodel stimulations, KIZZA et al., 2009 outputs from RCMS (Anyah,Sermazzi, 2006 ). Data
available for downscaling include (Max/Minimum temperature, Surface temperature, rainfall and
humidity from LVB) (see ANNEX 2)
Description of how it was carried out:
See the attached powerpoint presentaion (Shisanya_PRECIS_Presentation_Kisumu.ppt)
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Climate Support Facility – WO17 – Mission Report
Additional Tasks
Task 4: Land use/cover times series and change detection maps for Mt. Elgon study area (see
ANNEX 3)
Description of how it was carried out:
Geographical Information Systems (GIS) and Remote Sensing (RS) methodologies were used. These
methods helped to interpret satellite images and analyze the digital GIS layers that came from the
interpretation. Prior to interpretation and analysis, Landsat satellite images covering the area of study
were acquired in form of raw image bands for five different years, namely : 1973, 1984, 1995, 2002
and 2011. Figure 1 is a schematic summary of the approach and methodology used to achieve in this
task.
Fig.1: Study approach in land use/cover time series and change detection for Mt Elgon
Acquisition of satellite images
Processing of satellite images
Interpretation of satellite images
Land use/cover area computation
Change detection
Preparation of maps
Image processing
The raw image bands acquired were digitally processed prior to interpretation and classification. The
processing done involved image to image rectification to clear the shift between different year images,
mosaicing different image scenes covering the area of study, clipping the images based on the extent
of the area of study and combining of different image bands to come up with colour composite images
for ease of visual interpretation.
Image Interpretation
Interpretation and classification of the satellite images was done after image processing. The method
of interpretation used was interpreter guided on-screen classification where polygons were digitized
around homogenous areas on the satellite images and their respective land use/cover types assigned
based solely on the detection of different spectral signature patterns of the different land use and land
cover classes. These homogeneous areas had spectral signatures representing different land
use/cover types on the ground surface.
The above task was successfully accomplished.
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Climate Support Facility – WO17 – Mission Report
Task 5: Spatial rainfall interpolated maps (see ANNEX 4)
Description of how it was carried out:
Rainfall spatial maps were developed following the procedure outlined in Figure 2. Once the rainfall
data of the selected (point data) stations were acquired, they were used as input in generating their
spatial representation (maps) of both long and short rains seasons throughout the study area. Digital
elevation model (DEM) raster map were used to generate a 20 m interval contour feature in ArcGIS
software, which was further converted to point feature in order to generate substantial elevation points
within the study area. The process resulted to numerous elevation data points.Using historical rainfall
data, best fits were generated using scatter plot and trend lines in MS excel spread sheet. Based on
the goodness of fit of the relationship between rainfall trend and elevation, functions were selected
and used to generate spatial rainfall data for elevation points by adding a field into the contour point
feature shapefile attribute table and applying the MS Excel derived functions using field calculator with
the elevation point being x. Kriging method of interpolation in spatial tools was used to generate the
maps.
Study area
Geo-database
Figure 2. Rainfall data analysis and spatial maps development methodology flowchart
Rainfall Data
Excel operations
DEM
Best-fit functions
Orographic
derivation
Generation of
Contour
Interpolation by
Kriging method
Conversion
(Elevation)
Mean
monthly
rainfall maps
Reclassification,
Masking and extraction
Various
Shapes
Maps Development
Boundary
Shape
Spatial rainfall
maps
The above task was successfully completed.
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Climate Support Facility – WO17 – Mission Report
Task 6: Vulnerability maps (see ANNEX 5)
Vulnerability to: Drought and erosion, floods, landslides population pressure and malaria.
Description of how it was carried out:
The 50m DEM was used in the construction of the vulnerability maps. The 30m TM image was used
to generate NDVI for drought and erosion vulnerability. Population was captured at district level for
both Kenya and Uganda using the national census data sets for both countries.
Outputs
As specified in the ToR:
 Output 1: Mt Elgon pilot areas climate change models. RCM outputs validation with local
observation outputs from Ensemble models e.g. CORDEX or from PRECIS
Graphic presentation of rainfall trend analysis for stations in the study area as a first level of
outputs. The second level of outputs consist of RCM projected rainfall in the study area for the
main seasons up to 2100. This was done in collaboration with Dr. Alfred Opere and Mr Peter
Omeny
 Output 2: Mission reports, including putting the outputs in annex.
Reports covering climate change effects in mountainous ecosystems.
Additional Outputs
 Output 3: Land use/cover time series and change detection maps for Mt Elgon
A report on land use/cover changes in Mt Elgon study area between 1973-2011. This was done in
collaboration with Mr. Anthony Ndubi
 Output 4: Spatial rainfall interpolation maps
Annual, seasonal and monthly spatial rainfall distribution maps in the study area. This was done
in collaboration with Dr. Felix Ngetich.
 Output 5: Vulnerability maps
Vulnerability maps specifically to:
 Droughts and erosion
 Floods
 Landslides
 Population pressure
 Malaria
Problems encountered
Acquiring climatic data for the study area proved to be a major limitation of this mission. The only
option available was to purchase these data from the Kenya Meteorological Department (KMD) at an
exorbitant cost. This however was not budgeted for! We however managed to get the data through
own established networks in the world of academia
I was quite pleased with the manner in which the mission was coordinated by the local coordinator
(save for unforeseen delays like going to the field), the Climate Support Facility Administrator and by
the ACP Secretariat.
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Climate Support Facility – WO17 – Mission Report
Follow-up required
Technical assistance provided under the CSF should be a punctual contribution to an programme and
the local coordinator should be able to ensure the impact and sustainability of the CSF mission’s
outcomes.
We would welcome your opinion on the following aspects:
-
Possible impacts and outcomes of the mission and the eventual documents or projects they
could produce
I strongly believe that the outcomes from this mission will enable the beneficiary to look at the
outputs derived for the project from a different angle. The beneficiary will in particular appreciate
the power of GIS/Remote sensing (RS) tools in presenting spatial phenomena to the stakeholders
who may have NO idea whatsoever about these tools. I can predict a great demand for these
GIS/RS based outputs by the stakeholders in the study area. This will be a plus for this project!
Further steps required after the mission and your assessment regarding the ability of the local
coordinator to implement them.
The necessity of further technical assistance mission, its objectives and content.
There is still need for training in the RCM approaches and in particular the interpretation of the
RCM results with respect to understanding the impacts of the projected changes and what they
actually mean for the communities in the study area. Perhaps an area that could be looked at in
future in this regard is “Community disaster risk management”, given that projected climate
change is indeed going to be a disaster of un-imaginable proportion! I believe that communities
need to be well prepared to face these predicted challenges that will result from climate change.
Finally, if you have any suggestion of institution or training programmes other than the climate support
facility from which the beneficiary could get further assistance on the object of this mission, please
kindly list them.
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
Regional Center for Mapping of Resources for Development (RCMRD) in Nairobi (Training on
the use of spatial mapping tools, i.e. GIS and Remote Sensing).

Masinde Muliro University of Science & Technology in Kakamega (Programme on Disaster
Risk Management)
Climate Support Facility – WO17 – Mission Report
List of people consulted
Name
Institution
Email
Tel.
Prof.
Eric
Odada
University of Nairobi
[email protected]
+254733644845
Prof.
Daniel
Olago
University of Nairobi
[email protected]
+254722768536
Dr.
Alfred
Opere
University of Nairobi
[email protected]
+254722858660
Dr.
Lydia
Olaka
University of Nairobi
[email protected]
+254717555003
Dr.
Thomas
Ballatore
Director,Lake Basin Action Network (LBAN)
Affiliated Scientist, Kyoto University
Visiting Researcher,
International Lake Environment Committee
(ILEC)2043 Hoshika, Moriyama, 524-0063 Japan
Balla.orgtore@lakebasin
+81-77532-4433
Dr. Felix
Ngetich
Kenyatta University
[email protected]
+254721289269
Mr.
Simon
Thuo
Water Partnership Africa
[email protected]
+254703116932
Mr.
Peter
Omeny
Kenya Meteorological Department
[email protected]
+254722499058
Mr.
Anthony
Ndubi
FAO Somalia, SWALIM Project
[email protected]
or [email protected]
+254723490233
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Climate Support Facility – WO17 – Mission Report
ANNEX 1:
POTENTIAL IMPACTS OF CLIMATE CHANGE IN MOUNT ELGON TRANSBOUNDARY
ECOSYSTEM
1. Introduction
In June 1992, The United Nations Conference on Environment and Development (UNCED, Rio de
Janeiro) held in the year 1992 addressed a wide range of issues pertaining to sustainable
development as a means of reducing human-induced environmental stress, in a document popularly
referred to as Agenda 21. Chapter 13 of this document is devoted to mountain regions and, for the
first time, an official and explicit recognition that mountains and uplands are a major component of the
global environment emerged. This chapter sets the scene by stating the role of mountains within the
global ecosystem, and expresses serious concerns related to the decline in the general environmental
quality of many mountains. A summarized version of Agenda 21, Chapter 13 reads as follows (UN,
1992):
Mountains are important sources of water, energy, minerals, forest and agricultural products and
areas of recreation. They are storehouses of biological diversity, home to endangered species and an
essential part of the global ecosystem. From the Andes to the Himalayas, and from Southeast Asia to
East and Central Africa, there is serious ecological deterioration. Most mountain areas are
experiencing environmental degradation.
