A Case Study of River Mubuku-Sebwe Sub-Catchments

MAKERERE UNIVERSITY

COLLEGE OF AGRICULTURAL AND ENVIRONMENTAL SCIENCES DEPARTMENT OF ENVIRONMENTAL MANAGEMENT

SUSTAINABLE WATER USE IN LAKE EDWARD-GEORGE BASIN: A Case Study of River Mubuku-Sebwe Sub-Catchments

Presented by:

MWEBAZE CAROLINE EDNAH Bsc. Environmental Science Technology and Management, KYU Reg. No. 2015/HD02/555U Student No. 215021793

A DISSERTION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF A MASTER OF SCIENCE DEGREE IN ENVIRONMENT AND NATURAL RESOURCES OF MAKERERE UNIVERSITY

November 2018

Dedication

This work is dedicated to my beloved children Alvin Ahurira Musinguzi and Yvonne Ahurira Mbabazi. Whenever you read this report, may you be inspired concerning the great potential there is inside of you to transform communities.

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Acknowledgements

I thank the Ministry of Water and Environment, Uganda and the World Wildlife Fund’s “Education For Nature” programme for funding my master’s course for the 1st and 2nd years respectively. My sincere gratitude also goes to the German Academic Exchange Service (DAAD)’s Towards Demand Driven Training (DDT) programme which gave me an opportunity to work on my data and model with water resources experts at the Technical University of Cologne, Germany.

I acknowledge the Department of Environmental Management staff and lecturers on this course for their input at different levels to this research. Special thanks go to Professor Majaliwa J. Mwanjalolo and Dr. Joshua Wanyama for the support and supervision throughout this research and to Professor Jackson Roehrig for the supervision during my internship in Germany. My class mates, thank you for making this study easier and enjoyable. Your simplicity and team work has made these two years seem even shorter.

I also extend my sincere gratitude to the staff of Ministry of Water and Environment who supported me in one way or another especially with regards to acquisition and analysis of hydro- meteorological data. Special thanks go to Dr. Tindimugaya Callist, Mr. Epitu Joseph, Ms. Nakalyango Caroline, Mr. Sekamuli Benjamin, Mr. Wamala Sowed, Mr. Kiwalabye Charles, Mr. Kigozi Frank and Mr. Kitamirike Jackson, Team Leader Albert Water Management Zone.

My heartfelt gratitude to my husband, Ivan Akatuhurira, who has supported me spiritually, financially, emotionally and mentally during these two years of intensive study and research. You are a pillar in my life. I thank God for provision, wisdom, health, and grace to keep going. All glory and honor belong to you LORD.

Acronyms and abbreviations

AgMIP Agricultural Model Inter-comparison Project

ARC2 Africa Rainfall Climatology version 2

AWMZ Albert Water Management Zone

CBA Cost Benefit Analysis

CMORPH Climate Prediction Center morphing technique

CRU Climate Research Unit

DEM Digital Elevation Model

DHI Danish Hydraulic Institute

DSS Decision Support System

DST Decision Support Tool

DWRM Directorate of Water Resources Management

EFR Environmental Flow Requirement

ERA Interim European Reanalysis Interim

ETo Evapotranspiration

FAO Food and Agriculture Organization

GCM General Circulation Model

GIS Geographical Information System

GLDAS Global Land Data Assimilation System

GPS Global Positioning System

GoU Government of Uganda

GWP Global Water Partnership

HPP Hydro Power Plant

IBA Important Bird Area

IWRM Integrated Water Resources Management

IRWR Internal Renewable Water Resources

MAR Mean Areal Rainfall

MCA Multi Criteria Analysis

MCM Million Cubic Meters

MWE Ministry of Water and Environment

NAM NedborAfstromnings Model

NBDSS Nile Basin Decision Support System

NBI Nile Basin Initiative

NDP National Development Plan

NP National Park

OWAM Optimal Water Allocation Model

PET Potential Evapo-Transpiration

QENP Queen Elizabeth National Park

REALM Resource Allocation Model

RFE Rainfall Estimator

RMNP Rwenzori Mountains National Park

STDEV Standard Deviation

SWAT Soil and Water Assessment Tool

TAMSAT Tropical Application of Meteorology Using Satellite

TLU Tropical Livestock Unit

TRMM Tropical Rainfall Measuring Mission

UBOS Uganda Bureau of Statistics

UNDESA United Nations Department of Economic and Social Affairs

UNHS Uganda National Household Survey

UWA Uganda Wildlife Authority

WAM Water Allocation Model

WB World Bank

WD Water Demand

WDPs Water Demand Projections

WEAP Water Evaluation And Planning

WRM Water Resources Management

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Abstract

The need to make major decisions on sustainable water allocation in environmentally sensitive catchments in the face of high system complexity and uncertainty has aggravated water management problems across the globe. The goal of this study was to assess management options to support decision making on sustainable water use amidst the ever-increasing demands and pressures on the resource. This study was conducted in the Mubuku-Sebwe sub catchments of Lake Edward-George Basin in western Uganda. Hydro-meteorological data analysis techniques (including Rainfall-Runoff modeling) were employed to quantify the available water in the sub catchments. The Mike Hydro model was applied to allocate the water resource to developments based on different scenarios of the current (2015) and future (2040) water demand. Water use was simulated for six different uses (domestic, livestock, irrigation, hydropower, industry and ecosystem functions). The NBDSS was used to assess sustainability of the different water management scenarios/options. Sustainability was based on reliable water allocation, minimum deficits and meeting ecosystem demand/environmental flow with 2040 as the planning horizon.

The analysis revealed that in 2015, the available water was 60MCM in Sebwe and 365MCM in Mubuku sub catchments. It is expected to reduce by 11% (q = 33.08 – 0.02t, R2 = 0.32) and 15% (q = 157.08 – 0.07t, R2 = 0.32) by 2040 with Q75 of 1.0m3/s and 6.6m3/s (p < 0.05) for Sebwe and Mubuku respectively and that there are seasonal water surpluses that could be put to productive use. Domestic water demand is expected to increase by 64%, livestock by 44%, hydro power by 45%, industry by 400% and irrigation by 66% by 2040.

The study recommends that allocation of water should be based on the management option which prioritizes domestic and environmental water demand and allocates 90% to industry, 70% to irrigation and 60% to livestock demand. In addition, mechanisms to store surplus water at the different nodes where and when it occurs should be explored to further reduce deficits.

Key words: Sustainable Water Use, Water Allocation Modeling, Surface Water Quantification.

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Table of Contents

Declaration ...... Error! Bookmark not defined. Approval ...... i Dedication ...... iii Acknowledgements ...... iv Acronyms and abbreviations...... v Abstract ...... viii List of Figures ...... xii List of Tables ...... xiv CHAPTER ONE: INTRODUCTION ...... 1 1.1 Background to the study ...... 1 1.2 Statement of the Problem ...... 2 1.3 Objectives ...... 2 1.3.1 Main Objective ...... 2 1.3.2 Specific Objectives ...... 2 1.3.3 Research question ...... 3 1.4 Justification for the Study ...... 3 1.5 Significance of the study ...... 6 1.6 Conceptual framework for the research ...... 6 CHAPTER TWO: LITERATURE REVIEW ...... 8 2.1 Water allocation Policies in Uganda ...... 8 2.2 Quantifying available water within a catchment ...... 9 2.3 Quantification and projection of water demand ...... 11 2.3.1 Current WD computation ...... 11 2.3.2 WD projection ...... 12 2.4 Sustainable water use/allocation ...... 12 2.5 Performance of different Climate Models for the study area...... 14 2.6 Inferences from the Literature Review ...... 15 CHAPTER THREE: METHODOLOGY ...... 17

3.1 Description of the study area ...... 17 3.1.1 Location of the Mubuku-Sebwe sub catchments...... 17 3.1.2 Topography...... 18 3.1.3 Drainage patterns ...... 19 3.1.4 Geology and Soils...... 22 3.1.5 Natural vegetation and land use ...... 22 3.1.6 Climate ...... 24 3.1.7 Demographic Characteristics...... 26 3.1.8 Environmental concerns ...... 27 3.2 Quantification of available water in the sub-catchments...... 27 3.2.1 Rainfall analysis ...... 27 3.2.2 River flow analysis ...... 31 3.2.3 Rainfall Runoff Modeling ...... 32 3.2.4 Potentially available water in the catchment in 2040 ...... 34 3.3 Estimation of water demand in the Mubuku-Sebwe sub-catchments ...... 34 3.3.1 Mapping of water user abstraction points ...... 34 3.3.2 Computation of 2015 water demand...... 36 3.3.3 Estimation of future water demand for each use for the year 2040...... 44 3.3.3.1 Domestic WD ...... 44 3.3.3.2 Livestock WD ...... 44 3.3.3.3 Industrial WD ...... 45 3.3.3.4 Irrigation WD ...... 46 3.3.3.5 Hydropower WD ...... 46 3.3.3.6 Environmental flow ...... 47 3.4 Modeling Water demand and supply ...... 47 3.4.1 Model selection ...... 47 3.4.2 Water allocation modeling ...... 49 3.4.2.1 Baseline Scenario (2015) – Sc00 ...... 49 3.4.2.2 Future scenarios (2040) – Sc01 ...... 50 3.4.2.3 Reliable water allocation – Sc02 ...... 50 3.5 Identification of the most appropriate sustainable water management option...... 51 CHAPTER FOUR: RESULTS ...... 53

4.1 Quantity of water in the study area ...... 53 4.1.1 Available water in the Sebwe sub-catchment_2015 ...... 53 4.1.2 Available water in the Mubuku sub-catchment_2015 ...... 54 4.1.3 Potentially available water in the catchment in 2040 ...... 55 4.2 Current and projected water demand ...... 58 4.2.1 Domestic, Livestock, Industry and Environment WD ...... 58 4.2.2 Hydropower WD ...... 59 4.2.3 Irrigation WD ...... 60 4.3.1 Scenario 00; Baseline 2015 ...... 61 4.3.2 Scenario 01; Future 2040...... 63 4.3.3 Scenario 02; Reliable 2040 ...... 65 4.3.4 Reliability of Hydro Power Generation ...... 66 4.4 The sustainable water use option in the Mubuku-Sebwe sub catchments...... 67 CHAPTER FIVE: DISCUSSION OF RESULTS ...... 70 CHAPTER SIX: CONCLUSIONS ...... 73 CHAPTER SEVEN: RECOMMENDATIONS ...... 74 REFERENCES ...... 75 APPENDICES ...... 80 APENDIX I: Rainfall data analysis ...... 80 Observed Rainfall ...... 80 Double Mass Curves...... 82 Correlation matrix ...... 85 Gap filling matrices ...... 85 Theissen polygon weighting factors ...... 86 Comparison with global datasets ...... 88 APPENDIX II: River flow data analysis ...... 89 Hydrographs ...... 89 Analysis of rating curves ...... 90 Rainfall Runoff Modeling ...... 91 APPENDIX III: Photo gallery ...... 96

List of Figures

Figure 1: Conceptual frame work ...... 7 Figure 2: Location of the study area in Western Uganda ...... 18 Figure 3: Digital Elevation Model of the study area...... 19 Figure 4: Presentation of the drainage system of the study area...... 21 Figure 5: Spatial distribution of different land cover types in the sub catchments...... 24 Figure 6: Minimum and Maximum temperature for Kasese District ...... 25 Figure 7: Location of the rainfall stations in and around the study area ...... 29 Figure 8: Temporal distribution of the rainfall data coverage in and around the study area ...... 30 Figure 9: Computation of specific discharge in a catchment using discharge-area ratio ...... 31 Figure 10: Location of water abstraction points within the study area ...... 35 Figure 11: Mubuku-Sebwe MIKE HYDRO schematic diagram ...... 50 Figure 12: A screenshot of the registered Model in the NBDSS for scenario comparison ...... 52 Figure 13: Long term average flow for Sebwe (2015) ...... 54 Figure 14: Long term average flow for Mubuku (2015) ...... 55 Figure 15: Rainfall trend analysis for the period between 1956 and 2040 ...... 56 Figure 16: Long term average flow for Sebwe (2040) ...... 57 Figure 17: Long term average flow for Mubuku (2040) ...... 58 Figure 18: Time series presentation for hydropower water demand for N5 (Bugoye) and N3 (Eco gardens) ...... 59 Figure 19: Time series presentation of power demand as flow for Kilembe HPP ...... 60 Figure 20: Water demand time series for Mubuku Irrigation Scheme 4.3 Water allocation under different scenarios ...... 61 Figure 21: Monthly water demand deficit for Sebwe under Sc00 ...... 62 Figure 22: Monthly water demand deficit for Sebwe under Sc01 ...... 64 Figure 23: Monthly water demand deficit for Sebwe under Sc02 ...... 65 Figure 24: Analysis of power production reliability ...... 67 Figure 25: Water demand deficit at Kilembe HPP for 2015 ...... 67 Figure 26: Water demand deficit at Kilembe HPP in 2040 ...... 68

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Figure 27: Monthly water use reliability for the three scenarios for Sebwe sub catchment ...... 69 Figure 28: Monthly water use reliability for the three scenarios for Mubuku sub catchment ...... 69 Figure 29: Bugoye observed Rainfall ...... 80 Figure 30: Mubuku Prison Farm observed Rainfall ...... 80 Figure 31: Kasese MET observed Rainfall ...... 81 Figure 32: Kilembe Mines observed Rainfall ...... 81 Figure 33: Mubuku HEP observed Rainfall...... 82 Figure 34: Mubuku and Sebwe theissen polygons ...... 87 Figure 35: Comparing observed Rainfall with global data sets ...... 88 Figure 36: Mubuku and Sebwe hydrographs ...... 89 Figure 37: Sebwe rating curve ...... 90 Figure 38: Mubuku rating curve ...... 90 Figure 39: Sebwe NAM model calibration ...... 91 Figure 40: Sebwe NAM model validation ...... 92 Figure 41: Sebwe accumulated discharge ...... 92 Figure 42: Sebwe Flow Duration Curve ...... 92 Figure 43: Long term gap filled discharge for Sebwe_2015 ...... 93 Figure 44: Mubuku NAM model calibration ...... 93 Figure 45: Mubuku NAM model validation ...... 94 Figure 46: Mubuku accumulated discharge ...... 94 Figure 47: Mubuku Flow Duration Curve ...... 94 Figure 48: Long term gap filled discharge for Mubuku_2015 ...... 95

