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! "# $ % & % ' % ! !' "#$!#%!%!#%!#%!## &!'!# #! ' "# ' '% $$(' (!)*#(# -+.0#&#,1'4#7"0-*-%'!11#11+#,25'2&02'!3*0 #$#0#,!#2-#0'-**#71',Q#,7 "'11#022'-,13 +'22#"2-2&# !0"32#"!&--* -$2&##,'4#01'27-$',!',,2' ',.02'*$3*$'**+#,2-$2&# 0#/3'0#+#,21$-02&#"#%0##-$ %-!2-0-$&'*-1-.&7 ',2&#%#.02+#,2-$!#-%0.&7 -$2&#-**#%#-$021,""!'#,!#1 7 #2#0' -5#,'+-1-. T"T#,'4#01'27-$''0- ' (TT)#12#0,('!&'%,#,'4#01'27 SZ3%312TRSR -++'22##&'0S/)13,%/-,%Q&T%T ii Abstract The Kerio Valley Basin in Kenya has undergone periods of drought over the past century, yet drought patterns in the region are not well understood mainly because of the lack of climate data. This knowledge of drought pattern is important in mitigating drought related hazards and in planning for adaptation. Arid and Semi Arid lands are usually more susceptible to drought because of increasing climate variability. River Basins, including the Kerio Valley Basin, are frequently affected by droughts. In this study, precipitation and streamflow data were reconstructed to determine streamflows from the missing periods. Moreover, the Streamflow Drought Index (SDI) was used to examine the probability of the recurrence of hydrological drought in the Basin betw11een the periods 1965-1983 and 1992-2009. This study also applied Water Poverty Index (WPI) to assess and monitor water requirements for different communities in the Kerio Valley Basin. The water requirements of seventy five administrative locations within the Kerio Valley Basin were assessed. The results from the analysis showed that the Baringo and West Pokot districts scored a lower index compared to those located in Keiyo, Marakwet, Koibatek, and Uasin Gishu districts. However, disparities existed within every district. The WPI was able to identify administrative locations facing water scarcity; consequently local administrators in these locations can focus more on water development projects. It is hoped that local administrative units in addition to Water Resource Management Authority (WRMA) can use WPI as an indicator for evaluating water needs for different communities. In the United States where hydrologic and climatic data are available, better methods can be used in water resources research. For example, hydrologic models can be employed to examine iii nonpoint source pollution in a river basin. AnnAGNPS and SWAT were used in this dissertation to demonstrate their capabilities and effectiveness in hydrologic research. Results showed that both models were capable to identify the sources of nonpoint source pollution. iv v Acknowledgements First, I would like to thank my parents and siblings for their love and support throughout my education. Second, I would also like to thank the graduate students in the department geography for their inspiration … Andrew Miller, Yongming Cai, Benjamin Thomas, Teri Jacobs, Yu Sun, Andrew Rettig, Thushara Ranatunga, Carmen McCane, Kevin Magee, Barry Winston, Nissa Fink, Susan Jakubowski, and Deborah Kittner Third, I would like to thank my Dissertation Committee Chair, Dr. Susanna Tong for the great assistance and feedback I received throughout the entire dissertation process. I also want to thank my other dissertation committee members: Dr. Robert Frohn, Dr. Changjoo Kim, and Dr. Fulbert Namwamba for their tireless support. Last, I would like to thank Dr. Robert South, Dr. Roger Selya, Dr. Kevin Raleigh, Dr. Howard Stafford, Dr. Lin Liu, Dr. Nicholas Dunning, and Dr. Richard Beck from the University of Cincinnati, and also Dr. Gregory Veeck and Dr. Chansheng He from Western Michigan University for their wisdom and encouragement. vi Table of Contents CHAPTER 1 Page 1.1. Introduction 1 1.2. Methodology 3 1.2.1. Background Information 3 1.2.2. Analytical Methods 5 a) Streamflow data reconstruction 5 b) Streamflow Drought Index (SDI) 6 c) Water Poverty Index 7 d) Hydrologic modeling of non-point source pollution 9 CHAPTER 2 Assessment of hydrological drought in the Kerio Valley Basin 11 2.1. Introduction 12 2.1.1. Drought Indices 14 2.1.2. Drought in Kenya 17 2.1.3. Drought monitoring in Kenya 18 2.1.4. Assessment of hydrological drought in the Kerio Valley Basin 20 2.2. Methodology 23 2.2.1. The Study Area 23 i. Rock formation 24 ii. Soils 25 iii. Drainage 25 iv. Physiographic units 26 v. Agro-climatic zones 26 vi. Altitude and precipitation 27 2.2.2. Data 27 a. Data availability 28 b. Precipitation data 29 c. Reconstructing precipitation data from past datasets 30 d Streamflow data reconstruction for the Kerio Valley Basin (1990 - Present) 35 e. Streamflow Drought Index (SDI) 37 2.2.3. Analyzing hydrological drought using the reconstructed streamflow data 37 vii 2.3. Analysis and results 39 2.4. Discussion and conclusion 47 CHAPTER 3 Using Water Poverty Index as a tool for water policy making: