Assessment of Land Use Changes and Its Effect on Stream Discharge in Nanyuki River Watershed

TABLE OF CONTENTS

DEDICATION

ACKNOWLEDGEMENT

ABSTRACT

LIST OF FIGURES

LIST OF TABLES

LIST OF PLATES

LIST OF ABBREVIATIONS

CHAPTER ONE:
1.1 BACKGROUND OF THE STUDY
1.2 STATEMENT OF THE PROBLEM
1.3 RESEARCH QUESTIONS
1.4 RESEARCH OBJECTIVE
1.5 RESEARCH HYPOTHESIS
1.6 JUSTIFICATION
1.7 SIGNIFICANCE OF THE STUDY
1.8 SCOPE OF THE STUDY
1.9 LIMITATION OF THE STUDY
1.10 DEFINITION OF OPERATIONAL TERMS
1.11 CHAPTER OUTLINES

CHAPTER TWO:
2.0 INTRODUCTION
2.1 LAND USE/COVER CHANGES
2.2 LAND USE CLASSIFICATION
2.3 RAINFALL AND STREAM FLOW TREND DETECTION AND ANALYSIS
2.4 WAVELET ANALYSIS IN HYDROCLIMATIC STUDIES
2.5 THEORETICAL FRAMEWORK
2.6 CONCEPTUAL FRAMEWORK

CHAPTER THREE:
3.1 PHYSICAL SETUP
3.1.1 POSITION AND SIZE
3.1.2 CATCHMENT DESCRIPTION
3.1.3 PHYSIOGRAPHY
3.1.4 CLIMATIC CONDITION
3.2 SOCIO-ECONOMIC SETUP
3.2.1 DEMOGRAPHIC PROFILE
3.2.2 ETHNIC GROUPS
3.2.3 ECONOMIC ACTIVITIES
3.3 ECOLOGICAL SETUP
3.3.1 NATURAL RESOURCE IN THE AREA OF STUDY
3.3.2 CONTRIBUTORS AND EFFECTS OF ENVIRONMENTAL DEGRADATION IN THE AREA OF STUDY

CHAPTER FOUR:
4.1 RESEARCH DESIGN
4.2 NATURE AND SOURCES OF DATA
4.3 METHODS OF DATA COLLECTION
4.4 METHODS OF DATA ANALYSIS AND PRESENTATION
4.4.1 Vector and Raster Image Processing and Analysis
4.4.2 STREAM DISCHARGE DATA ANALYSIS
4.4.3 RAINFALL DATA ANALYSIS
4.5 CONSTRAINTS OF DATA ANALYSIS AND COLLECTION

CHAPTER FIVE
5.1 LAND USE/ LAND COVER CHANGE
5.2 RAINFALL AND STREAM DISCHARGE ANALYSIS
5.2.1 Rainfall analysis
5.2.2 Stream discharge analysis

The periodicity analysis of April and November showed similar results in the two continuous wavelet transform the majority periodicity appeared in the power spectrum of 2-3 year band between 1987 and 1997. This means that in the 10 years’ time the inter annual cycle of stream flow has been occurring after two to three years.

CHAPTER SIX:
6.1 SUMMARY OF THE FINDINGS
6.2 CONCLUSION
6.3 RECOMMENDATIONS
6.4 AREAS FOR FURTHER STUDY

REFERENCES:

APPENDICES:

APPENDIX 1: OBSERVATION CHECKLIST

APPENDIX 2: CONFUSION MATRIX

APPENDIX 3: MANN-KENDALL TEST RESULTS

DEDICATION

To the entire Tereri family, I express my gratefulness for the love, support and encouragement you gave me in making this work successful.

ACKNOWLEDGEMENT

My sincere gratitude to Dr. Clifford Obiero, Mr. Mathew Kigomo and Prof. Aggrey Thuo who followed my research work right from its inception to conclusion. I would like to thank them for their guidance, tolerance and willingness to share their knowledge throughout the project period. Additionally, gratitude to the staff members and fellow students who immensely contributed to the project work.

