VULNERABILITY, IMPACT AND ADAPTATION ASSESSMENT IN THE EAST AFRICA REGION

CHAPTER 9: WATER, AQUATIC ECOSYSTEMS, AND WATER SUPPLY INFRASTRUCTURE – FUTURE IMPACTS FROM CLIMATE CHANGE

NOVEMBER 2017

This report was produced for review by the United States Agency for International Development. It was prepared by Camco Advisory Services (K) Ltd. under subcontract to Tetra Tech ARD.

This report was produced for review by Camco Advisory Services (K) Ltd. under subcontract to Tetra Tech ARD, through USAID/Kenya and East Africa Contract No. AID-623-C-13-00003.

CAMCO WATER, AQUATIC ECOSYSTEM AND WATER SUPPLY TEAM Prof. Bancy Mati, Dr. Lydia Olaka, Dr. Titus Ndiwa, Dr. Omar Munyaneza, Dr. Sylvester Kasozi, Dr. Dorothy Nyingi, Ms. Rosemary Masikini, Dr. Susan Namaalwa, Ms. Stella Simiyu

TECHNICAL REVIEWERS Prof. Francis Mutua, Mr. Michael Mugarura, Mr. Robert Wanjara

Tetra Tech ARD 159 Bank Street, Suite 300 Burlington, Vermont 05401 USA

Tetra Tech ARD Contacts:

Anna Farmer Korinne Nevins Project Manager Assistant Project Manager Tetra Tech Tetra Tech Burlington, Vermont Burlington, Vermont Tel.: (802) 658-3890 Tel.: (802) 658-3890 [email protected] [email protected]

VULNERABILITY, IMPACT, AND ADAPTATION ASSESSMENT IN THE EAST AFRICA REGION

CHAPTER 8:

WATER RESOURCES, AQUATIC ECOSYSTEMS AND WATER SUPPLY INFRASTRUCTURE – FUTURE IMPACTS FROM CLIMATE CHANGE

NOVEMBER 2017

DISCLAIMER This report is made possible by the generous support of the American people through the United States Agency for International Development (USAID). The views expressed are those of the authors and do not necessarily reflect the views of USAID or the United States Government.

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CONTENTS

CONTENTS ...... iv ACRONYMS ...... v 1. IMPORTANCE OF THE WATER SECTOR...... 1 1.1 Sector Priority in the Five East African Community Countries ...... 1 1.2 Catchment Conservation ...... 2 1.3 Water Availability and Access for Increasing Population ...... 4 1.4 Importance of Hydro-Meteorological Monitoring ...... 5 2. IMPACT OF CLIMATE CHANGE ON THE SECTOR ...... 7 2.1 Impacts on Water Availability ...... 7 2.2 Impacts on Groundwater ...... 8 2.3 Impacts on Water Quality ...... 9 2.4 Impacts on Water Supply Infrastructure ...... 11 2.5 Impacts on Aquatic Ecosystems ...... 11 2.6 Summary of Key Potential Impacts on the Water Sector ...... 12 3. CLIMATE CHANGE AND SECTOR FUTURE PROJECTIONS IN EAST AFRICA .... 18 3.1 Projecting East Africa’s Future Climate ...... 18 3.2 Impacts of Climate Variability and Climate Change on Water Availability in the Lake Victoria Basin ...... 23 3.3 Future Projections of River Discharges into the Lake Victoria Basin...... 28 3.4 Discussion of Results ...... 44 4. ADAPTATION PRACTICES, OPTIONS, AND CONSTRAINTS...... 46 4.1 Emerging Water Resources Issues in the Lake Victoria Basin ...... 46 4.2 Policy, Institutional Frameworks, and Ongoing Initiatives ...... 51 4.3 Gaps in Institutional Capacities for Adaptation ...... 55 REFERENCES...... 57 APPENDIX ...... 63 Appendix I: River Discharge Statistical Results ...... 63

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ACRONYMS

AIC Akaike information criterion EAC East African Community EWS Early warning system IPCC Intergovernmental Panel on Climate Change IWRM Integrated Water Resource Management LVB Lake Victoria Basin LVEMP Lake Victoria Environmental Management Project NAPA National Adaptation Programmes of Action NBI Nile Basin Initiative NELSAP Nile Equatorial Lakes Subsidiary Action Program SAP Strategic Action Plan USAID United States Agency for International Development

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1. IMPORTANCE OF THE WATER SECTOR

1.1 SECTOR PRIORITY IN THE FIVE EAST AFRICAN COMMUNITY COUNTRIES

Water is essential for life, but only 2.53 percent of the world’s available water is freshwater that can be used to sustain societies and the natural ecosystems. People require a reliable and clean supply of drinking water to maintain health and to sustain agriculture, energy production, navigation, recreation, and manufacturing. These competing water demands put pressure on water resources and the water systems that keep ecosystems thriving and feed a growing human population. But rivers, lakes, and aquifers are drying up or becoming polluted. More than half the world’s wetlands have disappeared since 1900 (Barbier 1993). Climate change is likely to exacerbate these stresses. In many areas, climate change is likely to increase water demand while shrinking clean water supplies. This shifting balance will challenge water managers to simultaneously meet the needs of a growing population, sensitive ecosystems, irrigation supply, energy producers, and manufacturers.

Changes in climatic parameters have a direct impact on the social, economic, and environmental well-being the community. Major droughts in East Africa have resulted in sharp reductions in agricultural output, productive activity, and employment. This has lowered agricultural export earnings and led to other losses associated with decline in rural income, reduced consumption and investment, and destocking with additional multiplier effects on the monetized economy. Major droughts were recorded in East Africa in 1970, 1975, 1979–80, 1989–90, 1999/2000, and 2005. When weighted by impact on gross domestic product (GDP), drought poses a substantially higher impact risk than floods (EAC 2009). The economic impacts of drought in the East African Community (EAC) member states are summarized in Table 1.

Table 1: Economic Impacts of Drought in the East African Community Drought Rainfall deficiency Agricultural GDP GDP loss in Loss on export year (%) loss in % % earning 1970/71 15.2 0.50 0.07 17.00% 1979/78 22.0 1.58 1.13 7.98% 1980/83 29.0 27.00 10.00 20.00% 1990/91 10.2 (0.22) 0.43 17.50% 1992/94 11.9 3.64 (1.60) (9.00%) 1999/00 7.0 11.18 1.44 (8.48%) Source: Economic Impact of Climate Change in the East African Community—Final Report 2009.

Water and sanitation are at the core of sustainable development. Already water security for the EAC countries is low and is projected to be lower by 2025. The Sustainable Development Goals require countries to ensure availability and sustainable management of water and sanitation for all by 2030. Therefore, solutions that would spur progress toward long-term availability for all should be prioritized.

The EAC countries have outlined some priority areas of intervention to ensure sustainability in the water sector. The key priorities include catchment conservation, water availability and access, rainwater harvesting, and setting up hydro-meteorological stations. The five EAC Partner States

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have identified these priorities in their respective National Adaptation Programmes of Action (NAPAs) and National Vision Statements (Box 1).

Box 1: Water Sector as a Key Priority in the 5 EAC Partner States

v Burundi has as priorities, the popularizing of rainwater harvesting techniques, setting up mechanisms to control erosion-sensitive areas, and protecting strategic buffer zones around lakes in Bugesera (Republic of Burundi 2007). v Kenya’s Vision 2030 proposes development of two multipurpose water conservation structures along Nzoia and Nyando rivers, as well as implementation of a sewage initiative and water catchment management (Government of Kenya 2007). v Rwanda will adopt integrated water resource management (IWRM) and establish information systems for hydro-agrometeorological early warning and rapid intervention (Republic of Rwanda 2007). v Tanzania has prioritized development of water harvesting and storage programs for rural communities, particularly those in dry lands, and community-based catchment conservation and management programs, as well as water harvesting and recycling (United Republic of Tanzania 2007). v Uganda is scaling up safe water supply and sanitation using appropriate technologies, promoting community best practices of collaborative water resource management, and promoting appropriate and sustainable water harvesting, storage, and utilization technologies (Republic of Uganda 2007).

1.2 CATCHMENT CONSERVATION

Degradation of catchments threatens sustainability of water resources that provide domestic water, food, and energy for cities and their surrounding areas. Good catchment management is central to protecting basins, so restoring those already degraded should be a priority. The Lake Victoria Basin (LVB) supports about 28 million of the poorest rural inhabitants in the world (ICRAF 2006). Studies have shown that the magnitude of land degradation is linked to poverty levels in the basin.

Throughout the EAC countries, river catchments face threats, including siltation of the river from soil erosion and turbidity of water and floods caused by excess runoff from upstream; encroachment and degradation of river banks by cultivation; encroachment of waterways and eutrophication by invasive water hyacinth; degradation of riverine forests and the unique biodiversity and ecosystem of the groundwater forest in floodplains and encroachment of wetlands with a loss of their flow-regulating and filtering functions. This is a result of population pressure. Population in the Lake Victoria catchment more than doubled between 1980 and 2015, growing at a significantly higher pace than the rest of the region (Figure 1a and b). All these people required land for settlement, farming, timber and charcoal burning, activities that increase overland flow, flash floods, river pollution and soil erosion, and landslide and mudslide risks. This also occurred in the Mau catchment, which is the source of several rivers draining to Lake Victoria, as well as the Bugesera region, and Kagera catchment. Therefore, good catchment management and river conservation within the EAC countries is central to many issues also affecting the other sectors, such as water pollution and health, river siltation, and hydroelectric energy production.

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Figure 1: Projections—Population of Lake Victoria Basin

Figure 1a. Total population and projections in thousands for the EAC countries for the period 1950–2100 (source: United Nations, Department of Economic and Social Affairs, Population Division, 2015).

Figure 1b: Population Density Distribution and Trend Surrounding Lake Victoria, 1960– 2010 The NAPAs for the EAC countries mention projects that conserve the catchments, from reforestation and planting riparian vegetation to sustainable land management and Integrated Watershed Management. Among these initiatives are reforestation projects in Tanzania on Mount Kilimanjaro. In Kenya, the Forest Services and the African Wildlife foundation are working to reforest over 1,000 hectares of the Mau Forest with 1 million trees. Land degradation management projects are ongoing throughout Uganda. And in Burundi, reforestation is under way in the degraded zones of the subalpine stage of the Congo-Nile watershed. Plantation zones are being created in the degraded zones of the thickets of Murehe and mountain chains of Ruyigi and Cankuzo and identification and popularization of drought-resistant forest species are ongoing.

While these programs are useful and important, sustainability and conservation of these catchments may require other reforms, such as in land tenure, to ensure that responsibility is clear for land management decisions. Further, the riparian community must be involved as they use the forest resources for fuel. This can be done through training in sustainable land management practices that would promote off-farm, high-value livelihood activities that require less space, such as bee and rabbit keeping. Another key issue for sustainability is provision and promotion of

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alternative sources of fuel, such as charcoal briquettes made from charcoal dust, water, and a binding agent such as soil, paper, or starch. This would save trees that would have been otherwise cut down. Agricultural inputs, such as phosphate and nitrogen fertilizers, need to be used appropriately to avoid eutrophication of the lake.

1.3 WATER AVAILABILITY AND ACCESS FOR INCREASING POPULATION

Water is the primary medium through which climate change influences the Earth’s ecosystems and thus the livelihood and well-being of societies. Availability of water in an area mainly depends on two interlinked factors: rainfall and internal renewable resources. The renewable resources are replenished by rainfall, and if the rains fail, the groundwater stocks are not replenished. For water management to be sustainable, withdrawals must be carefully managed to ensure that water is not overused. Global climate change is expected to exacerbate current and future stresses on water resources from population growth and land use, and increase the frequency and severity of droughts and floods. It is anticipated that climate change will affect the availability of water resources through changes in rainfall distribution, soil moisture, glacier and ice/snow melt, and river flows and groundwater levels.

The total renewable water resources for East Africa is estimated to be 212.91 x109 cubic meters per year (FAO 2016). The sources of water are mainly rivers, lakes, rainfall, and groundwater. Surface water is mostly used for domestic supply; however, surface water sources are often highly polluted, and infrastructure to pipe water from fresh, clean sources to arid areas is too costly. Groundwater is the best resource to provide clean water to most areas in Africa, especially rural Africa (Awuah et al. 2009). It is naturally protected from bacterial contamination and is a reliable source during drought. Freshwater availability across EAC countries is not uniform due to both climatic and non-climatic issues, such as supply infrastructure and contamination. Hence, Burundi is classified as water-scarce while Rwanda and Kenya are water-stressed, and Uganda and Tanzania have relative sufficiency. This unequal distribution is a limiting factor for both agricultural and industrial growth, it also is a probable source of conflict between countries and a source of competition between rural community needs and urban industrial sector. Per capita water demand for freshwater is rising as prosperity increases and by 2025 there could be 3 billion living in water- stressed countries, compared to 700 million in 2006 (UNDP 2006).

Climate and water resources interact in two obvious ways: (i) rainfall drives run-off and recharge; and (ii) temperature, wind, humidity, and other climatic factors drive evapotranspiration and water demand. Changes in precipitation patterns will automatically affect each source and the consequences could be devastating depending on the magnitude. Managing water scarcity entails dealing with scarcity with the intention of overcoming it, either by supply-side increases or demand-side regulation. Various initiatives have been proposed by the EAC Partner States to increase water availability and accessibility, such as developing alternative water storage programs for both surface and subsurface water, water harvesting technologies for communities, and strengthening IWRM. Rainwater harvesting is a sustainable technique to raise the amount of available water in water-scarce areas.

Negative changes in water availability may have negative consequences for the rich biodiversity, both flora and fauna, in East Africa. Water managers should consider providing water for the competing water needs, such as human activities and protecting ecological resilience. This could be accomplished by establishing environmental flows for various rivers and watersheds that humans, animals and ecosystem rely on for survival (Pallangyo 2013).

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The EAC countries have plans to industrialize by 2040, which means that demand for water for domestic, industrial, and irrigation is going to increase. Thus, water managers and governments should think of ways to increase water supply while also avoiding disasters, by increasing water storage facilities, channelization, river modification, recycling of gray water, and reducing wastage.

1.4 IMPORTANCE OF HYDRO-METEOROLOGICAL MONITORING

Hydro-meteorological monitoring and access to historical climate data are critical to efficiently manage water resources. They are also important in forecasting and providing useful information for disaster risk reduction (hydro-meteorological–related hazards) and planning for agricultural, construction, tourism, health, and other environmental sectors.

Inadequate observational data in Africa remains a systemic limitation with respect to fully estimating future freshwater availability (Nuemann et al 2007, Batisani 2011). Three main issues are involved:

v The observation network is hindered by a lack of instruments, shortage of trained personnel, and in some cases problems associated with security. The telecommunications systems, as well as the infrastructure and facilities, have continued to deteriorate, leading to great difficulties in providing weather and climate services to meet national and regional needs (EAC 2004).

v The available hydrologic and meteorological data are scattered and are not arranged in a convenient way for users to access. Most data sets are often incomplete and are restricted to the public. There is often a lack of communication within countries and regions, creating data gaps at multiple levels. In some regions, historical data are recorded on paper and not cataloged electronically and are inaccessible to users (TAHMO 2016), and the temporal scale of data collection is not uniform across regions monitoring the same resources.

v Climate information is not widely used by water managers.

Detection of and attribution to climate change are difficult given that freshwater resources are governed by multiple interacting drivers and factors, such as land use change, water withdrawals, and natural climate variability. Additionally, the growing threats of hydro-meteorological disasters such as droughts, floods, water-related epidemics, and storms will affect the availability, supply, and quality of freshwater resources in East Africa. Unfortunately, a clear understanding of the processes and the ability to find appropriate adaptation strategies is hampered by inadequate monitoring data.

Specifically, plans for strengthening the core infrastructure for observation, telecommunications, and operational forecasting have been outlined. The installation of rainfall and streamflow gauges and use of real-time gridded rain data over regions will also help in early warning and potential risk assessment by all EAC Partner States. State-of-the-art monitoring equipment is required to manage information about the state, quality, and forecasts for water resources. This information should be available in real time. Building the capacity of water managers and mentoring the next generation to handle the large data sets that will be generated is also required. Here, the community can also be used for gathering information in remote areas and monitoring the health of the river. An innovative approach that involves school children in monitoring river health has

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been initiated in South Africa by the national Aquatic Ecosystem Health monitoring program (NAEHMP 2010).

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2. IMPACT OF CLIMATE CHANGE ON THE SECTOR

2.1 IMPACTS ON WATER AVAILABILITY

Climate and water resources interact in two ways: (i) rainfall drives run-off and groundwater recharge; and (ii) temperature, wind, humidity, and other climatic factors drive evapotranspiration and water demand. Water is essential for livelihoods, so any changes to its quantity or quality affects other sectors of the economy, including energy, infrastructure, health, and agriculture. In rivers with dams for hydroelectric power production, variability in streamflow will lead to variability in energy production. Climate change impacts on water supply and quality also affect tourism and recreation in rivers and lakes. Agriculture and livestock, key economic sectors in East Africa, are especially dependent on water. Heavy rainfall and flooding can damage crops, increase soil erosion, and delay planting. Areas that experience frequent droughts will have less water available for crops and livestock.

Changing water supply due to climate change will challenge planners in many sectors. They will likely adopt a variety of adaptation practices designed to conserve water supplies and improve water recycling, and they will likely develop alternative strategies for water management. Solutions for water security that incorporate natural infrastructure can enhance efficiency, effectiveness, and equity, but they can also spur implementation and progress toward long-term availability of water for all. Potential benefits include improved sanitation and wastewater management.

Climate change and climate variability may have both negative and positive impacts on water resources and the water sector (Table 2). Changes in rainfall patterns leading to heavy rainfall events, flooding, or droughts affect water supply, quality, and availability as well as freshwater ecosystems and their functions.

Warmer temperatures in the future will accelerate the hydrologic cycle, which in turn will alter precipitation amounts and the magnitude and timing of runoff as well as the intensity and frequency of floods and droughts. Higher temperatures will increase the energy available for evaporation, which will alter soil moisture and infiltration rates. River flows and recharge will be altered, which will eventually affect the catchment water balance (EAC 2011). Further, fewer rainfall days with more intense rainfall may lead to increased flooding, landslides, and associated impacts. However, increased precipitation also will boost groundwater systems in selected areas. Though the rainfall quantity may increase by the end of 21st century, the temporal rainfall distribution may not be adequate to meet future water demand.

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Table 2: Projected Impacts on the Water Sector due to Changes in Climate and Extreme Weather Events over the 21st Century (without considering adaptive capacity) Phenomenon and direction Likelihood of future Examples of major projected of trend trends based on phenomenon impacts on water projections for 21st sector century using special report on emissions scenarios Over most land areas, warmer and Likely Effect on water towers with snow fewer cold days and nights warmer and more frequent hot days and nights Frequency of warm spells increases Very likely Increased water demand and water over most land areas quality problems, algal blooms Frequency of heavy precipitation Very likely Adverse effects on surface and events increases over most areas groundwater quality; contamination of water supply; per capita water might increase Areas affected by drought increase Very likely Widespread water stress

2.2 IMPACTS ON GROUNDWATER

Groundwater resources are important for meeting the water demands for domestic, agricultural, and industrial uses in the EAC (Green et al. 2011). The total renewable resource is estimated to be 76.97 x 109 cubic centimeters (FAO 2016), mostly in volcanic and sedimentary rocks. Groundwater dynamics are connected to the dynamics of surface water. The projected precipitation and temperature changes will affect them both, though the magnitude and timing of runoff, base flow, and aquifer recharge will vary from the river flows. Higher temperatures will increase the energy available for evaporation, which will alter soil moisture and infiltration rates. However, changes in the magnitude of groundwater recharge will not always be in the same direction as precipitation changes. Recharge is influenced not only by the magnitude of precipitation but also by its intensity, seasonality, frequency, and type (Clifton et al. 2010). All these changes will eventually affect the catchment water balance.