List of Plates

Plate 1: Capital Auto Parts stone quarry at Mubuku bridge ...... 96 Plate 2: People washing clothes by the Mubuku River ...... 96 Plate 3: The water distribution canals in the Mubuku Irrigation Scheme ...... 97 Plate 4: Bricks production using boulders from the Mubuku River ...... 97 Plate 5: The intake to Kilembe Hydropower Plant ...... 98 Plate 6: Tertiary canals in the Mubuku Irrigation scheme ...... 98

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List of Tables

Table 1: Comparing observed monthly Evapotranspiration with Princeton dataset...... 25 Table 2: Reference Evapotranspiration from CROPWAT ...... 26 Table 3: Evaluation of NAM model performance ...... 34 Table 4: Annual growth rate for cattle ...... 37 Table 5: Annual growth rate for goats ...... 37 Table 6: Annual growth rate for sheep ...... 38 Table 7: Annual growth rate for pigs ...... 38 Table 8: 2008 Livestock census for Kasese District ...... 38 Table 9: Livestock in the study area ...... 38 Table 10: Livestock water demand ...... 39 Table 11: Crop-Area distribution for the Irrigation Schemes ...... 42 Table 12: Sowing calendar for the Irrigation Schemes ...... 42 Table 13: Projected Livestock population ...... 44 Table 14: Projected livestock water demand ...... 45 Table 15: Projected industrial water demand...... 46 Table 16: Average monthly flow for Sebwe in 2015 ...... 53 Table 17: Average monthly flow for Mubuku in 2015 ...... 54 Table 18: Average monthly flow for Sebwe in 2040 ...... 56 Table 19: Average monthly flow for Mubuku in 2040 ...... 57 Table 20: Domestic, livestock, industry and environmental water demand ...... 58 Table 21: Hydropower demand ...... 59 Table 22: Irrigation Area ...... 60 Table 23: Sebwe water use reliability for Sc00 ...... 61 Table 24: Mubuku water use reliability for Sc00 ...... 62 Table 25: Sebwe water use reliability for Sc01 ...... 63 Table 26: Mubuku water use reliability for Sc01 ...... 64 Table 27: Sebwe water use reliability for Sc02 ...... 65 Table 28: Mubuku water use reliability for Sc02 ...... 66

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Table 29: Water demand deficit ...... 68 Table 30: Rainfall stations correlation matrix ...... 85 Table 31: First priority gap filling matrix ...... 85 Table 32: Second priority gap filling matrix...... 85 Table 33: Third priority gap filling matrix ...... 86 Table 34: Fourth priority gap filling matrix ...... 86 Table 35: Mubuku weighting factors ...... 86 36: Sebwe weighting factors ...... 87 Table 37: Analysis of Sebwe Rating ...... 90 Table 38: Analysis of Mubuku Rating ...... 91

CHAPTER ONE: INTRODUCTION

1.1 Background to the study Globally, trends of water use are showing that there is a steady increase in water demand for all uses including water for the environment. As the global population grows and demand for food and energy increases, the pressure on freshwater ecosystems will intensify. Currently, global population is about 7.5bn and the United Nations Department of economic and social affairs projects that it will reach over 9bn by 2040 with 1.8bn experiencing absolute water stress and 2/3 of the world’s population under conditions of water stress by 2025 (UNDESA, 2015b). Uganda’s population is projected to reach 61.3m by 2040 (UBOS, 2008). To make it worse, the main effects of climate change are likely to aggravate the problem since they will be felt most through changes to the hydrological cycle (Tom et al., 2007). During 2015-2050, half of the world’s population growth is expected to be concentrated in nine countries: India, Nigeria, Pakistan, Democratic Republic of the Congo, Ethiopia, United Republic of Tanzania, United States of America, Indonesia and Uganda, listed according to the size of their contribution to the total growth (UNDESA, 2015b). Sustainable water use is the use of water that supports the ability of human society to endure and ﬂourish into the indeﬁnite future without undermining the integrity of the hydrological cycle or the ecological systems that depend on it (Gleick, 1998).

Water is at the center of socio-economic development and its management is critical to achieving the sustainable development goal targets. Sustainable development is an approach to development that takes the finite resources of the earth into consideration to ensure their continued supply for the present and future generations. Specifically, sustainable development goal No.6 “Ensure availability and sustainable management of water and sanitation for all” is directly targeting managing our water resources sustainably (UNDESA, 2015a). It is therefore clear that the need for managing our water resources sustainably is at the fore front of the global agenda yet this cannot be achieved without the appropriate information. The majority of water sources which qualify to be permitted are not – only less than 2% of the potential sources in the study area were permitted in total (ARK Consult, 2014).

The Mubuku-Sebwe sub-catchments drain into the Lake George which is a ramsar site and its protection is critical to the entire global environmental management drive yet with all these challenges, the department mandated to manage the country’s water resources does not have a water allocation tool for the river and its tributaries (WWF, 2012).

1.2 Statement of the Problem Despite the high environmental sensitivity of the Mubuku-Sebwe sub catchments, there is limited understanding of the availability of water resources in time and space and how the available resource can be effectively allocated to meet the various socio-economic needs of the present and future generations. Similarly, there is inadequate information on the existing usage of water resources in the various parts of the catchment making allocation and general regulation rather difficult. This has resulted in over exploitation of the resource and water use conflicts between the different uses. If business as usual continues, development will have to come to a forced halt since water is the driver of socio economic development. There is thus a need to establish how much water is available, how much is required by different users and match the resource with demand under properly conceptualized scenarios.

1.3 Objectives

1.3.1 Main Objective The main objective of this study was to assess management options to support decision making on sustainable water allocation within the Mubuku-Sebwe sub catchments amidst the ever- increasing demands and pressures on the resource.

1.3.2 Specific Objectives

The specific objectives of this study were:

i. To quantify potentially available water in Mubuku-Sebwe sub catchments in 2015 and 2040; ii. To estimate the current and projected water demand in 2015 and 2040 respectively; iii. To determine the impact of the water allocation framework on the sub catchments; iv. To determine the sustainable water management options to support decision making within the Mubuku-Sebwe sub catchments.

1.3.3 Research question What are the management options for sustainable water allocation in the Mubuku-Sebwe sub catchments and why should they be implemented?

1.4 Justification for the Study The United Nations projects that there will be more than 10 billion people living on the earth by the year 2100. This explosion in population is perhaps one of the greatest reasons why sustainable development is so important. It comes with several related challenges including growth in water abstractions, basin ‘closure’ and the lack of availability of more sites for water infrastructure, growth and change in the economy leading to a wider variety of water users with different water demands, decline of freshwater ecosystems and the loss of river system functions and in recent times, climate change. Nápoles-rivera et al., (2013) projects that by 2025 1.8 billion people will live in countries or regions with absolute water scarcity, and two-thirds of the world population could be under conditions of water stress.

In Uganda, the area covered by water constitutes 15% or 36,902km2 and 3% or 7,325km2 is covered by permanent or seasonal wetlands (GoU, 2010). While its water resources are quite abundant, with mean annual rainfall of 1200 mm, flows from the Nile that exceed 25 km3 per year, and large combined storage capacity in Lakes Victoria, Albert, Edward and Kyoga, there is nevertheless a growing perception that future water scarcity may affect economic development and food security. This perception is strengthened by the country’s rapid population growth, the uncertainties related to climate change, a severe hydrologic drought in recent memory (2004 - 2005) that lowered the level of Lake Victoria and compromised power production (DWRM, 2013). As a management tool, the Constitution of Uganda vests in the State the duty to protect important natural resources including water and to take all practical measures to promote a good water management system (GoU, 1995).. The water related economic activities are expected to generate revenue for the country over the vision period through effectively harnessing the opportunities presented by water resources and strengthening the relevant fundamentals to facilitate sustainable and more beneficial utilization of the resource (GoU, 2007). One of the strategic interventions under the objective “To improve the assessment and evaluation of permits for various water uses” in the National Development

Plan II is to improve water resources planning and regulation and use of other tools for water resources regulation (NPA, 2015).

DWRM, (2013) reported that the total natural renewable water resources of the Edward basin as 4.07km3/year. However, its distribution and availability in space and time remain a challenge and the case is not any different for the Mubuku-Sebwe sub-catchments. The area has a high economic status in the country because it is where the Mubuku irrigation scheme, which is the biggest in the country is located. The current land under irrigation is 516Ha and it is proposed to be expanded by another 480Ha in the near future.

The area has also suffered floods, killing several people and destroying important infrastructure like roads, dams, schools and hospitals. There is a cascade of hydro power plants along the R. Mubuku including Kilembe, Bugoye, KCCL, and possibility of others is inevitable.

This is in addition to proposed expansions for some of the existing plants which are underway and will require clear and systematic water management approaches while engaging all affected stakeholders.

Kasese National Water and Sewerage Cooperation is in final stages of expansion of its supply and has been considering abstraction from this sub catchment. Industry and mining has been revived and this comes with a lot of issues related to water abstraction, use and management. As a result, the water resources are under stress particularly during dry seasons causing conflicts between upstream and downstream users.

The situation is made worse by the current water permits assessment and evaluation process in the country which handles water allocation on a case by case basis hence the aggregated impact of developments on the catchments or basins as a whole are “vaguely” understood (COWI, 2010). In this regard, exploring adaptation pathways into an uncertain future can support decision making in achieving sustainable water management in a changing environment (Haasnoot et al., 2012). Sustainable water allocation can be a resolution for water disputes as it addresses simultaneously economic, social and environmental benefits (Roozbahani et al. 2015). From a sustainability point of view, we cannot use more than the rate of replenishment (Hoekstra, 2014). Uncertainty 4 about future conditions is becoming even more acute because of climate change and shifting economies, therefore the need to reconsider water use practices and develop strategies that can respond to all these challenges simultaneously. In this study, an interactive software that assists water managers to use data and information to provide scientific back up for decision making on complex issues has been developed for Mubuku-Sebwe sub-catchments.

Water resources management in Uganda is hampered by scanty hydro-meteorological information. In some seasons, excessive runoff has caused massive destruction of life and property, yet in subsequent seasons, water scarcity has led to death of animals and humans. Water allocation therefore is done as and when an application is made without considering the already existing users and the combined impact on the catchment as a whole.

Catchment based Integrated Water Resources Management has already been piloted in Rwizi, Mpanga, Awoja, Ruhezamyenda, Aswa and Semuliki among others. Although water allocation for present and future demands have been clearly emphasized in the “Catchment Management Planning Guidelines” for Uganda, they have not been carried out in the already developed Catchment Management Plans. Currently, only aspects of water balance are considered leaving a glaring gap on allocation.

In 1998, Uganda’s water sector was reformed, and implementation of catchment or basin based water resources management as a means of promoting effective management and development of the country’s water resources and sustainability of water-based infrastructure and services was effected in 2011. Implicitly, no water user in a catchment can be independent of other users.

Administratively, water abstraction and use is regulated through issuance and enforcement of water abstraction permits and in a study carried out to determine and map water use and demands in the George, Edward, Kafu basins it was recommended that Government considers detailed studies to determine the spatial and temporal demands for water and makes the water available at the time and locations where it is needed in the long term(ARK Consult, 2014). Uncertainty about future conditions is becoming even more acute due to climate change and shifting economies, thus the need to reconsider water use practices and develop strategies that can respond to all these challenges simultaneously. Exploring adaptation pathways into an uncertain 5 future can support decision making in achieving sustainable water management in a changing environment (Haasnoot et al., 2012). Sustainable water allocation can be a resolution for water disputes as it addresses simultaneously economic, social and environmental benefits (Roozbahani et al. 2015). From a sustainability point of view, we cannot use more than the rate of replenishment (Hoekstra, 2014).

This report provides a step by step procedure on how to incorporate sustainability in the water allocation framework of Uganda under catchment planning process.

1.5 Significance of the study This study contributes information to better understanding the spatial and temporal water availability in the Mubuku-Sebwe sub-catchments in order to achieve improved water resources planning and use of tools for water resources regulation; an objective under the strategic intervention “To improve the assessment and evaluation of permits for various water uses” in the National Development Plan II, well knowing that fresh water is a key strategic resource in Uganda and a driver to her economic development. This is important because it provides basis for improved management and ease of regulation, limiting conflicts among users, improved data collection, coordinated development and provision of scientific data that can guide decisions and policies. The data and information that has been generated is a basis for further research by incorporating climate change scenarios to improve resilience.

1.6 Conceptual framework for the research This is the scheme of concepts which have been followed in the study to achieve the above objectives. The study begins by quantifying how much water is available in the Mubuku-Sebwe sub catchments using rainfall, discharge and potential evapotranspiration data. This was followed by computation of water demand for six different uses of domestic, livestock, industry, irrigation, hydropower and environment. The available water was then matched with the demand for 2015 and 2040 using the Mike Hydro model and results analyzed for deficits and/or surpluses to build scenarios for water management. The performance of these scenarios was then evaluated in the NBDSS to come up with the most sustainable water use option for the study area. Figure 1 below shows the flow of concepts in this study.

Catchment characteristics Hydro- meteorological  Area NAM Rainfall data: -  Slope Runoff Model  Soils  Rainfall

 Surface/root zone  Discharge

storage e.t.c  PET Water balance for the Mubuku-Sebwe sub- catchments Water demands

 Agriculture Mike Hydro Water  Industry Allocation Model  Hydropower  Environment  Domestic

Scenario conceptualization and analysis (Seasonal / whole time deficits/surplus)

Nile Basin Decision Sustainable water Support System use Options (NBDSS)

Figure 1: Conceptual frame work

CHAPTER TWO: LITERATURE REVIEW

This section provides information about previous studies that have been carried out, the approach and methods employed in order to ensure that the method used is the most appropriate and relevant. It also reviews the enabling environment for water and environmental resources management in Uganda.

