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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) 2 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. 3 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. 4 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. 5 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. 6 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 7 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 8 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). 9 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 10 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 11 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 12 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 13 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. 14 Climate Support Facility – WO17 – Mission Report 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. 15 Climate Support Facility – WO17 – Mission Report 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. 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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. 19 Climate Support Facility – WO17 – Mission Report 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 20 Climate Support Facility – WO17 – Mission Report 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. 21 Climate Support Facility – WO17 – Mission Report 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. 22 Climate Support Facility – WO17 – Mission Report 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). 23 Climate Support Facility – WO17 – Mission Report 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. 24 Climate Support Facility – WO17 – Mission Report 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 25 Climate Support Facility – WO17 – Mission Report 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 26 Climate Support Facility – WO17 – Mission Report Figure 3b: OND seasonal rainfall trend over Mt Elgon for (a) Tororo (b) Kitale and Chebonet 27 Climate Support Facility – WO17 – Mission Report Figure 3c: JJA seasonal rainfall trend over Mt Elgon for (a) Tororo (b) Kitale and Chebonet Projected climate change 28 Climate Support Facility – WO17 – Mission Report 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). 29 Climate Support Facility – WO17 – Mission Report 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. 30 Climate Support Facility – WO17 – Mission Report 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 32 Climate Support Facility – WO17 – Mission Report 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). 33 Climate Support Facility – WO17 – Mission Report 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 34 Climate Support Facility – WO17 – Mission Report 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. 35 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. 36 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. 37 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 38 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. 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