Climate change will modify groundwater discharge, storage, biogeochemical reactions, and the fate and transport of chemical fluxes. The most noticeable impact will be changes in groundwater quality and quantity. Such changes already have been identified in aquifers and aquifer systems worldwide (Hanson et al 2006, Ngongondo 2006, White et al. 2007). Becker et al. (2010) also showed that groundwater storage around the Lake Victoria is directly related to the El Niño Southern Oscillation cycle, which increases water storage.

Prolonged heavy rainfall and concurrent river flooding can lead to groundwater flooding, which might occur when aquifer water levels (water table) rise to above ground level, as projected for most of the catchment by 2030. Because of the delayed response in vertical and horizontal flow in aquifers, flooding often takes place sometime after the causative rainfall events, and may persist for some time (days, weeks) after.

Climate variability and change influences groundwater systems both directly through replenishment by recharge and indirectly through changes in use, such as increased abstraction in

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times of drought and non-availability of surface water. These impacts also can be modified by non- climatic factors, such as land use change (Taylor et al. 2012). Thus, management of groundwater under the pressures of climate change and human activities would require knowledge of the functioning of the groundwater systems and their interactions and interlinked external factors. However, groundwater research in the EAC lags behind research on the climate change impacts on surface water. With the limited existing data, knowledge of the potential interactive effects of climate change on groundwater resources is a major gap for the sector (UNESCO 2008).

Over many areas groundwater recharge is projected to increase in the warming world, but many semi-arid areas that already suffer from water stress may face decreased groundwater recharge (Kundzewicz and Döll 2009). For Lake Victoria, the subsurface water component has two peaks, which occurred in mid-2005 and late 2006. The volume of water in the basin is also mainly governed by the surface water of the lake. There is a positive correlation between surface water volume and total water storage in the basin. Surface water volume showed a sharp increase from 2006 to 2007 (Figure 2). Climate projections for the region show that rainfall is likely to increase in the LVB; if the runoff and infiltration parameters are not modified, there would be increased groundwater recharge. Where increases in heavy rainfall events are projected, floods may wash away sanitation facilities, spreading wastewater and potentially contaminating groundwater resources. This may lead to increased risk of diseases especially in areas where pit latrines are used (Taylor et al. 2012).

Figure2: Left: changes in volume (km3) of the GRACE TWS1 in basin (square) and surface water of the lake from altimetry satellite (circle). Right: changes in volume (km3) of the soil moisture and groundwater: GRACE-Altimetry (diamond) and WGHM2 outputs (source: Becker et al. 2010) In a changing climate, sectors that depend on groundwater systems, such as irrigated agriculture, aquatic ecosystems, and rural and urban communities will be affected.

2.3 IMPACTS ON WATER QUALITY

Water quality in the EAC’s in rivers, lakes, and wetlands is influenced by both temperature and flow regime and is therefore vulnerable to climate change. Because oxygen solubility in water has an inverse relationship with water temperature, this, together with increased biological oxygen

1 GRACE TWS, The Gravity Recovery And Climate Experiment Total water storage 2 WGHM WaterGAP Global Hydrological Model

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demand of water systems, could lead to depressed levels of dissolved oxygen in many water systems (Kalff 2000).

Increased water temperatures in water bodies lead to reduced water quality due to transport of sediments, nutrient and pollutant loading, saltwater intrusion of aquifers, and other water bodies (EAC 2011). Increases in heavy rainfall and temperature are projected to change soil erosion and sediment yield, although the extent of these changes is highly uncertain and depends on rainfall seasonality, land cover, and soil management practices (IPCC 2014). Furthermore, high temperatures in rivers and lakes could lead to enhanced growth of algal blooms, hence affecting the levels of dissolved oxygen and the color, odor, turbidity, and pollutant and solid content (Ficke et al. 2007) (Figure 3).

Surface water resources are more vulnerable to climate change, as lower flows will imply less volume for dilution and, hence, higher concentrations of nutrients and other pollutants downstream of discharge points. For lake systems, more intense rainfall and flooding is likely to result into increased suspended solids, sediment yields, and associated contaminant metal fluxes as well as nutrient loads from degraded catchments (Swallow et al. 2009), leading to higher costs of treatment for municipal water supplies. Low levels of precipitation and increased temperatures will result in drought. This will lead to increased concentration of pollutants, as many East African countries depend on aquatic systems to purify their liquid effluents and wastes. Soil erosion will increase turbidity in aquatic ecosystems.

Figure 3: Winam Gulf affected by increased siltation and suspended sediments, the figure on the left is for 1986 and right is for 2001. Highly affected areas appear in red, least affected areas in blue (source: UNEP 2014). Increasing population has resulted in human encroachment on water catchment areas resulting in deforestation due to land conversion and cultivation of fragile environments. As the population increases, demand for land and informal settlements with no proper water supply and sewerage system will increase. These factors will contribute to degradation of water sources affecting both water quantity and quality, causing soil erosion and pollution and several threats to aquatic resources. Most rivers and watercourses in the region are not gauged and the rural and peri- urban water supply is not considered an essential part of most water treatment systems.

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Adaptation and planning of water resources is difficult, with poorly developed water quality monitoring infrastructure.

2.4 IMPACTS ON WATER SUPPLY INFRASTRUCTURE

Increases in heavy precipitation events could cause problems for water infrastructure, as sewer systems and water treatment plants are overwhelmed by the increased volumes of water. Heavy downpours can increase the amount of runoff into rivers and lakes, washing sediment, nutrients, pollutants, trash, animal waste, and other materials into water supplies, making them unusable, unsafe, or in need of treatment.

Water supply infrastructure, including water treatment plants, pipelines, and dams for irrigation will be affected by floods, sedimentation due to excessive runoff, as well as other climate hazards that will lead to huge financial loss. This has ramifications for public health, agricultural productivity, hydropower generation, fisheries, and socioeconomic development

Water-related hazards account for 90 percent of natural hazards the frequency and intensity of which are generally rising, with serious consequences for economic development. Between 1990 and 2000, natural disasters in several developing countries caused damage valued between 2 and 15 percent of their annual GDP. The cost of adapting to the impact of a 2°C rise in global average temperature could range from US$70 to US$100 billion per year between 2020 and 2050. Of these costs, between US$13.7 billion (drier scenario) and US$19.2 billion (wetter scenario) will be related the water sector, predominantly water supply and flood management (WWAP 2015).

2.5 IMPACTS ON AQUATIC ECOSYSTEMS

Climate variability already has been observed to have an impact on water resources and hydrology in East Africa. In the Great Lakes region (lakes Albert, Edward, Kivu, Malawi Tanganyika, and Victoria), deep-water temperatures have risen by 0.2–0.7°C since the early 1900s (IPCC 2007), changing their physico-chemical parameters. Many terrestrial, freshwater, and marine species have shifted their geographic ranges, seasonal activities, migration patterns, abundances, and species interactions in response to climate variability.

Receding shoreline and water levels in Lake Victoria are an effect of the changing climate combined with human activities. Similar observations have been reported in the Mara River systems, which have significantly reduced in the past ten years. Some local streams have completely dried up and some have reduced water levels and have become more seasonal. This decrease has negatively impacts on aquatic biodiversity (EAC 2011).

Climate change will affect aquatic systems by increasing water temperature, altering streamflow patterns, and increasing storm events. Water temperatures will rise in response to increased temperatures. Most aquatic organisms are ectothermic, so the temperature of their environment is important for their physiology, bioenergetics, behavior, and biogeography (Sweeney et al. 1992, Rahel 2002). Increased temperatures in aquatic habitats are predicted to affect these processes. Within the Pangani River Basin, the predicted 1.8–3.6°C increase in temperature will result in 6– 9 percent reductions in annual flow of the Pangani River with impact on the aquatic ecosystems.

These changes are expected to have profound effects on the functioning and, therefore, on the productivity of aquatic ecosystems. Altered patterns of runoff due to climate change will fundamentally modify many aquatic ecosystems and affect their functions (Poff et al. 2002). Aquatic

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goods and services are a significant source of revenue, employment, and proteins in all East African countries. Many individuals within the LVB depend on aquatic ecosystems for food (meat and fish), building materials (typha and papyrus reeds), and energy (timber and biomass). Decreased water levels in aquatic ecosystems will negatively affect these services.

Climate change and climate variability may affect the aquatic ecosystem both negatively and positively. Changes in rainfall patterns leading to heavy rainfall events, flooding, or droughts affect water supply, water quality, and availability, as well as freshwater ecosystems and their functions. However, change in precipitation due to climate change is concerning, as water sustains both terrestrial and aquatic biodiversity, fish reproductive patterns, distribution of macro-invertebrates and amphibians, and migration patterns for migratory water birds (EAC 2011).

For many rivers, hydrological flows are predicted to be highly variable. This will lead to high floods and drought. Such changes in magnitude and temporal distribution of extreme events will affect the survival of the aquatic ecosystems even more than changes in the mean conditions. This will be especially problematic where ecosystems are fragile, for example, where wetland vegetation is used for grazing during drought periods. Long droughts could have severe impacts for those who depend on that resource.

Climate change and climate variability will affect aquatic ecosystems and biodiversity at different scales of biological organization (i.e., genes, populations, species, communities, and ecosystems) and spatial scales (e.g., habitat, local, national, and regional). Changes in precipitation influence the hydrological regimes of freshwater ecosystems, so any considerable change in the amount and timing of precipitation is likely to have both direct and indirect effects on the characteristics of freshwater ecosystems, as well as on aquatic organisms inhabiting these ecosystems (Poff et al. 1997).

2.6 SUMMARY OF KEY POTENTIAL IMPACTS ON THE WATER SECTOR

2.6.1 Water Resources

v Climate and water resources interact in two ways: (i) rainfall drives runoff and recharge; and (ii) temperature, wind, humidity, and other climatic factors drive evapotranspiration and water demand. The impacts of climate change on water resources in some ways might be beneficial. For instance, high amounts of rainfall may recharge aquifers, but their effects may be diluted by high temperature and high abstraction rates during droughts. Increased annual runoff may produce benefits for a variety of in-stream and out-of-stream water users by increasing renewable water resources, but it may also generate harm by increasing flood risk in low-lying areas.

v The consequences of major droughts are sharp reductions in agricultural output, productive activity, and employment, with serious consequences for regional economies. The EAC Partner States face largely similar challenges in water resources. These are mainly droughts, floods, erratic rains, and water-borne diseases, but also include poorly developed water supply infrastructure. The impacts are likely to reduce agricultural export earnings and lead to losses associated with decline in rural income, reduced consumption and investment, and destocking with additional multiplier effects on the monetized economy. The impacts will be compounded by limits to adaptation among vulnerable communities. Pastoralists, for example, have few adaptation options.

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v Water scarcity and its negative impact on socioeconomic development of the EAC and specifically the LVB can be attributed to changes and variations in rainfall, which is the main source of both surface and subsurface water in the region. Climatic variability is already apparent in East Africa, which has undergone periods of both prolonged drought and of high rainfall changes and variations, resulting in prolonged floods. Future projections by the Intergovernmental Panel on Climate Change (IPCC) include warming from 0.2°C (low scenario) to more than 0.5°C per decade (high scenario), a 5–20 percent increase in precipitation from December to February (wet months) and a 5–10 percent decrease in precipitation from June to August (dry months) (IPCC 2001, 2007). Overall, annual mean rainfall in East Africa is projected to increase during the first part of the century. Changes precipitation or melting snow are altering hydrological systems, affecting water resource quantity and quality.

v Climate-related impacts on water resources and aquatic ecosystems will severely affect not only households but whole communities. Owing to the decrease of the water supply for domestic use, livestock and crop production, hydropower production, and industrial use, water-use conflicts may occur at different levels. Water stress will result in increased, protracted, and complex water allocation and rights conflicts between farmer and pastoralist communities, domestic and industrial users, users upstream and downstream within and between basins, states sharing transboundary water resources, and the public and private sectors among others. Increased migration may lead to other resource conflicts as well as exacerbation of other climate-related challenges, such as diseases, food insecurity, land degradation, and over- exploitation. Reduction in rainfall may compel people to encroach on marginal lands, such as water catchment areas, wetlands, and mountain ecosystems for cultivation and livestock grazing. This has already been observed in some parts of the region. Because of poor water management, water-borne diseases cause the highest morbidity and infant mortality in the region.

v Adaptation and planning of water resources is difficult, as many African countries have poorly developed water quality monitoring infrastructure. Another factor is the increasing population, which has resulted in human encroachment on water catchment areas resulting in deforestation due to land conversion and cultivation of fragile environments. These factors have contributed to degradation of water sources reducing both water quantity and quality, causing soil erosion and pollution, and threats to aquatic resources. Most of the rivers and watercourses are not gauged, and the rural and peri-urban water supply is not considered an essential part of most water treatment systems.

v Fewer rainfall days with more intense rainfall may lead to increased flooding, landslides, and associated impacts. However, increased precipitation will boost groundwater systems in selected areas. Though rainfall quantity may increase by the end of the 21st century, the temporal rainfall distribution may not be adequate to meet future water demand.

v Climate variability and change influences groundwater systems both directly through replenishment by recharge and indirectly through changes in groundwater use such as increased use of groundwater in times of drought and non-availability of surface water. These impacts can be modified by human activity such as land use change. Thus, management of groundwater under the pressures

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of climate change and human activities would require knowledge of the functioning of the groundwater systems and their interactions and interlinked external factors.

v Climate change is also bound to modify groundwater discharge, storage, saltwater intrusion in coastal areas, biogeochemical reactions, and the fate and transport of chemical fluxes. The most noticeable impact of climate change is the change in groundwater quality and quantity. Though this is recognized, groundwater research still lags research on climate change impacts on surface water. Due to currently limited data, knowledge about the interactive effects of climate change on groundwater resources is a major gap in understanding the availability and sustainability of groundwater resources. In the past decade, water resource managers and politicians have begun to recognize the important role of groundwater resources in meeting the water demand for domestic, agricultural, and industrial users.

v Water quality is vulnerable to climate change in the EAC Partner States. This is because the quality of water in rivers, lakes, and wetlands is influenced by both temperature and flow regime, hence the wide temporal variation. Climate change is expected to increase river and lake water temperatures. Because oxygen solubility in water has an inverse relationship with water temperature, this, together with increased biological oxygen demand of water systems, could lead to depressed levels of dissolved oxygen in many water systems. The increase in water temperatures in water bodies lead to reduced water quality in the rivers, lakes, groundwater and other water resources due to transport of sediments, nutrient and pollutants loading, and saltwater intrusion of aquifers and other water bodies. Furthermore, high temperatures could lead to enhanced growth of algal blooms in rivers and lakes, hence affecting the levels of dissolved oxygen and aesthetic quality of water in terms of color, odor, turbidity, and solids content.

v The impact of climate change and climate variability may be both negative and positive on water resources and the water sector. Changes in rainfall patterns leading to heavy rainfall events, flooding, or droughts affect water supply, water quality, and availability, as well as freshwater ecosystems and their functions. Change in precipitation due to climate change is of concern, as water sustains both terrestrial and aquatic biodiversity, fish reproductive patterns, distribution of macro-invertebrates and amphibians, and migration patterns for migratory water birds.

v Water supply infrastructure, including water treatment plants, pipelines, dams for irrigations will be affected by extreme floods, sedimentation due to excessive runoff, as well as other climate hazards, and lead to huge financial losses. This has ramifications for public health, agricultural productivity, hydropower generation, fisheries, and socioeconomic development. Increases in heavy rainfall and temperature are projected to change soil erosion and sediment yield, although the extent of these changes is highly uncertain and depends on rainfall seasonality, land cover, and soil management practices.

2.6.3 Aquatic Ecosystems v Climate change will affect aquatic systems by increasing water temperature, altering streamflow patterns, and increasing storm events. As air temperatures rise, water temperatures will also increase. Most aquatic organisms are ectothermic, so temperature is important to their physiology, bioenergetics, behavior, and biogeography. Therefore, increased temperatures in aquatic habitats are predicted to affect these

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processes. Various studies indicate long-term trends of increase in water temperatures of lakes Albert, Edward, Kivu, Nyasa, Tanganyika, and Victoria. Within the Pangani River Basin, the predicted 1.8–3.6°C increase in temperature will result in 6–9 percent reductions in annual flow of the Pangani River.

v Apart from affecting aquatic biodiversity, these changes are expected to have profound effects on the functioning and consequent productivity of aquatic ecosystems. Climate change will modify patterns of precipitation, evapotranspiration, and runoff. Altered patterns of runoff will fundamentally modify many aquatic ecosystems and affect their functions. Aquatic goods and services represent a significant source of revenue, employment, and proteins in all East African countries. Many individuals within the LVB depend on aquatic ecosystems for food (meat and fish), building materials (typha and papyrus reeds), and energy (timber and biomass). Decrease in water levels in aquatic ecosystems will negatively affect these services.

v Climate change and climate variability will affect aquatic ecosystems and biodiversity at different scales of biological organization (i.e., genes, populations, species, communities, and ecosystems) and spatial scales (e.g., habitat, local, national, and regional). Changes in precipitation influence the hydrological regimes of freshwater ecosystems, so any considerable change in the amount and timing of precipitation is likely to have both direct and indirect effects on the characteristics of freshwater ecosystems, as well as on aquatic organisms inhabiting these ecosystems.

2.6.4 Biodiversity

v Climatic changes will result in biodiversity loses through species extinction. Studies of freshwater ecosystems have shown that freshwater biodiversity is highly vulnerable to climate change with extinction rates and extirpations of freshwater species are predicted to match or even exceed those suggested for better-known terrestrial taxa (Heino et al. 2009).

v Climate change and reduced precipitation will lead to reduced resilience of aquatic plants, resulting in the disappearance of aquatic habitats, especially during dry months (Vanacker et al. 2005).

v A change in the intensity and duration of the rainy versus dry seasons could change relative breeding rates and, hence, genetic structures in aquatic populations (Poole 1989).

v Climate change has the potential to alter migratory routes (and timings) of species that both use seasonal wetlands, such as migratory birds, and track seasonal changes in vegetation, such as herbivores such as hippopotamus, leading to human-wildlife conflicts (Thirgood et al. 2004).

v Projections indicate that fish species inhabiting streams will be severely affected by climate change. Similarly, riverine fish species subjected to climate change and variability tend to react by restricting their range or by colonizing new climatically suitable habitats.