It is worth noting that orographic features occupy approximately 25% of continental surfaces (Kapos
et al., 2000) and, although only about 26% of the world’s population resides within mountains or in the
foothills of mountains (Meybeck et al., 2001), mountain-based resources indirectly provide
sustenance for over half. Additionally, 40% of world’s population lives in watersheds of rivers
originating in the planet’s different mountain ranges. Mountains also represent unique areas for
detection of climatic change and assessment of climate-induced impacts. An explanation for this is
that, as climate changes rapidly with height over relatively short horizontal distances, so does
vegetation and hydrology (Whiteman, 2000). As a result, mountains exhibit high biodiversity, often
with sharp transitions (ecotones) in vegetation sequences, and equally rapid changes from vegetation
and soil to snow and ice. Further, mountains ecosystems are often endemic, because many species
remain isolated at high elevations compared to lowland vegetation communities that can occupy
climatic niches spread over wider latitudinal belts. Certain mountain chains have been referred to as
‘islands’ rising above the surrounding plains (Hedberg, 1964), such as those found in East Africa. In
socio-economic terms, mountain landscapes attract large numbers of people in search of
opportunities. With the rapid industrialization and population growth that the 20th century has
witnessed worldwide, the natural environment has undergone unprecedented changes. While the
causal mechanisms of environmental and climatic change are numerous and complex, two factors
can be highlighted to explain the increasing stress imposed by human interference on the natural
environment: economic growth and population growth. The economic level of a country determines to
a large extent its resource requirements, in particular energy, industrial commodities, agricultural
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Climate Support Facility – WO17 – Mission Report
products and fresh water supply. Rising population levels, on the other hand, can weigh heavily upon
the resources available per capita, particularly in developing countries. Bearing these two factors in
mind, environmental degradation in mountains can be driven by numerous factors that include
deforestation, over-grazing by livestock and cultivation of marginal soils. Mountain ecosystems are
susceptible to soil erosion, landslides and the rapid loss of habitat and genetic diversity (Beniston,
2003). In many developing countries, in part because of the degradation of the natural environment,
there is widespread unemployment, poverty, poor health and inadequate sanitation (Price et al.,
2000). Such concerns have prompted a number of research and policy initiatives that have
acknowledged and highlighted the importance of mountain environments in environmental, economic,
and social terms. Perhaps the most notable action, at least in terms of policy, has been the
proclamation, by the UN General Assembly in 1998 (UN, 1998), of the year 2002 as the ‘International
Year of the Mountains’ (IYM), declaring that:
The aim of IYM is to ensure the well-being of mountain and lowland communities by promoting the
conservation and sustainable development of mountain regions. FAO (the United Nations Food and
Agricultural Organization), the lead agency for IYM, is working closely with UN and other
organizations to make sure the broadest possible range of expertise is focused on reaching the goals
of sustainable mountain development. One of IYM’s goals is to raise awareness about the challenges
in protecting mountain habitats and improving living standards in mountain communities.
The IYM aimed at furthering ongoing actions and stimulating new initiatives related to the following
sectors:

Natural resources, particularly climate, water, soils, biodiversity, and forests;

Resource use, namely water, agriculture, forestry, and mining;

Socio-economic issues, such as tourism, trade and transportation, people and culture, and
financial mechanisms and strategies;

Integrated themes, with a focus on health and well-being, risks and hazards, watershed
management, mountain protected areas, integrated mountain development, conflicts, and
policies.
In the general framework of IYM-2002, mountains indeed offer interesting research opportunities
(Beniston, 2003). Studies on Mt Kilimanjaro (see for example, Gamassa, 1991; Maro, 1974; 1998;
Soini, 2005; William, 2002; Yanda and Shishira, 2001) provide evidence of replacement of forests by
agriculture and settlements, leading to severe erosion, disruption of water sources and the drying up
of rivers. Mountain forest ecosystems are particularly important from an ecological perspective, as
they provide goods and services that are essential to maintain the life-support system on a local and
global scale. Greenhouse gas regulation, water supply, nutrient cycling, genetic and species diversity,
as well as recreation, are some of the services that mountain forests provide (Beniston, 2003;
Nagendra et al., 2004; Sivrikaya et al., 2007). There has been growing concern over the human
destruction of forests, especially in the tropical and subtropical countries' mountain environments and
the associated consequences on soil and water quality, biodiversity, global climatic and livelihood
systems (Armentaras et al., 2003; Laurence, 1999; Noss, 2001; Turner et al., 1995).
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Climate Support Facility – WO17 – Mission Report
In Mount Elgon transboundary ecosystem the uniqueness of the mountain and highland landscapes
presents both opportunities and management challenges. Opportunities because they comprise
important natural resources, ecosystems and services; including: Acting as water towers and hence
important watersheds/water sources – the mountains act as water towers due to the uniqueness of
the ecosystem to induce low evapo-transpiration and high precipitation. Most of the mountains are
home to indigenous communities (e.g., the Ndorobo or forest dwellers). The traditions, cultures and
practices of these people need to be preserved. The climate and soils in most mountainous areas are
conducive to important agricultural production; as well as important biodiversity enclaves (with both
endemic and threatened species). The challenges include the fragility of the ecosystems thereof,
including slopes that are prone to soil erosion and landslides and other mass wasting processes,
whose occurrences are dominantly of catastrophic proportions. Of recent, the impact of climate
change often associated with extreme weather events, has become yet an added and more serious
challenge; triggering disastrous soil erosion, landslides and floods. The environment is conducive,
leading to high population densities, whose livelihoods are dependent on subsistence agriculture that
lead to intense tillage of land; hence land pressures are extremely high, leading to high risks of
encroachment on fragile areas and environmental degradation. In some areas, we are to find some of
the highest rural population densities in the world and yet, in most cases, due to dominantly steep
slopes, over 50% of the land is too steep to cultivate without risking catastrophic erosion problems.
Low levels of awareness, land management technologies and high occurrence poverty among the
mountain communities exacerbates their inability to meet the new challenges of climate change
sustainable development. This paper examines the possible impacts of climate change on Mount
Elgon transboundary ecosystem.
2. Study area
According to Scott (1994), Mt. Elgon, a solitary volcano, is one of the oldest in East Africa. It was built
up from lava debris blown out from a greatly enlarged volcanic vent during the Pliocene epoch
(Knapen et al., 2006) and rises to a height of about 4320 m above sea level. The geology of the area
is dominated by basaltic parent materials and strongly weathered granites of the Basement Complex
(Claessens et al., 2007). Carbonatite intrusions on the lower slopes are reported by Knapen et al.
(2006) and Claessens et al. (2007) as having caused fenitization of the granites, rendering them
sensitive to slope instability. Identified as inorganic clays of high plasticity, the soils of the study area
were classified by Mugagga et al. (2011) as vertisols characterised by a clay content exceeding 41%.
Such properties qualify the soils as ‘problem soils’ that are susceptible to landslides. The area
receives a bimodal pattern of rainfall, generally, with the wettest period occurring from April to
October. The mean annual rainfall ranges from 1500 mm on the eastern and northern slopes, to 2000
mm in the southern and the western slopes. The mean maximum and minimum temperatures are 23°
and 15 °C respectively. Mid-slopes oriented towards the east and north, at elevations between 2000
and 3000 m tend to be wetter than either the lower slopes or the summit. The vegetation of Mt. Elgon
reflects the altitudinally controlled zonal belts commonly associated with large mountain massifs. Four
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Climate Support Facility – WO17 – Mission Report
broad vegetation communities are recognised, namely: mixed montane forest up to an elevation of
2500 m, bamboo and low canopy montane forest from 2400 to 3000 m, and moorland above 3500 m
(Scott, 1994).
Land in the study area of Uganda is divided between the National Park and farmland. Land use in the
latter area is itself divided between two topographic zones: an upland zone, characterised by
intensive coffee and maize farming, and a lowland zone, where beans, yams and onions are grown.
Arabica coffee is traditionally the major cash crop of the area, and bananas are the staple food. Much
of the cultivation takes place on steep slopes ranging between 36° and 58°. Despite cultivating on
steep slopes, there is inadequate use of soil conservation measures in the area, a significant factor
that leads to soil erosion, declining levels of soil fertility and decreasing crop yields. The use of soil
conservation methods varies with distance from the National Park. Farmers living far from the Park
boundary use far better soil conservation methods than their counterparts in and around the Park.
This is explained by the insecure land tenure and the constant fear of eviction by the Park authorities
(Mugagga et al., 2010).
3
Potential climate change impacts in mountain ecosystems: Case of Mt Elgon
Mountain ecosystems differ considerably from one geographic region to another in terms of their
complexity and topography. From an orographic point of view, they have some of the sharpest
gradients found in continental areas. Pertinent characteristics exhibited include rapid and systematic
changes in climatic parameters, in particular temperature and precipitation, over very short distances
(Becker and Bugmann, 1997); greatly enhanced direct runoff and erosion; systematic variation of
other climatic (e.g., radiation) and environmental factors, such as differences in soil types. Globally,
mountain ecosystems are susceptible to the impacts of a rapidly changing climate, and provide
interesting locations for the early detection and study of the signals of climatic change and its impacts
on hydrological, ecological, and societal systems. The complexity of mountain ecosystems and socioeconomic systems present significant challenges for potential climate change impacts studies
(Beniston et al., 1997). In the assessment of current and future trends in regional climate, the current
spatial resolution of General Circulation Models (GCM) is more often too crude to adequately
represent the complex orographic details of most mountain ecosystems. On the other hand, most
impacts research requires information with fine spatial definition, where the regional details of
topography or land-cover are important determinants in the response of natural and managed
systems to the potential change. Since the mid-1990s, the scaling problem related to complex
orography has been addressed through regional modeling techniques, pioneered by Giorgi and
Mearns (1991), and through statistical-dynamical downscaling techniques (e.g., Zorita and von
Storch, 1999).