Special thanks goes to Mr. Samuel Kirimi, Groundwater Officer and Mr. Felix Okoth, Catchment Management Officer of Nanyuki Sub-region Water Resource Management Authority office, and the database officers in the Kenya Meteorological Department and Regional Center of Mapping for Resource Development for their assistance in acquiring stream discharge data, rainfall data and satellite images. Their contribution was very important to the conclusion of the project work. For this reason, I extend my profound appreciation to staff members of these institutions..

To other people who contributed to the successful completion of the project and are not mentioned in this acknowledgement, I thank you all and may God bless you abundantly.

ABSTRACT

Kenya is endowed with an array of internal wetlands dispersed around the main water basins, as it is in many developing countries in the world, its rural population is characterized by rapid growth rate over the past years. As such, watersheds are continually facing environmental degradation due to the limited supply of the resource and over exploitation to cater for this increasing population. This has resulted to immense land use conversion in the watershed that has a ripple effect on several elements of the watershed. Per se, the research aimed at assessing the land use change and its influence on stream discharge in Nanyuki River Watershed.

The main objective of the study was to assess the land use changes and its effect on stream discharge in Nanyuki River watershed. This was done by identifying and mapping the land uses; assessing the trend and periodicity in stream discharge and precipitation; and the correlation between forest cover and stream discharge between 1985 and 2008.

Based on the satellite imagery the land use change was assessed, where the forest cover areas were calculated and correlated to stream discharge data. This correlation established the direction and significance of association. The results showed an increased land use change where the forest cover in the watershed is reducing and impervious surfaces were also increasing between 1985 and 2015. The precipitation and stream discharge decreased between 1985 and 2015.The correlation between land use changes and the stream discharge showed a negative association. This means that as the land use changes increases the stream discharge decreases. The study concluded with recommendations that may be employed so as to realize an effective integrated watershed management plan that provides guidance on coordinated management, conservation and development within Nanyuki River watershed.

LIST OF FIGURES

FIGURE 1 conceptual model (Author)

FIGURE 2: Nanyuki River Catchment Area (Author)

FIGURE 3: watershed’s topography (Author)

FIGURE 4: False color subsets used in the study

FIGURE 5: Thematic land use/land cover map 1985

FIGURE 6: Thematic land use/land cover map 1995

FIGURE 7: Thematic land use/land cover map 2000

FIGURE 8: Thematic land use/land cover map 2015

FIGURE 9: Trends in land cover between 1985 and 2015

FIGURE 10: Land Use/Land cover change between 1985 and 2015

FIGURE 11: monthly rainfall variation

FIGURE 12: mean annual rainfall trend for Nanyuki River Watershed

Figure 13 MAM_OND Rainfall

Figure 14 April and November rainfall

Figure 15: Annual mean stream discharge trend for Nanyuki River watershed.

Figure 16 MAM_OND Stream Flow

Figure 17 April_November Stream Flow

LIST OF TABLES

TABLE 1: Landsat Images specifications

TABLE 2: characteristics of Landsat TM and Landsat OLI_TIRS bands

TABLE 3 : specified land use/land cover classes

TABLE 4 : stream discharge and rainfall data

Table 5: METHODOLOGY MATRIX

TABLE 6: Class coverage in Percentage

LIST OF PLATES

PLATE 1: Anthropogenic disturbances of Nanyuki river watershed

LIST OF ABBREVIATIONS

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CHAPTER ONE: INTRODUCTION

1.1 BACKGROUND OF THE STUDY

Watersheds are land areas which catch and drain water to a particular location such as a stream, river, lake, ocean or other bodies of waters (Debarry, 2004).These land areas are examples of natural resources, as such they are limited in supply and valuable because they are the source of water for agricultural, industrial and domestic use.