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v Climate change may affect fisheries in East Africa through extinctions of fish species (WWF 2006). Many tropical fish can endure temperatures that are close to their temperature threshold. A 1°–2°C increase, however, may exceed these limits, in particular for populations that currently exist in thermally marginal habitats (Roessig et al. 2004). However, because there is little data on the ability of these species to adjust their tolerance to water temperature, their response to climate change is largely unknown (WWF 2006). Some warm-water species are predicted to respond to climate change in a direction opposite to that expected based on known physiological constraints (Ficke et al. 2007). Changing water temperatures would also affect the geographic range of fish species.

v Algae may be strongly affected by increased light and temperature after the removal of riparian vegetation, with increased algal biomass and altered community composition (Allan 2005b). In fact, fossil evidence indicates that historical changes dating back to 1920s in phytoplankton productivity and composition in Lake Victoria have been the primary cause of loss of fish diversity (Verschuren et al. 2002).

v Eutrophication due to increased flooding and consequent nutrient inputs from the catchment will undoubtedly lead to further extinctions and increased hyacinth (Eichhornia crassipes) coverage. Deep-water oxygen loss (anoxia) in Lake Victoria due to eutrophication already has been shown to have facilitated the decimation of demersal haplochromine fish stocks by Nile perch through the elimination of deep- water refuges that would have accorded protection for these fishes (Verschuren et al. 2002).

v Macro-invertebrates are also affected by land-based disturbances that deteriorate water quality. Muli and Mavuti (2001) found low oxygen to be a major factor in deep-water stations influencing the structure of benthic communities. Turbidity and temperature are also known to affect the abundance of snails by interfering with their reproduction processes; whereas chemical parameters influence gastropod abundance and distribution significantly (Ngupula and Kayanda 2010). The community structure of macro-invertebrates may change, with increased abundances of grazers and decreased abundances of shredders, following changes in the production base from allochthonous to autochthonous.

v Field observations by residents of the Lake Victoria region indicate that the impact of climate change is a gradual increase in the abundance and frequency of swarming of lake flies and elate termites relative to 5 to 10 years ago. The prolonged emergence of the insects is attributed to protracted periods of rainfall. Between March and November 2008 and 2009, for example, villagers in the lake region collected edible insects throughout the season (Ayieko et al. 2010).

v Reduction of the “spongy-like” effect of wetlands has also been affected by climate change, leading to floods in Nyando (Kenya) (EAC 2007). Biomasses of shrimps and lake flies have been observed to increase within the Lake Victoria region. This increase has been attributed to increased levels of eutrophication. The Nile Shrimp (Caridinanilotica) form an important basis for the diet of the Nile Perch and other species in the lake region, which are predicted to also increase (Ayieko et al. 2010).

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v Flooding will lead to loss of wetland vegetation due to submergence, as has already been witnessed within the East African Rift Valley lakes. Flooding will also have negative effects on aquatic fauna that depends on the vegetation. A significant rise in lake level due to very heavy rains will lead to submergence and eventual loss of the rooted fringing vegetation, as occurred in Lake Victoria in 1960s.

v O’Reilly et al. (2003) demonstrated the impacts of climate change on fisheries in Lake Tanganyika through the reduction of primary productivity by as much as 20 percent over the past 200 years. This disrupts the proportion and composition of phytoplankton available in the aquatic food web. Recent declines in fish abundance in East African Rift Valley lakes have also been linked to similar climatic impact on lake ecosystems (O’Reilly 2007).

v Increased water temperatures, changes in streamflow patterns and increased storms will affect the distribution and phenology of various species of aquatic biodiversity.

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3. CLIMATE CHANGE AND SECTOR FUTURE PROJECTIONS IN EAST AFRICA

3.1 PROJECTING EAST AFRICA’S FUTURE CLIMATE

Climate change analysis and projections were based on a regional downscaled climate model–– the CORDEX. The downscaling was done using multiple regional climate models as well as statistical downscaling techniques. Three climate scenarios were used to project rainfall and temperature changes. Three Representative Concentration Pathways (RCPs) were modelled: RCP2.6, RCP4.5, and RCP8.5. The numbers refer to radiative forcings (global energy imbalances), measured in watts per square meter by the year 2100.

The RCP2.6 emission pathway is representative for scenarios leading to very low greenhouse gas concentration levels (van Vuuren et al. 2007). RCP4.5 is a stabilization scenario where total radiative forcing is stabilized before 2100 by employment of a range of technologies and strategies for reducing greenhouse gas emissions (Wise et al. 2009). RCP8.5 is characterized by increasing greenhouse gas emission over time representative of scenarios leading to high greenhouse gas concentration levels (Riahi et al. 2007).

3.1.1 Rainfall

The projected changes in the annual rainfall component under each of the three different scenarios and time windows (2030s, 2050s and 2070s) show relatively little change compared to the projected changes in the seasonal rainfall components. The short rains (October–December, or OND, period) are projected to increase over most parts of the region under all the three scenarios (10–25 percent by 2030, and 25–50 percent by 2050 (Figure 4a and b). By contrast, the long rains (March–May, or MAM period) are projected to decease over the northern part but to increase over the southeastern part of the region. The dry season rainfall (June–September, or JJAS, period) is projected to decease over most parts of the region. The projected annual rainfall shows a tendency to increase over the LVB.

3.1.2 Maximum and Minimum Temperatures

The projected changes in the maximum temperature component for the three scenarios (RCP2.6, RCP4.5, and RCP8.5) in the 2030s and 2050s compared to the reference period (1971–2000) are shown in Figures 5a and b. By 2030, maximum temperatures during the long rains (MAM), the dry season (JJAS), and annual component will likely increase by 1.0–2.0°C over most of the region. The expected warming extent is greatest during the long rains (MAM) and the dry season (JJAS) and least during the short rains (OND). By 2050, annual maximum temperatures are expected to be 1.0–2.0°C higher under RCP2.6, 1.5–2.5°C higher under RCP4.5, and 2.5–3.5°C higher under RCP8.5 over most of the EAC, with slightly less warming expected in some coastal areas. The projected changes in the minimum temperatures through time for the three scenarios are shown in Figure 6b. The results suggest that there will likely be a greater increase in the minimum than the maximum temperatures in future. By 2030, almost all the EAC region will likely be 1.0–2.5°C warmer than the base period, with the greatest warming expected during the dry season months (JJAS) under the RCP8.5 scenario. By 2070, the projected increase in the annual minimum temperatures will likely be 4–5°C higher under the RCP8.5 scenario relative to the base period.

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Figure 4: Projected Rainfall Changes over EAC, 2030, 2050, 2070

Figure 4a: Projected rainfall changes over the EAC by the 2030s in annual (1st column), MAM (2nd column), JJAS (3rd column), and OND (4th column). Each row corresponds to emission scenarios RCP2.6 (1st row), RCP4.5 (2nd row), and RCP8.5 (3rd row).

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Figure 4b: Projected rainfall changes over the EAC by the 2050s in annual (1st column), MAM (2nd column), JJAS (3rd column), and OND (4th column). Each row corresponds to emission scenarios: RCP2.6 (1st row), RCP4.5 (2nd row), and RCP8.5 (3rd row).

Figure 5: Projected Maximum Temperature Changes over EAC––2030, 2050, 2070

Figure 5a: Projected maximum temperature changes over the EAC by the 2030s in annual (1st column), MAM (2nd column), JJAS (3rd column), and OND (4th column). Each row corresponds to emission scenarios RCP2.6 (1st row), RCP4.5 (2nd row), and RCP8.5 (3rd row).

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Figure 5b: Projected maximum temperature changes over the EAC by the 2050s in annual (1st column), MAM (2nd column), JJAS (3rd column), and OND (4th column). Each row corresponds to emission scenarios RCP2.6 (1st row), RCP4.5 (2nd row), and RCP8.5 (3rd row).

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Figure 6: Projected Minimum Temperature Changes over EAC––2030, 2050, 2070

Figure 6a: Projected minimum temperature changes over the EAC by the 2030s in annual (1st column), MAM (2nd column), JJAS (3rd column), and OND (4th column). Each row corresponds to emission scenarios RCP2.6 (1st row), RCP4.5 (2nd row), and RCP8.5 (3rd row).

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Figure 6b: Projected minimum temperature changes over the EAC by the 2050s in annual (1st column), MAM (2nd column), JJAS (3rd column), and OND (4th column). Each row corresponds to emission scenarios: RCP2.6 (1st row), RCP4.5 (2nd row), and RCP8.5 (3rd row). 3.2 IMPACTS OF CLIMATE VARIABILITY AND CLIMATE CHANGE ON WATER AVAILABILITY IN THE LAKE VICTORIA BASIN

3.2.1 Considerations in Assessing Impacts of Climate Change on Water Resources

It is vital to assess climate change impacts on the hydrological regime and present information on the nature of future streamflow in the LVB under future climatic conditions. Several factors would affect the outcome of the assessment. The following paragraphs discuss some of those factors.

Streamflow as a surrogate: Streamflow is usually used as a surrogate rather than rainfall (precipitation) in a climate change impact assessment, as it is easier to detect climate change in runoff than precipitation since changes in precipitation are usually amplified in runoff (Chiew and McMahon 1996). Furthermore, streamflow data may be perceived as representing the integrated effects of the spatial variability of precipitation within the catchment. Therefore, streamflow data may provide as much information as precipitation in a time series derived from several rainfall stations in the catchment (Chiew and McMahon 1996). Furthermore, the runoff directly influences the management of land and water resources that makes the study important.

For example, in the Mara Basin the long-term rainfall data show high variability of rainfall for the MAM (March–May) season, especially in the upper Mara Region (Figure 7). Therefore, variability is extended to the rivers flows. High variability is seen on the western side of the basin in Kenya and Tanzania.

Figure 7: The Coefficient of Variation of Rainfall in MAM, 1981–2014 (source: GEOCLIM) Downscaling: The output from GCMs simulates the present climate well with respect to annual or seasonal averages at large spatial scales, that is, a spatial resolution of 2.5° latitude (250 kilometers) and 3.75° longitude (350 kilometers). However, in assessing climate change impact on

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hydrological regime, using the raw output of GCMs is widely regarded as inappropriate due to the mismatch in spatial scale (Arnell 1996, Russo and Zack 1997, Robock et al. 1993). Another mismatch is the temporal resolutions, the hydrological models usually operate at daily or hourly time scale, even though GCMs run as a time scale as short as 15 minutes, there is little confidence in the predictions, especially for variables such as rainfall for time scales shorter than 1 month (Prudhomme et al. 2002) due to the coarse spatial resolution. GCM is also noted to be unreliable at individual and sub-grid box scales (Wilby et al. 1999). Hence, in assessing the climate impacts on the LVB, downscaling approaches should be adopted. Downscaling is a way of relating large- scale atmospheric predictor variables to local- or station-scale meteorological series (Semenov and Barrow 1997) (Figure 8).

Figure 8: Generic Approach to Determining Climate Change Impacts, Water Stress, and Adaptation Strategies (source: Adapted from FAO 2011) In assessing climate change impacts it is necessary to address certain critical aspects, such as downstream-upstream balances, transient stages of large-scale reservoirs, relevant flow retention policies, and associated downstream ramifications, as well as the implications of a variable and changing climate on water flow and riparian land use systems. The underlying climate change scenarios, represented by different SRES emission levels as well as the frequency of El Niño and La Niña events (Elshamy and Wheater 2009), reflect the potential for applying integrated management and cost-benefit analyses, as well as sensitivity or viability analysis of hydropower or irrigation projects. Even as this is done, a considerable degree of uncertainty must be factored into the consideration of such scenarios if the hydrological and climatic base data are to be useful. In addition to varying climate conditions, other uncertainties include prospective future irrigation and hydropower development projects. Water resources planning and strategizing between

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countries (both upstream and downstream) is pivotal to the success of such future projects as the balance between climate resilience, food production, and industrialization is considered at the same time as climate-responsive streamflow and water conservation.

3.2.2 Historical Trends of Rivers Discharging into the Lake Victoria Basin

Discharge projections for 23 rivers of the Lake Victoria Catchment were analyzed under three climate change scenarios, using the base period 1950–2006. These rivers are both transboundary (such as the Kagera, which flows through Burundi, Rwanda, Tanzania, and Uganda, and the Mara, which flows through Kenya and Tanzania) and national rivers. The rivers analyzed are as follows:

v In Kenya: North Awach, South Awach, Gucha-Migori, Nyando, Nzoia, Sio, Sondu, and Yala. v In Uganda: Bukora, Katonga rivers, and north shore streams. v Transboundary through Tanzania: Biharamulo, eastern shore streams, Gurumeti, Issanga, Kagera, Magogo Moame, Mara, Mbalageti, Nyashishi, Simiyu, and western shore streams (Figure 9).

Figure 9: Rivers of the Lake Victoria Basin Used in the Projection Studies (source: Masese and McClain 2012) This study examined climate variability associated with temperature and rainfall in the EAC region in the context of specific impacts on river discharge into Lake Victoria. It sought to define historical trends in river discharge for selected rivers in Kenya, Tanzania, and Uganda based on available data to establish the effect of rainfall on water availability. Analyses of temporal trends in rainfall,

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as well as maximum and minimum temperatures in the LVB and in its sections within each of the five countries of East Africa between 1900 and 2014 also were undertaken.

The analysis showed that river discharge trends in the LVB for the three countries increased with increasing rainfall but the rate of the increase varied considerably across rivers in the basin, reflecting the additional influences of other factors. The annual average discharge for each of 23 rivers in the LVB was related to the total annual rainfall using a standard linear regression model. Several nonlinear models were also considered, but they all had weaker supports than the linear model (Figures 10-12). For all the rivers except three in Uganda, the slopes of the linear regressions of discharge on the total annual rainfall were significant at the 5 percent level of significance based on the t test. In Uganda, the Katonga River had the strongest correlation between annual rainfall and river discharge confirming climate as the main contributor and hence climate, to river volume and water availability (Figure 10).

Figure 10: Annual Discharge in Relation to Rainfall for Selected Rivers in Uganda In Kenya, the Gucha, Nzoia, Yala, and Sondu showed very strong positive linear correlation (Figure 11) while in Tanzania, all the rivers analyzed except the western shore streams, Nyashishi, and Magogo Maome had very strong linear relationship (Figure 12). This study confirmed that climate change, especially rainfall, has a very significant impact on river volumes and hence water availability.

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Figure 11: Annual Discharge in Relation to Rainfall for Selected Rivers in Kenya

Figure 12: Annual Discharge in Relation to Rainfall for Selected Rivers in Tanzania All the established regression relationships were also visualized with the aid of graphical plots. The regression relationships were used to project the likely future trajectories of the sector indicator using the projected rainfall and temperature under various scenarios.

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3.3 FUTURE PROJECTIONS OF RIVER DISCHARGES INTO THE LAKE VICTORIA BASIN

3.3.1 Approach and Methodology Statistical methods for time series analysis and forecasting were used to analyze temporal variation in a variety of time series of historic observations. These time series (response variables) were related to various time series of predictors (explanatory variables). The relationships established for the historic response series were used to forecast the likely future trajectories of the response series. A primary goal of univariate time series analysis is to model variation in the series and use the model to forecast future values of the series. The same applies to multivariate time series. However, analysis of the relationships among the component series may be of additional interest for multivariate series. Other goals for analyzing time series, often only of secondary interest, include smoothing trend patterns, interpolating (estimating missing values), and modelling the structure of the series. The analysis of time series typically involves careful consideration and modelling of several characteristics of time series, including seasonality, cycles, trend, and autocorrelation.

In this case the VARMAX (Vector Autoregressive Moving-Average Processes) model was used to model the dynamic relationships between the response and the predictor variables and to forecast the response variables for most of the response variables. These extended models allow for several the following:

v Modelling of several time series together v Accounting for relationships among individual component series with current and past values of the other series v Feedback and cross-correlated explanatory series are allowed v Cointegration of time component series to achieve stationarity v Seasonality v Autoregressive errors v Moving-average errors v Mixed autorergressive and moving-average errors v Lagged values of the explanatory series and more v Unequal or heteroscedastic covariances for the residuals.

The VARMAX model can be represented in various forms, including in state space and dynamic simultaneous equation or dynamic structural equations forms. The model also allows representation of distributed lags in the explanatory variables. For example, power generation in year t can be related to power generation in year t-1, t-2 plus to annual rainfall in year t, t-1, t-2, minimum and maximum temperatures in years t, t-1, t-2, etc., simultaneously.

Univariate autoregressive moving-average models with rainfall, minimum and maximum temperature were used as the explanatory variables. Various lags in rainfall, minimum and maximum temperature were allowed and tested for so that most of the models can be characterized as autoregressive and moving-average multiple regression with distributed lags. The significance of seasonal deterministic terms for monthly time series data were also allowed and tested. For some response variables dead-start models were used; these do not allow for present (current) values of the explanatory variables. Heteroscedasticity was tested in residuals and, where appropriate, GARCH-type (generalized autoregressive conditional heteroscedasticity) conditional heteroscedasticity of residuals was allowed. Several information-theoretic model

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selection criteria were used to automatically determine the AR (autoregressive) and MA (moving- average) orders of the models. The specific criteria used were the Akaike information criterion (AIC), the corrected AIC (AICC), Hannan-Quinn criterion, Schwarz Bayesian criterion, also known as Bayesian information criterion, and final prediction error. As additional AR order identification aids, partial cross-correlations were used for the response variable, Yule-Walker estimates, partial autoregressive coefficients, and partial canonical correlations. The specific model selected and used for all the rivers had a second-order autoregressive component (AR2) and a second-order moving-average component (MA2) for the response variable (LN (river discharge+1)) and no seasonal component (VARMAX (2,2,0) model). The predictor variable was the total annual rainfall in each river basin divided by its long-term mean and represented by lags 0 to 4. Parameters of the selected full models were estimated using the maximum likelihood method. Roots of the characteristic functions for both the AR and MA parts (eigenvalues) were evaluated for the proximity of the roots to the unit circle to infer evidence for stationarity of the AR process and inevitability of MA process in the response series.

The adequacy of the selected models was assessed using various diagnostic tools. The specific diagnostic tools used were the following:

v Durbin-Watson test for first-order autocorrelation in the residuals v Jarque-Bera normality test for determining whether the model residuals represent a white noise process by testing the null hypothesis that the residuals are normally distributed v F tests for autoregressive conditional heteroscedastic (ARCH) disturbances in the residuals. This F statistic tests the null hypothesis that the residuals have equal covariances v F tests for AR disturbance computed from the residuals of the univariate AR(1), AR(1,2), AR(1,2,3) and AR(1,2,3,4) models to test the null hypothesis that the residuals are uncorrelated v Portmanteau test for cross-correlations of residuals at various lags.

Final forecasts and their 95 percent confidence intervals were then produced for the response series for lead times running up to 2100.

3.3.2 Future Projection for Lake Victoria Basin Rivers 2030, 2050, and 2070 All the three climate change scenarios predict that the LVB region is likely to see increases in both minimum and maximum temperature in future, but the annual rainfall component shows little change compared to the projected changes in the seasonal rainfall components. The short rains (OND) are projected to increase over most of the region under all the three scenarios (10–25 percent by 2020 and 2030, 25–50 percent by 2050 and 2070). By contrast, the long rains (MAM) are projected to decrease over the northern part but to increase over the southeastern part of the region. The dry season rainfall (JJAS) is projected to decrease over most of the region (25–50 percent by 2020 and 2030, 50–75 percent by 2050 and 2070). The projected annual rainfall shows a tendency to increase over the LVB. The combined effects of changing temperature and precipitation will generally decrease streamflow in some rivers across the region. In addition, streamflow will likely become more variable, with lower flows during drought periods and higher flows during wet periods than experienced in the past.

(a) Kenyan Rivers The river discharge values for eight rivers flowing into Lake Victoria on the Kenya side are projected for between 2016–2100 using the three emission scenarios 2.6., 4.6, and 8.5. The river discharge for 2020, 2030, 2050, and 2070 are considered based on where at least two emission scenarios agree. Rainfall contributes more than 80 percent of the Lake Victoria water budget

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(LVEMP 2002), with the projected increase in rainfall in the OND season, the lake levels are likely to peak after this season combined with the high temperatures in the Decedmber–February season. Evapotranspiration over the catchment will also be high. The projected changes in rainfall will affect the water supply systems that depend on lake water and activities along the beaches.