The so-called ‘nested’ approaches to regional climate simulations, whereby large-scale data or GCM
outputs are used as boundary and initial conditions for regional climate model (RCM) simulations,
have been applied to scenario computations for climatic change in the 21st century (Hewitson and
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Climate Support Facility – WO17 – Mission Report
Crane, 1996; Giorgi and Mearns, 1999). The technique is applied to specific periods in time (‘time
windows’) for which high-resolution simulations are undertaken. GCM results for a given time window
include the long-term evolution of climate prior to that particular time frame, based on an incremental
increase of greenhouse gases over time. The RCM focuses on a high-resolution simulation for the
limited time span of the selected time window over a given geographical area. The nested modeling
approach represents a tradeoff between decadal- or century-scale, high resolution simulations that
are today unattainable, even with currently-available computational resources, and relying only on
coarse resolution results provided by long-term GCM integrations. Although the method has a number
of drawbacks, in particular the fact that the nesting is ‘one-way’ (i.e., the climatic forcing occurs only
from the larger to the finer scales and not vice-versa), RCMs may in some instances improve regional
detail of climate processes. This can be an advantage in areas of complex topography, where for
example orographically-enhanced precipitation may represent a significant fraction of annual or
seasonal rainfall in a particular mountain region. Such improvements are related to the fact that RCM
simulations capture the regional detail of forcing elements like orography or large lakes, and the local
forcings of such features on regional climate processes, in a more realistic manner than GCMs
(Beniston, 2000). Over time, the increase in spatial resolution of RCMs has allowed an improvement
in the understanding of regional climate processes and the assessment of the future evolution of
regional weather patterns influenced by a changing global climate. Marinucci et al. (1995) tested the
nested GCM-RCM technique at a 20-km resolution to assess its adequacy in reproducing the salient
features of contemporary climate in the European Alps, while Rotach et al. (1997) repeated the
numerical experiments for a scenario of enhanced greenhouse-gas forcing. Over the past 15 years,
RCM spatial resolution has continually increased, partially as a response to the needs of the impacts
on community. Currently, detailed simulations with 5 km or even 1 km grids are used to investigate
the details of precipitation in relation to surface runoff, infiltration, and evaporation (e.g., Arnell, 1999;
Bergström et al., 2001), extreme events such as precipitation (Frei et al., 1998), and damaging wind
storms (Goyette et al., 2001).
3.1
Natural ecosystems
Plant life at high elevations like Mt Elgon is primarily constrained by direct and indirect effects of low
temperatures, radiation, wind and storminess or insufficient water availability (Körner and Larcher,
1988). Plants respond to these climatological influences through a number of morphological and
physiological adjustments such as stunted growth forms and small leaves, low thermal requirements
for basic life functions, and reproductive strategies that avoid the risk associated with early life
phases. Because temperature decreases with altitude by 5–10 oC/km, a first-order approximation
regarding the response of vegetation to climate change is that species will migrate upwards to find
climatic conditions in tomorrow’s climate which are similar to today’s (e.g., McArthur, 1972; Peters and
Darling, 1985). According to this paradigm, the expected impacts of climate change in mountainous
nature reserves like those of Mt Elgon would include the loss of the coolest climatic zones at the
peaks of the mountains and the linear shift of all remaining vegetation belts upslope. Because
mountain tops are smaller than bases, the present belts at high elevations would occupy smaller and
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Climate Support Facility – WO17 – Mission Report
smaller areas, and the corresponding species would have reductions in population and may thus
become more vulnerable to genetic and environmental pressure (Peters and Darling, 1985; HansenBristow et al., 1988; Bortenschlager, 1993). However, the migration hypothesis may not always be
applicable because of the different climatic tolerance of species involved, including genetic variability
between species, different longevities and survival rates, and the competition by invading species
(Dukes and Mooney, 1999). Huntley (1991) suggests that there are three responses that can be
distinguished at the species level, namely genetic adaptations, biological invasions through species
inter-competition, and species extinction. Adaptation pathways in the face of changing environmental
conditions include the progressive replacement of the currently dominant species by a more
thermophilous (heat-loving) species. A further mechanism is that the dominant species is replaced by
pioneer species of the same community that have enhanced adaptation capability (Halpin, 1994; Pauli
et al., 1998). A third possibility is that environmental change may favor less dominant species, which
then replace the dominant species through competition (Street and Semenov, 1990). These scenarios
are based on the assumption that other limiting factors such as soil type or moisture will remain
relatively unaffected by a changing environment.
It is expected that, on a general level, the response of ecosystems in mountain regions like Mt Elgon
will be most important at ecoclines (the ecosystem boundaries if these are gradual), or ecotones
(where step-like changes in vegetation types occur). Guisan et al. (1995) note that ecological changes
at ecoclines or ecotones will be amplified because changes within adjacent ecosystems are
juxtaposed. In steep and rugged topography, ecotones and ecoclines increase in quantity but
decrease in area and tend to become more fragmented as local site conditions determine the nature
of individual ecosystems. Even though the timberline is not a perfect ecocline in many regions, it is an
example of a visible ecological boundary that may be subject to change in coming decades. This
change could either take place in response to a warmer climate, or as a result of recolonization of
pastures that have been cleared in the past for pastoral activities. McNeely (1990) has suggested that
the most vulnerable species at the interface between two ecosystems will be those that are
genetically poorly adapted to rapid environmental change. Those that reproduce slowly and disperse
poorly, and those which are isolated or are highly specialized, will therefore be highly sensitive to
seemingly minor stresses.
3.2
Human health : case of malaria
The occurrence of vector-borne diseases such as malaria is determined by the abundance of vectors
and intermediate and reservoir hosts, the prevalence of disease-causing parasites and pathogens
suitably adapted to the vectors, and the human or animal hosts and their resilience in the face of the
disease (McMichaels and Haines, 1997). Local climatic conditions, especially temperature and
moisture, are also determinant factors for the establishment and reproduction of the Anopheles
mosquito (Epstein et al., 1998). The possible development of the disease in mountain regions thus
has relevance, because populations in uplands where the disease is currently not endemic may face
a new threat to their health and well-being as malaria progressively invades new regions under
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Climate Support Facility – WO17 – Mission Report
climatic conditions favorable to its development (Martens et al., 1999). Debate continues over which
factors, climatic or non-climatic, are most responsible for influencing malaria’s transmission rate and
distribution in the past, present and future. The IPCC (2001) and WHO (2003, 2009a), among others,
argue that climate change, by leading to rising temperatures, changes in precipitation, and climate
variability, could increase malaria infection. For example, such changes could favour proliferation of
malaria-carrying mosquitoes at higher altitudes (USAID, 2008). Others (e.g. Gething, et.al., 2010; Hay
et al., 2002) argue that socio-economic factors such as economic development, population changes,
immunity and drug resistance, urbanization, and land-use changes “have exerted a substantially
greater influence on the geographic extent and intensity of malaria worldwide during the twentieth
century than have climatic factors” (Gething et al. 2010: 343). According to these authors, any impact
that climate change could have on disease distribution can be more than offset by expanding disease
control techniques, such as bednets and drugs (Gething et al., 2010; Tatalović, 2010). To support
their case, they point to the “global decrease [since 1900] in the range and intensity of malaria
transmission” during a century marked by a rise in global temperature (Gething, et al., 2010). The
picture is complex. Climate change may increase the risk of malaria becoming more prevalent, but
whether this greater risk is translated into reality (i.e. more prevalence) and whether it becomes more
of a danger to human health (e.g. more prevalence, but greater treatment improves outcomes) will
depend on many other factors and will be context-specific. Overall, the potential impact of climate
change on malaria remains uncertain (Confalonieri et al., 2007; Paaijmans et al., 2009). The difficulty
lies in determining which factors are most responsible for influencing the distribution and intensity of
the disease (WHO, 2003).
A recent study (UNDP/BCPR, 2012) reveals that in recent years, malaria has spread to over 2o
districts in Western Highlands of Kenya, where Mt Elgon is located. The issue is rather complicated in
that whereas the disease had been decreasing in Kenya’s traditional endemic lowland areas, it has
been expanding in highland zones. Abnormal temperatures followed by rainfall exceeding certain
thresholds can create conditions favourable to malaria epidemics, depending on topography (Githeko,
2010). In the western highlands, the outbreaks of malaria are seasonal. Peak transmission generally
occurs in June-July-August—after the long rainfall season of March-April-May—and when climatic
conditions favour mean temperatures around 18o C (DOMC/MOPHS, 2011). Overall, the link between
unusual climate variables (temperature, precipitation and humidity) and malaria in the area has
already been documented (Githeko, 2001).The UNDP/BCPR (2012) report notes that in Western
Highlands of Kenya climate change has combined with socio-economic factors to create conducive
conditions for malaria epidemics. Some of the counties at risk include: Kisii, Kakamega, Nandi,
Kericho, Trans Nzoia and Mount Elgon.
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4
Conclusion
Mountains of East Africa including highlands are complex ecosystems, which cover largely the
volcanic, fault or horst formations in Uganda, Kenya, Tanzania and Rwanda. They have a multitude of
rivers, varied soil types, vegetation and favourable climate conditions that have attracted high
population density. Recent observations, however, reveal decline in the resource capacity to sustain
increasing demands. There is evidence of land pressure which coupled with climate change threatens
sustainable use of these mountain ecosystem complex Because mountain ecosystems are often
referred to as ‘hotspots of biodiversity’ (Price et al., 2000), they warrant protection in order to maintain
ecosystem integrity and adaptability. Furthermore, montane vegetation is important in terms of its
protective role against slope erosion and as a component of mountain hydrology and water quality.