Watersheds are vulnerable to change, because they form an integrated network and they are continuously being subjected to various disturbances (Debarry, 2004).These dynamics in the watershed that cause disturbances in the watersheds may be due to natural processes or anthropogenic activities. Examples of the natural processes that may cause these disturbances include climate change or erosion, while the anthropogenic activities include urbanization, deforestation and intensive agricultural practices. These changes in LULC are among the most important changes on the earth surface (Nunes & Auges, 1999).For instance, in the tropics grassland, woodland and forest conversion into cropland and pasture during the last few decades has risen dramatically (Lambin, et al., 2003).

Land is one of the vital components of life supporting system and thus a good health status should be maintained through its sustainable use. Unfortunately this is not the case, it has been used in an unsustainable way in the course of human civilisation (Showqi, et al., 2013). The LULC changes could be as a result of complicated interactions of socio economic diversification, technological advancement, demographic pressure and many other related conditions (Reid, et al., 2000).

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PLATE 1: Anthropogenic disturbances of Nanyuki river watershed: (A) built up area in the watershed (B) agricultural activities in the watershed (C) a reforested stretch of the watershed with dominant eucalyptus trees

Anthropogenic activities have profound impacts upon the natural setting of global system (Rashid, et al., 2013).Decreases in rainfall interception, transpiration and surface soil hydraulic conductivities are associated with watershed disturbances i.e. forest disturbances and LULC modifies the water cycle (Chhabra, et al., 2006) and may have significant effects on watershed water yields and stream flow dynamics (Zhang, et al., 2001 cited in Munoz-Villers & McDonnell, 2013).

1.2 STATEMENT OF THE PROBLEM

Nanyuki river sub-watershed is part of the largest catchment area i.e. Ewaso Ngiro North Catchment. According to WRMA, Ewaso Ngiro North catchment area has an area of 210 233Km2 with a population of 3.6 million and 70% of it falls in a semi-arid area. The rapid growing nature of the population, as is the case in many developing countries, has led to heavy reliance on land in this area. Due to this fact the resource base faces a lot of changes. One such effect is the changes in land use, which has a ripple effect. For example, instances of water use conflict may develop when the lower communities within Ewaso Ngiro North catchment do not get water during the dry seasons. Per se, the project aimed at providing a comprehensive assessment of land use changes and its effect on stream discharge based on scientific data analysis in a bid to give direction to coordinated management, conservation and development within Nanyuki River watershed.

1.3 RESEARCH QUESTIONS

The research project answered the following question which constituted the statement of the problem.

i. How has the watershed’s land use changed from 1985-2015?
ii. What is the rainfall and stream discharge trend from 1985-2015?
iii. What is the periodicity of the rainfall and stream flow from 1985-2015?

1.4 RESEARCH OBJECTIVE

The main objective of this study was to assess the land use changes and how it affects stream discharge in Nanyuki River watershed.

The specific objectives of the study were:

i. To examine the land use change from 1985-2015
ii. To study rainfall and stream flow trend from 1985-2015 in the catchment.
iii. To study the rainfall and stream flow periodicity from 1985-2015 in the catchment.

1.5 RESEARCH HYPOTHESIS

The research hypothesis which guides data collection, analysis and interpretation is as follows:

There is no significant relationship between land use changes and stream discharges in the Nanyuki River watershed.

1.6 JUSTIFICATION

The choice of the study is fostered by the need to plan and manage the watershed resources in order to ensure efficient supply of fresh water to the water uses and avoid some eventualities that result from lack of enough fresh water. The choice of the study area on the other hand is first influenced by familiarity with the area. Secondly, the fact that Nanyuki sub-watershed is part of the largest catchment area in Kenya, that is, Ewaso Ngiro North catchment area. This catchment lies in a semi-arid area where the supply of fresh water all year round is a problem.

1.7 SIGNIFICANCE OF THE STUDY

The study is significant in that, the output may be used by government agencies such as WRMA, environmental planners and other stakeholders in the area. The data collected and processed in this project may be used in other studies within the Nanyuki River Sub-watershed, also apart from that the methodology of the study may be replicated for other natural resource management studies.