For most of the rivers, there is not a clear increasing or decreasing trend projected but rather high variability in the coming decades (Figures 13–20, summarized in Table 3). With this variability, the contribution rank of each river to the Victoria is likely to remain the same; for example, Nzoia is currently second after Kagera. The coefficients of variability for the predicted flows for each river is discussed below.

Table 3: Projected River Discharge Trends Agreed by at Least Two Emissions Scenarios and River Contribution Rank Country Rivers Contribution 2016–2030 2030–2050 2050–2070 ranking Kenya Gucha-Migori 3 Variable Highly variable Highly variable Kenya North Awach 20 Variable Highly variable Highly variable Kenya Nyando 12 Variable Highly variable Highly variable Kenya Nzoia 2 Variable Highly variable Highly variable Kenya Sio 15 Variable Highly variable Highly variable Kenya Sondu 4 Highly variable Highly variable Highly variable Kenya South Awach 17 Highly variable Highly Variable Highly variable Kenya Yala 6 Highly variable Highly variable Highly variable

For the Gucha-Migori, the coefficients of variability for the period 2011–2030 are 48 percent (2.6), 53.5 percent (4.5), and 42.4 percent (8.5). In 2031–2050 they are 50.7 percent (2.6), 61.6 percent (4.5), and 97.7 percent (8.5). In 2051–2070 they are 51.4 percent (2.6), 79.2 percent (4.5), and 59 percent (8.5). The highest variability of river discharge will be in the 2031–2050 period.

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Figure 13: River Gucha-Migori Projections under MPI 2.6, 4.5, and 8.5, Note: River discharge levels plotted in natural log. River flows are likely to be higher by 2030 under the MPI 2.6 and 4.5 scenarios. For the North Awach River, the coefficients of variability for the 2011–2030 period are 28.8 percent (2.6), 34.8 percent (4.5), and 23.6 percent (8.5). For the period 2031–2050 the coefficients of variability are 37.1 percent (2.6), 40.4 percent (4.5), and 58.2 percent (8.5). In the 2051–2070 the values are 33.7 percent (2.6), 41.9 percent (4.5), and 37.5 percent (8.5). The highest variability will be experienced in 2031–2050.

For the Nyando River, the coefficients of variability for the 2011–2030 period are 69.6 percent (2.6), 92.8 percent (4.5), and 53 percent (8.5). In the 2031–2050 period the values are 119.5 percent (2.6), 92.5 percent (4.5), and 152 percent (8.5). In the 2051–2070 period the coefficients of variability are 90 percent (2.6), 124.4 percent (4.5), and 124.7 percent (8.5). In general, the Nyando River displays the highest variability, this could be because its source is in the Mau Forest, which is projected to have highly variable rainfall (Figure 15).

For the Nzoia River, the coefficients of variability for the 2011–2030 are 45.1 percent (2.6), 55.9 percent (4.5), and 39.6 percent (8.5). For the 2031–2050 period the values are 59.8 percent (2.6), 54 percent (4.5), and 80.4 percent (8.5). For the 2051–2070 period, the coefficients of variability are 53 percent (2.6), 73.2 percent (4.5), and 40.9 percent (8.5). The 2031–2050 period is likely to experience the highest variability (Figure 16).

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Figure 14: North Awach River projections under MPI 2.6, 4.5, and 8.5. Note: River flows are likely to be higher by 2030 under the different scenarios with a reduction in flows by 2050 in all scenarios and an increase in 2070 with higher variability between 2050 and 2070.

Figure 15: Nyando River projections under MPI 2.6, 4.5, and 8.5. An increase by 2030 is likely with high variability in river flows. Drought and flooding periods likely.

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Figure 16: Nzoia River projections under MPI 2.6, 4.5, and 8.5. Shows increased flows by 2030, but a reduction in 2050. High variability in river flows is expected. v For the Sio, the coefficients of variability for 2011–2030 are 48.2 percent (2.6), 56.8 percent (4.5), and 43.7 percent (8.5). For the 2031–2050 period, the coefficients of variability are 57.3 percent (2.6), 62.5 percent (4.5), and 84.5 percent (8.5). In the 2051– 2070 period the values are 53 percent (2.6), 73.8 percent (4.5), and 51.3 percent (8.5). The 2031–2050 period is projected to have the highest variability (Figure 17).

v For the Sondu river, the coefficient of variability values for the 2011–2030 period are 42.6 percent (2.6), 52.4 percent (4.5), and 41.5 percent (8.5). For the 2031–2050 period the values are 57.3 percent (2.6), 59.3 percent (4.5), and 76.8 percent (8.5). For the 2051– 2070 period they are 48.9 percent (2.6), 71.9 percent (4.5), and 47.7 percent (8.5). The 2031–2051 is likely to have the highest variability (Figure 18).

v For the South Awach, the coefficients of variability for the 2011–2030 period are 28.6 percent (2.6), 33.4 percent (4.5), and 24.4 percent (8.5). For the 2031–2050 period, the values are projected to be 36.5 percent (2.6), 37.1 percent (4.5), and 49.9 percent (8.5). For the 2051–2070 period the values are 31.7 percent (2.6), 42.4 percent (4.5), and 33.4 percent (8.5). The 2031–2051 is likely to have the highest variability (Figure 19).

v For the Yala River the coefficient of variability values for the 2011–2030 period are 31.7 percent (2.6), 40.7 percent (4.5), and 28.5 percent (8.5). For the 2031–2050 period the values are 39.1 percent (2.6), 48.3 percent (4.5), and 63.4 percent (8.5). The values for the 2051–2070 period are 38.8 percent (2.6), 51 percent (4.5), and 44.3 percent (8.5). The 2031–2051 period is likely to have the highest variability (Figure 20).

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Figure 17: Sio River projections under MPI 2.6, 4.5, and 8.5. Shows increased flows by 2030 but a reduction in 2050. High variability in river flows is expected.

Figure 18: Sondu River projections under MPI 2.6, 4.5, and 8.5. Shows increased flows by 2030 but a reduction in 2050. High variability in river flows is expected.

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Figure 19: South Awach River projections under MPI 2.6, 4.5, and 8.5. Shows increased flows by 2030 but a reduction in 2050. High variability in river flows is expected.

Figure 20: Yala River projections under MPI 2.6, 4.5, and 8.5. Shows high variability after 2030. (b) Transboundary Rivers in the LVB The projected discharge values for 2030, 2050, and 2070 show high variability within the decades. For most of the rivers, there is not a clear increasing or decreasing trend but rather high variability, except for the Issanga, Kagera, and Mara rivers, which are likely to increase after 2020. The Issanga shows the highest increase in flows after 2016 (Figures 21–32). Twelve rivers flow to Lake Victoria in Tanzania, all are transboundary and their ranking in hydrological contribution is shown in Table 4.

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Table 4: Projected River Discharge Trends under Three Emission Scenarios and River Contribution Rank Country River Contribution 2016–2030 2030–2050 2050–2070 rank Kenya/Tanzania Biharamulo 13 Highly variable Highly variable Highly variable Kenya/Tanzania Eastern shore 11 Highly variable Highly variable Highly variable streams Kenya/Tanzania Grumeti 14 variable Highly variable Highly variable Kenya/Tanzania Issanga 8 Increase Increase Increase Kenya/Tanzania Mara 7 Variable Variable Variable Kenya/Tanzania Magogo Moame 16 Variable Variable Variable Kenya/Tanzania Mbalageti 19 Variable Variable Variable Kenya/Tanzania Southern shore 9 Variable Variable Variable streams Kenya/Tanzania Nyashishi 22 Variable Variable Variable Kenya/Tanzania Simiyu 3 Variable Variable Variable Kenya/Tanzania Western shore 10 Variable Variable Variable streams Burundi/ Rwanda/ Kagera 1 Variable Variable Increase Uganda/ Tanzania

For the Biharamulo River, a transboundary river between Tanzania and Kenya (Figure 21), the 2011–2030 coefficients of variability in river discharge are 25.4 percent (2.6), 32.8 percent (4.5), and 16.4 percent (8.5). The coefficients of variability for the 2031–2050 period are 22.4 percent (2.6), 36.1 percent (4.5), and 31.1 percent (8.5). The projections for the 2051–2070 are 26.3 percent (2.6), 32.5 percent (4.5), and 24.5 percent (8.5). The 2031–2050 period shows the highest variability and the 4.5 emission scenario predicts the highest variability (Figure 21).

For the east shore streams the coefficient of variability values in the 2011–2030 period are 48.6 percent (2.6), 92.5 percent (4.5), and 45 percent (8.5). For the 2031–2050 period the values are 52.4 percent (2.6), 83 percent (4.5), and 97.6 percent (8.5). For the 2051–2070 period the values are 70.2 percent (2.6), and 66.1 percent (4.5), and 52.5 percent (8.5). The 2051–2070 period shows the highest variability and the 4.5 emission scenario predicts the highest variability in the 2011–2030 and 2031–2050 periods (Figure 22).

For the Gurumeti River the coefficients of variability in the 2011–2030 period are 35.7 percent (2.6), 46.5 percent (4.5), and 38.4 percent. In the 2031–2050 period the values are 48.4 percent (2.6), 36.6 percent (4.5), and 59.8 percent (8.5). In the 2051–2070 period the values are 54.2 percent (2.6), 48.5 percent (4.5), 38.6 percent (8.5). The 2051–2070 period will likely experience higher variability though the difference is not significantly higher than the other periods (Figure 23).

For the Issanga River the coefficients of variability for the 2011–2030 period are 115 percent (2.6), 156.1 percent (4.5), and 178.1 percent (8.5). For the 2031–2050 period the projections are 110.8 percent (2.6), 215.6 percent (4.5), and 119.1 percent (8.5). For the 2051–2070 period the values are125.3 percent (2.6), 196.3 percent (4.5), and 100.5 percent (8.5). The Issanga is likely to experience higher discharge by 2030 and even higher by 2050 (Figure 24).

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Figure 21: Biharamulo River projections under MPI 2.6, 4.5, and 8.5. Shows increased flows by 2030 but a reduction in 2050. High variability in river flows is expected.

Figure 22: Eastern shore streams projections under MPI 2.6, 4.5, and 8.5. High variability in river flows is expected.

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Figure 23: Grumeti River projections under MPI 2.6, 4.5, and 8.5. High internal annual variability is highly likely.

Figure 24: Issanga River projections under MPI 2.6, 4.5, and 8. Shows increased flows by 2030 and 2050 and high variability is highly likely. For the Mara River the predicted coefficients of variation for the 2011–2030 period are 56.9 percent (2.6), 111 percent (4.5), and 58.4 percent (8.5). For the 2031–2050 period the variability is 61.7 percent (2.6), 102.9 percent (4.5), and 106.5 percent (8.5). For the 2051–2070 period the predicted the coefficients of variability are 62.8 percent (2.6), 73.7 percent (4.5), and 82.2 percent (8.5) (Figure 25).

For the Magogo Moame River the coefficients of variability for the 2011–2030 period are 112.8 percent (2.6), 226.6 percent (4.5), and 115.5 percent (8.5). For the 2031–2050 period the values are 104.9 percent (2.6), 235.5 percent (4.5), and 234.4 percent (8.5). The coefficients of variability for the period 2051–2070 are 128.5 percent (2.6), 129 percent (4.5), and 93.8 percent (8.5). This river is likely to experience the highest variability with average predictions all above 100 percent (Figure 26).

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For the Mbalageti River the coefficients of variability in the 2011–2030 period are 52.1 percent (2.6), 100.4 percent (4.5), and 53.2 percent (8.5). For the 2031–2050 period the values are 57.1 percent (2.6), 83.7 percent (4.5), and 112.2 percent (8.5). For the 2051–2070 period the coefficients of variability are 72.2 percent (2.6), 65.1 percent (4.5), and 55 percent (8.5). The 2031– 2050 period is likely to experience the highest variability (Figure 27).

For the Nyashishi River the coefficients of variation for the 2011–2030 period are 63.9 percent (2.6), 88.8 percent (4.5), and 61.5 percent (8.5). In the 2031–2050 period the values are 72.1 percent (2.6), 93.6 percent (4.5), and 106.3 percent (8.5). For the 2051–2070 period the coefficients of variability are 73.8 percent (2.6), 96.4 percent (4.5), and 74.3 percent (8.5). The 2031–2050 period is likely to experience the highest variability (Figure 28).

Figure 25: Mara River projections under MPI 2.6, 4.5, and 8.5. Shows increased flows by 2030 but a reduction in 2050. High variability in river flows is expected.

Figure 26: Magogo Moame River projections under MPI 2.6, 4.5, and 8.5, Shows increased flows by 2030 but a reduction in 2050. High variability in river flows is expected.

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Figure 27: Mbalageti River projections under MPI 2.6, 4.5, and 8.5

Figure 28: Nyashishi River projections under MPI 2.6, 4.5, and 8.5. Shows increased flows by 2030 but a reduction in 2050. High variability in river flows is expected. For the south shore streams the coefficients of variability for the predicted streamflow for the 2011–2030 period are 101.7 percent (2.6), 153.5 percent (4.5), and 72.3 percent (8.5). For the 2031–2050 period the values are 86.3 percent (2.6), 197.5 percent (4.5), and 145.9 percent (8.5). In the 2051–2070 period the values are 114.6 percent (2.6), 98.1 percent (4.5), and 77.5 percent (8.5) (Figure 29).

For the Simiyu River the coefficients of variability for the 2011–2030 period are 45.4 percent (2.6), 82.1 percent (4.5), and 42.4 percent (8.5). For the 2031–2050 period the predicted values are 55.1 percent (2.6), 82.2 percent (4.5), and 92.6 percent (8.5). For the period 2051–2070 the values are 71.3 percent (2.6), 62.8 percent (4.5), and 52.8 percent (8.5) (Figure 30).

The western shore streams have the lowest coefficients of variability on the predicted river discharge. For the 2011–2030 period, the coefficient of variability values are 6.6 percent (2.6), 7.1

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percent (4.5), and 3.7 percent (8.5). For the 2031–2050 period the values are 5.3 percent (2.6), 5.8 percent (4.5), and 5.4 percent (8.5). For the 2051–2070 period the coefficients of variability for are 4.8 percent (2.6), 9.4 percent (4.5), and 7.9 percent (8.5) (Figure 31).

The Kagera River is an important contributor to the Lake Victoria hydrology and flows through four of the EAC countries. The coefficients of variability for 2011–2030 are 18.6 percent (2.6), 25.7 percent (4.5), and 16.2 percent (8.5). For the 2031–2050 period values are 21.7 percent (2.6), 30.1 percent (4.5), and 29.7 percent (8.5). For the 2051–2070 period the projections are 23.5 percent (2.6), 27.4 percent (4.5), and 26.6 percent (8.5) (Figure 32).

Figure 29: Southern shore streams projections under MPI 2.6, 4.5, and 8.5. Shows increased flows by 2030 but a reduction in 2050. High variability in river flows is expected.

Figure 30: Simiyu River projections under MPI 2.6, 4.5, and 8.5. Shows increased flows by 2030 but a reduction in 2050. High variability in river flows is expected.

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Figure 31: Western shore streams projections under MPI 2.6, 4.5, and 8.5. Shows increased flows by 2030 and in 2050.

Figure 32: Kagera River projections under MPI 2.6, 4.5, and 8.5. Shows increased flows by 2030 and 2050 and high variability is highly likely. (c) Uganda Rivers

The projected discharge values for 2020, 2030, 2050, and 2070 show high variability within the decades (Figures 33–35). The contribution of the rivers in Uganda to the lake are lower, ranking 18, 21, and 23 (Table 5).

The coefficient of variation is a statistical measure of the potential seasonal and interannual fluctuations in river discharge. The coeffecients of variation of monthly river discharge for the period 2011–2070 in percent at 20-year intervals for the three emission scenarios (2.6, 4.5, and 8.5) are discussed below as they represent the annual variation in discharge for the rivers.

In general, the MPI 4.5 projections show relatively higher hydrological flows in the 2016–2030 period compared to the MPI 2.6 and MPI 8.5.

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Table 5: Projected River Discharge in Uganda and River Contribution Rank Country Rivers Contribution 2011–2030 2031–2050 2051–2070 rank Uganda Bukora 21 Variable Variable Highly variable Uganda Katonga 18 Low variability Low variability Variable Uganda Northern shore 23 Highly variability Highly variability Highly variability streams

For the Bukora River the coefficients of variation for the period 2011–2030 are 19.6 percent (2.6), 38.1 percent (4.5), and 17.1 percent (8.5). For 2031–2050 the projections are 35.4 percent (2.6), 25 percent (4.5), and 34 percent (8.5). For 2051–2070 the values are 22.5 percent (2.6), 24.5 percent (4.5), and 36.2 percent (8.5). The 2031–2050 period is projected to have the highest variability (Figure 33).

For the Katonga River the coefficients of variability for the period 2011–2030 are 23 percent (2.6), 43.7 percent (4.5), and 24.5 percent (8.5). For the 2031–2050 period the values are 33.6 percent (2.6), 25.9 percent (4.5), and 40.3 percent (8.5). For the 2051–2070 period the values are 29.6 percent (2.6), 43.6 percent (4.5), and 30.3 percent (8.5). The general coefficients of variability for the Katonga are between 23 percent and 43.7 percent. The 2.6 and 8.5 projections are close to each other compared to the 4.5 projections (Figure 34).

For the northern shore streams the coefficients of variability for the period 2011–2030 are 39 percent (2.6), 72.5 percent (4.5), and 30.5 percent (8.5). For the 2031–2050 period the values are 32.5 percent (2.6), 37.9 percent (4.5), and 40.9 percent (8.5). For 2051–2070 the values are 31.1 percent (2.6), 50.8 percent (4.5), and 37.4 percent (8.5). The northern shore streams show a higher variation between 30.5 percent and 72.5 percent. The 2.6 and 8.5 projections are in range of +/- 10 while the 4.5 is much different apart from the 2031–2050 projection, which is closer to the other projections (Figure 35).

Figure 33: Bukora River projections under MPI 2.6, 4.5, and 8.5. Shows increased flows by 2030 but a reduction in 2050. High variability in river flows is expected.

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Figure 34: Katonga River projections under MPI 2.6, 4.5, and 8.5. Shows highly variable flows by 2030 and 2050, no trend.

Figure 35: Northern shore streams projections under MPI 2.6, 4.5, and 8.5. Shows increased flows by 2030 but a reduction in 2050. High variability in river flows is expected and an increase in discharge is likely between 2050 and 2065; toward 2070 the river flows are projected to decrease. 3.4 DISCUSSION OF RESULTS

Results from this study show that the Issanga, north shore streams, Kagera, and Mara are projected to increase mean discharge significantly after 2030. These projected changes will have a great impact on water quantity and water availability in the rivers and lakes of the LVB. Flooding conditions are likely to destroy water supply infrastructure, as it did during the 1997/1998 El Niño rains. Additionally, water has a high likelihood of becoming polluted due to high runoff from agricultural farms and with the sewerage systems. This will increase water treatment costs and the water might be expensive for most of the population that is already poor.

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The projected temporal and spatial rainfall variations will result in a decrease of river flows and the drying up of other many natural water sources by 2030 in the 2.6 scenario. This will affect water resources and aquatic ecosystems and have severe consequences for households and whole communities.