Whatever the ecosystem response to multiple environmental stress factors, adaptation of natural
ecosystems to climatic change in many regions cannot be achieved without some kind of human
intervention, in the form of ecosystem management. Reforestation would in some cases be a viable
adaptation option, and so would afforestation of abandoned agricultural land. Freshwater biological
systems can be assisted in a number of ways which could help mitigate the impacts of climate
change, particularly through the increase and protection of riparian vegetation, and restoring river and
stream channels to their natural morphologies. One approach to ecosystem conservation inmountains
and uplands is the setting up of refugia and migration corridors. Refugia are buffer-zones that can
play the role of allowing ecosystems to adapt or migrate to change. National parks with restricted
access, and biosphere reserves are one form of refugia.
Some experts contend that mountains are forgotten ecosystems by the world community, and
probably a stand alone international convention to conserve these ecosystems should have been
considered at the time other environment conventions were being negotiated. At this advent of climate
change and its impacts, the mountain ecosystems and communities need special and urgent
attention; given the importance fragility/extreme vulnerability and unique value, functions, and cultures
of the ecosystems and communities in these areas. At this time when climate change negotiations are
ongoing, the attention of the mountain ecosystems and communities should be put high on the
agenda, ensure that they do not lose out like it appears to have been, since the 1992 Earth Summit.
Advocacy for climate change mitigation and adaptation and general environmental sustainability the
mountain ecosystems of the world must rise higher than ever to ensure that the integrity and security
of these ecosystems are not compromised or decimated by the predicted, as well as already visible
impacts of climate change. The Mt Elgon ecosystem communities face insurmountable challenges, of
continued existence in a fragile environment whose ability to sustain their livelihoods has been greatly
undermined by impacts of climate change, a cause of which is none of their own making. As a special
priority, these people deserve and need help from the international community, to be able to adapt to
the new challenges.
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References
Armentaras, D., Gast, F., Villareal, H., 2003. Andean forest fragmentation and the representativeness
of protected areas in the Eastern Andes, Colombia. Biological Conservation 113, 245–256.
Beniston, M. 2003. Climate change in mountain regions: A review of possible impacts. Climate
Change, 59: 5-31.
Bortenschlager, S. 1993. ‘Das höchst gelegene Moor der Ostalpen “Moor am Rofenberg” 2760m’,
Festschrift Zoller, Diss. Bot. 196: 329–334.
Confalonieri, U., and others. 2007. Human health. In: Climate Change 2007: Impacts, Adaptation and
Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change, M.L. Parry and others, eds. Cambridge,
UK, and New York: Cambridge University Press, pp. 391-431.
Division of Malaria Control, Ministry of Public Health and Sanitation (DOMC/MOPHS), Kenya National
Bureau of Statistics (KNBS), and ICF Macro. 2011. 2010 Kenya Malaria Indicator Survey.
Nairobi, Kenya.
Dukes, J. S. and Mooney, H. A. 1999. ‘Does Global Change Increase the Success of Biological
Invaders?’.Trends in Ecology and Evolution 14: 135–139.
Epstein, P. R., Diaz, H. F., and Elias, S. 1998. Biological and Physical Signs of Climate Change.
Focus on Mosquito-Borne Diseases. Bulletin of American. Meteorological Society. 78:
410–417.
Gamassa, D.M., 1991. Historical change in human population on Mount Kilimanjaro and its
implications. In: Newmark, W.D. (Ed.), The Conservation of Mount Kilimanjaro. The IUCN
Tropical Forest Programme, pp. 1–8.
Gething, P.W., and others. 2010. Climate change and the global malaria recession. Nature, 465: 342–
345.
Githeko, A.K. 2010. Transferring the Malaria Epidemic Prediction Model to End Users in East Africa.
International Development Research Centre.
Githeko, A.K. and Ndegwa, W. 2001. Predicting malaria epidemics in the Kenyan highlands using
climate data: a tool for decision makers. Global Change and Human Health, 2(1): 54–63.
Guisan, A., Holten, J., Spichiger, R., and Tessier, L. (eds.). 1995. Potential Impacts of Climate
Change on Ecosystems in the Alps and Fennoscandian Mountains. Annex Report to the
IPCC Working Group II Second Assessment Report, Publication Series of the Geneva
Conservatory and Botanical Gardens, University of Geneva, Switzerland, 194 pp.
Halpin, P. N. 1994. ‘Latitudinal Variation in Montane Ecosystem Response to Potential Climatic
Change, in Beniston, M. (ed.), Mountain Ecosystems in Changing Climates, Routledge
Publishing Company, London and New York, 180–203.
16
Climate Support Facility – WO17 – Mission Report
Hansen-Bristow, K. J., Ives, J. D., and Wilson, J. P. 1988. Climatic Variability and Tree Response
within the Forest-Alpine Tundra Ecotone. Annals of the Association of American
Geographers 78: 505–519.
Hay, S., and others. 2002. Climate change and the resurgence of malaria in the East African
highlands. Nature, 415: 905–909.
Hedberg, O. 1964. The Phytogeographical Position of the Afroalpine Flora. Recent Advances in
Botany, 914–919.
Hewitson, B.C., Crane, R.G. 1996. Climate downscaling techniques and application. Climate
Research 7 : 85-95.
Huntley, B. 1991. How Plants Respond to Climate Change: Migration Rates, Individualism and the
Consequences for Plant Communities. Annals of Botany 67: 15–22.
IPCC. 2001. Climate Change. The IPCC Third Assessment Report, Cambridge University Press,
Cambridge and New York. Vols. I (The Scientific Basis), II (Impacts, Adaptation, and
Vulnerability) and III (Mitigation).
Jenkins, G.S., Brron, E.J. 1996. General circulation model and coupled regional climate model
simulations over eastern United States : GENESIS and RegCM2 simulations. Global Planet
Change 23 :157-167.
Kapos, V., Rhind, J., Edwards, M., Ravilious, C., and Price, M. 2000. Developing a Map of the
Körner, C. and Larcher, W. 1988. Plant Life in Cold Climates. In Long, S. F. and Woodward, F. I.
(eds.), Plants and Temperature, The Company of Biol Ltd, Cambridge, pp. 25–57.
Laurence, W.F., 1999. Reflections on the tropical deforestation crisis. Biological Conservation 91,
109–117.
Maro, P.S., 1974. Population and land resources in northern Tanzania: the dynamics of change
1920–1970, PhD Thesis, University of Minnesota, Minneapolis.
Maro, P.S., 1998. Agricultural land management under population pressure: the Kilimanjaro
experience, Tanzania. Mountain Research and Development Journal 8, 273–282.
McArthur, R. H. 1972. Geographical Ecology, Harper and Row, New York.
McMichael, A. J. and Haines, A. 1997. Global Climate Change: The Potential Effects on Health.
British Medical Journal 315: 805–809.
McNeely, J. A. 1990. Climate Change and Biological Diversity: Policy Implications, LandscapeEcological Impact of Climatic Change. In Boer, M. M. and de Groot, R. S. (eds.), IOS
Press, Amsterdam.
Meybeck, M., Green, P., and Vörösmarty, C. 2001. A New Typology for Mountains and other Relief
Classes: An Application to Global Continental Water Resources and Population
Distribution. Mountain. Research and Development, 21: 34–45.
Nagendra, H., Munroe, D.K., Southworth, J., 2004. From pattern to process: landscape fragmentation
and the analysis of land use/cover change. Agriculture, Ecosystems and Environment 101,
111–115.
17
Climate Support Facility – WO17 – Mission Report
Noss, R.F., 2001. Forest fragmentation in the Southern Rocky Mountains. Landscape Ecology 16,
371–372.
Paaijmans, K.P, and others. 2010. Relevant microclimate for determining the development rate of
malaria mosquitoes and possible implications of climate change. Malaria Journal, 9, No.
196.
Pauli, H., Gottfried, M., and Grabherr, G. 1998. Effects of Climate Change on Mountain Ecosystems.
Upward Shifting of Alpine Plants. World Resources Review 8: 382–390.
Peters, R. L. and Darling, J. D. S. 1985. The Greenhouse Effect and Nature Reserves: Global
WarmingWould Diminish Biological Diversity by Causing Extinctions among Reserve
Species. Bioscience 35: 707–717.
Price, M., Kohler, T., and Wachs, T. (eds.). 2000. Mountains of the World: Mountain Forests and
Sustainable Development, CDE, University of Bern, Switzerland, 42 pp.
Sivrikaya, F.G., Cakir, A.I., Kadiogullari, S., Keles, E.S., Baskent, T.S., 2007. Evaluating land use/land
cover changes and fragmentation in the Camli Forest Planning Unit of north-eastern Turkey
from 1972 to 2005. Land Degradation and Development 18, 383–396.
Soini, E., 2005. Land use change patterns and livelihood dynamics on the slopes of Mt Kilimanjaro,
Tanzania. Agricultural Systems 85, 306–323.
Street, R. B. and Semenov, S. M. 1990. Natural Terrestrial Ecosystems. In Tegart, W. J. K., Sheldon,
G. W., and Griffiths, D. C. (eds.), Climate Change: The First Impacts Assessment Report,
Australian Government Publishing Service, Chapter 3.
Tatalović, M. 2010. Debate heats up over climate impact on malaria spread. Guardian Environment
Network, 21 May 2010.
Turner, D.P., Koerper, G.J., Harmon, M.E., Lee, J.J., 1995. A carbon budget for forests of the
Conterminous, United States. Ecological Applications 5, 421–436.