1.8 SCOPE OF THE STUDY

The study was conducted in Nanyuki River watershed within Ewaso Ngiro North Catchment Area. It covers a length of 95 Km before its confluence with Ewaso Ngiro River. This traverses between Laikipia East and Laikipia North sub counties. The study subject on the other hand, focused on the assessment of the land use changes and its effect on stream discharge in the watershed.

1.9 LIMITATION OF THE STUDY

- There are various types of land uses/land cover in the watershed. However, due to limitations of the Landsat images resolution and ground true exercise limitations the land uses/land cover used were classified as forest, water, agriculture and settlements.
- The watershed is covered by dense forest which limited the access to some of the areas.

1.10 DEFINITION OF OPERATIONAL TERMS

i. Land use; this is the way human beings use land and its resources. It entails the management and modification of the natural environment into built environment such as agricultural fields and residential areas.
ii. Land cover; this refers to the physical material at the surface of the earth. Land cover includes grassland, bare land, and water among others. There are two primary methods for capturing information on land cover this includes field surveys and analysis of remotely sensed imagery.
iii. Land use change; is the dynamics in the way human beings utilize land and its resources.
iv. Stream discharge: is the volume rate of water flow in a river
v. Watershed: is the area of land from which rainfall or snowmelt discharges into a stream or lake.
vi. Periodicity: this refers to the inter-annual cycle of a certain phenomenon.

1.11 CHAPTER OUTLINES

This chapter gives an overview of the study. This includes the background of the study, statement of the problem, the research questions, objectives, premises, justification and significance of the study. Limitations, definition and chapter outlines of terms include the chapter.

Chapter two is the literature review. In reviewing the literature objectives of this study was considered. Review of literature on land use changes and stream discharge in a watershed was done. Gaps were identified and a conceptual model was made.

Chapter three presents information on the study area. Relevant information from Laikipia CIDP and the NRWUA SCMP are presented.

Chapter four comprises of the research methodology. Information on methods of data collection, analysis and presentation as well as constraints to data collection and analysis is given.

Chapter five presents the findings of the research using a variety of ways such s thematic maps, tables, graphs and photographs. The findings identified the problem which was the basis of recommendations.

Chapter six gives a summary of the findings, conclusion and recommendations. An integrated action plan and areas for further research are also included in this chapter.

CHAPTER TWO: LITERATURE REVIEW

2.0 INTRODUCTION

In the past, the concept of watershed management mainly dealt with the management of natural resources in medium or large river valleys, designed to slow down rapid run off and excessive soil erosion and soil erosion ,and to slow the rate of siltation of reservoirs and limit the occurrence of potentially damaging flash flooding in river courses (Paul, 1997).At present, the overall objective of watershed development and management programs take the watershed as the hydrological unit, and aim to adopt suitable measures for soil and water conservation, provide adequate water for agriculture and domestic use and improve the livelihoods of the inhabitants (Habtamu, 2011).This new concept is more complex compared to the old one. The complexity in this new concept is because the watershed has been recognized as a unit for integrated resource management. In this case management is not only limited to land, water and biomass, but also worried with integration for self-reliance and holistic development of the rural poor (Mollinga, 2000).

2.1 LAND USE/COVER CHANGES

According to Ayodeji, 2006 every parcel of land on the Earth’s Surface is unique in the cover it possesses.Land use and land cover are distinct but they are closely linked characteristics of the earth surface.The use to which land can be put into use could be mining,agriculture or urban development,while the land cover categories could be cropland,forest,wetland,pasture and roads among others.The term land cover was originally used to refer to the kind and state of vegetation but its usage has broaden to include other things such as human structure,soil type,biodiversity,surface and ground water (Meyer, 1995).

Land use affects land cover and changes in land cover affect land use. Changes in land cover by land use do not necessarily imply degradation of the land. However, many shifting land use patterns driven by a variety of social causes, result in land cover changes that affects biodiversity, water and radiation budgets, trace gas emissions and other processes that come together to affect climate and biosphere (Riebsame, et al., 1994).