A case study of the Kerio Valley Basin, Kenya 48 3.1. Introduction 49 3.2. Kenya and Kerio Valley Basin 52 3.2.1. The Kenya Water Policies 52 3.2.2. Decentralization of water development projects 53 3.2.3. The Constituency Development Fund (CDF) 54 3.2.4. The Kerio Valley Basin study area 54 3.2.5. Poverty in Kenya 58 3.3. Methodology 61 3.3.1. The Millennium Development Goal (MDGs) on drinking water 61 3.3.2. Human and water development indicators 62 3.3.3. Water Poverty Index 64 3.4 Results and discussion 68 3.4.1. CDF-Funded Water Projects in the Kerio Valley Basin 75 3.5. Conclusion 76 CHAPTER 4 Comparison of AnnAGNPS and SWAT in estimating nonpoint source pollution in a Lake Michigan Watershed 78 Conclusion 79 Works Cited 81 viii List of Maps and Figures Page Fig. 2.1 Association of drought types and duration 14 Fig. 2.2 The ALRMP target districts 20 Fig. 2.3 East-West geological cross section of the Kerio Valley Basin 22 Fig. 2.4 Location of the Kerio Valley Basin, Kenya 23 Fig. 2.5 The formation of the Kerio Valley Basin 24 Fig. 2.6 Locations of the rain gauge stations 31 Fig. 2.7 Graph of average monthly precipitation plotted against the month 32 Fig. 2.8 Total Precipitation amounts for the period 1985-2009 40 Fig. 2.9 Cumulative probability curve for precipitation in Kerio Valley Basin (1973-2009) 41 Fig. 2.10 Box plots for cumulative precipitation for Kerio Valley Basin (1973-2009) 42 Fig. 2.11 Average monthly Streamflow for River Kerio – Actual (1965 – 1975), and predicted (1992 – 2009) 43 Fig. 2.12 Average annual Streamflow for River Kerio – Actual (1965 – 1975), and predicted (1992 – 2009) 44 Fig. 2.13 Graph of SDI Vs. percent exceedance: 1965-1983 45 Fig. 2.14 Graph of SDI Vs. percent exceedance: 1992 – 2009 46 Fig. 3.1 Administrative districts 56 Fig. 3.2 Administrative locations 57 Fig. 3.3 Poverty rates in Kenya – 1999 60 Fig. 3.5 Conceptual framework for WPI 66 Fig. 3.6 Water Poverty Index – Kerio Valley Basin 73 Fig. 3.7 Comparison of Water sector allocation of CDF in three districts (2003-2007) 76 ix List of Tables Page Table 2.1 Incidences of drought in the Kerio Valley Basin since 1883. 21 Table 2.2 Locations of the selected rain gauge stations. 30 Table 2.3 Average monthly rainfall for seven meteorological stations 32 Table 2.4 Logarimi Estate Kipkabus (pre-1978 vs. the current average monthly precipitation) 34 Table 2.5 The actual monthly precipitation (pre-1978) vs. predicted average monthly rainfall 34 Table 2.6 States of streamflow drought 39 Table 2.7 SDI percent state expectation 46 Table 3.1 Goalposts used to calculate HDI 62 Table 3.2 Data selected as WPI component variables for the Kerio Valley Basin 69 Table 3.3 The WPI components and values for the Kerio Valley Basin 70 Table 3.4 Average WPI for the 6 districts 74 Table 3.5 Water sector allocation of CDF (2003-2007) 75 x CHAPTER 1 1.1 INTRODUCTION Water is a vital resource which is important to all forms of life. Yet increased global climate variability and rapid population growth is putting pressure on the available water resources. The earth is three-quarters occupied by surface water, but only 2.78% of this water supply constitutes fresh water reserves (Christopherson 2006). The scarcity and stress on the available fresh water in relation to water demands is increasing worldwide (Postel 2000, Goudie 2006). Numerous studies and publications have shown that fresh water demand tripled between the period of 1950 and 1990 (Gleick 1996, Postel 2000). If this trend continues, many countries especially those in the developing world will face enormous challenges in water supply in the future, which could have serious ramifications on food security, political stability, social structure, and human health (Goudie 2006, Madulu 2003). Additionally, climate variability has led in increase in drought incidences. Climate variability in Kenya is affected by regional climate controls. Among the factors that have been identified to affect climate variability in the region include: the Intertropical Convergence Zone (ITCZ), the subtropical anticyclones, the East African monsoonal winds, tropical cyclones, and the jet streams. Rainfall variability in the region is also affected by the complex topography. The moist and wet regions in Kenya are concentrated around large water bodies and over the highlands. The dry areas are concentrated in the Arid and Semi-Arid Lands (ASALs), where the annual rainfall values 1 are low. Approximately 80 percent of Kenya is in the form of ASALs, which are known to face drought recurrence and frequent rainfall deficits (Ogallo 2000). Drought has been identified as a major natural disaster in Kenya, affecting agricultural productivity and water availability. Drought in Kenya has also led to substantial loss of lives.
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