United Nations Development Programme (UNDP), Bureau for Crisis Prevention and Recovery
(BCPR). 2012. Climate Risk Management for Malaria Control in Kenya : The Case of the
Western Highlands. New York, NY :UNDP BCPR.
UN. 1992, Earth Summit: Agenda 21. The United Nations Programme of Action from Rio, The Final
Text of Agreements Negotiated by Governments at the United Nations Conference on
Environment and Development (UNCED), 3–14 June 1992, Rio de Janeiro, Brazil, 294
pp.
UN. 1998, Proclamation of the International Year of the Mountains, Report on the 1998 UN General
Assembly Meeting, New York. Text and signatories can be accessed via Internet at
URL:http://www.mtnforum.org/resources/library/uniym99a.htm
United States Agency for International Development (USAID). 2008. Malaria in Kenya. USAID
Knowledge
Services
Center.
http://pdf.usaid.gov/pdf_docs/PNADM024.pdf.
18
Available
from
Climate Support Facility – WO17 – Mission Report
Wandiga, S.O., and others. 2010. Vulnerability to epidemic malaria in the highlands of lake Victoria
basin: The role of climate change/ variability, hydrology and socio-economic factors.
Climate Change, 99: 473–497.
William, C.M.P., 2002. The implications of changes in land use on forests and biodiversity. A case of
the Half Mile Strip on Mount Kilimanjaro, Tanzania. Unpublished M.A. Dissertation,
University of Dar es Salaam.
World Health Organization (WHO). 2003. Methods of assessing human health vulnerability and public
health adaptation to climate change. Health and Global Environmental Change Series
No. 1.
World
Health
Organization
(WHO).
2009.
World
Malaria
Report
2009.
Available
from
http://whqlibdoc.who.int/ publications/2009/9789241563901_eng.pdf.
World’s Mountain Forests. In: Price, M. F. and Butt, N. (eds.), Forests in a Sustainable Mountain
Environment, CAB International, Wallingford.
Yanda, P.Z., Shishira, E.K., 2001. Forestry conservation and resource utilization on the southern
slopes of Mount Kilimanjaro: trends, conflicts and resolutions. In: Ngana, J.O. (Ed.), Water
Resources Management in the Pangani River Basin: Challenges and Opportunities. Dar es
Salaam University Press, Dar es Salaam, pp. 104–117.
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ANNEX 2
ASSESSMENT OF SEASONAL RAINFALL PREDICTABILITY USING PRECIS REGIONAL
MODELING SYSTEM OVER MT ELCON
Abstract
The purpose of this study was to evaluate the accuracy and skill of the UK Met Office Hadley Center
Regional Climate Model (HadRM3P) regional model, part of the PRECIS (providing Regional
Climates for Impact Studies) in describing the seasonal variability of the main climatological features
over Mt Elgon in long-term simulations (30 years, 1961–1990). The analysis was performed using
seasonal averages from observed and simulated precipitation, temperature, and lower- and upperlevel circulation. Precipitation and temperature patterns as well as the main general circulation
features, including details captured by the model at finer scales than those resolved by the global
model, were simulated by the model. The findings of this study indicate the presence of increasing
positive trend in the long term observed rainfall record over the eastern and southern sectors of Mt
Elgon during the major rainfall season of March-May (MAM) and second most important season of
October-December (OND). The third season of June-August (JJA) however, showed insignificant
downward trend in all the stations used for the studied. The linear trend is probably an indicator of the
sensitivity of the region’s extremes to climate change due to possible external enhancement of the
natural climate agitation. The latter has implications for flood risks if the trend is maintained. The
analysis results of the model projections relative to the reference period 1961-1990 indicates that Mt
Elgon area will continue to experience steady increase in temperature under a range of IPCC
scenarios. Going by the ensemble means, temperatures are projected to steadily increase uniformly
in all the seasons at a rate of about 0.5oC/decade leading to an approximate temperature increase of
2oC by 2100. The rainfall on the other hand indicates seasonal differences but with major parts of the
area getting wetter during MAM, JJA and OND seasons by the end of 21 st century under both A2 and
B2
1
Introduction
Global models have allowed for a better scientific understanding of anthropogenic global climate
change and this has brought commensurate developments in mitigation strategies. However, at the
regional scale, there remains an urgent need for relevant, targeted projections of regional climate
change. Furthermore, adaptation, as opposed to mitigation, is inherently a local and regional-scale
issue, and is limited by the measure of confidence in the projected changes at these scales. Demand
for regional climate change scenarios has generated increased interest in the downscaling of global
climate model simulations. These downscaling methods can be statistical, using empirical transfer
functions, or dynamical, using Regional Climate Models (RCM). Many studies around the world have
carried out simulations of present and future climates (see reviews in Meehl et al., 2007). There is a
consensus that for impacts or vulnerability applications, dynamic downscaling using RCMs is the most
appropriate option. The hypothesis behind the use of high-resolution RCMs is that they can provide
meaningful small-scale features over a limited region at affordable computational cost compared to
high resolution AOGCM simulations. The value-added information that is expected from RCMs should
come not only from the spatial details but also from better-simulated temporal variability. This
variability aspect is often a weakness in GCMs (Giorgi, 1990; Giorgi and Mearns, 1999; Wang et al.,
2004). Regional climate modeling has become an important tool for the prediction of climate variability
and change. High-resolution scenarios developed from regional climate model results have been
obtained in various parts of the world: China (Zhang et al., 2006), Pakistan (Islam and Rehman,
2007), Europe (Christensen and Christensen 2003; Frei et al., 2006), and in South America (Nuñez et
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al., 2006; Marengo and Ambrizzi, 2006, Ambrizzi et al., 2007; Marengo et al. 2007, 2009; Solman et
al., 2007). During the last decade, several national and international researches in regional climate
modeling has demonstrated that RCMs is a useful downscaling tool for providing climate information
at the scale appropriate for societal use (see reviews on international projects using regional climate
change scenarios in Marengo et al., 2009). All these have all followed a standard experimental design
of using one or more GCMs to drive various regional models from meteorological services and
research institutions in the regions to provide dynamically downscaled regional climate projections.
Typically, present-day climate (e.g., 1961–1990) and future climate (2070–2100) time slices are
simulated to calculate changes in relevant climatic variables.
In this study, we focus on the analysis of a 30-year simulation, 1961–1990 (referred to as “presentday”), from the HadRM3P regional model, part of the PRECIS (Providing Regional Climates for
Impacts Studies) modeling system (Jones et al., 2004) which has been used to develop regional
climate change scenarios worldwide (Hudson and Jones, 2002; Xu et al., 2006; Zhang et al., 2006).
The HadRM3P simulations have been driven with boundary conditions from HadAM3P (the GCM on
which the CREAS simulations are based) and are run at 40 km resolution. The analysis provides an
opportunity to examine the behavior of the PRECIS model in simulating the mean climatological
features of Mt Elgon, drawing attention to possible systematic errors and biases in the simulation.
2
Study area
Mount Elgon is located on the border of Kenya and Uganda, 140 km North East of Lake Victoria. It is
one of Kenya’s five major water sources that influence the Kenyan, the Ugandan and the wider Nile
Basin ecosystems and is a source for at least 12 rivers and streams. It is an important water
catchment for the Nzoia River which flows to Lake Victoria and for the Turkwel River which flows into
Lake Turkana. It is uniquely split down the middle by the Kenyan-Ugandan border. It is an extinct
shield volcano on the border of Uganda and Kenya, north of Kisumu and west of Kitale (Figure1a and
b). The mountain's highest point, named "Wagagai", is located entirely within the country of Uganda.
The vegetation of Mt. Elgon reflects the attitudinally controlled zonal belts commonly associated with
large mountain massifs. Four broad vegetation communities are recognized, namely: mixed montane
forest up to an elevation of 2500 m, bamboo and low canopy montane forest from 2400 to 3000 m,
and moorland above 3500 m (Scott, 1994).
Climate change effects such as flooding, droughts, reduced water availability, massive soil erosion,
loss of soil moisture and biodiversity loss is likely to put the Mt. Elgon district’s ecosystem services
under severe stress. In addition, land use change from forest to agriculture has reduced volume in the
downstream water flow creating water stress. The Mt. Elgon district’s economy majorly depends on
agriculture, but the agricultural production is inefficient since it is affected by low land productivity due
to decreasing soil fertility, unsuitable agricultural practices and erosion problems.
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Climate change presents a serious threat to smallholder farmers who are already suffering from
increased climate variability. Impacts include longer drought periods and heavier rainfall which in turn
lead to poor crops yields and severe erosion. Elgon's slopes support a rich variety of vegetation
ranging from montane forest to high open moorland studded with the giant lobelia and groundsel
plants. The vegetation varies with altitude. Climate change effects such as flooding, droughts,
reduced water availability, massive soil erosion, loss of soil moisture and biodiversity loss is likely to
put the Mt. Elgon district’s ecosystem services under severe stress. In addition, land use change from
forest to agriculture has reduced volume in the downstream water flow creating water stress. The Mt.
Elgon district’s economy majorly depends on agriculture, but the agricultural production is inefficient
since it is affected by low land productivity due to decreasing soil fertility, unsuitable agricultural
practices and erosion problems.
The Ugandan side of Mt. Elgon National Park (MENP) was formerly gazetted as a natural forest
reserve in 1938, with a variety of wild animals. In October 1993, the Government of Uganda declared
the area a National Park — in an effort to strengthen the conservation status of the ecosystem
(Mugagga et al 2012). Encroachment for cultivation into the National Park is a major threat to the Mt.
Elgon ecosystem, due to the amount of degradation caused by the removal of natural vegetation.