2.1 Water allocation Policies in Uganda The constitution of the Republic of Uganda clarifies that the management of water resources is the state’s duty unless otherwise decreed by parliament. Government, either local or central, holds natural resources in trust for Ugandans in accordance with the provisions of the Constitution (GoU, 1995).

Water policy in Uganda has been based on the Integrated Water Resource Management (IWRM) approach since the Water Action Plan in 1995, the Water Policy in 1999 and the Water Act Cap 152. The 2005 Water Sector Reform Study and the 2006 Joint Sector Review (JSR) both recommended the implementation of IWRM at the catchment level. The National Water Policy provides an overall policy framework and defines the Government’s policy objective as: “To manage and develop the water resources of Uganda in an integrated and sustainable manner, so as to secure and provide water of adequate quantity and quality for all social and economic needs of the present and future generations and with the full participation of all stakeholders” (GoU, 1999).

“The Water Act” for regulating water use is to ensure provision of adequate quantities and quality of water for all social and economic needs of present and future generation (COWI, 2010). Water use regulation is carried out through enforcement of the Water Act Cap 152, the Water Resources Regulations S.1 No. 152-1 and through implementation of Water Policy (GoU, 1997; GoU, 1998; GoU, 1999). Despite the regulatory framework, these resources are being developed and depleted at a fast rate, a situation that requires assessment to establish present status of water resources in the country (Nsubuga et al., 2014).

The Directorate of Water Resources Management (DWRM) of the Ministry of Water and Environment (MWE) is mandated through the Water Act (1998) to ensure optimal use of water resources through issuance of permits for abstraction and discharge of water and monitoring compliance with stipulated conditions for water abstraction and discharge (Muwanga, 2015). In 1998, Uganda’s water sector underwent a reform and implementation of catchment or basin based water resources management as a means of promoting effective management and development of the country’s water resources and sustainability of water-based infrastructure and services was effected in 2011. The Directorate of Water Resources Management de- concentrated some water management functions, such as data collection, compliance monitoring, compliance assistance and awareness raising to four Water Management Zones (WMZs) of Kyoga, Victoria, Albert and Upper Nile. The Edward-George basin falls under the AWMZ.

GoU, (1998a) states that water abstraction qualifies to be regulated when;

a) the user wishes to construct, use/own, occupy or control the works on or adjacent to land on or adjacent to which there is a motorized pump for pumping water from a borehole or water-way; b) there is a weir, dam, tank or other works capable of diverting or impounding an inflow of more than 400m3 in any period of 24 hours; and, c) there are works of non-consumptive uses. The water users considered in this study therefore were those that abstract 400m3 of water or more per day. For purposes of completeness, those abstracting less were aggregated and future water demand projected to the year 2040 in reference to Uganda’s vision 2040. The proposed developments were based on the NDP II and the Uganda Vision 2040. ARK Consult, (2014) recommended that Government considers detailed studies to determine the spatial and temporal demands for water and makes the water available at the time and locations where it is needed in the long term.

2.2 Quantifying available water within a catchment The overall system concept is related to the recognition that a catchment forms a unit and must be managed as a whole. The clear guiding principle in the management of a system is that upstream use has direct and indirect consequences on downstream users. Problems with water resources security resulting from water scarcity are occurring worldwide, and the most feasible solution to such problems is to increase the productivity of limited water resources through 9 rational water use and allocation within the affected region. The optimization of water resources allocation is a complex process.

An Optimal Water Allocation Model (OWAM) based on Water Resources Security assessment was applied to improve the water use benefits in Zhangjiakou Region of northern China in 2020 (Wang et al., 2012). Improving the existing methodology or creating new methodology to make the water allocation program more accurate is always the emphasis. The Resource Allocation Model (REALM) was used to build the water allocation tool to analyze alternate policy scenarios and investigate possible water allocation options in the Malaprabha catchment, India (George et al., 2008). Parks, (2015) recommended that a harmonized approach to the exploitation of water resources at an integrated water shed management level should be urgently developed.

In the Water Allocation Models for the Umbeluzi River Basin in Mozambique, the WAM that was developed was based on the WEAP framework (Droogers et al., 2014). It is well accepted that the best model does not exist, but is a function of the questions to be answered. A common approach of model selection is to look at the spatial scale to cover and the amount of physical detail to be included but also taking into consideration other factors such as resource (time and money) availability, access to data, knowledge level, and support among others.

MODSIM 8.0 is a generic river basin management decision support system for analysis of long term planning, medium term management, and short-term operations on desktop computers operating under MS Windows 2000/XP.

MODSIM is free from expensive licenses for proprietary software since all components are developed from native code or shareware under the MS .NET Framework (Labadie, 2006). Its generic nature however, does not take into account the uniqueness of local hydrological conditions in a given basin.

The NAM (NedborAfstromnings Model) is deterministic, lumped and conceptual rainfall-runoff model that operates by continuously accounting for the moisture content in three different and mutually interrelated storages that represent overland flow, interflow and base flow. It treats each sub-catchment as one unit, therefore the parameters and variables are considered for representing average values for the entire sub-catchments. The result is a continuous time series of the runoff from the catchment throughout the modeling period. Thus, the NAM model provides both peak

10 and base flow conditions that accounts for antecedent soil moisture conditions over the modelled time period. The model was prepared with 9 parameters representing the Surface zone, Root zone and the Ground water storages. In addition to these parameters, NAM contains provision for:  Extended description of the ground water component.  Two different degree day approaches for snow melt.  Irrigation schemes.  Automatic calibration of the 9 most important NAM parameters. The input data was daily rainfall, runoff and potential evapotranspiration which was converted to dfso format using MIKE ZERO software which was then used for the model development.

2.3 Quantification and projection of water demand Water demand is the amount of water that will be used by all groups of consumers, assuming that no limiting factor such as lack of resource, lack of pressure, negatively perceived water quality, inaccurate distribution etc. (MWE, 2013).

To effectively plan for meeting future water resource needs, it is important to understand current use trends as well as future changes that may be anticipated (Longworth, 2013). The water demand computation was done in reference to Ministry of Water and Environment’s “Water Supply Design Manual” (MWE, 2013).

2.3.1 Current WD computation There is a global need for management of river flows to be informed by science to protect and restore biodiversity and ecological function while maintaining water supply for human needs. However, a lack of data at large scales presents a substantial challenge to developing a scientifically robust approach to flow management that can be applied at a basin and valley scale.

In most large systems, only a small number of aquatic ecosystems have been studied well enough to reliably describe their environmental water requirements.

The umbrella environmental asset (UEA) approach uses environmental water requirements developed for information-rich areas to represent the water requirements of a broader river reach or valley while relying on detailed knowledge about key representative assets. It was applied in the Murray–Darling Basin in eastern Australia (Swirepik et al., 2015).

Conventional water-use assessments for example as used in California’s water foot print assessment tend to focus on water supply and direct water use in discrete sectors of society. This approach, however, offers incomplete information on impacts to water resources, which are interconnected through economic activity to our daily consumption of goods (Fulton et al., 2012).

In the Malaprabha catchment, India, the quantity of water used in agriculture was considered a function of crop area, type of crop and climatic conditions. The cropping data collected from district level statistical handbook was used to estimate crop area. The water requirements of crops were estimated using the Penman-Monteith approach based on climate and crop culture (George et al., 2008). Water demand for industries should be studied in detail by consulting the proprietors concerned and other relevant agencies (MWE, 2013). Environmental flow is the flow regime required in a river to achieve desired ecological objectives (Mike et al., 2004). The practice of EFR’s (Environmental Flow Requirement) began as a commitment to ensuring a ‘minimum flow’ in the river, often arbitrarily fixed at 10% of the mean annual runoff (World Commission on Dams, 2000) however, the dynamics of river flow have to be taken into consideration.

2.3.2 WD projection Review of the global WDPs shows that the current water withdrawals follow a higher rate of growth than that of most post-1990 demand projections with the domestic sector having the lowest overall demand at present, especially in the low-income countries (Upali et al., 2014).

2.4 Sustainable water use/allocation While scientists often complain that their input is ignored by decision makers, the latter have also expressed dissatisfaction that critical information for their decision making is often not readily available or accessible to them, or not presented in a usable form.

Scenario analysis is a practical, effective way to put integrated environmental models into more beneficial use for long-term real-world decision making under uncertainty. For Integrated Water Resources Management studies, scenario development involves making explicit and/or implicit assumptions about potential future conditions, such as climate change, land cover and land use changes, population growth, economic development, and technological changes (Liu et al., 2008). 12

MIKE HYDRO (Basin Module) is a basin-scale simulation model and accommodates a basin- wide representation of water availability and water demand. It is a physical and conceptual model system for catchments, rivers and floodplains, structured as a network model in which the rivers and their main tributaries are represented by a network consisting of branches and nodes. The branches represent individual stream sections while the nodes represent confluence, locations where certain water activities may occur, or important locations where model results are required (Jha et al., 2003)

MIKE HYDRO is a comprehensive deterministic, semi-distributed and physically-based modeling system for the simulation of water flow, water consumption, water quality, and sediment transport. It has an integrated modular structure with basic computational modules for hydrology and hydrodynamics. One basic MIKE HYDRO module is MIKE HYDRO basin, containing modules for evapotranspiration, snow melt, overland and channel flow, crop irrigation, and exchange between aquifer and rivers. A map layer coordinates the parallel running of the process components. Rivers and catchments are depicted in the map layer. Parameters and data requirements are outlined in a tabular layer. Simulation results are obtained from a result layer (Yu et al., 2015). The Nile Basin Initiative developed the NBDSS that enables analysis of different management options and scenarios, making it possible for the optimal allocation of water resources in the Nile Basin region (NBI, 2009).

The NBDSS is a common computer-based platform for communication, information management and analysis of water resources that provides a framework for sharing knowledge, understanding river system behavior, evaluating alternative development and management strategies, and supporting informed decision making.

It offers tools for integrating environmental, social and economic objectives thus greatly facilitating multi-sector water resources planning at river basin level.

With its modeling tools and scenario manager, it helps in the development of scenarios and defining their indicators and then simulate those scenarios and quantify the indicators in the output time series.

2.5 Performance of different Climate Models for the study area.

Although ARC2 extends back to 1983 and thus might be useful for rainfall-variability analyses in gauge-deficient regions in equatorial Africa, it greatly underestimates rainfall in the Rwenzori Mountains, the largest mountain range in the study region (Diem et al., 2014).

ERA-Interim showed a persistent tendency to overestimate (14 mm per dekad on average with higher bias at the start of the season). ERA-40 tended to underestimate throughout (10 mm per dekad on average) (Maidment et al., 2013). A good discharge simulation depends on a good coalition of a hydrological model and a precipitation product, and a better precipitation product does not necessarily guarantee a better discharge simulation.

This suggests that, although the satellite-based precipitation products are not as accurate as the gauge-based product, they could have better performance in discharge simulations when appropriately combined with hydrological models (Qi et al., 2016).

The TRMM 3B42 and the TAMSAT products show greatest similarity to gauge data across most aspects of rainfall estimation. CMORPH shows greater similarity to historical data in the seasonal progression of rainfall than spatial patterns of rainfall when considered in relation to other products. On the other hand, the RFE 2.0 product shows greater spatial ability than temporal, when compared with the other products. Whilst the PERSIANN system compares favorably to other products in terms of seasonal patterns of rainfall in the regions with lower elevation, it estimates very different amounts of rainfall from the other products and resembles poorly the rainfall regimes in the highland regions (Asadullah et al., 2008).

TRMM3B42V7 exhibits significantly better error statistics than the GLDAS re-analysis product in terms of basin average rainfall and simulated runoff values with respect to the reference radar- rainfall dataset. However, the availability of reanalysis products over long time frames (e.g. GLDAS, since 1979) makes these datasets desirable for deriving precipitation and flood frequency analyses for data poor areas (Seyyedi, 2014).

The Climate Research Unit (CRU) precipitation dataset from the University of East-Anglia consists of a long record of rain-gauge data from ground stations from 1901 to the present day and uses a complex interpolation method to fill in gaps between stations (Williams et al., 2015).

2.6 Inferences from the Literature Review The Literature Review has extensively covered the concepts of sustainable water use through appropriate water allocation and catchment based IWRM as follows: a) Meeting water-demands in an economically efficient, socially equitable and environmentally sustainable way is a daunting task that requires an IWRM approach where every user is responsible and it should be applied at catchment/basin level. b) The river basin is widely acknowledged to be the most appropriate unit for water resources planning and development because it is a natural bounded area that acts as a distinct hydrologic system, for which meteorological and hydrological inflows and outflows can be defined. c) A systems approach should be used as this recognizes the individual components as well as the linkages between them, and that a disturbance at one point in the system will be translated to other parts of the system. d) The use of such techniques as systems analysis and mathematical modeling as planning and management tools is recommended and by establishing a simulation model of the entire river system, the performance of the system as a whole in response to changes in the individual components can be evaluated. e) The simulation model chosen for this study is MIKE HYDRO because of its ability to carry out in depth analysis of a river system and define new rules intended to maximize overall benefits and its availability. f) Current water demand was considered as water required for different uses in 2015 in the whole sub catchment. All water users abstracting 400m3 or more of water per day were mapped using a hand-held GPS and located on the catchment map. The users abstracting less than 400m3/day were aggregated on sub-catchment by sub-catchment basis. Data on how much water is abstracted and target demand at the different points was obtained from the water permits data base and from the individual water

users during field visits. Water demands computations were guided by the Water supply design manual (MWE, 2013). g) Environmental demand is a key water use and should always be considered in any water allocation exercise. In this study, it will be considered as 10% of the river flow. h) The ERA-Interim and TRMM3B42V7 products have the required daily time step and good performance in estimation of rainfall over the study area. CRU is also appropriate although only available on monthly time step.

CHAPTER THREE: METHODOLOGY

3.1 Description of the study area The study area is about 346km2, with Mubuku covering 260km2 and Sebwe 86km2. The head waters originate from the steep slopes of the Rwenzori ranges characterized by scattered settlements and high flow velocities that cause massive erosion. The midstream is mainly farm land and more dense settlements than the highlands. The river channels here are characterized by deposited materials including boulders and soil causing morphological changes.