The short rains (OND) are projected to increase over most of the region under all the three scenarios (10–25 percent by 2020 and 2030, and 25–50 percent by 2050 and 2070). By contrast, the long rains (MAM) are projected to decrease over the northern part but to increase over the southeastern part of the region. The dry season rainfall (JJAS) is projected to decrease over most of the region (25–50 percent by 2020 and 2030, and 50–75 percent by 2050 and 2070).

Apart from three rivers in the LVB that show an increasing trend, 20 out of 23 rivers are projected to have highly variable discharge. The coefficients of variability in the predicted streamflow are quite high, in some cases above 70 percent. The most variable period for most of the rivers is the 2031–2050 period. Increased annual runoff may produce benefits for a variety of in-stream and out-of-stream water users by increasing renewable water resources, but may simultaneously generate harm by increasing flood risk in some low-lying areas. The managers of the water resource in the region must be prepared for these extremes, dealing with too much and too little in a short time span. During drought periods, there will be stiff competition for access and use. If the supply is not able to meet the demand for the resource, water-use conflicts are likely to occur between various users. Owing to the decrease of the water supply for domestic use, agriculture, hydropower production, and industrial use, water-use conflicts may occur at level of the family, among villages, between ethnic groups, between government institutions and local people, between different livelihood systems, and between countries.

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4. ADAPTATION PRACTICES, OPTIONS, AND CONSTRAINTS

4.1 EMERGING WATER RESOURCES ISSUES IN THE LAKE VICTORIA BASIN

Water demand in East Africa is linked to population growth and to developments in the agricultural and industrial sectors. Hence, the economic growth of the region is linked to the availability and access to water in the future. The supply should be able to meet the demand under the changed climate scenario.

Rainfall determines the availability of water in rivers and lakes. Most of the rivers are a source of municipal water supply. Water stress will affect vulnerable communities, who have limited options (Mwiturubani and van Wyk 2010). In the recent past, droughts have led to great losses within pastoral communities. Increased migration may lead to other resource conflicts as well exacerbation of other climate-related challenges, such as diseases, food insecurity, land degradation, and over-exploitation. Reduction in rainfall may compel people to encroach on marginal lands, such as water catchment areas, wetlands, and mountain ecosystems for cultivation and livestock grazing. This has already been observed in some parts of the EAC. Because of poor water management, water-borne diseases cause the highest morbidity and infant mortality in the region.

Climatic variability is already manifest in East Africa, as evidenced by changes in the timing and amount of precipitation and increased frequency and severity of extreme weather events (EAC 2011), which have significant impact on the availability of pasture and water. Particularly vulnerable are the pastoralists, such as the Maasai in the Mara Basin. The large animals that depend on the savannas also will be affected. The changes in the seasonal and spatial distribution of rainfall and water availability with thin the LVB might lead to changes in nomadism for communities that have done this for a long period. Increased rainfall in the OND season will lead to flooding in some areas, which will affect water quality. Floods are the second most common disaster in East Africa. Rainfall over Lake Victoria is the highest contributor of the lake water budget. Changes in the magnitude of seasonal rainfall will affect the water level in lake. Direct climate change impacts on the water resources will depend on how the amount and timing of precipitation are altered and how they affect baseflow, stormflow, groundwater recharge, and flooding. Lake Victoria is a source of water for several cities such as, Entebbe, Kampala, Jinja, Kisumu, Masaka, and Musoma, whose water supply will vary with the changes in rainfall.

Of the rivers draining into Lake Victoria, 12 are transboundary and all are used by communities within their catchments. Variability in water availability and supply will result in increased, protracted, and complex water allocation rights and conflicts between crop farmers and pastoralists, domestic and industrial users, upstream and downstream users within and between basins, states with transboundary water resources, and the public and private sectors among others. During periods of low river water and high demand, this will also affect the economy of the EAC states as socioeconomic activities are directly connected to the water supply. Water scarcity is bound to have a negative impact on socioeconomic development, while water abundance might affect some sectors positively and others negatively. This calls for the formulation and implementation of national policies and other legal frameworks that are geared toward addressing issues of climate variability and change, particularly the adaptation to and mitigation of climate variability and change impacts. Potential options for adaptation to these emerging constraints are summarized in Table 6.

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Table 6: Climate Adaptation Policy Options for Water Resources Resilience Policy Option Advantage Disadvantage v National institutions should v Better information may result in improved v Can be costly consistently monitor levels and water resource management, distribution, v Can be difficult to represent the entire quality of water sources (rivers, allocation, etc. range of interests lakes, groundwater) v Multisector partnerships are essential v Increase investments in research to measure the economic value of goods and services in the water sector v Regional climate organizations v Provides critical insight into the future, v Can have significant barriers that limit such ICPAC, EAC, and national improving decision making their use and application (e.g., meteorological departments of accessibility, uncertainty, credibility, the EAC countries provide cost, difficult to understand, and

annual, monthly, and seasonal accuracy) climate projections to various stakeholders managing terrestrial ecosystems v International, regional research v Establishes ‘buy-in’ early in knowledge creation v Can be costly and time-intensive organizations, EAC, and and decision-making processes and increases v Need to develop modelling capacities universities develop scenarios of likelihood of outcomes and actions of various institutions involved in possible future climate v Allows decision makers to contemplate managing terrestrial ecosystems Information based Information conditions (and the respective uncertainties associated with climate change v Can be difficult to represent the entire impacts and adaptation decisions range of interests of the various water systems) with stakeholders involved with management of water resources v International, regional research v Can increase public understanding of v Can risk delayed action in the name of organizations, EAC and importance of climate risk and value of more knowledge and understanding universities educate the public, ecosystem services v Can be costly in the collection of data decision makers and media so v May foster support for appropriate policy and processing of information that they can understand climate measures v Barriers and institutional constraints risk and its importance in v Results in increases in data for evaluating exist between countries and regions managing Water resources and climate processes and for monitoring for standardizing and sharing of data ecosystems effectiveness of adaptation decisions v Can lead to empowerment through citizen- science

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Policy Option Advantage Disadvantage v Regional and national institutions v Can be a common regulatory means to achieve v Requires effective monitoring and and judiciary (national and EAC environmental objectives penalties for noncompliance to be

levels) develop IWRM and legal v Can be clear and easy to enforce in cases where successful; enforcement capacity can protection laws prioritizing ecosystem damage can be easily identified (e.g., be weak or nonexistent (e.g., fragile protection of key water towers pollutants and land contamination) nations and communities) and groundwater recharge zones v Will help build common interest in the protection and conservation of shared water, ecosystems, and other resources v Sustainable ecosystem products v Can encourage and support shifts to more v Standards for eco-labeling can be

voluntary methods) (eco-labeling, green marketing) sustainable production practices (e.g., reduction uneven, have poorly expressed of chemical use in farming and industries) environmental standards, and lack v Can encourage conservation of the appropriate certification mechanisms environment and the ecosystem health in the absence of additional protection v Labels can help to inform consumer choice and policies and regulations shift consumption patterns toward more sustainably produced goods and services v Can result in financial savings to producers in the long term v Regional and national financial v Have been used to close the gap between v Distributional issues can be prevalent, and environmental institution socially optimal level of using ecosystem equity and rights of individuals and through public-private services and level of user-based, more narrow community will be difficult to negotiate partnerships should provide private benefits v Few financial services will provide such payments for environmental funding unless the returns are services and subsidies that guaranteed command and control and informal informal and control and command - protect water quality and encourage technological innovations that promote efficient water use v National governments should v Water purification costs and water-borne v Competing interests of stakeholders enact laws on effective waste diseases reduced management v National land use and planning v Can be effective at directing various types of v Requires state capacity to effectively ministries within the EAC and ecosystem uses to clearly demarcated carry out appropriate zoning actions LVB need to zone the land for geographical areas, or harmful development v Equity issues can be prevalent; often the various land uses (e.g., away from particular recharge zones and water the marginalized are forced to slums Regulatory (includes formal (includes Regulatory limiting floodplain development, purification areas such as swamps and ecologically sensitive areas building in sensitive areas, pit subsequent protection of which can lead to displacement and relocation

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Policy Option Advantage Disadvantage latrines located near shallow groundwater regions v The EAC governments should v Multiple benefit actions support human v May imply challenging cultural initiate programs that encourage development and poverty reduction transitions for people (e.g., livelihood diversification to v Multiple benefits to governments and the transitioning from agriculture based reduce pressure on land. environment livelihoods to other forms of making a v EAC governments should living in areas designated exclusively encourage / subsidize companies for protection) developing alternative sources

of energy to preserve the trees v The EAC governments and v Devolution of management to local users can v Land tenure and land use rights can be working with local communities result in multiple benefits for people and costly to administer and politically should preserve state and local ecosystems challenging to confer land reserves, including v Can support empowerment and autonomy v Uncertainty about ecological shifts as a communal management of v Can serve as a basis for supporting sustainable result of changing climate regime can protected areas, with particular use of ecosystem services and practices be difficult to anticipate and scale to emphasis on recharge areas that the level of user groups and land use will be priorities clean water units availability

v EAC countries should v Can protect and ensure supply of clean water. v Costly Public Investment Programs Investment Public encourage innovations and Improve efficiency improvements in infrastructure that can reduce leakage, enhance delivery, help prevent floods, water storage facilities and recycling to increase adaptation to the high variability of river discharge v The EAC countries are part of v Most environmental conventions are v Can have enforceability issues (i.e., the international treaties and framework conventions where general these are not “hard laws”) conventions (e.g., UN obligations are established, and new information v Can be difficult to make substantive Commission on Sustainable may be amended to the treaty (e.g., RAMSAR) changes once a treaty is signed Development, RAMSAR, CITES, v Further protocols may also be developed (e.g., UNCCD Desertification, CBD) Kyoto Protocol to the United Nations Framework Convention on Climate Change and

International Cartagena Protocol to CBD)

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Policy Option Advantage Disadvantage v Bi- and multi-level agreements v Though usually not legally binding, can later be v Are often non-binding; consequences on cross-boundary resource transformed into treaties for noncompliance are difficult to management issues (e.g., river v Enables states to take on obligations they enforce basin commissions and water- otherwise might not use agreements) and “soft laws” (e.g., UN Statement of Principles for a Global Consensus on the Management, Conservation, and Sustainable Development of All Types of Forests) v International agreements v Trade and investment agreements (NAFTA, v Requires that knowledge of outside the environmental ASEAN Free Trade Area, CONOSUR) can environmental principles be sector that protect water support ESG by incorporating environmental incorporated into multiple arenas of resources (e.g., development, measures as a part of their performance decision making, but institutional and trade, human rights, requirements financial barriers to this may be anticorruption) substantial

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4.2 POLICY, INSTITUTIONAL FRAMEWORKS, AND ONGOING INITIATIVES

Water security is a priority for the rural and urban communities of the EAC Partner States for livelihoods and economic development. Water demand (for domestic, commercial, and industrial uses or energy) far outstrips available supply, even without the impacts of climate change, which with further complicate the challenge of balancing demand and supply. In general, lakes, rivers, and wetlands in East Africa provide major economic benefits, such as the following:

v Domestic and industrial water: The primary economic use of water resources is for domestic, industrial, and commercial supply, but wetlands are important for cleaning up water by removing pollutants such as phosphorous, heavy metals, and toxins that are trapped in the sediments. They therefore reduce the cost of water treatment. v Hydroelectric power generation: National and transboundary hydropower projects are being implemented on the region’s rivers to meet the increasing demand for energy in the EAC Partner States. These include technologies such as run of river hydropower, storage hydropower, and pumped storage hydropower. v Agriculture, livestock, and food security: With growing demand for agricultural production, the EAC Partner States are shifting from rain-fed agriculture to the intensification and expansion of irrigation, which greatly increases water abstraction. v Fisheries: Fisheries are a major source of income, employment, food, and foreign exchange for East Africa. Lake Victoria annually produces more than 800,000 tons of fish, currently worth about US$590 million for Kenya, Tanzania, and Uganda. The lake fisheries provide incomes for almost 2 million people and meet the fish consumption needs of almost 22 million people in the region. v Transport and communications: Lake Victoria supports inland water transport that is important for the region’s trade and its tributaries provide the means of transport for the populations along their shores. v Biodiversity and tourism employment: The rivers, wetlands, and lakes of the EAC are important tourism and recreation sites and support local employment. Wetlands and freshwaters also provide habitats for many important species and provide valuable ecosystem services. v Change Control and flood mitigation: Wetlands play an important role in flood control and climate regulation, and influence the micro-climates of the regions. They also have a role in climate adaptation and mitigation strategies.

However, water quantity and quality are under increasing pressure due to high population, poverty, agricultural intensification, and degradation of aquatic ecosystems. Various initiatives, plans, and policies address the potential impacts of climate change and climate variability on water resources, water infrastructure and aquatic ecosystems in East Africa. Given the significance of this sector, it is imperative that strategic options are developed for communities to cope with the likelihood of water stress in the future, especially in the context of climate change and climate variability. Vulnerability Impact Assessments in selected hotspots should provide increased understanding of these systems and build the resilience of the communities that depend on water and natural resources from the sector to adapt to inevitable changes. In addition, measures need to be in place to secure priority aquatic ecosystems, address potential threats to aquatic biodiversity, and sustain ecosystem function. Climate change and variation are expected to reduce water supply, which will intensify competition for access to and use of water. As a result, water resource conflicts will be more likely to occur.

The EAC and the five Partner States have a robust policy and legal framework in place at the regional and national levels and a framework for shared transboundary water resources and aquatic ecosystems is emerging. However, the institutional framework is not robust enough to cope with the potential

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challenges of climate change and major investment is needed in human, technical, technological, and institutional capacity.

4.2.1 Regional Cooperation After the signing of the EAC Treaty in 1999, the LVB was declared an Economic Growth Zone and the Partner States committed to coordinated and sustainable development of the basin. The EAC Treaty, Article 114 on Management of Natural Resources, 1(c) stipulates that the Partner States shall “adopt common regulations for the protection of shared aquatic and terrestrial resources.” Furthermore, the Protocol for Sustainable Development of Lake Victoria Basin (1999) in Article 25 on Water Resources Monitoring, Surveillance and Standard Setting states as follows:

v The Partner States shall establish and harmonize their water quality standards. v The Partner States shall, in their respective territories, establish water quality and quantity monitoring and surveillance stations and water quality and quantity control laboratories. v The Partner States shall exchange water quality and quantity data in accordance with guidelines to be established by the Partner States.

Under Article 94 (p), the Treaty commits the signatories to jointly tackle issues of inland water pollution with a view to achieving effective monitoring and control thereof. In addition, the EAC’s Vision and Strategy Framework for Management and Development of Lake Victoria Basin (2011) under the Sector Strategies for Water Resources Management, requires the Partner States to “Promote water quality and quantity monitoring.” Thus, the EAC integration and cooperation is cognizant of a regional approach, while also being sensitive to country-specific concerns.

The governments of the five Partner States are making serious efforts not only to address water resources issues in their countries but also to use transboundary water resources for the social and economic benefit of the region. These efforts have included developing and strengthening of national policies, institutional frameworks, and laws and regulations that are essential to meet the existing and emerging water resources challenges. They also have proposed to strengthen regional cooperation to jointly address transboundary threats and challenges and seek opportunities for enhancing sustainable management of shared water resources. Burundi is developing its National Water Policy while Rwanda’s policy of 2011 is in the final stage of approval. Kenya developed Sessional Paper No. 1 of 1999 on National Water Policy on Water Resources Management and Development. Uganda had its National Water Policy in 1997; and Tanzania replaced its 1991 Water Policy with National Water Policy 2002. All these polices promote comprehensive water resources management and development framework.

The Key Legal Principles of Transboundary Water Resources Management are included in the United Nations Convention on the Non-Navigational Uses of International Water Courses of 1997, and in the Protocol for the Sustainable Development of the Lake Victoria Basin, 2003. They are:

i. The principle of international cooperation, which refers to undertakings on international water resources that are governed by two or more states. ii. The concept of an international watercourse, which is a system of surface waters and ground waters constituting, by virtue of their physical relationship, a unitary whole, parts of which are situated in different states and normally flowing into a common terminus. iii. The principle of equitable utilization of the water resources in shared watercourses. iv. The obligation not to cause significant harm to co-riparians: In using the resources, states are required not to cause significant harm to the interests of other states by pollution or other conduct.

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v. The protection of present reasonable and beneficial uses of the water: International law favors the protection of present beneficial uses of shared waters and discourages wasteful uses. vi. Notification and information sharing: The 1997 Convention and general international law require states to cooperate and share information regarding the development of shared water resources. The principle of prior notification requires that each of the riparian states should notify other basin states of planned measures or planned activities within its territory that may have adverse effects upon those other states. vii. The principle of regular exchange of data and information is essential for confidence building, and basis upon which states can build a reliable and comprehensive knowledge base. viii. The principle of the prevention, minimization, and control of pollution of watercourses to minimize adverse effects on freshwater resources and their ecosystems, including fish and other aquatic species, and on human health. ix. The principle of the protection and preservation of the ecosystems of international watercourses whereby ecosystems are treated as units, all components of which are necessary to their proper functioning, and that they be protected and preserved. x. The principle of community of interest in an international watercourse whereby all states sharing an international watercourse system have an interest in the unitary whole of the system. xi. The principle that water is a social and economic good. (The Rio-Dublin Principles 1992)

4.2.2 Ongoing Programs and Initiatives Initiatives already exist in the East African as commitments and efforts toward responding to the emerging challenges of climate change. Some these are:

v The Lake Victoria Environmental Management Project (LVEMP) is a comprehensive regional development program that covers the whole of Lake Victoria and its catchment areas. The project aims at collecting information on the environmental status of the lake and its catchment, as well as on the practices being used by the communities living around the lake. It also helps establish institutions, build capacity, and supports actions to deal with environmental problems, such as water hyacinth control, improving water quality, land use management, and sustainable use of the wetlands to protect both their buffering capacity and the products they contain.

v The Nile Basin Initiative (NBI)/The Nile Equatorial Lakes Subsidiary Action Program (NELSAP) is a partnership among the Nile riparian states that seeks to develop the river in a cooperative manner, share substantial socioeconomic benefits, and promote regional peace and security. Having begun as a dialog among the riparian states it resulted in a shared vision to achieve sustainable socioeconomic development through the equitable use of, and benefit from, the common Nile Basin water resources. NELSAP is an NBI investment program that promotes investments in power development and trade, water resources management, management of lakes, fisheries, and watersheds as well as agricultural trade and productivity.

v The Global Water Partnership, with its regional headquarters in Uganda, is a working partnership among all actors involved in water management: government agencies, public institutions, private companies, professional organizations, multilateral development agencies and others committed to the Dublin-Rio principles.

v The Programme on Climate Change Adaptation and Mitigation in the COMESA-EAC-SADC region is a five-year initiative that started in 2010. It aims to inject Africa’s Unified Position on Climate Change into the post-2012 United Nations Framework Convention on Climate Change

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global agreement and to unlock resources for promoting strategic interventions that sustain productivity and livelihood improvements for millions of climate-vulnerable people in the region. The program is linked to the AU-NEPAD Climate Change Adaptation-Mitigation Framework and its Investment Platform for Climate-Smart Agriculture.