According to Bewket & Sterk, 2005 resource degradation brought about by the decrease in the area under natural vegetation and its conversion into other types of land use and land cover that are human-managed systems is a common concern.One of the forms of resource degradation believed to follow fom LULC changes is the disturbance in stream flow regimes in a watershed.They further urgue using emperical evidence that the underlying simple assumption is that land under little vegetative cover is subjected to high surface runoff amounts,low infiltration rates and reduced groundwater recharge.The reduced infiltration and groundwater recharge,eventually,leads to lowering of water tables and intermittence of once-perennial streams

2.2 LAND USE CLASSIFICATION

Supervised and unsupervised classification

Supervised classification requires that an operator is familiar with the area of interest. The operator needs to know where to find the classes of interest in the scene. This information can be derived from general knowledge of the scene or from dedicated field observation. One of the main steps in image classification is the partitioning of the feature space. In supervised classification this is realized by an operator who defines the spectral characteristics of the classes by identifying sample areas, training areas, (ITC, 2009). Supervised classification method rely heavily on both the quality and representation of the training area (Campbell, 2007).This means that the training area should be sufficient to produce homogenous cell values for a particular class and to have a more superior representation in the output. On the other hand unsupervised classification method uses cluster algorithms to partition the feature space into a number of clusters and furthermore it requires no prior knowledge of the study area (ITC, 2009).

2.3 RAINFALL AND STREAM FLOW TREND DETECTION AND ANALYSIS

Rainfall and stream flow are hydro-climatic variables that can be used to show how the climate of a given area has changed over a given period of time. To realize the objective of assessing the climate evolution over time trend analysis is a vital method. Additionally, to extract an underlying behavioral pattern in a time series that would otherwise be partly or fully hidden by noise, trend detection methods are applicable (Nalley, 2012).Over the past years researchers have spent their time exploring into details the detection of changes, both gradual and sudden. From a practical and scientific perspective, the information on the spatial-temporal variance over a given time series is very vital. For example according to Smakhtin, 2001 trend detection and forecasting of low flow is of importance due to quantity of water to be released downstream a dam, in order to protect ecological integrity and sustainability.

There is an inherent variability of hydrologic time series when trying to detect trends in natural series. Also it is wise to be aware that the process of differentiating between natural variability and distinct trend it is not straightforward (Burn, 1994).According to Pekarova and Pekar, 2007 so as to draw accurate and useful conclusion about the changes, acquisition of long term data is key. This approach is seconded by various authors who have conducted trend analysis and detection using different length of data. For instance Burn and Elnur, 2002 considered a minimum of 25 years while Kahya and Kalayci, 2004 considered a minimum of 30 years.

In analysis and detection of rainfall and stream flow there are several different methods that a researcher can apply. Land based data, satellite data, statistical tests, computer intensive approaches and models play significant roles in enhancing the understanding of the complex time and space variations in hydro-climatic systems (Nalley, 2012). However, the choice of method used should depend on data’s characteristics. The bootstrap method, Spearman’s rho test, regression models, Mann-Kendall trend test and wavelet analysis are the approaches used in detection and estimation of trends

2.4 WAVELET ANALYSIS IN HYDROCLIMATIC STUDIES

According to Torrence and Compo, 1998 wavelet analysis (WA) is a tool which has gained popularity in many studies that is used to analyze localized variation of power within a time. By decomposing a time series into time-frequency space, one is able to determine both the dominant modes of variability and how those modes vary in time. As such, it is used in isolation of different periodicities embedded in a time series and closely examines the composition of a signal (Zume and Tarhule, 2006).

The use of wavelet transformation (WT) has been used in a number of fields including astronomy, medical research, geophysics and signal & image processing. WT utilization in the analysis of hydroclimatic time series have been found to be advantageous as it effectively uncovers phenomena in the time series that would be otherwise hidden (Nalley, 2012).WT allows the time series observed to be transformed into wavelet coefficients according to time and scale simultaneously.