Kasese district has a vibrant population in terms of development and the major source of water is the Mubuku and Sebwe Rivers. The irrigation schemes, several hydro power generation plants, industries like the Tibet Hima Mining Company, Municipal water supply and livestock together present pressure on the available water resource in the catchment. This area also suffers critical water related disasters with devastating floods during the rainy season and acute water shortage during the drier months. The Mubuku-Sebwe sub-catchments drains into the Lake George which is a ramsar site of global importance hence the management and sustainable allocation of the resource to the different competing uses is very crucial at such a time.

3.1.1 Location of the Mubuku-Sebwe sub catchments The Mubuku-Sebwe sub-catchment is located in Kasese district in western Uganda in the sub- counties of Maliba, Bugoye and Karusandara in Busongora County within the Lake Edward- George basin. The upstream is the mountain Rwenzori ranges and the downstream is the Lake George. Figure 2 shows the location of the study area.

South Sudan

Study Area

DRC

Kenya

Tanzania

Figure 2: Location of the study area in Western Uganda 3.1.2 Topography The topography of the catchment is very pronounced. The highest point in the catchment is formed by the tops of the Rwenzori Mountains with heights of up to 5,109 m asl. The lowest point is the swamp area in which the Mubuku River discharges. The swamp is linked to Lake George and has an altitude of approximately 915 m asl. The slopes of the valleys in the main upper catchment are more than 25% and reduce in the middle zone to about 10-20%. The lower low land slopes are mild up to about 5% (WWF, 2012). Elevations in the study area are shown in the map in Figure 3.

Figure 3: Digital Elevation Model of the study area

3.1.3 Drainage patterns Most of the wetlands in the sub-catchment are related to relief. The majority are found around Lake George. Other swamps are along the river banks with U-shaped valleys.

On top of Mount Rwenzori are bogs occupying depressions. The low land wetlands exist between 800-1,200m above sea level while high up in the Rwenzori, they are at altitude 2,000- 5,100 m above sea level. The edge of Lake George is occupied by wetlands containing a very rich ecosystem that requires protection. The enormous climatic, social and economic importance of the wetlands was recognized by the Uganda Government in 1986. Subsequently, on 4th March 1988, Uganda became a signatory to the Ramsar convention naming Lake George wetland as a wetland of international importance. The Ramsar site is the internationally recognized wetland of Uganda situated around L. George and shared by the districts of Kasese, Bushenyi and Kamwenge with the largest part being in Kasese district. Covering an area of approximately 250 km2, this wetland was included on the Ramsar convention’s list of wetlands of international importance in 1988. Much of this site lies in Queen Elizabeth National Park (QENP) area.

The L. George Ramsar site contains extensive permanent lakeside swamps, riverine swamps, marshes, swamp forests and savanna grasslands which are seasonally or permanently flooded with water and support organisms adopted to water logged conditions. Dominant vegetation in this wetland is the papyrus communities around the lake, cyperus and patches of woody vegetation.

The Mubuku-Sebwe sub-catchments has a total area of about 346 km2 with 86.3 and 260 square kilometers covered by the sebwe and mubuku sub-catchments respectively. The two rivers drain the eastern slopes of Rwenzori mountain. Their catchments are part of the Lake Edward-George drainage system. River mubuku is the largest river draining the Rwenzori mountains complex. Its main tributaries are Isya, Kitajuka, Ruboni, Choho, Karyankoko, Bujuku among others. Figure 4 shows the drainage pattern of the mubuku-sebwe sub-catchments.

River Esya

River Mubuku

River Sebwe

Figure 4: Presentation of the drainage system of the study area

3.1.4 Geology and Soils Most of the Mubuku-Sebwe sub catchment is underlain by metamorphic rocks while a very small part is underlain by sediments. The latter part of the catchment is located in the lower section of the catchment. Sediments have been deposited by the Mubuku and other rivers that are part of the L. George system. Based on the geological map, the metamorphic rocks consist of undifferentiated gneisses and the sediment formation is formed by alluvium, black soils and moraines. The soils are Organic, ferrosols, podsols/eutrophic, and hydromorphic. These soils are vulnerable and are degraded because of unwise human activities such as over cultivation, cultivation on steep slopes, poor agronomic practices and over grazing causing soil erosion and fertility loss.

3.1.5 Natural vegetation and land use The Mubuku-Sebwe sub catchments have three main belts of vegetation which are the Rwenzori Mountains National Park, Low land/Queen Elizabeth National Park (QENP) and the middle belt mainly covered with farmland. Over 63% of the study area lies in RMNP.

The vegetation of Rwenzori Mountains National Park is largely determined by factors related to elevation aspect and five distinct zones can be distinguished. These are grassland (1000-2000m), Montane forest (2000-3000m), bamboo/mimolopsis zone (2,500-3,500m), healther/riparian zone (3,000-4,000m) and the afro-alpine moorland zone (4,000-4,500m). Tall dense pennisetum purperum (elephant grass) grows in the valleys with shorter grasses and many flowering plants on the hill slopes where the thorny, red-flowered Erythrina abassinica is often conspicuous. Flat crowned Albizia spp. are abundant in the small valley forest.

In the lower lying areas up to about 2,400m the montane forest vegetation is characterized by the tree species such as Symphonia globulifera, Prunus africana, Albizia and Dombeya spp. There are very few large trees exceeding 30m in height and a canopy is very broken except in valley bottoms and along ridge tops where gradient are comparatively gentle. Here the trees are very dense and layered with larger tree specimens.

On moderate slopes with a deep soil, Arundinaria alpina forms a dense stand with few other plants among it, though nettles are sometimes painfully obvious. On steep and rocky slopes this is replaced by a frightful tangle of Mimulopsis ellotii.

On poor soil (ridge-tops, rock, or moderately boggy ground) grow dense thickets of tree heathers, philipia trimera and p. kingaensis. On well drained slopes there is a greater variety of plants, with small trees standing over a tangled undergrowth. Bogs in this zone are occupied by various kinds of sedge, chiefly carex runsorrensis that forms huge tussocks up to 1m high between which grow sphagnum and other mosses.

The most abundant vegetation in the zones is a tangled thicket about 5ft high of Helichrysum stuhlmanii, with white flowers that open quickly in any sunny period; at the higher altitudes the same species is only 1ft high, covered with white wooly hairs. Thickets of tree groundsels, senecioadnivalis occupy gullies and other sheltered or well watered sites, and scattered individuals occur throughout the zone. Carex runsorrensis bogs are abundant in this zone too, and small brilliant yellow or orange moss bogs occur at the highest levels.

The landscape is generally covered by farms and plantations, grazing land and settlements. A few areas especially on hill slopes are bare due to stone crashing and minor quarrying activities especially in areas of Kabukero which presents issues on steep slopes like erosion and high sediment loads in water due to high flow velocities and ease of access issues. There are forested areas too especially in the hilly slopes upstream and in the conservation areas of the Rwenzori National Park and Mubuku Forest Reserve. Figure 5 shows the land cover types in the study area.

Figure 5: Spatial distribution of different land cover types in the sub catchments 3.1.6 Climate The climate in the area is equatorial and rainfall contribution to river flow and lakes volumes cannot be underestimated. The area receives bi-model rainfall in two seasons of March-May and August-November with annual average ranging from 800mm – 1600mm.

While the total amounts of rainfall might be adequate to cover the demands, the spatial and temporal variations in the rainfall makes it impossible for all the basin water uses to have sufficient quantities from rainfall at all times. Glacial recession will have a minimal impact on alpine river flow. Glacial melt water flows, based on fluxes during the dry season, constitute a very small proportion (<0.5%) of the total river discharge realized at the base of the Rwenzori Mountains (Taylor et al., 2007).

The rapid decline in the area covered by glaciers in the Rwenzori Mountains reflects a rise in alpine air temperatures and/or reductions in alpine precipitation and cloud cover (Bob and Taylor, 2009). The temperature of Kasese town ranges from 13 – 35oC and exhibits very low monthly variation as shown in Figure 6.

Figure 6: Minimum and Maximum temperature for Kasese District

Monthly Evapotranspiration data from the Mubuku Irrigation scheme design report and a daily global dataset from Princeton University were used in this study. Princeton University dataset was preferred for use since it provides a much longer record on a daily time step. Table 1 shows a comparison between Princeton and observed monthly evapotranspiration.

Table 1: Comparing observed monthly Evapotranspiration with Princeton dataset

Source Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avg.

ETo Design Report 4.95 5.14 5.7 4.59 4.37 4.23 4.04 4.28 5 4.63 4.44 4.54 4.7 mm/day

Princeton 5.19 4.99 4.64 4.13 4.22 4.48 4.6 4.36 4.2 4.08 4.16 4.6 4.5

The average monthly ETo was obtained from CROPWAT 8.0 using the climatic data (Temperature, relative humidity, wind speed, sunshine hours, and rainfall) from FAOClim2 for

Mubuku Meteorological station as the input. The model calculates ETo using the recommended

FAO Penman-Monteith method. Table 2 shows monthly ETo variations. Table 2: Reference Evapotranspiration from CROPWAT

Min Max Month Temp Temp Humidity Wind Sunshine Radiation ETo °C °C % km/day hours MJ/m²/day mm/day January 15.80 30.90 64.00 199 7.50 20.30 4.95 February 16.70 31.10 65.00 216 7.30 20.70 5.14 March 17.00 30.30 42.00 199 6.90 20.30 5.70 April 17.30 29.60 68.00 173 7.00 19.90 4.59 May 16.80 29.80 72.00 147 8.00 20.30 4.37 June 16.10 29.80 71.00 147 8.00 19.60 4.23 July 15.50 29.70 68.00 147 6.50 17.70 4.04 August 16.50 30.00 67.00 147 6.50 18.60 4.28 September 16.10 30.30 65.00 199 7.50 20.90 5.00 October 16.10 29.30 66.00 199 6.30 19.10 4.63 November 16.30 28.80 70.00 199 6.80 19.30 4.44 December 15.30 29.70 72.00 173 8.20 21.10 4.54

3.1.7 Demographic Characteristics The study area is located in Kasese District in mid-western Uganda. The population of the district in 2014 was about 694,992 with a population density of 236 and annual growth rate of 2.4 for the period 2002 to 2014. The district covers a total surface area of approximately 3,390Km2. The population is vibrant in terms of development and has increasingly caused a lot of pressure on the water resources in the study area.

The catchment covers three sub counties of Maliba, Karusandara and Bugoye and their 2014 population was 47,585, 11,890 and 35,367 respectively. Maliba has the highest population but Bugoye has the highest population density.

The people in the sub catchments are mostly concentrated in the mid and downstream since the upstream are high slopes of the Mountain Rwenzori and the Rwenzori National Park.

3.1.8 Environmental concerns The area has protected zones including the Mt. Rwenzori NP, Mubuku FR, and part of Queen Elizabeth NP. The mountains are habitat to several endemic, endangered, threatened and rare species of the Albertine Rift and also an Important Bird Area (IBA). As this represents only a moderate level of species richness, the forest harbours many rare, threatened and endemic species (Kizza, 2014). The catchment drains into L. George which is a ramsar site of international importance and allocation of water should recognize the peculiar requirements of these important ecosystems.

3.2 Quantification of available water in the sub-catchments. The output from this exercise is the knowledge of the flow rates or volumetric amount of water available at the outlet of the Mubuku-Sebwe sub-catchments.

3.2.1 Rainfall analysis

3.2.1.1 Data checking Five rainfall stations within and around the catchment were selected as shown in Figure 7. These stations are Kilembe, Bugoye, Mubuku Prisons, Mubuku HEP and Kasese MET. Figure 8 shows their data coverage.

The data was observed to understand the data sets and identify any suspicious data periods for ease of reference in case of any anomalies in the analysis.

The daily rainfall time series was converted to monthly to be used for filling the correlation matrix. This was done in excel using the Data (subtotal) tool from the toolbar. The correlation matrix in the appendices was generated. After gap filling, thiessen polygons were constructed in Arc GIS to compute the weights of each rainfall station for computing the MAR. From the theissen polygons, the contributing stations to flow for Mubuku are Mubuku HEP and Bugoye, but the most significant contributor is Mubuku HEP. MAR was computed using these two stations.

The stations which influence flow at Sebwe were found to be Bugoye, Mubuku HEP and Kilembe. Kilembe had the least influence but was considered because its data was quiet reliable.

In addition, considering that the Sebwe sub catchment area is not very big, it was important to include Kilembe in the analysis. The computed weighting factors and the gap-filled rainfall data records were then used to compute the mean areal rainfall for each sub catchment separately.

From the correlation matrix, the suitability of stations to gap fill other stations was considered for correlation coefficients ≥ 0.6. The higher the coefficient, the stronger the relationship hence the stations gap filling priority as shown in Table 31, Table 32, Table 33 and Table 34 in appendices.

With the most suitable stations identified, double mass curves were plotted using accumulated rainfall values and a trend line was added to generate the model expressing the relationship between the two data sets.

The Double Mass Curve can smooth a time series and surpress ramdom elements thus showing the main trend in the data time series. The slope of the trend line represents the constant of proportionality between the quantities. To estimate the missing record at station A using the record of station B, data set B, corresponding to the missing period was multiplied by the ratio of the slope of the double-mass curve of station A to the slope of the double-mass curve for station B.

Figure 7: Location of the rainfall stations in and around the study area

Figure 8: Temporal distribution of the rainfall data coverage in and around the study area 3.2.1.2 Comparison with global data sets The observed rainfall was compared with daily satellite rainfall products that were identified to fairly predict rainfall around the study area like TRMM and ERA INTERIM. The comparison showed significant variation with a general under estimation of rainfall from the two satellites as can be observed in Figure 35 .

3.2.1.3 Selection of model period After data processing and analysis, Mubuku HEP gap filled rainfall data gave a better estimation of the precipitation in the study area and a period of 60 years from 1956 to 2015 was selected as the model time frame using the following criteria: - a) The period that was long enough to cover the expected inter-annual variations and to include typical dry and wet years, as well as extremely dry and wet years. b) The period that was representative of the present climate.

c) Availability of a coherent and consistent record of adequate quality.