4.2.3 Examples of Current Efforts to Support Adaptation to Climate Change

(a) Water Resources Improved water stress management requires strategies to address risks related to flow regimes, including floods and drought, and to health and poverty since they are linked to future water resource conditions. Water management measures must target the reduction of wastewater, improved storage, and the balance of green and blue water under various climate change–induced water demand levels. Technological innovations and water-use efficiency remain priorities, especially in land use planning, crop production technologies, and other forms of withdrawals. A strong emphasis should be put on information to support adaptation through forecasting, prediction, and early warning, as well as traditional knowledge and raising awareness. Improved water governance and cross-country and basin-wide cooperation will continue to be instrumental in addressing water-use conflicts, policy support needs, and equitable water and water benefit sharing and trade.

v Kenya has incorporated climate change adaptation strategies in its national planning documents. Short-term measures include implementing a duty waiver on imported maize, conserving water, irrigation, and constructing cattle troughs and introducing watering of animals. Long-term measures include sensitizing communities to efficient and effective use of water, supporting and encouraging the use of rainwater harvesting techniques, de-silting or building new water pans and dams, and adopting energy-saving technologies (FAO 2014).

v Rwanda, through the Africa Adaptation program, has established the Rwanda Environment and Climate Information System and acquired and installed modern meteorological and information technology equipment and trained 50 people from Rwanda’s Meteorological Agency and Environmental Management Authority (REMA) on climate modelling, forecasting, and disaster management preparedness. An enormous range of capacity has been built through these interventions. A climate change database and a geographic information system have been established. An atlas of Rwanda’s vulnerability to likely impacts of climate change and a map of risk zones were created. Vulnerability and risk assessments were carried out. The NAPA was amended to include an early warning system (EWS), with representatives from 11 institutions who were trained on modelling, forecasting, and preparedness. This group now constitutes an EWS Disaster Management Steering Committee and professionals from other agencies are being trained on the deployment and use of early warning information. A series of agriculture and water-related pilot projects also provided key information on adaptation opportunities.3

v In Tanzania, the water sector has been the focus of significant reform and investment with support from a US$800 million fund to implement the Water Sector Development Programme. The program focuses on infrastructure investment and provision for urban and rural water supply and sanitation alongside implementation of a reformed water resource management regime. The donor funding, which comes through the Water Resource Management function of the Ministry of Water and nine semi-autonomous basin water offices, has been key in prioritizing the needs of the environment and poor communities over water needed for industry and production.

3 https://www.undp-aap.org/countries/rwanda

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Nonetheless, a fully functional system of water resource regulation and administration—including scaled responses to drought events, which prioritize vulnerable users, is imperative if Tanzania is to be resilient to climate shocks (Nepworth 2010).

v In Uganda, the Directorates of Water Resources Management and Water Development within the Ministry of Water and Environment take the lead for vulnerability and adaptation responses. As a result, a National Assessment and Strategy in Water Resources has been put together and is being used to incorporate climate change into strategic planning in the water resource sector. Meanwhile, the Joint Water and Sanitation Sector Programme Support, with a total commitment of US$150 million from 2007–2012, sought to improve the management of water resources and delivery of water services with a view to reducing Uganda’s vulnerability to climate change. The World Bank has supported Uganda’s effort to develop its Water Resources Assistance Strategy 2011, which aims to assist the government in identifying priority actions for building on successful outcomes, tackling remaining challenges, and exploiting Uganda’s water sector (Nepworth 2010).

(b) Aquatic Ecosystems Ogutu-Ohwayo et al. (2011) finds that the local population in the LVB lack sufficient understanding of the possible impacts of climate change on water resources, aquatic ecosystems, resources, and their livelihoods. This lack of knowledge exposes the already vulnerable ecosystems to the effects of climate change. Since most of the households in the LVB rely on rain-fed farming, their food sufficiency depends on their success in farming. Thus, anything that affects farming, such as drought and flooding, would ultimately affect household food sufficiency. This makes the populations in the LVB vulnerable to the effects of climate change, with negative impacts on aquatic biodiversity (LVBC 2011).

Adaptations by populations within the LVB are necessary to respond to opportunities and threats to resources and livelihoods caused by climate change. Small-scale fishermen and riparian communities within the basin have responded by adjusting and taking on other livelihood occupations, such as diversifying to crops, livestock, forestry, and other income-generating activities; changing and diversifying fisheries operations; migrating to less affected areas; or resorting to social capital and community support (Ogutu- Ohwayo et al. 2011).

Other adaptations put in place by the local communities include planting drought-tolerant crops, timely planting of crops, use of organic pesticides to control diseases, and irrigation during dry seasons. Water security has been enhanced through water harvesting and storage at homes, support by nongovernmental organizations (NGOs) to construct tanks, drilling of shallow wells, and enacting laws (LVBC 2011). Promotion of aquaculture through an Economic Stimulus Plan to reduce pressure on the dwindling catches from natural fisheries has also been employed (Manyala 2011).

Further adaptation measures proposed include the need to collectively identify, agree, and prioritize the most appropriate locally available and affordable strategies, improve on them, and provide EWSs to increase resilience and sustain livelihoods (Ogutu-Ohwayo et al. 2011).

4.3 GAPS IN INSTITUTIONAL CAPACITIES FOR ADAPTATION

The following gaps in institutional capacity and capability may compromise design, implementation, and monitoring of adaptation measures at local and national levels:

v Human technical capacity in water resources management is limited, which compromises effective design and implementation of adaptive measures and actions. This situation is compounded by

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lack of financial and technological resources at the national and local levels. Most of the projects in the region are implemented by international agencies with limited technology transfer.

v Gaps in meteorological infrastructure and data in the region hinder efforts to undertake much- needed meteorological research and channel it to where it is most needed.

v The impacts of climate change on gender and marginalized groups are underappreciated. Gender, age, and disability are important determinants of adaptive capacity. In most cases, women, the aged, youth, and children make up a significant share of the poor in communities that are highly dependent on local natural resources for their livelihood and are disproportionately vulnerable to and affected by climate change. Women in rural areas have the major responsibility for household water supply, energy for cooking and heating, and for food security. As a result, they are negatively affected by drought, uncertain rainfall, and deforestation. Because of their roles, unequal access to resources and limited mobility, women in many contexts are disproportionately affected by natural disasters.

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APPENDIX

APPENDIX I: RIVER DISCHARGE STATISTICAL RESULTS

The parameter estimates and univariate model diagnostics for assessing how well the selected VARMAX (2,2,0) models fit the data.

Table 7: Model Parameter Estimates for LN (River Discharge+1) (Rain_dev is Annual rainfall divided by its mean)

Region River Parameter Estimate Standard t Value Pr > |t| Variable error Kenya Gucha-Migori CONST1 -0.3383 0.42953 -0.79 0.4349 1 Kenya Gucha-Migori XL0_1_1 2.646 0.6002 4.41 0.0001 rain_dev(t) Kenya Gucha-Migori XL1_1_1 0.29291 0.94146 0.31 0.7571 rain_dev(t-1) Kenya Gucha-Migori XL2_1_1 -0.7575 0.77366 -0.98 0.3325 rain_dev(t-2) Kenya Gucha-Migori XL3_1_1 -1.2997 0.62273 -2.09 0.0423 rain_dev(t-3) Kenya Gucha-Migori XL4_1_1 0.37119 0.51566 0.72 0.4752 rain_dev(t-4) Kenya Gucha-Migori AR1_1_1 0.44882 0.20241 2.22 0.0315 log_discharge(t-1) Kenya Gucha-Migori AR2_1_1 0.32302 0.22917 1.41 0.1653 log_discharge(t-2) Kenya Gucha-Migori MA1_1_1 0.33539 0.23347 1.44 0.1575 e1(t-1) Kenya Gucha-Migori MA2_1_1 0.66461 0.23075 2.88 0.006 e1(t-2) Kenya North Awach CONST1 0.03005 0.14247 0.21 0.8338 1 Kenya North Awach XL0_1_1 1.43695 0.1974 7.28 0.0001 rain_dev(t) Kenya North Awach XL1_1_1 0.24034 0.40904 0.59 0.5596 rain_dev(t-1) Kenya North Awach XL2_1_1 0.0858 0.31761 0.27 0.7882 rain_dev(t-2) Kenya North Awach XL3_1_1 -0.3779 0.22064 -1.71 0.0934 rain_dev(t-3) Kenya North Awach XL4_1_1 -0.2634 0.18574 -1.42 0.1627 rain_dev(t-4) Kenya North Awach AR1_1_1 0.07874 0.19603 0.4 0.6898 log_discharge(t-1) Kenya North Awach AR2_1_1 0.1672 0.13293 1.26 0.2147 log_discharge(t-2) Kenya North Awach MA1_1_1 0.39064 0.16942 2.31 0.0256 e1(t-1) Kenya North Awach MA2_1_1 0.60936 0.16346 3.73 0.0005 e1(t-2) Kenya Nyando CONST1 0.19432 0.12771 1.52 0.1348 1 Kenya Nyando XL0_1_1 3.79209 0.62242 6.09 0.0001 rain_dev(t) Kenya Nyando XL1_1_1 -5.0671 1.29039 -3.93 0.0003 rain_dev(t-1) Kenya Nyando XL2_1_1 1.23713 1.24152 1 0.3241 rain_dev(t-2) Kenya Nyando XL3_1_1 -1.3421 0.80834 -1.66 0.1035 rain_dev(t-3) Kenya Nyando XL4_1_1 1.43846 0.32166 4.47 0.0001 rain_dev(t-4) Kenya Nyando AR1_1_1 1.62773 0.06597 24.68 0.0001 log_discharge(t-1) Kenya Nyando AR2_1_1 -0.7123 0.06483 -10.99 0.0001 log_discharge(t-2) Kenya Nyando MA1_1_1 1.99351 0.07564 26.36 0.0001 e1(t-1) Kenya Nyando MA2_1_1 -1 0.07552 -13.24 0.0001 e1(t-2) Kenya Nzoia CONST1 4.72861 2.26152 2.09 0.042 1 Kenya Nzoia XL0_1_1 2.7027 0.48114 5.62 0.0001 rain_dev(t) Kenya Nzoia XL1_1_1 1.10681 1.46599 0.75 0.454 rain_dev(t-1) Kenya Nzoia XL2_1_1 -0.7063 1.06792 -0.66 0.5116 rain_dev(t-2) Kenya Nzoia XL3_1_1 -0.3293 0.52093 -0.63 0.5304 rain_dev(t-3)

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Region River Parameter Estimate Standard t Value Pr > |t| Variable error Kenya Nzoia XL4_1_1 -0.3145 0.58161 -0.54 0.5913 rain_dev(t-4) Kenya Nzoia AR1_1_1 -0.5239 0.47653 -1.1 0.2772 log_discharge(t-1) Kenya Nzoia AR2_1_1 -0.0234 0.30634 -0.08 0.9395 log_discharge(t-2) Kenya Nzoia MA1_1_1 -0.9837 0.46664 -2.11 0.0404 e1(t-1) Kenya Nzoia MA2_1_1 -0.3631 0.25416 -1.43 0.1597 e1(t-2) Kenya Sio CONST1 -0.0914 0.04553 -2.01 0.0504 1 Kenya Sio XL0_1_1 2.69487 0.40715 6.62 0.0001 rain_dev(t) Kenya Sio XL1_1_1 -3.5413 0.94968 -3.73 0.0005 rain_dev(t-1) Kenya Sio XL2_1_1 0.80823 0.97959 0.83 0.4135 rain_dev(t-2) Kenya Sio XL3_1_1 0.69291 0.60899 1.14 0.261 rain_dev(t-3) Kenya Sio XL4_1_1 -0.1833 0.23419 -0.78 0.4377 rain_dev(t-4) Kenya Sio AR1_1_1 1.52057 0.08928 17.03 0.0001 log_discharge(t-1) Kenya Sio AR2_1_1 -0.6742 0.0874 -7.71 0.0001 log_discharge(t-2) Kenya Sio MA1_1_1 1.993 0.08028 24.82 0.0001 e1(t-1) Kenya Sio MA2_1_1 -1 0.08015 -12.48 0.0001 e1(t-2) Kenya Sondu CONST1 -0.1696 0.11571 -1.47 0.1495 1 Kenya Sondu XL0_1_1 2.83106 0.34096 8.3 0.0001 rain_dev(t) Kenya Sondu XL1_1_1 -3.0374 0.60877 -4.99 0.0001 rain_dev(t-1) Kenya Sondu XL2_1_1 0.73279 0.64767 1.13 0.2636 rain_dev(t-2) Kenya Sondu XL3_1_1 -0.5464 0.52833 -1.03 0.3063 rain_dev(t-3) Kenya Sondu XL4_1_1 0.19381 0.28197 0.69 0.4952 rain_dev(t-4) Kenya Sondu AR1_1_1 1.20437 0 . . log_discharge(t-1) Kenya Sondu AR2_1_1 -0.2044 0 . . log_discharge(t-2) Kenya Sondu MA1_1_1 1.27239 0.16747 7.6 0.0001 e1(t-1) Kenya Sondu MA2_1_1 -0.2729 0.16769 -1.63 0.1103 e1(t-2) Kenya South Awach CONST1 0.15661 0.17989 0.87 0.3884 1 Kenya South Awach XL0_1_1 1.57215 0.23691 6.64 0.0001 rain_dev(t) Kenya South Awach XL1_1_1 -0.0519 0.43583 -0.12 0.9058 rain_dev(t-1) Kenya South Awach XL2_1_1 -0.0770 0.36396 -0.21 0.8334 rain_dev(t-2) Kenya South Awach XL3_1_1 -0.0240 0.26343 -0.09 0.9277 rain_dev(t-3) Kenya South Awach XL4_1_1 -0.1670 0.21562 -0.77 0.4426 rain_dev(t-4) Kenya South Awach AR1_1_1 0.15248 0.19942 0.76 0.4483 log_discharge(t-1) Kenya South Awach AR2_1_1 0.11452 0.1541 0.74 0.4611 log_discharge(t-2) Kenya South Awach MA1_1_1 0.33596 0.16744 2.01 0.0506 e1(t-1) Kenya South Awach MA2_1_1 0.66404 0.16082 4.13 0.0001 e1(t-2) Kenya Yala CONST1 0.56318 0.60754 0.93 0.3587 1 Kenya Yala XL0_1_1 1.959 0.14123 13.87 0.0001 rain_dev(t) Kenya Yala XL1_1_1 0.88532 0 . . rain_dev(t-1) Kenya Yala XL2_1_1 0.1593 0 . . rain_dev(t-2) Kenya Yala XL3_1_1 0.12891 0 . . rain_dev(t-3) Kenya Yala XL4_1_1 -0.2014 0.22658 -0.89 0.3785 rain_dev(t-4) Kenya Yala AR1_1_1 -0.0081 0 . . log_discharge(t-1) Kenya Yala AR2_1_1 0.04904 0 . . log_discharge(t-2) Kenya Yala MA1_1_1 -0.1135 0.24108 -0.47 0.6401 e1(t-1) Kenya Yala MA2_1_1 -0.3633 0 . . e1(t-2)

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Region River Parameter Estimate Standard t Value Pr > |t| Variable error Tanzania Biharamulo CONST1 0.5466 0.82215 0.66 0.5094 1 Tanzania Biharamulo XL0_1_1 1.5389 0.35445 4.34 0.0001 rain_dev(t) Tanzania Biharamulo XL1_1_1 0.76371 0.48486 1.58 0.1219 rain_dev(t-1) Tanzania Biharamulo XL2_1_1 -0.9022 0.50717 -1.78 0.0817 rain_dev(t-2) Tanzania Biharamulo XL3_1_1 -0.0915 0.37397 -0.24 0.8078 rain_dev(t-3) Tanzania Biharamulo XL4_1_1 -0.6434 0.34896 -1.84 0.0716 rain_dev(t-4) Tanzania Biharamulo AR1_1_1 0.03199 0.21124 0.15 0.8803 log_discharge(t-1) Tanzania Biharamulo AR2_1_1 0.54951 0.18088 3.04 0.0039 log_discharge(t-2) Tanzania Biharamulo MA1_1_1 -0.2631 0.27564 -0.95 0.3448 e1(t-1) Tanzania Biharamulo MA2_1_1 0.23296 0.22274 1.05 0.301 e1(t-2) Tanzania E. shore streams CONST1 -1.0180 0.95704 -1.06 0.2929 1 Tanzania E. shore streams XL0_1_1 3.6671 0.55866 6.56 0.0001 rain_dev(t) Tanzania E. shore streams XL1_1_1 0.23877 0.94769 0.25 0.8022 rain_dev(t-1) Tanzania E. shore streams XL2_1_1 -0.2958 0.7449 -0.4 0.6931 rain_dev(t-2) Tanzania E. shore streams XL3_1_1 1.49828 0.68951 2.17 0.0349 rain_dev(t-3) Tanzania E. shore streams XL4_1_1 -1.4279 0.4508 -3.17 0.0027 rain_dev(t-4) Tanzania E. shore streams AR1_1_1 0.47767 0.16173 2.95 0.0049 log_discharge(t-1) Tanzania E. shore streams AR2_1_1 -0.4230 0.11982 -3.53 0.0009 log_discharge(t-2) Tanzania E. shore streams MA1_1_1 0.79177 0.08117 9.75 0.0001 e1(t-1) Tanzania E. shore streams MA2_1_1 -1 0.08483 -11.79 0.0001 e1(t-2) Tanzania Grumeti CONST1 0.98681 4.4755 0.22 0.8264 1 Tanzania Grumeti XL0_1_1 1.51473 0.74952 2.02 0.049 rain_dev(t) Tanzania Grumeti XL1_1_1 3.69442 1.43179 2.58 0.0131 rain_dev(t-1) Tanzania Grumeti XL2_1_1 2.6523 1.59527 1.66 0.103 rain_dev(t-2) Tanzania Grumeti XL3_1_1 -0.9003 0.96301 -0.93 0.3546 rain_dev(t-3) Tanzania Grumeti XL4_1_1 -1.6104 0.58656 -2.75 0.0085 rain_dev(t-4) Tanzania Grumeti AR1_1_1 -1.3372 0.11979 -11.16 0.0001 log_discharge(t-1) Tanzania Grumeti AR2_1_1 -0.4317 0.11524 -3.75 0.0005 log_discharge(t-2) Tanzania Grumeti MA1_1_1 -1.9936 0.06885 -28.96 0.0001 e1(t-1) Tanzania Grumeti MA2_1_1 -1 0.06862 -14.57 0.0001 e1(t-2) Tanzania Issanga CONST1 1.65461 0.20117 8.23 0.0001 1 Tanzania Issanga XL0_1_1 6.99315 1.34401 5.2 0.0001 rain_dev(t) Tanzania Issanga XL1_1_1 -9.007 2.76567 -3.26 0.0021 rain_dev(t-1) Tanzania Issanga XL2_1_1 0.21073 2.32824 0.09 0.9283 rain_dev(t-2) Tanzania Issanga XL3_1_1 -0.23224 0.52519 -0.44 0.6604 rain_dev(t-3) Tanzania Issanga XL4_1_1 0.40532 0.53696 0.75 0.4541 rain_dev(t-4) Tanzania Issanga AR1_1_1 1.46302 0 . . log_discharge(t-1) Tanzania Issanga AR2_1_1 -0.4630 0 . . log_discharge(t-2) Tanzania Issanga MA1_1_1 1.99999 0 . . e1(t-1) Tanzania Issanga MA2_1_1 -1.0 0 . . e1(t-2) Tanzania Kagera CONST1 0.18591 1.04206 0.18 0.8592 1 Tanzania Kagera XL0_1_1 0.98082 0.23159 4.24 0.0001 rain_dev(t) Tanzania Kagera XL1_1_1 1.62189 0.18553 8.74 0.0001 rain_dev(t-1) Tanzania Kagera XL2_1_1 -0.3471 0.41807 -0.83 0.4106 rain_dev(t-2) Tanzania Kagera XL3_1_1 -0.8431 0.29263 -2.88 0.006 rain_dev(t-3)