The fundamental wavelet functions used in WT can localize time and frequency component of a signal simultaneously and also conserve the temporal features and periodic cycle patterns of a series without assuming stationary, which makes WT an ideal tool for hydrologic related time series analyses because climate and hydrology generally have a non-stationary nature. Wavelets can be stretched and translated into different resolutions in both frequency and time.

(Kim, 2004)

2.5 THEORETICAL FRAMEWORK

Common property theory

Watershed resources are characterized by high exclusion costs and sub-tractability, these are the two attributes of common pool resources. A high exclusion cost is a nonexclusive resource that is difficult to exclude others from using, for a sub-tractable or rival resource one user’s welfare is diminished by other users. Many natural resources in a watershed are often held in common but others, such as agricultural land and captured runoff water, are managed individually. Despite this fact a watershed is defined by the hydrological linkages among all the resources in it. Collective action among all watershed resource users is needed to manage hydrological process for maximum productivity of the whole watershed system. Through these hydrological linkages, a watershed system is in fact a common pool resource that faces typical commons management problem (Kerr, 2007).In complex, multiple use commons like watersheds, interests must be balanced both within and across diverse interest groups to generate agreement on regulations about resource access, allocation and control (Steins & Edwards, 1990). Agrawal (2001) synthesized and revised factors that encourage successful commons management, focusing on those that enable sustainable governance of the commons. For watershed projects, the key issue is a group’s ability to establish a new governance system to effectively manage the watershed commons (Kerr, 2007).

2.6 CONCEPTUAL FRAMEWORK

The project addressed the assessment of land use changes and its effect on stream discharge. Per se, it needed elaborate data on the natural and physical resources in the watershed; the conceptual framework for informed watershed management decision is shown below.

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FIGURE 1 conceptual model (Author)

CHAPTER THREE: AREA OF STUDY

3.1 PHYSICAL SETUP

3.1.1 POSITION AND SIZE

The watershed is situated in Nanyuki. Nanyuki is a town in Laikipia County and lies northwest of Mt. Kenya. It’s situated in the north of the equator and it is 1448.2 Km in size. The town was founded in 1907 by the British settlers. According to the geographical coordinates, the town lies between latitude; 1 00’ ’’ longitude; 37 04’ 00’’.The altitude of the area varies between 1500 m above sea level and 2611 m above sea level (G. O. K 2013).

3.1.2 CATCHMENT DESCRIPTION

The Nanyuki River watershed covers a length of 95km and spreads over three administrative boundaries of Laikipia east, Laikipia North and Nyeri North subcounties. The catchment area extends from a high altitude area,i.e the slopes of Mt.Kenya, through medium zone around Nanyuki Town to low lying area at Dol Dol before it condluences with Ewaso Nyiro River.

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FIGURE 2: Nanyuki River Catchment Area (Author)

3.1.3 PHYSIOGRAPHY

The top soil are composed of loamy soils. The flat topography is dictated by tectonic and volcanic disturbance of Mt. Kenya. The volcanic activity of Mt. Kenya has contributed to the complexity of the physiography of this area. The topography is generally undulating with small open valleys and ridges as shown in figure 2. Soils are developed on basement system rocks mainly migmatites and biotite gneisses. The area is geologically associated with Mt. Kenya volcanic suite which technically controls the geological setting. The volcanic rocks associated with this suite comprise tuffs, basalts, trachytes, phonolites, kenytes and agglomeratic constituents of the above rocks (NAREDA, 2010).

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FIGURE 3: watershed’s topography (Author)

3.1.4 CLIMATIC CONDITION

The area experiences a relief type of rainfall due to its location and altitude and the annual average rainfall varies between 400mm and 750 mm. The seasonal distribution of rainfall in the area is as a result of the influences of Northeast and South trade winds, the Inter-Tropical Convergence Zone and the Western winds. The long rains occur from March to May while the short rains are in October and November. The annual mean temperature of the county ranges between 16o C and 26o C. The average duration of sunshine is between ten and twelve hours daily (G. O. K, 2013).