3.2.2 River flow analysis

3.2.2.1 Data checking The discharge datasets for Mubuku and Sebwe gauging station was studied to understand its distribution. The R. Mubuku observed data available with DWRM ranges from 1954 to 1971 while that for R. Sebwe at Bugoye ranges from 1950 to 1968. The hydrographs in Figure 36 show the data from these stations. 3.2.2.2 Flow Quantification

The existing gauging stations were used for delineation for model calibration while water abstraction points were used for delineation of water source catchments. In addition, topography and slope were also taken into consideration since specific discharge on steep areas is not necessarily the same as other parts of the catchment. This is illustrated in Figure 9.

Q2, S2

Q1, S1

Figure 9: Computation of specific discharge in a catchment using discharge-area ratio

If S1 = Average slope for whole catchment with discharge Q1,

And S2 = Average slope for a sub-catchment with discharge Q2,

Then Q1/Q2 = S1/S2 And discharge of any sub-catchment within the bigger catchment can be got as: -

Q2 = S2*Q1/S1

3.2.3 Rainfall Runoff Modeling Rainfall data was obtained from the Department of Meteorology, MWE and some global sources to gap fill discharge data. Selected rainfall stations within and around the catchment were used for the calculation of MAR using the thiessen polygon method in order to be able to attach weights on the stations in Arc GIS.

Having computed the MAR over the catchments, the missing data for the discharge stations was gap-filled using NAM “Precipitation-Runoff-Model”. A global dataset for Evapotranspiration for Lake George was used in the model after comparison with the observed data.

Sebwe NAM The observed discharge, rainfall and potential evapotranspiration time series for the calibration period (1956 – 1960) were inputted into the model and the default model parameters were kept same and model was run in auto-calibration mode. The model output simulation results were checked for coefficient of determination (R2) value and graphically analyzed for degree of agreement between simulated and observed runoff. The model parameters were then adjusted one by one using trial by error method to obtain the set of best fit model parameters which could simulate runoff with high degree of agreement with observed runoff in terms of timings, peaks and total volume.: -

The model was validated to using a portion of data records (1961 – 1968) which were not used for the calibration. During validation the set of model parameters obtained during the calibration was maintained and the model was run without auto-calibration mode to simulate runoff.

The statistics of the simulated results were analyzed and output of the model checked to compare the simulated and observed runoff to verify the capability of calibrated model to simulate the runoff. Figure 39 and Figure 40 show the calibration and validation plots respectively. Model performance To quantitatively evaluate the performance of the model calibration, three error indices were considered: (a) the Nash-Sutcliffe efficiency (NSE); (b) the RMSE-observations standard deviation ratio (RSR); and (c) the % Bias.

NSE compares the relative magnitude of squared residuals to the variance of the observed flow. RMSE is used to examine the square root of the mean squared difference between the observed and simulated flows.

RSR standardizes RMSE using the standard deviation in the observations and % Bias is to assess the values of residuals to the observed flow from the gauging stations. Table 11shows the performance of the Rainfall Runoff Model for Sebwe and Mubuku.

From the analysis showing a good match between observed and simulated daily runoff during validation period, it was deduced that the model would work well during the extended period also with reasonable accuracy. It could also be concluded that the NAM model thus developed for Sebwe could be used to simulate the runoff in other sub catchments of similar characteristics.

Mubuku NAM As was earlier done for Sebwe, the observed discharge, rainfall and potential evapotranspiration time series for Mubuku for the calibration period (1964 – 1966) were inputted into the model and the default model parameters were kept same and model was run in auto-calibration mode. The model output simulation results were checked for coefficient of determination (R2) value and graphically analyzed for degree of agreement between simulated and observed runoff. The model parameters were adjusted one by one to obtain the set of best fit model parameters which could simulate runoff with high degree of agreement with observed discharge. The Mubuku model was difficult to fit, most probably attributed to the changing channel characteristics caused by a lot of erosion and boulder deposition and to the observed discharge covering only low flows. The constant changes in the rating curves can be seen in Figure 38 and Table 38.

Although the water level data seems consistent, the flow estimation by the different rating curves were suspected to be erroneous with discharge values for the maximum stage of 15m very outrageous. The best achievable fit for the model calibration and validation periods were plotted in Figure 44 and Figure 45 respectively.

Table 3: Evaluation of NAM model performance

Station No. Name NSE RSR %Bias

84219 Sebwe 0.565 0.433 25

84222 Mubuku 0.461 0.617 31

3.2.4 Potentially available water in the catchment in 2040 An ensemble mean of 29 downscaled GCM was obtained from the AgMIP team in Uganda to make projection of available water in the study area for 2040. This provided the projected weather data for Kasese which was used to project the Mubuku and Sebwe rainfall up to 2040 using DMCs. With the potential rainfall time series, NAM model was employed to generate the potential discharge in the study area for the projection period. The Mann Kendal test, inbuilt within the NBDSS was used to test for the trend of the complete data set. The trend was then analyzed in python using Fourier analysis (Williams, 1998; Tularam et al., 2010).

3.3 Estimation of water demand in the Mubuku-Sebwe sub-catchments

3.3.1 Mapping of water user abstraction points Some major water users have or are under assessment for issuance of abstraction permits with the Ministry of Water and Environment. Therefore, the starting point here was to visit the Ministry’s Regulation Department and get all the available GPS locations of these water users. Although it is a requirement on the permit application form, it was noted that most of the applicants did not fill this space and so it was not possible to locate them on a map. Only about 50 water users had locations filled so this data was sorted and put in an excel sheet. On plotting however, most of the points fell in clearly different locations. This was evident when the map was overlaid with administrative boundaries hence need for primary data collection and verification of some erroneous points.

Field data collection trips were taken to map water user abstraction points and to collect data concerning future development plans for use in projecting future water demand. The major abstractions in the study area are shown in the Figure 10.

Figure 10: Location of water abstraction points within the study area

3.3.2 Computation of 2015 water demand.

3.3.2.1 Domestic WD Population figures from the 2014 population census report were used to estimate the water demand for domestic use. This report presents the population of Kasese district in 2014 as 695,000 with an annual growth rate of 2.4 and a population density of 236 people per unit square km. The same report gives the total 2014 population of the three sub counties in the catchment as 94,842. This figure was used to compute the 2015 population using the geometric progression method (James M. Mandon, 2003) and subsequent water demand as follows: - n Pt = Po (1+r)

Where Pt = population after t years, Po = present population, and r = annual growth rate (%), n=period of projection in decades. Hence the population in the catchment in 2015 (base year) was computed. Water demand was then computed using the equation: - Water Quantity= Per capita demand x Population Taking per capita water demand of 25 litres per day (MWE, 2013):- 2015 domestic water demand for the study area was computed.

3.3.2.2 Livestock WD According to the Water Act of Uganda 1997 in Part I – Interpretations, “livestock unit” means a mature animal with a live weight of 500 kilograms and for the purposes of this definition:- One head of cattle shall be deemed to be 0.7; One horse shall be deemed to be 0.6; One donkey/pig shall be deemed to be 0.4; One goat shall be deemed to be 0.15; and One sheep shall be deemed to be 0.15; of a livestock unit; Per capita water demand for one TLU = 25liters per day (MWE, 2013). Hence current livestock WD = per capita WD x Livestock population.

Cattle

Previous estimates of the total number of cattle in Uganda based on the results of the Agricultural Module of the Uganda National Household Survey (UNHS) 2005/06 showed that

36 the national cattle herd stood at 7.5 million cattle as of 2005/06. The estimates show that the national cattle herd stood at 11.4 million as of 2008. The average annual growth rate was as shown in Table 4.

Annual Growth Rate over multiple years = {(f/s)1/y}*100 where f is the final value, s is the starting value, and y is the number of years.

Table 4: Annual growth rate for cattle

Year No. of cattle 1/y (f/s)1/y (f/s)1/y-1 ((f/s)1/y-1)*100 2002 5,200,000 0.3333 1.13 0.13 12.85 2005 7,500,000 0.3333 1.15 0.15 14.82 2008 11,400,000 Avg. Annual growth rate 13.83

Goats

Compared to previous censuses and large scale surveys there was an increase in the total goat herd in Uganda. For instance the 2002 Population and Housing Census estimated the total goat herd in Uganda to be 5.2 million as of 2002. The Uganda National Household Survey 2005/06 estimated the total goat herd to be 8.5 million in 2005/06 with an average annual growth rate of 15.59 as shown in Table 5.

Table 5: Annual growth rate for goats

Year No. of goats 1/y (f/s)1/y (f/s)1/y-1 ((f/s)1/y-1)*100 2002 5,200,000 0.3333 1.18 0.18 17.61 2005 8,500,000 0.3333 1.14 0.14 13.57 2008 12,500,000 Avg. Annual growth rate 15.59

Sheep

The number of sheep in western region was estimated at 0.27 million in 2008. The average annual growth rate is shown in Table 6.

Table 6: Annual growth rate for sheep

Year No. of sheep 1/y (f/s)1/y (f/s)1/y-1 ((f/s)1/y-1)*100 2002 1,555,000 0.3333 0.92 -0.08 -7.77 2005 1,217,000 0.3333 1.40 0.40 40.50 2008 3,410,000 Avg. Annual growth rate 16.36

Pigs

Table 7 shows the computation of the average annual growth rate for pigs.

Table 7: Annual growth rate for pigs Year No. of pigs 1/y (f/s)1/y (f/s)1/y-1 ((f/s)1/y-1)*100 2002 750,000 0.3333 1.31 0.31 31.00 2005 1,700,000 0.3333 1.22 0.22 21.93 2008 3,100,000 Avg. Annual growth rate 26.46

Computation of livestock water demand

Table 8 shows the 2008 census livestock numbers in Kasese District

Table 8: 2008 Livestock census for Kasese District

District Cattle Goats Sheep Pigs

Kasese 97,243 227,518 24,890 85,812 Source: Livestock census report, 2008.

Table 9 shows the livestock numbers in the study area based on the 2008 livestock census. Table 9: Livestock in the study area

Livestock type Po (2008) r r+1 n Pn Kasese 2015 Pn Mubuku

Cattle 97,243 0.14 1.14 7 243,328 21,100.63 38

Livestock type Po (2008) r r+1 n Pn Kasese 2015 Pn Mubuku

Goats 227,518 0.16 1.16 7 643,016 55,760.27

Sheep 24,890 0.16 1.16 7 70,345 6,100.06

Pigs 85,812 0.26 1.26 7 432,655 37,518.46

Water demand for base year taking per capita for TLU of 25 liters per day

After computing the 2015 livestock population, the water demand was computed using per capita requirement for a mature Tropical Livestock Unit of 25liters per day as shown in Table 10that follows.

Table 10: Livestock water demand

Livestock WD for Body weight to WD Cubic type Popn 2015 TLU TLU (litres/day) meters/day

Cattle 21,101 25 0.7 369,261.10 369

Goats 55,760 25 0.15 209,101 209

Sheep 6,100 25 0.15 22,875 23

Pigs 37,518 25 0.4 375,185 375

Total 976,422 976

The total livestock water demand for the study area was computed by adding the volume of water required per day for the different livestock types.

3.3.2.3 Industrial WD The industries in the catchment are mainly mining which include Tibet Hima, KCCL, and Stone quarrying at the banks of River Mubuku. They were mapped, and their water requirements 39 quantified. During the field visits, the growth and expansion projections were obtained from these individual companies. Water demand for Tibet Hima was mainly domestic since mining hadn’t yet started and the team was informed that water abstraction for domestic use was from River Nyamwamba which is outside the study area. The industry was only carrying out mineral exploration which doesn’t require water.

Stone quarrying has also gained ground from the accumulated boulders in the Mubuku River. At the time of visit, two quarries, and the bigger one being “Capital Auto Parts” were operational. This quarry uses a maximum of 6,000 liters (6 m3) of water a day for watering the site to reduce dust, making pavers and for domestic use by the workers.

The activities on this site however, are expected to intensify and require more water since a more heavy duty machine has been brought to the site. Taking an average of 6 m3 per day for the 3 quarries, the total industrial water demand was 18m3/day.

3.3.2.4 Irrigation WD The catchment has some irrigation schemes with Mubuku as the major one. It has two diversions from River Sebwe, an open canal and an underground canal. In all cases, furrow irrigation is implemented.

Mubuku Irrigation scheme The Irrigation Scheme currently occupies 516Ha. The crops grown mainly include rice, maize, onions and mangos.

The development of the scheme was phased for coordinated development and currently, Phase I (400 Ha, developed in 1970s) and Phase II (116 Ha, developed in 1980s) is fully equipped with irrigation infrastructure. The other phases, (Phase 2B-100 Ha, and Phase 3-380 Ha) are not yet equipped and are planned for during the expansion of the scheme.

Water supplying the scheme is abstracted at two locations from River Sebwe using two weirs, one through a piped system and the other through an open canal. This source currently supports irrigation of the entire 516 Ha of irrigated area under Phase I and Phase II of the Mubuku irrigation project with no storage provided. A night storage of 140,000 m3 capacity was constructed to store water at times when there is no irrigation (at night) to supplement irrigation the following day in case of a deficit.

The Sebwe River provides the water for all the currently irrigated areas. However, with the planned expansion of the irrigation scheme by additional approximately 480 Ha (Phase IIB, 100 Ha; Phase III, 380 Ha), the river doesn’t provide sufficient water to meet the irrigation water demand of the expansion hence possibilities of abstracting from the Mubuku river are being explored.

Nyabubaare Irrigation scheme The scheme covers an area approximately 32 acres with crops like rice and vegetables grown on most of the farms. Irrigation is by furrow and water floods the gardens all the time during dry and wet seasons. The abstracted water was estimated at 0.102m3/s or 8,813m3 from River Nkoko. The scheme has no water abstraction permit.

Kabukero Irrigation Scheme The area under irrigation here is about 500 acres with ⅓ used as grazing land for animals and ½ for farming. The grazing area is slowly being converted to farms as irrigation continues to improve farm yields. Mostly vegetables and rice are grown in this scheme. The animals here include about 1000 cattle, 800 goats and 6 sheep. No pigs are kept in this scheme.