Encroachment has resulted in the destruction of approximately 25,000 ha within the past generation,
or about one fifth of Elgon's forest (Mugagga et al 2011, Mugagga et al 2012). Virtually all of the forest
cover below an elevation of 2000 m has been removed, as a result of encroachment. The breakdown
of civil order in the 1970s and 1980s provided a social and economic climate within which
encroachment was rife (Malpas, 1980; UWA, 2000).
This study was conducted over an area located on the slopes of Mount Elgon on the Eastern Uganda
and western Kenya extending from 34.10 E to 350E, 0.70N to 1.50N (Figure 1). The climate change
concerns for this area include among others recurring dry periods and, or intensive periods of
incessant rainfall; increasing incidences of landslides in the upper elevations and flash flooding
downstream; and high incidences of Malaria being reported. Population pressure leading to
increasing demand for agricultural land and settlement on marginal and steep slopes; incapability of
adjacent local communities to sustain themselves from current livelihood strategies; lack of capital
and incentives for investments into natural resources development; poor markets and marketing
systems; and threats to biodiversity conservation arising from policy and institutional weaknesses are
some of the environmental concerns for the area.
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Figure 1: Mt Elgon area used for the study is marked by the red box
Current climate characteristics
The climate of Mt Elgon area is generally moist to moderate dry. The dry season runs from December
to March, although it can rain at any time. The mean annual rainfall ranges from 1500 mm on the
eastern and northern slopes, to 2000 mm in the southern and the western slopes. The mean
maximum and minimum temperatures are 23° and 15 °C respectively. Mid-slopes oriented towards
the east and north, at elevations between 2000 and 3000 m tend to be wetter than either the lower
slopes or the summit.
The equatorial sector (north of about 5°S) covering most parts of Uganda and Kenya generally exhibit
bimodal rainfall regime, with peaks during March to May (long rains) and October to December (short
rains).The latitude and longitude of Mount Elgon peak is 1.010N and 35.00E, respectively just slightly
north of the equator. Because it lies across the equator, much of the area experiences two rainy
seasons. A longer rainy season starts around March through to June, with the peak occurring from
March to May (MAM) season (Figure 2).
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180.0
160.0
140.0
120.0
100.0
80.0
60.0
40.0
20.0
0.0
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
LTM
Figure 2: The Long Term Mean rainfall over Mt Elgon area showing strong bimodal regime
The shorter rainy season runs from September and tapers off in November or December (Figure 2).
The area also receives substantial amount of rainfall during the northern hemisphere summer i.e.
June – August (JJA) season. This is as a result of incursions of shallow, westerly, moist airmass from
the Atlantic Ocean and the Congo Basin within the Democratic Repuplic of Congo (DRC) (Anyamba,
1984).
3
Methodology
Investigation of trends in the historical meteorological records formed the first objective of this study.
Trend presents the long term movement of the time series. Trend analysis results do not only provide
information on the general direction of observed change but also unravel significant changes that
have occurred over and above the expected natural climate variability and may link them to past
consequences. Record of extremes may provide evidence of a significant shift from the natural trend
to that which is enhanced by, for example, anthropogenic forcing. Since the effects of climate change
are unleashed more through the occurrence of extremes, the analysis of long term records of rainfall
from 3 selected stations within the study area is considered in this study.
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Climate models are adopted in the study of future climate over Mt Elgon area. They use quantitative
methods to simulate the interactions of the atmosphere, ocean, land surface, ice, etc. Notwithstanding
the marked progress made in recent years, particularly with model assessments (e.g., in parts of
Africa, see Christensen et al., 2007, Otieno and Anyah, 2012), the climate of many parts of Africa is
still not fully understood. Climate scenarios developed from Global Climate Models (GCMs) are very
coarse and do not usually adequately capture important regional variations in Africa’s climate.
Future changes in climate depend on Greenhouse gases (GHG) concentration levels which in turn
depend on a number of factors including population growth, economic activity, type of technology
used and policy measures adopted by governments. The future states of these factors cannot be
predicated precisely but scenarios can be made. Scenarios are not predictions but plausible future
states. IPCC has developed six emission scenarios ranging from low (B1) to high (A1F1) emission
scenarios (IPCC 2001).
The credibility of the Regional Climate Models (RCMs) in representing the current and past climate
over eastern Africa region using PRECIS has been undertaken. Details of the PRECIS-RCM can be
obtained from Omondi et al., 2010. Thorough assessment was done before the RCM was employed
to aid in future projection of climate over the region. Thus, the second task in this study was to use the
output of PRECIS simulation runs for the 20th century (present day climate) to study future impacts
over Mt Elgon area. Change in rainfall regimes due to projected change in climate necessitates
comparing the future time series with that of the current or observed climate. RCMs directly provide
future climatic parameters such as rainfall and temperature based on Special Report on Emission
Scenarios (SRES) of the Intergovernmental Panel on Climate Change (IPCC). The use of PRECIS
nested to Global Climate Models (GCMs) is conventionally termed as downscaling. This technique
enables investigation of climate impacts on the study area by extraction of climate change signals at
larger scale and transferring them to local scale while accounting for the signals.
4
Results
4.1
Trend analysis
Analyses of observed insitu rainfall are shown in Figure 3a-3c of MAM and OND seasons for some of
the stations adopted in this study. It is noteworthy that trend analysis is the long-term movement in a
time series. Examination of the trend component in any time series analysis is significant since it
shows whether the time series is stationary or non-stationary. Trend can be linear or non-linear, and
the objective approach to examine this is through graphical and statistical approaches (WMO 1966).
A graph of the time series can indicate whether or not a linear relationship provides a good
approximation to the long-term movement, regression analysis may give the curve of the best fit.
The analysis indicated increasing trend for the two seasons of MAM and OND. The statistical tests
show that the slopes of the regression lines are not statistically significant which are indicative of the
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lack of significant trends. The eastern and southern parts of Elgon area show increasing trends in
rainfall during the two main seasons of MAM and OND. The north western parts represented by
Chebonet, however, show decreasing trend. The JJA seasonal rainfall over this study region
nevertheless, show decreasing trend in all the sub-sectors of Elgon area.
Figure 3a: MAM seasonal rainfall trend over Mt Elgon for (a) Tororo (b) Kitale and Chebonet
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Figure 3b: OND seasonal rainfall trend over Mt Elgon for (a) Tororo (b) Kitale and Chebonet
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Figure 3c: JJA seasonal rainfall trend over Mt Elgon for (a) Tororo (b) Kitale and Chebonet
Projected climate change
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The UK-Met Office PRECIS RCM has been used to characterize projected changes in mean seasonal
climate over Mt Elgon area based on IPCC fourth assessment report (AR4) A2 and B2 scenarios.
Quantitative estimates of the model’s precipitation biases and a more detailed analysis of its mean
annual cycle can be observed in Figure 4, which show simulated and observed precipitation averaged
over the study area. In the northwestern part of Mt Elgon (Figure 4a), the RCM simulates well the
timing of the peak rainfall season, although the amounts are overestimated during the OND season.
In eastern Elgon (Figure 4b), the simulated peak in April occurs in March after the mean observed
peak, while in the second season the model captures correct peaking but underestimates rainfall. In
the southwestern part, the agreement between model and observation is good in timing but the
simulated rainfall is slightly overestimated.
Overall, the analysis shows that the simulations exhibit only a slight or negligible dispersion from
observed. This implies good skill in simulating the annual cycle of precipitation in the area. The
interesting feature in the annual cycle simulated by the model, considering the local characteristics of
the area, is the agreement with the observational annual cycle in almost all seasons. We however,
take note of discrepancy in the model and corrected it appropriately in future projections.
Projection of precipitation over equatorial eastern Africa is more complicated due to physical features
that modify climate systems (IPCC 2007). Precipitation is highly variable spatially and temporally and
data are limited in some regions (IPCC 2007). As indicated by Sivakumar et al. (2005) rainfall
changes in Africa projected by most Atmosphere-Ocean General Circulation Models (AOGCMs) are
relatively modest, at least in relation to present-day rainfall variability. Seasonal changes in rainfall are
not expected to be large. Great uncertainty exists, however, in relation to regional-scale rainfall
changes simulated by GCMs. Over much of Kenya, Uganda, Rwanda, Burundi and southern Somali
there are indications for an upward trend in rainfall under global warming (Thornton et al. 2007). The
increase in rainfall in East Africa, extending into the Horn of Africa, is robust across the ensemble of
GCMs, with 18 of 21 models projecting an increase in the core of this region, east of the Great Lakes
(Christensen et al. 2007).
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Figure 4: Annual cycle of observed and modeled rainfall (a) Tororo (b) Kitale (c) Chebonet. The
black/grey line shows the observed/projected rainfall.
Quantitative estimates of the model’s precipitation biases and a more detailed analysis of its mean
annual cycle can be observed in Figure 4, which show simulated and observed precipitation averaged
over the study area. In the northwestern part of Mt Elgon (Figure 4a), the RCM simulates well the
timing of the peak rainfall season, although the amounts are overestimated during the OND season.
In eastern Elgon (Figure 4b), the simulated peak in April occurs in March after the mean observed
peak, while in the second season the model captures correct peaking but underestimates rainfall. In
the southwestern part, the agreement between model and observation is good in timing but the
simulated rainfall is slightly overestimated.
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Overall, the analysis shows that the simulations exhibit only a slight or negligible dispersion from
observed. This implies good skill in simulating the annual cycle of precipitation in the area. The
interesting feature in the annual cycle simulated by the model, considering the local characteristics of
the area, is the agreement with the observational annual cycle in almost all seasons. We however,
take note of discrepancy in the model and corrected it appropriately in future projections.