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Region River Parameter Estimate Standard t Value Pr > |t| Variable error Tanzania Kagera XL4_1_1 -0.1707 0.23675 -0.72 0.4743 rain_dev(t-4) Tanzania Kagera AR1_1_1 -0.0984 0.15939 -0.62 0.5401 log_discharge(t-1) Tanzania Kagera AR2_1_1 0.84125 0.14298 5.88 0.0001 log_discharge(t-2) Tanzania Kagera MA1_1_1 -0.4628 0.23105 -2 0.0509 e1(t-1) Tanzania Kagera MA2_1_1 0.53716 0.2291 2.34 0.0233 e1(t-2) Tanzania Magogo Moame CONST1 -5.8766 3.85567 -1.52 0.1342 1 Tanzania Magogo Moame XL0_1_1 7.29101 1.07287 6.8 0.0001 rain_dev(t) Tanzania Magogo Moame XL1_1_1 0.08659 2.77557 0.03 0.9752 rain_dev(t-1) Tanzania Magogo Moame XL2_1_1 0.76944 1.96537 0.39 0.6972 rain_dev(t-2) Tanzania Magogo Moame XL3_1_1 0.69852 1.68541 0.41 0.6804 rain_dev(t-3) Tanzania Magogo Moame XL4_1_1 -1.7256 1.14837 -1.5 0.1396 rain_dev(t-4) Tanzania Magogo Moame AR1_1_1 0.40988 0.36412 1.13 0.266 log_discharge(t-1) Tanzania Magogo Moame AR2_1_1 -0.2287 0.24108 -0.95 0.3476 log_discharge(t-2) Tanzania Magogo Moame MA1_1_1 0.32843 0.38921 0.84 0.403 e1(t-1) Tanzania Magogo Moame MA2_1_1 -0.2718 0.32665 -0.83 0.4096 e1(t-2) Tanzania Mara CONST1 -7.6347 2.25551 -3.38 0.0014 1 Tanzania Mara XL0_1_1 4.1245 0.7391 5.58 0.0001 rain_dev(t) Tanzania Mara XL1_1_1 1.38387 1.00676 1.37 0.1758 rain_dev(t-1) Tanzania Mara XL2_1_1 2.79941 0.93389 3 0.0043 rain_dev(t-2) Tanzania Mara XL3_1_1 3.5101 0.71444 4.91 0.0001 rain_dev(t-3) Tanzania Mara XL4_1_1 0.47683 0.96487 0.49 0.6235 rain_dev(t-4) Tanzania Mara AR1_1_1 0.38765 0.12073 3.21 0.0024 log_discharge(t-1) Tanzania Mara AR2_1_1 -0.7896 0.07848 -10.06 0.0001 log_discharge(t-2) Tanzania Mara MA1_1_1 0.40579 0.1256 3.23 0.0023 e1(t-1) Tanzania Mara MA2_1_1 -1 0.06799 -14.71 0.0001 e1(t-2) Tanzania Mbalageti CONST1 -2.5220 1.18994 -2.12 0.0394 1 Tanzania Mbalageti XL0_1_1 2.98832 0.65699 4.55 0.0001 rain_dev(t) Tanzania Mbalageti XL1_1_1 0.27863 0.92046 0.3 0.7634 rain_dev(t-1) Tanzania Mbalageti XL2_1_1 -0.0810 0.86596 -0.09 0.9259 rain_dev(t-2) Tanzania Mbalageti XL3_1_1 1.20288 0.71152 1.69 0.0975 rain_dev(t-3) Tanzania Mbalageti XL4_1_1 -0.6604 0.4876 -1.35 0.1821 rain_dev(t-4) Tanzania Mbalageti AR1_1_1 0.71332 0.15285 4.67 0.0001 log_discharge(t-1) Tanzania Mbalageti AR2_1_1 -0.53058 0.13395 -3.96 0.0003 log_discharge(t-2) Tanzania Mbalageti MA1_1_1 0.78251 0.09205 8.5 0.0001 e1(t-1) Tanzania Mbalageti MA2_1_1 -1 0.08442 -11.85 0.0001 e1(t-2) Tanzania Nyashishi CONST1 -4.4304 3.66315 -1.21 0.2325 1 Tanzania Nyashishi XL0_1_1 2.96732 0.71662 4.14 0.0001 rain_dev(t) Tanzania Nyashishi XL1_1_1 2.46129 1.27013 1.94 0.0587 rain_dev(t-1) Tanzania Nyashishi XL2_1_1 0.41024 1.51367 0.27 0.7876 rain_dev(t-2) Tanzania Nyashishi XL3_1_1 0.11376 1.04417 0.11 0.9137 rain_dev(t-3) Tanzania Nyashishi XL4_1_1 -0.2230 0.84185 -0.26 0.7922 rain_dev(t-4) Tanzania Nyashishi AR1_1_1 -0.7030 0.46889 -1.5 0.1405 log_discharge(t-1) Tanzania Nyashishi AR2_1_1 -0.0353 0.37301 -0.09 0.925 log_discharge(t-2) Tanzania Nyashishi MA1_1_1 -0.8705 0.4539 -1.92 0.0612 e1(t-1) Tanzania Nyashishi MA2_1_1 -0.0428 0.40732 -0.11 0.9167 e1(t-2)

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Region River Parameter Estimate Standard t Value Pr > |t| Variable error Tanzania S. shore streams CONST1 -3.0345 5.37972 -0.56 0.5754 1 Tanzania S. shore streams XL0_1_1 6.17175 1.59318 3.87 0.0003 rain_dev(t) Tanzania S. shore streams XL1_1_1 3.68818 2.77303 1.33 0.1899 rain_dev(t-1) Tanzania S. shore streams XL2_1_1 0.07696 3.52755 0.02 0.9827 rain_dev(t-2) Tanzania S. shore streams XL3_1_1 -0.4180 2.0466 -0.2 0.839 rain_dev(t-3) Tanzania S. shore streams XL4_1_1 -3.1620 1.62189 -1.95 0.0572 rain_dev(t-4) Tanzania S. shore streams AR1_1_1 -0.3189 0.38376 -0.83 0.4102 log_discharge(t-1) Tanzania S. shore streams AR2_1_1 0.01237 0.38413 0.03 0.9744 log_discharge(t-2) Tanzania S. shore streams MA1_1_1 -0.4013 0.37707 -1.06 0.2926 e1(t-1) Tanzania S. shore streams MA2_1_1 0.02604 0.37741 0.07 0.9453 e1(t-2) Tanzania Simiyu CONST1 -0.8313 0.74514 -1.12 0.2703 1 Tanzania Simiyu XL0_1_1 3.15385 0.70998 4.44 0.0001 rain_dev(t) Tanzania Simiyu XL1_1_1 2.96243 1.40231 2.11 0.04 rain_dev(t-1) Tanzania Simiyu XL2_1_1 -1.0154 1.15459 -0.88 0.3836 rain_dev(t-2) Tanzania Simiyu XL3_1_1 -1.1213 1.05712 -1.06 0.2942 rain_dev(t-3) Tanzania Simiyu XL4_1_1 -0.7226 0.68311 -1.06 0.2956 rain_dev(t-4) Tanzania Simiyu AR1_1_1 -0.0635 0.3086 -0.21 0.8379 log_discharge(t-1) Tanzania Simiyu AR2_1_1 0.34697 0.21373 1.62 0.1112 log_discharge(t-2) Tanzania Simiyu MA1_1_1 0.31156 0.25124 1.24 0.2211 e1(t-1) Tanzania Simiyu MA2_1_1 0.68844 0.24792 2.78 0.0079 e1(t-2) Tanzania W. shore streams CONST1 0.54398 0.64286 0.85 0.4017 1 Tanzania W. shore streams XL0_1_1 0.36557 0.22905 1.6 0.1172 rain_dev(t) Tanzania W. shore streams XL1_1_1 -0.4375 0.35335 -1.24 0.2218 rain_dev(t-1) Tanzania W. shore streams XL2_1_1 0.39911 0.35895 1.11 0.2719 rain_dev(t-2) Tanzania W. shore streams XL3_1_1 -0.1623 0.29536 -0.55 0.5854 rain_dev(t-3) Tanzania W. shore streams XL4_1_1 0.1809 0.23367 0.77 0.4427 rain_dev(t-4) Tanzania W. shore streams AR1_1_1 0.93302 0.26456 3.53 0.001 log_discharge(t-1) Tanzania W. shore streams AR2_1_1 -0.2232 0.21139 -1.06 0.2965 log_discharge(t-2) Tanzania W. shore streams MA1_1_1 0.98949 0.17964 5.51 0.0001 e1(t-1) Tanzania W. shore streams MA2_1_1 -0.7287 0.2532 -2.88 0.006 e1(t-2) Uganda Bukora CONST1 0.08232 0.32176 0.26 0.7992 1 Uganda Bukora XL0_1_1 0.80838 0.47314 1.71 0.0941 rain_dev(t) Uganda Bukora XL1_1_1 -0.1990 0.762 -0.26 0.7951 rain_dev(t-1) Uganda Bukora XL2_1_1 -1.0335 0.54206 -1.91 0.0627 rain_dev(t-2) Uganda Bukora XL3_1_1 1.33912 0.39714 3.37 0.0015 rain_dev(t-3) Uganda Bukora XL4_1_1 -0.6359 0.30016 -2.12 0.0394 rain_dev(t-4) Uganda Bukora AR1_1_1 1.57052 0.07708 20.37 0.0001 log_discharge(t-1) Uganda Bukora AR2_1_1 -0.8302 0.0716 -11.6 0.0001 log_discharge(t-2) Uganda Bukora MA1_1_1 1.51908 0.08253 18.41 0.0001 e1(t-1) Uganda Bukora MA2_1_1 -1 0.08817 -11.34 0.0001 e1(t-2) Uganda Katonga CONST1 0.53867 1.96075 0.27 0.7847 1 Uganda Katonga XL0_1_1 0.92502 0.91228 1.01 0.3158 rain_dev(t) Uganda Katonga XL1_1_1 0.09703 1.07264 0.09 0.9283 rain_dev(t-1) Uganda Katonga XL2_1_1 0.4356 0.95918 0.45 0.6518 rain_dev(t-2) Uganda Katonga XL3_1_1 0.5785 0.86331 0.67 0.5061 rain_dev(t-3)

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Region River Parameter Estimate Standard t Value Pr > |t| Variable error Uganda Katonga XL4_1_1 -1.4393 0.91016 -1.58 0.1205 rain_dev(t-4) Uganda Katonga AR1_1_1 0.61266 0.39719 1.54 0.1297 log_discharge(t-1) Uganda Katonga AR2_1_1 -0.3314 0.25905 -1.28 0.2071 log_discharge(t-2) Uganda Katonga MA1_1_1 0.08179 0.46451 0.18 0.861 e1(t-1) Uganda Katonga MA2_1_1 -0.2482 0.19825 -1.25 0.2168 e1(t-2) Uganda N. shore streams CONST1 0.25138 0.06252 4.02 0.0002 1 Uganda N. shore streams XL0_1_1 0.2245 0.33738 0.67 0.509 rain_dev(t) Uganda N. shore streams XL1_1_1 0.19163 0.62572 0.31 0.7608 rain_dev(t-1) Uganda N. shore streams XL2_1_1 -1.5883 0.4257 -3.73 0.0005 rain_dev(t-2) Uganda N. shore streams XL3_1_1 1.08486 0.16725 6.49 0.0001 rain_dev(t-3) Uganda N. shore streams XL4_1_1 -0.0707 0.17533 -0.4 0.6885 rain_dev(t-4) Uganda N. shore streams AR1_1_1 1.83152 0.03797 48.23 0.0001 log_discharge(t-1) Uganda N. shore streams AR2_1_1 -0.9374 0.03692 -25.39 0.0001 log_discharge(t-2) Uganda N. shore streams MA1_1_1 1.99202 0.08601 23.16 0.0001 e1(t-1) Uganda N. shore streams MA2_1_1 -1 0.08584 -11.65 0.0001 e1(t-2)

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Table 8: Portmanteau Test for Cross-Correlations of Residuals for LN (River Discharge+1) The results show tests for white noise residuals based on the cross-correlations of the residuals. Insignificant test results show that we cannot reject the null hypothesis that the residuals are uncorrelated.

County River Up to lag Degrees of Chi-square Pr > ChiSq freedom Kenya Gucha-Migori 5 1 3.16 0.0754 Kenya Gucha-Migori 6 2 6.65 0.0359 Kenya Gucha-Migori 7 3 6.71 0.0816 Kenya Gucha-Migori 8 4 7.93 0.0944 Kenya Gucha-Migori 9 5 11.69 0.0393 Kenya Gucha-Migori 10 6 12.98 0.0434 Kenya Gucha-Migori 11 7 13.41 0.0626 Kenya Gucha-Migori 12 8 13.64 0.0915 Kenya North Awach 5 1 8.33 0.0039 Kenya North Awach 6 2 9.55 0.0084 Kenya North Awach 7 3 10.61 0.0141 Kenya North Awach 8 4 11.19 0.0246 Kenya North Awach 9 5 11.32 0.0454 Kenya North Awach 10 6 17.32 0.0082 Kenya North Awach 11 7 21.11 0.0036 Kenya North Awach 12 8 22.28 0.0044 Kenya Nyando 5 1 6.58 0.0103 Kenya Nyando 6 2 8.17 0.0168 Kenya Nyando 7 3 8.18 0.0424 Kenya Nyando 8 4 8.18 0.0852 Kenya Nyando 9 5 10.24 0.0686 Kenya Nyando 10 6 11.84 0.0656 Kenya Nyando 11 7 12.44 0.0871 Kenya Nyando 12 8 12.75 0.1207 Kenya Nzoia 5 1 2.1 0.1476 Kenya Nzoia 6 2 2.58 0.2747 Kenya Nzoia 7 3 3.42 0.3309 Kenya Nzoia 8 4 3.7 0.448 Kenya Nzoia 9 5 3.73 0.5891 Kenya Nzoia 10 6 4.81 0.5683 Kenya Nzoia 11 7 5.51 0.5977 Kenya Nzoia 12 8 5.72 0.6784 Kenya Sio 5 1 1.76 0.1849 Kenya Sio 6 2 4.33 0.115 Kenya Sio 7 3 4.68 0.1968 Kenya Sio 8 4 5.23 0.2644 Kenya Sio 9 5 6.34 0.2748 Kenya Sio 10 6 10.16 0.1182 Kenya Sio 11 7 10.16 0.1798 Kenya Sio 12 8 10.18 0.2526 Kenya Sondu 5 1 2.89 0.0889

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County River Up to lag Degrees of Chi-square Pr > ChiSq freedom Kenya Sondu 6 2 3.98 0.1366 Kenya Sondu 7 3 6.16 0.1042 Kenya Sondu 8 4 6.18 0.1861 Kenya Sondu 9 5 6.34 0.2744 Kenya Sondu 10 6 7 0.3212 Kenya Sondu 11 7 7.76 0.3538 Kenya Sondu 12 8 7.77 0.4558 Kenya South Awach 5 1 1.18 0.277 Kenya South Awach 6 2 1.31 0.5205 Kenya South Awach 7 3 1.5 0.683 Kenya South Awach 8 4 1.51 0.8249 Kenya South Awach 9 5 3.87 0.5679 Kenya South Awach 10 6 3.99 0.6776 Kenya South Awach 11 7 4.05 0.7735 Kenya South Awach 12 8 5.22 0.7337 Kenya Yala 5 1 2.78 0.0955 Kenya Yala 6 2 3.86 0.1452 Kenya Yala 7 3 4.17 0.2432 Kenya Yala 8 4 4.18 0.3819 Kenya Yala 9 5 4.24 0.5158 Kenya Yala 10 6 5.61 0.4682 Kenya Yala 11 7 5.64 0.5821 Kenya Yala 12 8 8.07 0.4266 Tanzania Biharamulo 5 1 3.87 0.0491 Tanzania Biharamulo 6 2 4.61 0.0998 Tanzania Biharamulo 7 3 4.68 0.1969 Tanzania Biharamulo 8 4 4.96 0.291 Tanzania Biharamulo 9 5 6.21 0.2865 Tanzania Biharamulo 10 6 6.23 0.398 Tanzania Biharamulo 11 7 6.28 0.5074 Tanzania Biharamulo 12 8 7.43 0.4912 Tanzania E. shore streams 5 1 2.67 0.102 Tanzania E. shore streams 6 2 2.81 0.2453 Tanzania E. shore streams 7 3 3.03 0.3865 Tanzania E. shore streams 8 4 3.75 0.4412 Tanzania E. shore streams 9 5 3.82 0.5756 Tanzania E. shore streams 10 6 5.32 0.5032 Tanzania E. shore streams 11 7 5.38 0.6135 Tanzania E. shore streams 12 8 5.44 0.7097 Tanzania Grumeti 5 1 4.57 0.0324 Tanzania Grumeti 6 2 5.93 0.0515 Tanzania Grumeti 7 3 6 0.1117 Tanzania Grumeti 8 4 9.82 0.0436 Tanzania Grumeti 9 5 9.84 0.08 Tanzania Grumeti 10 6 9.84 0.1315

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County River Up to lag Degrees of Chi-square Pr > ChiSq freedom Tanzania Grumeti 11 7 11.23 0.1291 Tanzania Grumeti 12 8 11.57 0.1715 Tanzania Issanga 5 1 2.46 0.1164 Tanzania Issanga 6 2 2.94 0.2303 Tanzania Issanga 7 3 3 0.3913 Tanzania Issanga 8 4 4.79 0.3099 Tanzania Issanga 9 5 5.86 0.3197 Tanzania Issanga 10 6 5.86 0.4386 Tanzania Issanga 11 7 7.21 0.4076 Tanzania Issanga 12 8 7.21 0.5143 Tanzania Kagera 5 1 0.48 0.49 Tanzania Kagera 6 2 0.55 0.7612 Tanzania Kagera 7 3 0.65 0.8844 Tanzania Kagera 8 4 0.72 0.9493 Tanzania Kagera 9 5 1.09 0.9554 Tanzania Kagera 10 6 1.31 0.9712 Tanzania Kagera 11 7 1.47 0.9835 Tanzania Kagera 12 8 1.51 0.9925 Tanzania Magogo Moame 5 1 1.29 0.2568 Tanzania Magogo Moame 6 2 3.11 0.2116 Tanzania Magogo Moame 7 3 3.12 0.3742 Tanzania Magogo Moame 8 4 3.87 0.4245 Tanzania Magogo Moame 9 5 4.27 0.511 Tanzania Magogo Moame 10 6 4.5 0.6096 Tanzania Magogo Moame 11 7 4.53 0.7168 Tanzania Magogo Moame 12 8 4.66 0.7937 Tanzania Mara 5 1 2.55 0.11 Tanzania Mara 6 2 2.61 0.2711 Tanzania Mara 7 3 2.65 0.4485 Tanzania Mara 8 4 3.11 0.5394 Tanzania Mara 9 5 4.52 0.4778 Tanzania Mara 10 6 4.58 0.5983 Tanzania Mara 11 7 8.24 0.3123 Tanzania Mara 12 8 8.52 0.3848 Tanzania Mbalageti 5 1 0.57 0.4496 Tanzania Mbalageti 6 2 0.89 0.6416 Tanzania Mbalageti 7 3 2.52 0.4717 Tanzania Mbalageti 8 4 5.81 0.2135 Tanzania Mbalageti 9 5 8.59 0.1264 Tanzania Mbalageti 10 6 10.9 0.0915 Tanzania Mbalageti 11 7 11.34 0.1244 Tanzania Mbalageti 12 8 11.44 0.1782 Tanzania Nyashishi 5 1 5.51 0.0189 Tanzania Nyashishi 6 2 5.81 0.0549 Tanzania Nyashishi 7 3 6.28 0.0986