Water abstraction is from Kabaka stream, a tributary of the Mubuku River. This scheme also has no abstraction permit.

Computation of Irrigation WD Data required for irrigation WD computation like the current and proposed area under irrigation, the types of crops grown, cropping patterns clearly stating the start and end of seasons was collected and is summarized in Table 11 and Table 12.

For modeling purposes, data regarding length of crop development stage, maximum height

(MH), basal crop coefficient (Kcb), and rooting depth (RD) for the respective crop development stages was got from literature like FAO Irrigation and Drainage Paper No. 56 and the Global Crop Water Model. Growing stages were categorized as follows: Initial stage (INI), development stage (DEV), mid-season (MID), and late season (LAT). The crop modules were established in MIKE HYDRO based on field surveys and statistical data. The sowing days for the crops correspond with the start of the rainy season. The dominant soil type was got from the Mubuku

41 irrigation scheme design report and average moisture content for different soil types from the FAO Irrigation manual module 2 (Savva et al., 2002).

Table 11: Crop-Area distribution for the Irrigation Schemes

Scheme Crop-Area distribution Crop Area irrigated (Ha) Percentage of Total Area Mubuku Maize 256 50 Mangoes 150 29 Rice 74 14 Onions 36 7 Kabukero Crop Area irrigated (Ha) Percentage of Total Area Rice 160 32 Onions 340 68 Nyabubaare Crop Area irrigated (Ha) Percentage of Total Area Rice 4.3 33 Onions 8.7 67

Table 12: Sowing calendar for the Irrigation Schemes

Crop Sowing date, Season 1 Sowing date, Season 2

Maize 15-20 Feb. 15-20 Aug.

Onions 15-20 Feb. 15-20 Aug.

Rice 15-20 Feb. 15-20 Aug.

Mangoes 1/Jan – all year round

3.3.2.5 Hydropower WD

The study area has a number of hydro power plants all of which are run-of-the-river. However, in MIKE HYDRO, to model hydropower schemes, there has to be a reservoir(s) onto which the hydropower node is connected. In this case, imaginary reservoirs will be created in the model and it shall be assumed that the total inflow is equal to the total outflow at the reservoir node. 42

These reservoirs will be designed in a way that they are very shallow but also the top of dam crest, the initial water level, the flood control level, are the same as the minimum operating level to ensure that all the water that comes in, flows out implying zero storage in the reservoir.

It should be noted that although hydro power generation is non-consumptive, power plants are in danger of not maintaining the required water head given the seasonal changes in river flow, increasing abstraction for other demands and increasing siltation in the river. The problem escalates particularly for HPPs downstream making it harder for them to operate at maximum capacity. The water requirement for hydro power was got from the power station offices.

Bugoye Power Station is a mini hydro power plant with an installed capacity of 13 MW. It is also referred to as Mubuku II or Tronder Power Station. The power station is across the Mubuku River, in Bugoye, Kasese District. The 2015 power generation was 7.2MW from 10m3/s of water abstracted from the river. There is no expansion envisaged in the near future.

Mubuku I Power Station (formerly operated by Kilembe Mines, now Tibet Hima) is a mini- hydroelectric power station. It is located between Bugoye and Kitoko, straddling the Mubuku River in Kasese District. The station has an installed capacity of 5 MW from3.8m3/s of water from Mubuku River.

River Ruboni which is seasonal is used as a reserve source when water in Mubuku is not sufficient. Tibet Hima has plans to increase capacity of the Kilembe power plant to 17.6MW in their expansion plan using water from River Mubuku.

Mubuku III (KCCL) has an installed capacity of 10MW and abstracts water from river Mubuku near Mubuku town, Kasese District. The plant produced 7.2 MW to 7.5 MW from 6.7 m3/s of water in 2015, drawn from Mubuku River and returned through the tail race into river Nkoko. It was noted that the plant had no plans for expansion in the planning year.

Greenewus Energy Africa Ltd is operated by ECO Gardens Ltd. and abstracts water from River Esya through a pipe. Installed capacity is 8 KW, but 2015 generation was 2 KW from 5.5 liters/sec (0.0055 m3/s) of water. The plant runs for about 8hrs a day during week days and 24hrs a day during the week end. There are plans for expansion to 20 KW which would require 20 liters of water per sec (0.2 m3/s). The plant has an abstraction permit allowing withdraw of up to10liters/sec from river Esya.

3.3.2.6 Environmental WD Environmental flows are defined as the ‘quantity, timing and quality of water flows required to sustain freshwater and estuarine ecosystems and the human livelihoods and well-being that depend on these ecosystems’ (Pahl-wostl et al., 2013). Rwenzori Mountains NP is located in the study area and is a UNESCO World Heritage Site located in the Rwenzori Mountains. Almost 1,000 km2 in size, the park has Africa's third highest mountain peak and many waterfalls, lakes, and glaciers. The other ramsar site is the L. George. In addition, other ecosystems of importance like Queen Elizabeth NP shall be identified and considered.

Environmental flow components, or events were quantified in terms of magnitude (the volumetric flow rate or level in m3/s). Some ecosystems for example fish in L. George require maintaining a minimum water level while others require a minimum discharge. All this was catered for by constraining 10% of the river flow at the most downstream node.

3.3.3 Estimation of future water demand for each use for the year 2040. Determining the amount of water needed in the future is one of the key building blocks of the catchment water planning process.

Projection of water demand took into account expansion and planned investments derived from the Uganda Government Vision 2040, NDP II, and the District Development Plan for Kasese district or on expansion prospects by the different water users.

3.3.3.1 Domestic WD Using the annual growth rate for Kasese district and assuming geometric growth for population n forecasting; Pn = Po (1+r)

Where Pn = population after n years, Po = present population, and r = annual growth rate (%) Projected population for 2040 was computed.

3.3.3.2 Livestock WD For projection of livestock WD, the ratio method which considers the rate of livestock population growth per annum was employed under geometric series. The projected livestock populations and subsequent water demand are shown in Table 13 and Table 14.

Table 13: Projected Livestock population

Livestock type Po r r+1 n Pn (2040) 44

Livestock type Po r r+1 n Pn (2040)

Cattle 21,100.63 0.14 1.14 25 558,363

Goats 55,760.27 0.16 1.16 25 2,279,159

Sheep 6,100.06 0.16 1.16 25 249,335

Pigs 37,518.46 0.26 1.26 25 12,120,169

Table 14: Projected livestock water demand Popn WD for Body weight to WD Cubic Livestock type 2040 TLU TLU (litres/day) meters/day

Cattle 558,363 25 0.7 977,135 977

Goats 2,279,159 25 0.15 854,684 855

Sheep 249,335 25 0.15 93,500 94

Pigs 12,120,169 25 0.4 12,120,168 12,120

Total 1,404,549 1,405

3.3.3.3 Industrial WD For industrial demand, information on the proposed industries was obtained from Kasese District Local government and estimation of water demand based on purpose and target production done as follows:-

Baseline WD = Existing WD (2015) and Projected WD = Individual industries projections + planned industries by 2040 as shown in Table 15.

Table 15: Projected industrial water demand

Existing WD 18 m3/day Demand for proposed expansion of existing industries No clear expansion plan. Provided for 5.7% (GOU, 2016).

72 m3/day

Total industrial demand projection 90 m3/day

3.3.3.4 Irrigation WD Projected irrigation WD was computed using data on potential, target/expansion area under irrigation and extending each of the above data conditions to the proposed expansion area of 480Ha for Mubuku. The other irrigation schemes have no clear expansion plan since they are limited by land ownership.

3.3.3.5 Hydropower WD Projected hydro power WD was computed using the data on target power (installed capacity). With knowledge of how much water is required to generate a known power capacity, it was possible to compute the water requirement for the target power. The information on the proposed HPPs was obtained from the Ministry of Energy, and Mineral Development. With this data, i.e. power generated and target power in MIKE HYDRO, the total hydro power water requirement at a specified efficiency was computed as follows: -

P = ∆h(Q). Q.Ɛ(∆h).ɡ.Pwater …………………………………………………………….(i)

Where P is the power generated, ∆h is the effective head (difference) [L], Q is the discharge/release through turbine(s) [L3/T], Ɛ is the machine (power) efficiency [-], ɡ is the 2 3 gravitational constant [L/T ] and Pwater is the density of water [M/L ]. Machine (power) efficiency Ɛ may also be specified as a function of Q, in which case Ɛ(∆h) is replaced by Ɛ(Q) in equation above. The effective head difference is:

∆h(Q)=hreservoir – htailwater(Q) - ∆hconveyance(Q) ………………………….(ii) The hydropower formula is non-linear because of the dependencies of head difference on 46 discharge and machine efficiency. Tail water levels are generally a function of discharge, and so are additional conveyance head losses in the channel (both increase with discharge).

In addition the tailwater can also become governed by backwater from the reservoir downstream rather than discharge of the supplying reservoir. In the simulations, the applicable tailwater level for use in the power equation is found from:

htailwater = max(htailwater.(Q),hdownstream_reservoir ………………………………….(iii)

In MIKE HYDRO, the following inter-dependencies between variables were assumed constant or insignificant by leaving out the respective detailed specifications: htailwater(Q) = hreservoir_bottom_level (Leaving out the tail water table) ………………….(iv)

∆hconveyance = 0 (Leaving out the conveyance head loss table) Ɛ(∆h) = 0.86 (Leaving out the power efficiency table)

Water demand for power generation was then calculated by solving the power equation, for Q (the solution must be found iteratively). When the effective head difference is small, turbines are shut off, both because they are inefficient and because the required discharge would grow very large. Accordingly, a minimum head for operation can also be specified. If the head is less than the minimum head, Q is set to zero, i.e., no water is routed through the turbines, regardless of demand.

3.3.3.6 Environmental flow The water demand computation made provision for all ecosystems downstream in the catchment to receive their water requirement either by allowing the required discharge to flow in the rivers. The model allowed for 10% of river flow which was constrained at the most downstream node thereby achieving demands for each ecosystem.

3.4 Modeling Water demand and supply 3.4.1 Model selection From reviewed literature, several models like SWAT, WEAP, REALM and others have been used for water allocation studies, but the basin module of MIKE HYDRO was selected for this research because: -

 It was found to have a more developed graphical user interface where customized plugins can be designed, and it’s fully integrated with GIS including several other applications (Dresser, 2001).  With remote simulation in MIKE 2016, sharing powerful hardware among users has never been easier (Powered, 2016).  A number of licenses are available with DWRM and was therefore easily accessible for the study.  The model is not data intensive and given the data challenges in Uganda, it came in handy.  Mike has associated Rainfall Runoff Model-NAM, making it more appropriate for this study.  It is inbuilt within the NBDSS hence offers access to more data analysis tools, scenario creation and evaluation capabilities which are available with the DSS.  Also, the model has a proven record of use within the Ministry of Water and Environment.  All data sets for input into the model were converted to dfs0 format in MIKE Zero.

Calibration is a process of standardizing predicted values, using deviations from observed values for a particular area to derive correction factors that can be applied to generate predicted values that are consistent with the observed values. Once the NAM model was set up with the input information, the model was calibrated. During calibration, the default model parameters were kept same and model was run in auto-calibration mode. The model output simulation results were then checked for coefficient of determination (R2) value and graphically analyzed for degree of agreement between simulated and observed runoff. The model parameters were again adjusted one by one using trial and error method to obtain the set of best fit model parameters which could simulate runoff with high degree of agreement with observed runoff in term of timings, peaks and total volume.

Model validation means judging the performance of the calibrated model over the portion of historical records which have not been used for the calibration. The NAM model thus calibrated was then validated with another data period apart from the one used for calibration. During validation the set of model parameters obtained during the calibration was used and model was 48 run without auto-calibration mode to simulate runoff. The statistics of the simulated results were analyzed and output of the model were checked to compare the simulated and observed runoff to verify the capability of calibrated model to simulate the runoff.

3.4.2 Water allocation modeling The model design comprised two types of water users: “irrigation water users” (Mubuku, Kabukero and Nyabubaare) and “regular water users”. Each irrigation water user was assigned with irrigation fields where in a crop module, soil type and irrigation method, were specified. On each irrigated field, a crop sequence was characterized by a crop type, sowing date and reference to the irrigation method used to irrigate the crop.

For each regular water user, a water demand time series was specified to represent the total amount of water required to fulfill the water requirement in each sub-catchment. In the Mubuku system, reservoirs were included to simulate the operations using operating rule curves but ensured no storage since the power generation is run of the river.

3.4.2.1 Baseline Scenario (2015) – Sc00 With the long-term discharge time series for the two catchments, the model was prepared to cover the period 1956 – 2015, a period of 60 years. The schematic diagram is shown in Figure 11.

Wetlands and their potential attenuation of stream flows, as well as the seasonal fluctuation in storage, consumptive demands including domestic and livestock, estimated environmental water demands under present conditions were modelled and issues such as seasonal deficits, whole period deficits, and environmental flows identified.

Figure 11: Mubuku-Sebwe MIKE HYDRO schematic diagram 3.4.2.2 Future scenarios (2040) – Sc01 With the projected discharge time series for the two catchments, the model was prepared to cover the period 1956 – 2040. This scenario considered the baseline together with future water demand without demand management.

This scenario therefore allocated 100% demand for all water uses while ensuring a minimum flow of at least 10% of the total sub-catchment flow along the reach.

3.4.2.3 Reliable water allocation – Sc02 This scenario was to test management options for water allocation adopted from “The Contribution of Water Resources Development and Environmental Management to Uganda’s Economy Final Report | 12 October 2016” (Strzepek et al., 2016). The allocation was based on computation of these thresholds while allowing a minimum flow of 10% of the total sub- catchment flow along the river reach. The thresholds for the different categories of water uses are given below. • Domestic 100% • Livestock (Not in cattle corridor) 60% 50

• Industry including Hydropower 90% • Irrigation (Type A) 70% • Environment 100%

Reliability was computed as a percentage of met demand against total demand in each of the sub catchments in question.