Projection of precipitation over equatorial eastern Africa is more complicated due to physical features
that modify climate systems (IPCC 2007). Precipitation is highly variable spatially and temporally and
data are limited in some regions (IPCC 2007). As indicated by Sivakumar et al. (2005) rainfall
changes in Africa projected by most Atmosphere-Ocean General Circulation Models (AOGCMs) are
relatively modest, at least in relation to present-day rainfall variability. Seasonal changes in rainfall are
not expected to be large. Great uncertainty exists, however, in relation to regional-scale rainfall
changes simulated by GCMs. Over much of Kenya, Uganda, Rwanda, Burundi and southern Somali
there are indications for an upward trend in rainfall under global warming (Thornton et al. 2007). The
increase in rainfall in East Africa, extending into the Horn of Africa, is robust across the ensemble of
GCMs, with 18 of 21 models projecting an increase in the core of this region, east of the Great Lakes
(Christensen et al. 2007).
4.2
Projected Mean Precipitation
Table 1 provides the median estimate of projected rainfall for Mt Elgon area based on PRECIS RCM
projections. The RCM projects an increase in mean seasonal rainfall compared to its 1961-1990
averages. Nonetheless, projected precipitation increase varied across seasons (Figure 5).
Table 1: Median estimate of the overall projected rainfall over study area based on PRECIS
Period average
Projected changes
Rainfall intensity
projections
MAM
2030
+0.3%
2050
+0.7%
2100
+1.0%
OND
+0.2%
+0.4%
+1.5%
+1-2%
JJA
+0.1%
+0.3%
+0.5%
+1-2%
31
+1-4%
Climate Support Facility – WO17 – Mission Report
Figure 5: Observed and simulated precipitation anomalies over Mt Elgon area with reference to
the 1961-1990 climatology. The panels represent a) MAM b) OND and c) JJA seasons. The
black vertical lines display the observed twentieth century precipitation from observed data.
The white line shows the mean trend with the darker gray shadings indicating 50% and 95% of
the distribution respectively.
Time series plots for rainfall during the twentieth (observed and simulated) and twenty-first centuries
(simulated) and in each rainy season are shown in Figure 5. A trend toward predominantly positive
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precipitation anomalies is present in the long rains twenty- first century time series. It is noteworthy
that the signal-to-noise ratio in these projections is low.
The spatial plots of rainfall indicate seasonal differences but with generally high percentage increase
over eastern than western parts of Mt Elgon area (Figure 6). The intensity for MAM is highest
compared to other seasons. These projections are in agreement with the fourth assessment report of
the IPCC 2007. An ensemble of the model projected a gradual increase in precipitation anomalies
over the entire area under both A2 and B2 scenarios. For example, over the eastern and western
segments, both OND and MAM seasonal precipitation anomalies showed a positive trend by the
middle of the century under A2 and B2 emission scenarios, respectively. The RCM analysis indicates
seasonal differences with major parts of the area getting wetter during MAM, JJA and OND seasons
by 2100. In MAM, the mean seasonal rainfall is expected to increase, in relation to the 1961-1990
reference period, by 1%, 1.5% and 0.5% during 2030, 2050 and 2100, respectively. In OND, it would
increase by 0.2%, 0.4% and 1.5% while in JJA 0.1%, 0.3% and 0.5%, respectively (Table 1).
The spatial plots of rainfall indicate seasonal differences but with generally high percentage increase
over eastern than western parts of Mt Elgon area (Figure 6). The intensity for MAM is highest
compared to other seasons. These projections are in agreement with the fourth assessment report of
the IPCC 2007. An ensemble of the model projected a gradual increase in precipitation anomalies
over the entire area under both A2 and B2 scenarios. For example, over the eastern and western
segments, both OND and MAM seasonal precipitation anomalies showed a positive trend by the
middle of the century under A2 and B2 emission scenarios respectively. The RCM analysis indicates
seasonal differences with major parts of the area getting wetter during MAM, JJA and OND seasons
by 2100. In MAM, the mean seasonal rainfall is expected to increase, in relation to the 1961-1990
reference period, by 1%, 1.5% and 0.5% during 2030, 2050 and 2100, respectively. In OND, it would
increase by 0.2%, 0.4% and 1.5% while in JJA 0.1%, 0.3% and 0.5% respectively (Table 1).
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Figure 6: Projected % change in mean precipitation over Mt Elgon area (enclosed by the
rectangular box) by 2100 for (a) DJF (b) MAM (c) JJA and (d) OND seasons (base period 19611990 for A2).
4.3
Projected Mean Surface Temperature
Hulme et al. (2001) point out that climate change in Africa is not simply a phenomenon of the future,
but one of the relatively recent past. The Fourth Assessment Report of the Intergovernmental Panel
on Climate Change (IPCC, 2007) indicates that climate model projections for the period between
2001 and 2100 suggest an increase in global average surface temperature of between 1.1°C and
5.4°C, the range depending largely on the scale of fossil-fuel burning within the period and on the
different models used.
Since the first IPCC report in 1990, assessed projections have suggested global average temperature
increases between about 0.15°C and 0.3°C per decade for 1990 to 2005. This can now be compared
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with observed values of about 0.2°C per decade, strengthening confidence in near-term projections
(IPCC, 2007). The climate model simulations under a range of possible emissions scenarios suggest
that for Africa in all seasons, the median temperature increase lies between 3°C and 4°C, roughly 1.5
times the global mean response. Half of the models project warming within about 0.5°C of these
median values (Christensen et al., 2007).
In this study, the annual cycles of temperature show maximum and minimum attained during
December-February (DJF) and June-August (JJA) seasons respectively (Figure 7). The general
direction of the observed temperature is simulated and this shows a better agreement. Unlike
precipitation, surface temperature in the tropical region is generally homogeneous with minimal
variability (King’uyu et al., 2000).
Table 2 shows results of the RCMs projection analyzed for 30 year time slices centered around 2030,
2050 and 2100, and changes were calculated relative to the reference period 1961-1990.
Table 2: Mean estimate of projected temperature for Mt Elgon using RCM
Mean projected changes (o C )
Period average
2030
0.7
2050
2.4
2100
3.2
March - May (MAM)
0.8
2.5
3.5
June - August (JJA)
0.7
2.8
3.8
October - December (OND)
0.9
2.1
3.1
December - February (DJF)
The results of the projection analyzed for 30 year time slices centered on 2030, 2050 and 2100
together with changes were calculated relative to the reference period 1961-1990. The ensemble
mean of the RCM indicates that the area has been experiencing a steady increase in temperature and
will continue to increase in future when both A2 and B2 scenarios are used. The mean temperature is
projected to steadily increase uniformly in all the seasons over the area leading to an approximate
temperature increase of 20C to 30C by the end of the century.
The major shortcoming of the regional model over this area is the slight systematic overestimation of
temperature in almost all seasons. It is important to remark that due to inadequate coverage of
observing stations in these regions, these results should be examined with caution. The
overestimation shown in Figure 8 is due to possibly the representation of physical processes in the
model. The under or overestimation in temperature is also observed in various studies (e.g. Anyah
and Qiu, 2012, Otieno and Anyah, 2013) over the region using regional climate models.
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Climate Support Facility – WO17 – Mission Report
Figure 7: Annual cycle of observed and modeled mean surface temperature (°C) in (a) Tororo
(b) Kitale (c) Chebonet of Mt Elgon area. The black/grey line shows the observed/simulated
temperature.
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Climate Support Facility – WO17 – Mission Report
Figure 8: Projected 2070-2100 surface temperature over Mt Elgon area for (b) MAM (c) JJA (c )
OND season.
In Figure 9, the spatial distribution of mean surface temperature changes by the end of the 21st
century in A2 with reference to the 1961–1990 average depict near uniform increase across the area
of study. This is observed during all the seasons, but the largest warming is projected to occur during
the DJF.
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Climate Support Facility – WO17 – Mission Report
Figure 9: Projected % change in mean surface temperature over Mt Elgon area (enclosed by
the rectangular box) by 2050 for (a) DJF (b) MAM (c) JJA and (d) OND seasons (base period
1961-1990).
5
Conclusions
This study analyzed recent observed trends in rainfall records (1920-2003) and future projection
scenarios using the Regional Climate Model (RCM) known as Providing Regional Climates for
Impacts Studies (PRECIS). The statistical trend analysis of rainfall reveals increasing positive trend in
the long term observed rainfall record over the eastern and southern sectors of Mt Elgon during the
major MAM rainfall season and second most important OND season. The midyear season of JJA
however, showed decreasing rainfall trend. The skill of the high resolution PRECIS RCM in
downscaling of climate information over the area of study showed general agreement in directions of
the annual cycles but with minimal biases. The biases were reduced when several model runs were
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Climate Support Facility – WO17 – Mission Report
used to produce ensemble mean. The projected model results relative to the reference period 19611990 indicates that Mt Elgon area will continue to experience steady increase in temperature under
A2 and B2 IPCC scenarios during all seasons studied. It is noteworthy that future changes projected
by IPCC (Cubasch et al. 2001) show global increase in the surface air temperature projected under all
scenarios and by all models. AOGCM project a global average surface air temperature increase of
+3ºC (ranging from 1.30C to 4.50C) for a “globally high emissions” scenario (referred as the “A2”
scenario), and of +2.2ºC (ranging from 0.9ºC to 3.4ºC) for a “lower CO2 emissions” scenario (referred
as the “B2” scenario) for the period 2071-2100 relative to 1961-1990. The rainfall on the other hand
indicates seasonal differences but with major parts of the area getting wetter during MAM, JJA and
OND seasons by the end of 21st century under similar scenarios. Similar findings by Christensen et al.