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County River Up to lag Degrees of Chi-square Pr > ChiSq freedom Tanzania Nyashishi 8 4 6.29 0.1785 Tanzania Nyashishi 9 5 6.87 0.2305 Tanzania Nyashishi 10 6 6.93 0.3274 Tanzania Nyashishi 11 7 8.47 0.2931 Tanzania Nyashishi 12 8 9.68 0.2879 Tanzania S. shore streams 5 1 3.68 0.055 Tanzania S. shore streams 6 2 4.33 0.1145 Tanzania S. shore streams 7 3 5.12 0.1629 Tanzania S. shore streams 8 4 12.43 0.0144 Tanzania S. shore streams 9 5 12.51 0.0285 Tanzania S. shore streams 10 6 13.29 0.0387 Tanzania S. shore streams 11 7 14.82 0.0384 Tanzania S. shore streams 12 8 15.09 0.0575 Tanzania Simiyu 5 1 2.24 0.1349 Tanzania Simiyu 6 2 2.27 0.3212 Tanzania Simiyu 7 3 3.56 0.313 Tanzania Simiyu 8 4 5.77 0.2168 Tanzania Simiyu 9 5 9.3 0.0978 Tanzania Simiyu 10 6 9.3 0.1576 Tanzania Simiyu 11 7 9.34 0.2295 Tanzania Simiyu 12 8 9.54 0.2989 Tanzania W. shore streams 5 1 1.83 0.1766 Tanzania W. shore streams 6 2 3.78 0.1507 Tanzania W. shore streams 7 3 4.09 0.252 Tanzania W. shore streams 8 4 4.57 0.3347 Tanzania W. shore streams 9 5 4.76 0.4455 Tanzania W. shore streams 10 6 4.99 0.545 Tanzania W. shore streams 11 7 5.19 0.6364 Tanzania W. shore streams 12 8 5.51 0.7024 Uganda Bukora 5 1 2.77 0.096 Uganda Bukora 6 2 5.93 0.0516 Uganda Bukora 7 3 6.29 0.0984 Uganda Bukora 8 4 13.3 0.0099 Uganda Bukora 9 5 15.6 0.0081 Uganda Bukora 10 6 15.83 0.0147 Uganda Bukora 11 7 15.86 0.0264 Uganda Bukora 12 8 15.98 0.0426 Uganda Katonga 5 1 4.26 0.039 Uganda Katonga 6 2 4.72 0.0942 Uganda Katonga 7 3 4.72 0.1931 Uganda Katonga 8 4 5.41 0.2475 Uganda Katonga 9 5 5.72 0.3341 Uganda Katonga 10 6 5.78 0.448 Uganda Katonga 11 7 9.47 0.2204 Uganda Katonga 12 8 11.02 0.2005

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County River Up to lag Degrees of Chi-square Pr > ChiSq freedom Uganda N. shore streams 5 1 5.14 0.0234 Uganda N. shore streams 6 2 5.23 0.0733 Uganda N. shore streams 7 3 5.37 0.1468 Uganda N. shore streams 8 4 9.32 0.0537 Uganda N. shore streams 9 5 9.37 0.0953 Uganda N. shore streams 10 6 12.49 0.0518 Uganda N. shore streams 11 7 13.31 0.0649 Uganda N. shore streams 12 8 13.56 0.094

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Table 9: Univariate model ANOVA diagnostics for LN (River discharge +1) (The results show that each model is significant.)

Region River R-Square Standard F value Pr > F deviation Kenya Gucha-Migori 0.6236 0.37215 6.81 <0.0001 Kenya North Awach 0.7559 0.13621 12.73 <0.0001 Kenya Nyando 0.8733 0.32099 28.34 <0.0001 Kenya Nzoia 0.5444 0.31822 4.91 0.0002 Kenya Sio 0.7062 0.25267 9.88 <0.0001 Kenya Sondu 0.564 0.23821 5.32 0.0001 Kenya South Awach 0.685 0.16515 8.94 <0.0001 Kenya Yala 0.7478 0.16768 12.19 <0.0001 Tanzania Biharamulo 0.5199 0.21997 4.45 0.0005 Tanzania E. shore streams 0.6088 0.33073 6.4 <0.0001 Tanzania Grumeti 0.4997 0.51634 4.11 0.001 Tanzania Issanga 0.7085 0.78871 9.99 <0.0001 Tanzania Kagera 0.6657 0.14514 8.19 <0.0001 Tanzania Magogo Moame 0.66 0.68885 7.98 <0.0001 Tanzania Mara 0.5985 0.5223 6.13 <0.0001 Tanzania Mbalageti 0.5391 0.39008 4.81 0.0003 Tanzania Nyashishi 0.4041 0.47601 2.79 0.0133 Tanzania S. shore streams 0.4421 0.9501 3.26 0.0052 Tanzania Simiyu 0.5924 0.47477 5.97 <0.0001 Tanzania W. shore streams 0.3468 0.15347 2.18 0.0463 Uganda Bukora 0.5563 0.22087 5.15 0.0002 Uganda Katonga 0.4575 0.47528 3.47 0.0034 Uganda N. shore streams 0.6555 0.14432 7.82 <0.0001

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Table 10: Univariate Model White Noise Diagnostics for LN(River Discharge +1) The results test whether the residuals are correlated and heteroscedastic. The Durbin-Watson test tests the null hypothesis that the residuals are uncorrelated. The Jarque-Bera normality test tests the null hypothesis that the residuals are normally distributed. The F statistics and their p-values for ARCH(1) disturbances test the null hypothesis that the residuals have equal covariances.

Country River Durbin- Jarque-Bera normality test ARCH(1) test Watson statistics Chi-square Pr > ChiSq F value Pr > F Kenya Gucha-Migori 2.05448 3.38 0.1848 0.31 0.5808 Kenya North Awach 1.83708 0.25 0.8818 0.37 0.544 Kenya Nyando 1.76345 0.37 0.8303 3.07 0.0869 Kenya Nzoia 1.90663 1.39 0.4983 0.69 0.4097 Kenya Sio 1.88991 10.84 0.0044 0 0.976 Kenya Sondu 1.66005 41.12 <0.0001 1.51 0.2261 Kenya South Awach 1.77779 8.15 0.017 0.14 0.7129 Kenya Yala 1.96923 0.99 0.6106 2.37 0.1306 Tanzania Biharamulo 1.99324 1.14 0.5662 0.16 0.6904 Tanzania E. shore streams 1.65326 0.44 0.802 3.09 0.0857 Tanzania Grumeti 1.93961 1.04 0.5944 0.43 0.5154 Tanzania Issanga 1.84259 0.77 0.6809 2.01 0.163 Tanzania Kagera 2.05729 11.5 0.0032 0.01 0.9422 Tanzania Magogo Moame 1.94314 0.78 0.6765 0.45 0.5047 Tanzania Mara 1.69206 1.79 0.4091 1.84 0.1822 Tanzania Mbalageti 1.90046 7.22 0.0271 0.02 0.888 Tanzania Nyashishi 1.97824 1.48 0.476 1.72 0.1961 Tanzania S. shore streams 1.90595 0.16 0.9211 3.18 0.0813 Tanzania Simiyu 1.90239 0.03 0.9848 2.84 0.099 Tanzania W. shore streams 1.70983 0.21 0.9008 0.02 0.8953 Uganda Bukora 1.57199 0.46 0.7958 1.29 0.2623 Uganda Katonga 2.00198 17.1 0.0002 0 0.9854 Uganda N. shore streams 1.83187 1.22 0.5424 1.53 0.2225

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Table 11: Univariate AR Model Diagnostics for LN(River discharge +1) The F statistics and their p-values for AR(1), AR(1,2), AR(1,2,3), and AR(1,2,3,4) models of residuals test the null hypothesis that the residuals are uncorrelated.

Country River AR(1) AR(1,2) AR(1,2,3) AR(1,2,34) F Value Pr > F F Value Pr > F F Value Pr > F F Value Pr > F Kenya Gucha- 0.67 0.4183 2.04 0.1428 1.01 0.3992 0.86 0.4944 Migori Kenya North 0.11 0.7421 0.15 0.8615 0.1 0.9572 0.42 0.7919 Awach Kenya Nyando 0.27 0.6071 0.33 0.723 0.25 0.8589 0.22 0.9285 Kenya Nzoia 0.03 0.8609 0.08 0.9269 0.21 0.8914 0.46 0.7615 Kenya Sio 0 0.9862 0.21 0.8112 0.36 0.78 0.5 0.7363 Kenya Sondu 0 0.9838 0.67 0.5188 0.08 0.9721 0.1 0.9823 Kenya South Awach 0.06 0.8004 0.15 0.8624 0.32 0.8087 0.33 0.8569 Kenya Yala 0.19 0.6635 0.01 0.9881 0.55 0.6536 0.56 0.692 Tanzania Biharamulo 0.01 0.9169 0.01 0.9934 0.53 0.6674 0.64 0.637 Tanzania E. shore 0.76 0.3895 0.47 0.6267 0.49 0.6931 0.53 0.712 streams Tanzania Grumeti 0.01 0.9153 0.29 0.7527 1.1 0.3594 1.15 0.3484 Tanzania Issanga 0.14 0.7093 1.04 0.3619 0.76 0.522 0.67 0.617 Tanzania Kagera 0.04 0.8494 0.1 0.9028 0.08 0.9719 0.06 0.9929 Tanzania Magogo 0.03 0.8568 0.03 0.97 0.05 0.9862 0.04 0.997 Moame Tanzania Mara 0.85 0.3611 0.62 0.5419 0.57 0.6397 0.49 0.7396 Tanzania Mbalageti 0.07 0.7877 0.04 0.9605 0.2 0.8962 0.17 0.9544 Tanzania Nyashishi 0 0.9512 0.29 0.7527 1.13 0.3488 1.44 0.2398 Tanzania S. shore 0.01 0.9436 0.14 0.8709 1.25 0.3047 1.03 0.4038 streams Tanzania Simiyu 0.01 0.9377 0.05 0.9516 0.1 0.9623 0.07 0.9896 Tanzania W. shore 0.18 0.6773 0.77 0.4716 0.58 0.6309 0.45 0.7748 streams Uganda Bukora 1.85 0.1805 1.24 0.2987 0.8 0.5016 0.57 0.6882 Uganda Katonga 0 0.9823 0.05 0.9505 0.27 0.8439 0.45 0.7732 Uganda N. shore 0.19 0.6624 0.4 0.6755 0.3 0.823 0.46 0.7678 streams

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Table 12: Roots of AR and MA characteristic polynomials The modulus of the roots of its AR polynomial should be less than 1 for a time series to be stationary.

Country River Process Index Real part Imaginary Modulus Arctangent Degree part Kenya Gucha-Migori AR 1 0.83546 0 0.8355 0 0 Kenya Gucha-Migori AR 2 -0.38664 0 0.3866 3.1416 180 Kenya Gucha-Migori MA 1 1 0 1 0 0 Kenya Gucha-Migori MA 2 -0.66461 0 0.6646 3.1416 180 Kenya North Awach AR 1 0.45016 0 0.4502 0 0 Kenya North Awach AR 2 -0.37142 0 0.3714 3.1416 180 Kenya North Awach MA 1 1 0 1 0 0 Kenya North Awach MA 2 -0.60936 0 0.6094 3.1416 180 Kenya Nyando AR 1 0.81386 0.22345 0.844 0.2679 15.3522 Kenya Nyando AR 2 0.81386 -0.22345 0.844 -0.2679 -15.3522 Kenya Nyando MA 1 0.99676 0.08048 1 0.0806 4.616 Kenya Nyando MA 2 0.99676 -0.08048 1 -0.0806 -4.616 Kenya Nzoia AR 1 -0.04929 0 0.0493 3.1416 180 Kenya Nzoia AR 2 -0.47457 0 0.4746 3.1416 180 Kenya Nzoia MA 1 -0.49187 0.34805 0.6026 2.5258 144.7164 Kenya Nzoia MA 2 -0.49187 -0.34805 0.6026 -2.5258 -144.7164 Kenya Sio AR 1 0.76028 0.31012 0.8211 0.3873 22.1906 Kenya Sio AR 2 0.76028 -0.31012 0.8211 -0.3873 -22.1906 Kenya Sio MA 1 0.9965 0.08362 1 0.0837 4.7966 Kenya Sio MA 2 0.9965 -0.08362 1 -0.0837 -4.7966 Kenya Sondu AR 1 1 0 1 0 0 Kenya Sondu AR 2 0.20437 0 0.2044 0 0 Kenya Sondu MA 1 0.9993 0 0.9993 0 0 Kenya Sondu MA 2 0.27309 0 0.2731 0 0 Kenya South Awach AR 1 0.42313 0 0.4231 0 0 Kenya South Awach AR 2 -0.27065 0 0.2707 3.1416 180 Kenya South Awach MA 1 1 0 1 0 0 Kenya South Awach MA 2 -0.66404 0 0.664 3.1416 180 Kenya Yala AR 1 0.21744 0 0.2174 0 0 Kenya Yala AR 2 -0.22553 0 0.2255 3.1416 180 Kenya Yala MA 1 -0.05673 0.60004 0.6027 1.6651 95.4012 Kenya Yala MA 2 -0.05673 -0.60004 0.6027 -1.6651 -95.4012 Tanzania Biharamulo AR 1 0.75746 0 0.7575 0 0 Tanzania Biharamulo AR 2 -0.72547 0 0.7255 3.1416 180 Tanzania Biharamulo MA 1 0.36873 0 0.3687 0 0 Tanzania Biharamulo MA 2 -0.6318 0 0.6318 3.1416 180 Tanzania E. shore AR 1 0.23884 0.60495 0.6504 1.1948 68.4559 streams Tanzania E. shore AR 2 0.23884 -0.60495 0.6504 -1.1948 -68.4559 streams Tanzania E. shore MA 1 0.39589 0.9183 1 1.1638 66.6788 streams

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Country River Process Index Real part Imaginary Modulus Arctangent Degree part Tanzania E. shore MA 2 0.39589 -0.9183 1 -1.1638 -66.6788 streams Tanzania Grumeti AR 1 -0.54501 0 0.545 3.1416 180 Tanzania Grumeti AR 2 -0.79214 0 0.7921 3.1416 180 Tanzania Grumeti MA 1 -0.99681 0.07981 1 3.0617 175.4225 Tanzania Grumeti MA 2 -0.99681 -0.07981 1 -3.0617 -175.4225 Tanzania Issanga AR 1 1 0 1 0 0 Tanzania Issanga AR 2 0.46302 0 0.463 0 0 Tanzania Issanga MA 1 0.99999 0.0003 1 0.0003 0.0173 Tanzania Issanga MA 2 0.99999 -0.0003 1 -0.0003 -0.0173 Tanzania Kagera AR 1 0.86933 0 0.8693 0 0 Tanzania Kagera AR 2 -0.9677 0 0.9677 3.1416 180 Tanzania Kagera MA 1 0.53716 0 0.5372 0 0 Tanzania Kagera MA 2 -1 0 1 3.1416 180 Tanzania Magogo AR 1 0.20494 0.43213 0.4783 1.128 64.6271 Moame Tanzania Magogo AR 2 0.20494 -0.43213 0.4783 -1.128 -64.6271 Moame Tanzania Magogo MA 1 0.16421 0.49476 0.5213 1.2503 71.6387 Moame Tanzania Magogo MA 2 0.16421 -0.49476 0.5213 -1.2503 -71.6387 Moame Tanzania Mara AR 1 0.19382 0.8672 0.8886 1.3509 77.4012 Tanzania Mara AR 2 0.19382 -0.8672 0.8886 -1.3509 -77.4012 Tanzania Mara MA 1 0.20289 0.9792 1 1.3665 78.2937 Tanzania Mara MA 2 0.20289 -0.9792 1 -1.3665 -78.2937 Tanzania Mbalageti AR 1 0.35666 0.63512 0.7284 1.0591 60.6831 Tanzania Mbalageti AR 2 0.35666 -0.63512 0.7284 -1.0591 -60.6831 Tanzania Mbalageti MA 1 0.39125 0.92028 1 1.1688 66.9675 Tanzania Mbalageti MA 2 0.39125 -0.92028 1 -1.1688 -66.9675 Tanzania Nyashishi AR 1 -0.05445 0 0.0544 3.1416 180 Tanzania Nyashishi AR 2 -0.64852 0 0.6485 3.1416 180 Tanzania Nyashishi MA 1 -0.05236 0 0.0524 3.1416 180 Tanzania Nyashishi MA 2 -0.81816 0 0.8182 3.1416 180 Tanzania S. shore AR 1 0.03497 0 0.035 0 0 streams Tanzania S. shore AR 2 -0.35385 0 0.3539 3.1416 180 streams Tanzania S. shore MA 1 0.05683 0 0.0568 0 0 streams Tanzania S. shore MA 2 -0.45815 0 0.4582 3.1416 180 streams Tanzania Simiyu AR 1 0.55814 0 0.5581 0 0 Tanzania Simiyu AR 2 -0.62164 0 0.6216 3.1416 180 Tanzania Simiyu MA 1 1 0 1 0 0 Tanzania Simiyu MA 2 -0.68844 0 0.6884 3.1416 180

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Country River Process Index Real part Imaginary Modulus Arctangent Degree part Tanzania W. shore AR 1 0.46651 0.0743 0.4724 0.1579 9.0488 streams Tanzania W. shore AR 2 0.46651 -0.0743 0.4724 -0.1579 -9.0488 streams Tanzania W. shore MA 1 0.49475 0.69563 0.8536 0.9526 54.579 streams Tanzania W. shore MA 2 0.49475 -0.69563 0.8536 -0.9526 -54.579 streams Uganda Bukora AR 1 0.78526 0.46217 0.9112 0.532 30.479 Uganda Bukora AR 2 0.78526 -0.46217 0.9112 -0.532 -30.479 Uganda Bukora MA 1 0.75954 0.65046 1 0.7082 40.5763 Uganda Bukora MA 2 0.75954 -0.65046 1 -0.7082 -40.5763 Uganda Katonga AR 1 0.30633 0.48736 0.5756 1.0096 57.8486 Uganda Katonga AR 2 0.30633 -0.48736 0.5756 -1.0096 -57.8486 Uganda Katonga MA 1 0.04089 0.4965 0.4982 1.4886 85.2915 Uganda Katonga MA 2 0.04089 -0.4965 0.4982 -1.4886 -85.2915 Uganda N. shore AR 1 0.91576 0.31425 0.9682 0.3306 18.9401 streams Uganda N. shore AR 2 0.91576 -0.31425 0.9682 -0.3306 -18.9401 streams Uganda N. shore MA 1 0.99601 0.08925 1 0.0894 5.1202 streams Uganda N. shore MA 2 0.99601 -0.08925 1 -0.0894 -5.1202 streams

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