3.5 Identification of the most appropriate sustainable water management option. The NBDSS was used to evaluate the different scenarios in order to identify the one that generated minimum deficits and no adverse impact on the environment and water ecosystem in the planning horizon (2040).

The model schematic for the different scenarios which were designed in the MIKE HYDRO environment were registered in the DSS. A MIKE HYDRO model is stored in two files; the .mxd and the .mdb file formats. The files contain all model objects, data as well as all associations designed in the model. The NBDSS then loads these two files through its Model Setup registration wizard using a MIKE HYDRO model adapter. All the registered models were observed and analyzed as shown in Figure 12.

The scenario that gave the best catchment situation in terms of maximum water allocation with least deficit and ecosystem degradation was then proposed as the sustainability scenario for the study area.

Figure 12: A screenshot of the registered Model in the NBDSS for scenario comparison

CHAPTER FOUR: RESULTS

This section presents the research findings on how much water is available in the study area, the water demands for six different uses and proposed water management options to ensure sustainability of the water resource.

4.1 Quantity of water in the study area The mean water flow for both Mubuku and Sebwe is bi-model corresponding to the received rainfall. It was found to be low in January-March and June-August with peaks in April-March and October-November.

4.1.1 Available water in the Sebwe sub-catchment_2015 Table 16 and Figure 13 show the available water in the Sebwe sub catchment. The lowest mean flow value is 0.95 ±0.41 m3/s occurring in February and the highest is 3.07 ±1.97 m3/s in April. This represents approximately 2.29 MCM and 7.94 MCM respectively. The long term average flow shows that it has been significantly fluctuating though not in a linear manner (R2 = 0.081, p = 0.03). However, the annual flow declined with time (q = 69.25 – 0.23t) up to 1991 (R2 = 0.43, p < 0.001) and remained constant from 1992 to 2015 (R2 = 0.03, p < 0.05). The mean annual water volume in Sebwe sub-catchment by 2015 was 60 MCM.

Table 16: Average monthly flow for Sebwe in 2015

Month Flow (m3/s) Volume (MCM) STDEV Jan 1.12 3.00 0.58 Feb 0.95 2.29 0.41 Mar 1.30 3.49 0.74 Apr 3.07 7.94 1.97 May 2.77 7.42 1.45 Jun 1.60 4.15 0.83 Jul 1.10 2.94 0.65 Aug 1.35 3.60 0.79 Sept 1.97 5.10 1.31 Oct 2.66 7.13 1.77 Nov 3.05 7.90 1.66 Dec 1.88 5.04 0.95 Total 60.01

Figure 13: Long term average flow for Sebwe (2015) 4.1.2 Available water in the Mubuku sub-catchment_2015 Table 17 and Figure 14 show the available water in the Mubuku sub catchment. Like Sebwe, the lowest mean flow occurs in February and the highest in April with 6.46 ± 2.65 m3/s and 17.60 ± 8.91 m3/s respectively. This flow translates to approximately 15.64 MCM and 45.61 MCM. The long term average flow shows that it has been significantly fluctuating though not in a linear manner (R2 = 0.119, p = 0.01). However, the annual flow declined with time (q = 12.89 – 0.06t) up to 1991 (R2 = 0.44, p < 0.001) and remained constant from 1992 to 2015 (R2 = 0.02, p > 0.05). The mean annual water volume in Mubuku sub-catchment by 2015 was 365 MCM

Table 17: Average monthly flow for Mubuku in 2015

Month Flow (m3/s) Volume (MCM) STDEV Jan 7.21 19.32 3.12 Feb 6.46 15.64 2.65 Mar 9.31 24.93 4.42 Apr 17.60 45.61 8.91 May 15.74 42.15 6.68 Jun 9.77 25.33 4.38 Jul 7.02 18.81 3.40 Aug 8.97 24.03 4.41 Sep 12.24 31.72 6.28 Oct 15.62 41.84 8.23 Nov 17.36 44.99 7.49 Dec 11.36 30.42 4.59 54

Total 364.78

Figure 14: Long term average flow for Mubuku (2015) 4.1.3 Potentially available water in the catchment in 2040

4.1.3.1 Analysis of Rainfall trends for 1956 to 2040 The graphs in Figure 15 show the rainfall time series decomposition. The observed rainfall exhibits no obvious trend. This is also seen in the residuals which is the differences between the observed rainfall values and the predicted values. Positive values for the residual (on the y-axis) mean the prediction was too low, and negative values mean the prediction was too high; zero means the guess was exactly correct. As expected, the residual graph shows no obvious pattern and no unusual values. The seasonal graph however, shows a regularly repeating pattern across the year.

Figure 15: Rainfall trend analysis for the period between 1956 and 2040

4.1.3.2 Available water in the sub-catchments_2040 Figure 16 and Table 18 show a declining trend of flow up to 2040 for Sebwe sub catchment (q = 33.08 – 0.02t, R2 = 0.32). However, the projected flow from 2016 is expected to remain constant (R2 = 0.07, p < 0.05). The lowest mean annual flow of 0.82 ± 0.43 m3/s still occurs in February although less than in 2015 and the highest flow of 2.57 ± 1.97 m3/s also less than 2015 occurs in the same month of April. The potentially available water in Sebwe sub catchment by 2040 is 52 MCM.

Table 18: Average monthly flow for Sebwe in 2040

Month Flow (m3/s) Volume (MCM) STDEV Jan 0.99 2.66 0.60 Feb 0.82 1.98 0.43 Mar 1.12 2.99 0.82 Apr 2.57 6.67 1.97 May 2.51 6.72 1.54 Jun 1.33 3.45 0.87 Jul 0.92 2.46 0.64 Aug 1.13 3.03 0.78 Sep 1.70 4.39 1.25 Oct 2.30 6.15 1.68 Nov 2.83 7.33 1.72

Month Flow (m3/s) Volume (MCM) STDEV Dec 1.72 4.60 1.07 Total 52.42

Figure 16: Long term average flow for Sebwe (2040) Figure 17 and Table 19 show a declining trend of flow up to 2040 for Mubuku sub catchment (q = 157.08 – 0.07t, R2 = 0.32). However, the projected flow from 2016 is expected to remain constant (R2 = 0.07, p < 0.05). The lowest and highest mean annual flow like in 2015 occur in February and April respectively.

The lowest is projected to be 5.60 ± 2.67 m3/s and the highest 15.51 ± 8.93 m3/s representing 13.54 MCM and 40.20 MCM respectively. The potentially available water in the sub catchment by 2040 is 330 MCM

Table 19: Average monthly flow for Mubuku in 2040

Month Flow (m3/s) Volume (MCM) STDEV Jan 6.54 17.51 3.23 Feb 5.60 13.54 2.67 Mar 8.05 21.55 4.73 Apr 15.51 40.20 8.93 May 14.73 39.44 7.09 Jun 8.44 21.87 4.51 Jul 6.10 16.33 3.31 Aug 7.99 21.40 4.33

Month Flow (m3/s) Volume (MCM) STDEV Sep 11.09 28.75 6.12 Oct 14.10 37.77 7.76 Nov 16.47 42.70 7.64 Dec 10.73 28.73 5.04 Total 329.785

Figure 17: Long term average flow for Mubuku (2040) 4.2 Current and projected water demand

4.2.1 Domestic, Livestock, Industry and Environment WD Domestic water demand for Sebwe in 2015 was 0.0004 MCM and for Mubuku, domestic water demand was 0.002 MCM, livestock 0.001 MCM, industry 0.00002 MCM. Domestic water demand is expected to increase by 64%, livestock by 44% and industry by 400% by 2040 (See Table 20).

Table 20: Domestic, livestock, industry and environmental water demand

2015 2040 Water Use Sebwe (m3/day) Mubuku (m3/day) Sebwe (m3/day) Mubuku (m3/day) Domestic 442 1,976 725 3,243 Livestock - 976 - 1,405 Industry - 18 - 90

Environment 10% of river flow 10% of river flow 10% of river flow 10% of river flow

4.2.2 Hydropower WD Table 21 shows that the water demand for hydro power generation is expected to remain constant for Bugoye, KCCL and Eco Gardens while that for Kilembe is expected to rise by 252% in 2040. Overall, hydropower demand will rise by 66%. Figure 18 shows the hydropower water demand for Bugoye and Eco Gardens as generated from the model and for

Figure 19 Kilembe.

Table 21: Hydropower demand

2015 2040 Water Use Details Mubuku (MW) Mubuku (MW) Hydropower (Installed capacity) Bugoye 13 Eco Gardens 13 0.008 0.002 10 KCCL 10 17.6 Kilembe 5

Figure 18: Time series presentation for hydropower water demand for N5 (Bugoye) and N3 (Eco gardens)

Figure 19: Time series presentation of power demand as flow for Kilembe HPP

4.2.3 Irrigation WD Table 22 shows that irrigation water demand for Mubuku irrigation scheme will increase by 93% in 2040. The demand for Kabukero and Nyabubaare will remain constant. In Figure 20 the water demand for Kilembe HPP is presented.

Table 22: Irrigation Area

2015 2040 Water Use Details Sebwe (Ha) Mubuku (Ha) Sebwe (Ha) Mubuku (Ha) Irrigation Mubuku 516 996 Kabukero 202 202 Nyabubaare 13 13

Figure 20: Water demand time series for Mubuku Irrigation Scheme

4.3 Water allocation under different scenarios 4.3.1 Scenario 00; Baseline 2015

4.3.1.1 Sebwe Sub Catchment The highest water demand was 1.197 MCM, observed in March and the lowest demand is 0.422 MCM in January. On the other hand, reliability was highest in January and July of up to 100% and was lowest in March at only 87%. Table 23 shows the variation in demand and associated reliability for Sebwe sub-catchment while Figure 21 shows the water demand deficit in 2015.

Table 23: Sebwe water use reliability for Sc00 demand met Month demand (MCM) (MCM) Reliability (%) Jan 0.422 0.422 100 Feb 0.768 0.718 94 Mar 1.197 1.047 87 Apr 0.935 0.834 89 May 1.104 1.049 95 Jun 0.958 0.87 91 Jul 0.473 0.473 100 Aug 0.685 0.656 96

demand met Month demand (MCM) (MCM) Reliability (%) Sep 1.152 1.042 90 Oct 0.834 0.766 92 Nov 0.654 0.638 98 Dec 0.545 0.535 98

Figure 21: Monthly water demand deficit for Sebwe under Sc00 4.3.1.2 Mubuku Sub Catchment For mubuku, the highest monthly water demand was 0.807 MCM and it was observed in May whereas the lowest was 0.092 MCM in January. The reliability analysis in Table 24 shows that all the demand in the sub catchment was met throughout the year.

Table 24: Mubuku water use reliability for Sc00 Demand met Month Demand (MCM) Reliability (%) (MCM) Jan 0.092 0.092 100 Feb 0.193 0.193 100 Mar 0.483 0.483 100 Apr 0.668 0.668 100 May 0.807 0.807 100 Jun 0.682 0.682 100 Jul 0.092 0.092 100 Aug 0.234 0.234 100 Sep 0.567 0.567 100 Oct 0.574 0.574 100 62

Demand met Month Demand (MCM) Reliability (%) (MCM) Nov 0.466 0.466 100 Dec 0.311 0.311 100

4.3.2 Scenario 01; Future 2040

4.3.2.1 Sebwe Sub-Catchment In this scenario, the monthly water demand for Sebwe sub catchment, presented in Table 25shows that March and September have the highest demand reaching 2.71 MCM and July the lowest of only 0.98 MCM. The reliability ranges from 35% in March to 76% in January. The monthly water demand deficit is shown in Figure 22.

Table 25: Sebwe water use reliability for Sc01 Demand Month (MCM) Demand met (MCM) Reliability (%) Jan 1.092 0.83 76 Feb 1.873 0.835 45 Mar 2.701 0.949 35 Apr 2.143 1.014 47 May 2.125 1.276 60 Jun 1.943 0.9 46 Jul 0.978 0.66 68 Aug 1.394 0.821 59 Sep 2.706 1.31 48 Oct 2.096 1.298 62 Nov 1.645 1.288 78 Dec 1.45 0.92 63

Figure 22: Monthly water demand deficit for Sebwe under Sc01 4.3.2.2 Mubuku Sub-Catchment Table 26 shows the monthly water demand in Mubuku sub catchment with the highest of 1.28 MCM in May and lowest of 0.56 MCM in January and July. The reliability analysis shows that all the water demands are met all year round.

Table 26: Mubuku water use reliability for Sc01

Month Demand (MCM) Demand met (MCM) Reliability (%) Jan 0.560 0.560 100 Feb 0.616 0.616 100 Mar 0.951 0.951 100 Apr 1.120 1.120 100 May 1.275 1.275 100 Jun 1.134 1.134 100 Jul 0.560 0.560 100 Aug 0.702 0.702 100 Sep 1.020 1.020 100 Oct 1.042 1.042 100 Nov 0.918 0.918 100 Dec 0.778 0.778 100

4.3.3 Scenario 02; Reliable 2040

4.3.3.1 Sebwe Sub-Catchment The highest water demand was 2.17 MCM observed in the months of March and September while the lowest of 0.71 MCM was observed in May. The reliability is highest in November at 98% and lowest in March at 44%. Table 27 shows the monthly variations in demand and reliability and Figure 23, the water demand deficit.

Table 27: Sebwe water use reliability for Sc02 Month Demand (MCM) Demand met (MCM) Reliability (%) Jan 0.879 0.830 94 Feb 1.502 0.835 56 Mar 2.165 0.949 44 Apr 1.719 1.014 59 May 1.705 1.276 75 Jun 1.558 0.900 58 Jul 0.787 0.660 84 Aug 1.119 0.821 73 Sep 2.169 1.310 60 Oct 1.681 1.298 77 Nov 1.321 1.288 98 Dec 1.164 0.920 79

Figure 23: Monthly water demand deficit for Sebwe under Sc02

4.3.3.2 Mubuku Sub-Catchment Table 28 shows the monthly water use in the Mubuku sub catchment under scenario 02. The maximum water demand was 1.13 MCM in May and the lowest 0.56 MCM in January and July with reliability of 100% across the year.