2007 show that increase in rainfall over East Africa, extending into the Horn of Africa, is robust across
the ensemble of GCMs, with 18 of 21 models projecting an increase in the core of this region, east of
the Great Lakes. Again increase in rainfall could be due to plausible hydrological response to a
warmer atmosphere, a consequence of the increase in water vapour and the resulting increase in
vapour transport in the atmosphere from regions of moisture divergence to regions of moisture
convergence (IPCC 2007).
References
Ambrizzi, T., Rocha, R., Marengo, J.A., Pisnitchenko, I., Alves, L.M. 2007. Cenários regionalizados de
clima no Brasil para o Século XXI: Projeções de clima usando três modelos regionais.
Relatório
3,
MINISTÉRIO
DO
MEIO
AMBIENTE-MMA,
SECRETARIA
DE
BIODIVERSIDADE E FLORESTAS–SBF, DIRETORIA DE CONSERVAÇÃO DA
BIODIVERSIDADE–DCBio
Mudanças
Climáticas
Globais
e
Efeitos
sobre
a
Biodiversidade-Sub projeto: Caracterização do clima atual e definição das alterações
climáticas para o território brasileiro ao longo do Século XXI. Brasília, Fevereiro 2007.
Anyah, R.O and Qiu, W., 2012. Characteristic 20th and 21st century rainfall and temperature patterns
and changes over the Greater Horn of Africa. International Journal of Climatology, 32, 347–363;
DOI: 10.1002/joc.2270.
Anyamba, E K, 1984. Some aspects of the origin of rainfall deficiency in East Africa. Proceeding of
the WMO regional scientific conference; On GATE, WAMEX and Tropical Meteorology, Dakar,
Senegal, pp 110-112.
Christensen, J.H., Christensen, O.B. 2003. Climate modeling: severe summertime flooding in Europe.
Nature 421:805–806.
Christensen, J.H., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I., Jones, R., Kolli, R.K., Kwon,
W.-T., Laprise, R., Magaña Rueda, V., Mearns, L., Menéndez, C.G., Räisänen, J., Rinke, A., Sarr,
A. and Whetton, P. 2007. Climate change 2007: The physical science basis: Regional climate
projections. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M.
and Miller, H.L. (eds). Contribution of Working Group I. Fourth Assessment Report of the
Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, and
New York, USA.
39
Climate Support Facility – WO17 – Mission Report
Frei, C., Scholl, R., Fukutome, S., Schmidli, J., Vidal, P.L. 2006. Future change of precipitation
extremes in Europe: Intercomparison of scenarios from regional climate models. Journal
of Geophysics Research.doi:10.1029/2005JD005965.
Giorgi F, Mearns L.O. 1999. Introduction to special section: Regional climate modeling revisited.
Journal of Geophysics Research 104(D6): 6335–6352.
Giorgi, F. 1990. On the simulation of regional climate using a limited area model nested in a general
circulation model. Journal of Climatology 3: 941–963.
Hudson, D.A., Jones, R.G. 2002. Regional climate model simulations of present-day and future
climates of southern Africa. Bracknell: Met Office (Note 39).
Hulme, M., Doherty, R.M., Ngara, T., New, M.G. and Lister, D. 2001. African climate change: 1900–
2100. Climate Research 17:2:145–168.
IPCC (Intergovernmental Panel on Climate Change). 2007. The Physical Science Basis. Contribution
of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate
Change. Cambridge University Press, Cambridge, UK, and New York, USA.
Islam S, Rehman, N. 2007. Future change in the frequency of warm and cold spells durations over
Pakistan simulated by the PRECIS regional climate Model, Submitted, Climatic Change
Jones, R.G., Noguer, M., Hassell, D.C., Hudson, D., Wilson, S.S., Jenkins, G.J., Mitchell J.F.B. 2004.
Generating high resolution climate change change scenarios using PRECIS. Met Office
Hadley Centre, Exeter, UK.
King’uyu, S.M., L.A. Ogallo and E.K. Anyamba, 2000. Recent trends of minimum and maximum
surface temperatures over Eastern Africa. J. Climate, 13, 2876-2886.
KNMI (Royal Dutch Meteorological Institute). 2007. Climate change in Africa. Changes in extreme
weather under global warming. Royal Netherlands Institute of Meteorology. (Available from
http://www.knmi.nl/africa_scenarios/ ).
Malpas, R. 1980. Wildlife in Uganda, 1980: a survey. Ministry of Tourism and Wildlife. Gland,
Switzerland: World Wildlife Fund.
Marengo, J., Ambrizzi, T. 2006. Use of regional climate models in impacts assessments and
adaptations studies from continental to regional and local scales: The CREAS (Regional
Climate Change Scenarios for South America) initiative in South America. Proceedings of
8 ICSHMO, Foz do Iguaçu, Brazil, April 24-28, 2006, pp. 291-296.
Marengo, J.A., Alves, L., Valverde, M., Rocha, R., Laborbe, R. 2007. Eventos extremos em cenários
regionalizados de clima no Brasil e América do Sul para o Século XXI: Projeções de
clima future usando três modelos regionais. Relatório 5, Ministério do Meio Ambiente MMA, Secretaria de Biodiversidade e Florestas – SBF, Diretoria de Conservação da
Biodiversidade – DCBio Mudanças Climáticas Globais e Efeitos sobre a Biodiversidade Sub projeto: Caracterização do clima atual e definição das alterações climáticas para o
território brasileiro ao longo do Século XXI. Brasília, Fevereiro 2007.
Marengo, J.A., Jones, R., Alves Muniz, L., Valverde, M. 2009. Future change of temperature and
precipitation extremes in South America as derived from the PRECIS regional climate
modeling system. Internationa Journal of Climatology doi:10.1002/joc.1863
40
Climate Support Facility – WO17 – Mission Report
Meehl, G.A. et al. 2007. Global climate projections. In: Solomon, S., Qin, D., Manning, M., Chen, Z.,
Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (eds) Climate change 2007: The
physical science basis. Contribution of Working Group I to the Fourth Assessment Report
of the Intergovernmental Panel on Climate Change. Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA.
Mugagga F., V. Kakembo, and M. Buyinza, 2012. Land use changes on the slopes of Mount Elgon
and
the
implications
for
the
occurrence
of
landslides.
Catena
90
(2012)
39–46,
doi:10.1016/j.catena.2011.11.004
Mugagga, F., Kakembo, V., Buyinza, M., 2011. A characterization of the physical properties of soil
and the implications for landslide occurrence on the slopes of Mount Elgon, Eastern Uganda.
Natural Hazards. doi:10.1007/s11069-011-9896-3.
Nuñez Solman, M.S., Cabré, M.F. 2006. Mean climate and annual cycle in a regional climate change
experiment over Southern South America. II: Climate change scenarios (2081-2090),
Proceedings of 8 ICSHMO, Foz do Iguaçu, Brazil, April 24-28, 2006, pp. 325-331
Omondi P. A, Ogallo L. A., Muthama, J. N. and Ininda, J., 2010. Teleconnections between Decadal
Rainfall Variability and Global Sea Surface Temperatures and Simulation of future Climate
Scenarios over East Africa, Ph.D. Dissertation, Dept. of Met: University of Nairobi.
Osbahr, H and Viner, D. 2006. Linking climate change adaptation and disaster risk management for
sustainable poverty reduction. Kenya. Country Study. A study carried out for the Vulnerability and
Adaptation Resource Group (VARG) with support from the European Commission.
Otieno V. O., Anyah R.O., 2012. CMIP5 simulated climate conditions of the Greater Horn of Africa
(GHA). Part 1: contemporary climate. Climate Dynamics: DOI: 10.1007/s00382-012-1549-z
Scott, P., 1994. An assessment of natural resource use by communities from Mt. Elgon National Park.
UNDP/Technical Report No. 15, 1994.
Sivakumar, M.V.K., Das, H.P. and Brunini, O. 2005. Impacts of present and future climate variability
and change on agriculture and forestry in the arid and semi-arid tropics. Climatic Change (2005)
70: 31–72.
Solman, S.A., Nuñez, M.N. et al. 2007. Regional climate change experiments over southern South
America. I: present climate. Climate Dynamics 30:533–552.
Thornton, P.K., Jones, P.G., Owiyo, T., Kruska, R.L., Herrero, M., Kristjanson, P., Notenbaert, A.,
Bekele, N. and Omolo, A., with contributions from Orindi, V., Otiende, B., Ochieng, A., Bhadwal,
S., Anantram, K., Nair, S., Kumar, V. and Kulkar, U. 2007. Mapping climate vulnerability and
poverty in Africa. Report to the Department for International Development. ILRI, Nairobi, Kenya
Uganda Wildlife Authority (UWA), 2000. Mt Elgon National Park General Management Plan. UWA,
Kampala, Uganda.
Wang, B., Kang, I.S., Li, J.Y. 2004. Ensemble simulation of Asian-Australian monsoon variability by
11 AGCMs. Journal of Climate 17: 803–81.
Xu, Y.L., Zhang, Y., Lin, E.D., Lin, W.T., Dong, W.J., Jones, R., Hassell, D., Wilson, S. 2006.
Analyses on the climate change responses over China under SRES B2 scenario using
PRECIS. Chinese Science Bulletin 51:2260–2267.
41
Climate Support Facility – WO17 – Mission Report
Zhang, Y., Yinlong, X., Wenjie, D., Lijuan, C., Sparrow, M. 2006. A future climate scenario of regional
changes in extreme climate events over China using the PRECIS climate model.
Geophysics Research Letters. doi:10.1029/2006GL027229.
42