Table 28: Mubuku water use reliability for Sc02

Demand Month (MCM) Demand met (MCM) Reliability (%) Jan 0.560 0.560 100 Feb 0.593 0.593 100 Mar 0.872 0.872 100 Apr 1.004 1.004 100 May 1.131 1.131 100 Jun 1.016 1.016 100 Jul 0.560 0.560 100 Aug 0.673 0.673 100 Sep 0.924 0.924 100 Oct 0.945 0.945 100 Nov 0.843 0.843 100 Dec 0.734 0.734 100

4.3.4 Reliability of Hydro Power Generation In 2015, 2.7MW of power was produced at Kilembe 90% of the time was and this is expected to dropped to 2.5 MW by 2040. For Bugoye and KCCL, the power produced will almost remain constant by the planning year. The reliability analysis for selected hydro power plants is shown in Figure 24.

KCCL Power production reliability (%)_2040 Bugoye Power production reliability (%)_2040

Kilembe Kilembe Power production reliability (%)_2040 Power production reliability (%)_2015 Figure 24: Analysis of power production reliability

4.4 The sustainable water use option in the Mubuku-Sebwe sub catchments. Figure 25 and Figure 26 show the water demand deficit at Kilembe Power Plant for the three scenarios. Sc00 exhibits fewer days with deficits as compared to Sc01 and Sc02. Sc02 however, generally presents lower deficits as compared to Sc01.

Figure 25: Water demand deficit at Kilembe HPP for 2015

Figure 26: Water demand deficit at Kilembe HPP in 2040 From Table 29, the water demand deficit in Sebwe sub catchment for the base year was 0.827 MCM. It increases to 10.04 MCM in Sc01 and drops to 5.667 MCM in Sc02. For Mubuku sub catchment, there is no deficit in the three scenarios.

Scenario comparison sc00 sc01 sc02 (MCM) Mubuku Sebwe Mubuku Sebwe Mubuku Sebwe

Total Demand 5.168 9.73 10.676 22.145 9.856 17.77

Met Demand 5.168 8.903 10.676 12.102 9.856 12.102 Deficit 0 0.827 0 10.043 0 5.667 Table 29: Water demand deficit

Comparing the three scenarios, Sc00 generally has the highest reliability, followed by Sc02 and then Sc01 with the least for Sebwe whereas for Mubuku, all the scenarios meet the demand all the time. Figure 27 and Figure 28 show reliability for Sebwe and Mubuku for the different scenarios.

Figure 27: Monthly water use reliability for the three scenarios for Sebwe sub catchment

Figure 28: Monthly water use reliability for the three scenarios for Mubuku sub catchment

CHAPTER FIVE: DISCUSSION OF RESULTS

The results show that although there are seasonal deficits, the flow available in the Mubuku- Sebwe sub catchments will meet the demand by the year 2040. This can be enhanced by putting in place mechanisms to store surplus water at the different nodes as and when it occurs to meet the seasonal deficits. The deficit volumes are a guide to the capacity of the required storage facility.

The finding that the available water in the study area will reduce by the planning year by 8 MCM and 35 MCM with Q75 of 1.0m3/s and 6.6m3/s for Sebwe and Mubuku respectively, replicates the findings of earlier research that precipitation in the study area will generally decrease while temperatures will rise in the dry periods. These changes will have significant impact on the river discharge by reducing the flows in the dry periods especially between May and September, while heavy floods were simulated for the wet months between November and March. In the 2020’s these changes were shown to be small, but they will increase significantly beginning the 2050’s (Nyenje, 2007).

The variability of water availability and demand in the sub catchments was also reported in the neighboring Mara River Basin in Kenya even when another model, the Soil and Water Assessment Tool (SWAT) was used (Dessu et al., 2014).

The analysis of demand and deficits shows that development increases the demand for water and hence the water demand trend can only continually rise. Although deficits occur in the 3 scenarios for Sebwe sub catchment, they are higher in sc01 implying that if development continues without applying management on the water use, the deficits will eventually put a halt on development.

The need for sustainable water use and management in the Mubuku-Sebwe sub catchments cannot be over emphasized.

There are some seasonal water surpluses within the system as indicated by the “outflow to node”. These may be due to increase in rainfall in certain wet seasons aggravated by climate change as

70 reported (Conway, 2009). Praskievicz & Chang, (2009) also reported that hydrological impacts of climate change and urban development are likely to significantly affect future water resource management.

Although the reliability analysis for Mubuku was 100%, there are seasonal deficits at some nodes within the system shown in the model. This is because the reliability computation does not include hydropower because it is non-consumptive and at the same time, all the plants in the study area are “run of the river” technology. It should be noted that at the point of abstraction, the water is not available for other uses until it returns in the system at the tail race. The results show that Kilembe, Bugoye, and KCCL hydropower plants will be operating below 50%, 90% of the time by the planning year. Already, some very expensive equipment in KCCL are out of use due to low water flow and this has led to loss of revenue to the country. With the sustainable allocation framework, as long as priority needs are met, then all remaining demands on water are acceptable as long as they do not impair the renewability of the resource and as long as allocations between both present and future generations are equitable.

The seemingly high reliability of Sebwe in January and July is attributed to Mubuku irrigation scheme which is the major water user in this sub catchment. The farmers at the scheme engage in supplementary irrigation which literally means that they practice rain fed agriculture and only supplement when the rainfall is not sufficient. In January and July, the farmers are mostly engaged in harvesting and preparation of nurseries for the next cropping cycle and in such periods, the demand for water is low.

Like previous studies highlighted, incomplete hydrological and climatic data records and data gaps impact on the results of the study cannot be ignored. Gap filling and extrapolation was inevitable. The quality challenges were reflected in the fit of the simulated and observed discharge which was rather difficult especially for Mubuku high flows. In addition, the Mike Hydro and NAM are global tools and calibrating them to local conditions was challenging considering that certain conditions are really different at a local scale. Important to note though is that data quality control was carried out to sufficient standards for modeling, reflected in the model performance analysis.

At the core of the issue of managing water within a catchment is a key question: how do we decide and control who can abstract water for domestic, industry, irrigation, hydro power, 71 livestock or any other use? This study provides a mechanism to prioritize certain water uses through a set of rules in a given scenario. In Uganda, domestic and environmental water requirements are given priority and allocation to other uses should only commence after these two are met.

Having adequate knowledge and information about the water resources availability and demand of a country is a very vital aspect of WRM. Allocation should not be spontaneous but rather long term framework capturing all existing and projected demands. Effective management requires hydro-meteorological data, analytical tools and adequate information about the country’s water resources. The results from this study give a basis for future water management framework and optimum utilization of the resource to drive socio-economic development since abstractions can be targeted where surpluses occur in the system.

With this tool, new water user applications is only required to give information on the proposed abstraction point and the quantity of water required. This information, when entered into the tool as a new water user node, is simulated and the effect on all other water users and the catchment as a whole can be studied before a decision on allocation is made. This will significantly reduce the application assessment time and improve water resources management of the catchment as a hydrological unit.

CHAPTER SIX: CONCLUSIONS

The available water in the Mubuku-Sebwe sub catchment in 2015 was 60MCM and 365MCM for Sebwe and Mubuku respectively. It is likely to reduce by 15% in Sebwe and 11% in Mubuku with Q75 of 1.03/s and 6.6m3/s by the planning year 2040.

For Mubuku generally, the water available in the base and planning years meets the total water demand. However, it should be noted that although hydropower is a non-consumptive water use thereby taking no water out of the system, abstracted water is not available for different uses from the point of abstraction to the tail race inflow back into the river hence some seasonal deficits along such sections in the river reach. Sebwe on the other hand, has a water deficit of 0.827MCM in 2015, which will increase to about 10.043MCM by 2040.

The water demand is expected to increase by 64% for domestic, 44% for livestock, 400% for industry, 45% for hydro power and 66% for irrigation by 2040.

Given that the results show a general reduction in the available water in the sub catchments, it is recommended that allocation of water should be based on the management options presented in scenario 02 which prioritizes domestic and environmental water demand and allocates 90% to industry, 70% to irrigation and 60% to livestock demand.

CHAPTER SEVEN: RECOMMENDATIONS

Mechanisms and strategies for implementing scenario 02 should be identified in order to ensure sustainable allocation of the water resources in the sub catchments.

Surplus water that has previously caused havoc as life and property are lost should be stored where and when it occurs to further reduce water deficits. The computed deficits may guide storage capacity for the different water storage technologies.

The three “run of the river” hydropower plants cascaded along the river Mubuku present numerous water use and power production reliability challenges since the river is not regulated by a stable control. There is need for research on alternative technology for hydro power production in order to optimize water use.

With research showing that the main effects of climate change will be felt most through changes in hydrological cycle, additional research on the extent of climate change and variability on the hydrology of the Mubuku-Sebwe sub catchments is pertinent to analyze the different scenarios and how best to build resilience from the water resources management angle.

Water managers will find this research approach and methodology very useful and may wish to extend such studies to other sub catchments in the country to address challenges of water allocation and sustainable use.

Further research on the social dynamics related to sustainable water use in the Mubuku-Sebwe sub-catchments should be considered.

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APPENDICES

APENDIX I: Rainfall data analysis

Observed Rainfall

Figure 29: Bugoye observed Rainfall

Figure 30: Mubuku Prison Farm observed Rainfall

Figure 31: Kasese MET observed Rainfall

Figure 32: Kilembe Mines observed Rainfall

Figure 33: Mubuku HEP observed Rainfall Double Mass Curves

(a) Sebwe (b) Mubuku

Correlation matrix

Table 30: Rainfall stations correlation matrix

Station Kilembe Bugoye Mobuku Prisons Mobuku HEP Kasese MET Kilembe 1.000 0.569 0.542 0.542 0.723 Bugoye 0.569 1.000 0.266 0.620 0.425 Mobuku Prisons 0.542 0.266 1.000 0.458 0.685 Mobuku HEP 0.542 0.620 0.458 1.000 0.701 Kasese MET 0.723 0.425 0.685 0.701 1.000

Gap filling matrices

Table 31: First priority gap filling matrix

Station Refrence station Gap-filling Equation Correlation (R2) Kilembe Kasese MET 1.642*Kasese MET 0.9992 Bugoye Mubuku HEP 0.6595*Mubuku HEP 0.9835 Mubuku Prison Kasese MET 0.8317*Kasese MET 0.9927 Mubuku HEP Kasese MET 1.5029*Kasese MET 0.9991 Kasese MET Kilembe 0.6089*Kilembe 0.9991

Table 32: Second priority gap filling matrix

Stn. to gap fill 1st 2nd 3rd Kilembe Kasese MET Bugoye Mobuku HEP Mobuku Prisons Kasese MET Mobuku HEP Kasese MET Bugoye Kasese MET Kilembe Mobuku HEP Mobuku Prison

Table 33: Third priority gap filling matrix

Station Reference Station Gap-filling Equation Correlation (R2) Mubuku HEP Bugoye 1.5114*Bugoye 0.9861 Kasese MET Mubuku HEP 0.5841*Mubuku HEP 0.9973

Table 34: Fourth priority gap filling matrix

Station Reference Station Gap-filling Equation Correlation (R2)

Kasese MET Mubuku Prison 1.2029*Mubuku Prison 0.9928

Theissen polygon weighting factors Table 35: Mubuku weighting factors

Station Name Station No. Thiessen Polygon Area (Km2) Weighting Factor Mubuku HEP 8930057 242 0.93 Bugoye 89300090 18 0.07 TOTAL 260 1

Station Name Station No. Thiessen Polygon Area (Km2) Weighting Factor Bugoye 89300090 48 0.56 Mubuku HEP 8930057 25 0.29 Kilembe 13 0.15 TOTAL 86 1

36: Sebwe weighting factors

Figure 34: Mubuku and Sebwe theissen polygons

Comparison with global datasets

Figure 35: Comparing observed Rainfall with global data sets

APPENDIX II: River flow data analysis

Hydrographs

Figure 36: Mubuku and Sebwe hydrographs

Analysis of rating curves

Figure 37: Sebwe rating curve

Table 37: Analysis of Sebwe Rating

c Start End Q = A * (H - b) Period Period max Rating H (m) A b c max Rating A 08-Aug-1966 15-Jun-1967 12.01 4.01 2.80 12

Rating B 16-Jun-1967 31-Dec-1993 0.91 3.22 5.50 12

Figure 38: Mubuku rating curve

Table 38: Analysis of Mubuku Rating

Rating Q = A * (H - b)c Discharge (m3/s) H Start End max max Period Period H Q = 93.4 A b c max H = 15m H = 10m (m) max max (m3/s) A 1-01-54 20-06-57 3.80 7.08 2.80 15 1247.34 76.32

B 21-06-57 24-04-59 17.39 7.99 3.10 15 7283.86 151.88

C 15 10768.86 153.01 25-04-59 16-07-63 0.06 5.79 5.43 D 15 3466.20 182.74 17-07-63 26-05-66 13.90 7.43 2.73 E 15 470.58 41.14 27-05-66 10-05-71 0.87 6.21 2.90 F 15 228.511 32.03 11-05-71 31-12-00 0.94 5.86 2.48

Rainfall Runoff Modeling

Figure 39: Sebwe NAM model calibration

Figure 40: Sebwe NAM model validation

Figure 42: Sebwe Flow Duration Curve Figure 41: Sebwe accumulated discharge

Figure 43: Long term gap filled discharge for Sebwe_2015

Figure 44: Mubuku NAM model calibration

Figure 45: Mubuku NAM model validation

Figure 46: Mubuku accumulated discharge Figure 47: Mubuku Flow Duration Curve

Figure 48: Long term gap filled discharge for Mubuku_2015

APPENDIX III: Photo gallery

Plate 1: Capital Auto Parts stone quarry at Mubuku bridge

Plate 2: People washing clothes by the Mubuku River

Plate 3: The water distribution canals in the Mubuku Irrigation Scheme

Plate 4: Bricks production using boulders from the Mubuku River

Plate 5: The intake to Kilembe Hydropower Plant

Plate 6: Tertiary canals in the Mubuku Irrigation scheme