3.2 SOCIO-ECONOMIC SETUP

3.2.1 DEMOGRAPHIC PROFILE

The total population of the area stood at 49,233 people of which males were 25,046 and women were 24,187.It was also projected that by 2015 the population will be at 56421 and at 59080 by 2017.The ratio of men to women is approximately one to one (KNBS, 2009).

3.2.2 ETHNIC GROUPS

The area around Nanyuki is home to the Maasai, Agikuyu, Somali and the Ameru ethnic groups.

3.2.3 ECONOMIC ACTIVITIES

The main economic activities in the study area include crop, livestock and fish production, tourism and at a small scale industry processing. The main crop grown includes wheat, maize, beans and potatoes while the tourism sector is boosted by the presence of wildlife and unique Maasai cultural practices. The industry processing on the other hand is minimal with Nanyuki milk plant being the major firm. There are also several jua kali associations with artisans who are involved in welding, fabrication, carpentry among other activities (G. O. K, 2013).

3.3 ECOLOGICAL SETUP

3.3.1 NATURAL RESOURCE IN THE AREA OF STUDY

The study area is generally a semi-arid area and is endowed with several natural resources. These include pasture rangeland, forest, wildlife and undulating landscapes. The high and medium potential land constitutes 20.5 per cent of the total land area while the remaining 79.5 per cent is low potential hence unsuitable for crop farming (G.O.K, 2013). Black cotton soil which has inherent fertility spreads in most parts of the area. The dark reddish brown to red friable soils and rocky soils are mainly found on the hillsides. The limiting factors to agricultural production are the poor weather conditions characterized by frequent dry spells and poor rainfall distribution (G.O.K, 2013).Wildlife is another natural resource and is widely distributed in the Mt. Kenya corridors and it has been a source of conflict between the farming and pastoralist communities.

3.3.2 CONTRIBUTORS AND EFFECTS OF ENVIRONMENTAL DEGRADATION IN THE AREA OF STUDY

According to G.O.K (2013), population pressure on limited land resources and the growth of Nanyuki town has strained the provision of social amenities. The establishments of informal settlements have resulted to high levels of pollution, poor sanitation and disposal of waste. Other factors contributing to environmental degradation in the area of study include; overgrazing, cutting down of trees for charcoal burning and farming along the river banks . Environmental degradation has contributed to reduced productivity of land, quality and quantity of water sources, high levels of pollution for both air and water masses, constraining existing effluent and solid waste disposal facilities especially in the urban areas. Increased farming activities in forests are also a threat to the area’s rich biodiversity.

CHAPTER FOUR: METHODOLOGY

4.1 RESEARCH DESIGN

The research project employed a quasi-experimental research design. This research design was effective as the research project aimed at exploring causes and effect relationship where causes can be manipulated. In this case the cause and effect relationship is the land use changes and its effects on the stream discharge.

4.2 NATURE AND SOURCES OF DATA

Existing Maps

The topographic map sheets 121/1 and 107/3 for Mt. Kenya region at a scale of 1:50,000 were used as base maps for compiling Nanyuki River watershed map. The maps were obtained from survey of Kenya in digital form. From the maps the hydrographic information were extracted. The topographic maps were in Arc 1960 datum and Clarke 1880 spheroid modified datum.

Remote Sensing Imagery

Satellite images from Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper and Landsat-8 Operation Land Imager and Thermal Infrared Sensor were used for mapping the land uses and detecting changes. Images used in the study were chosen on the basis of data availability and suitability. Suitability in this case refers to the time series and image clarity. Images selected were between the months of January and February with less than 10% cloud cover. Landsat images for the year 1995, 2000 and 2010 were acquired from RCMRD while that of 1985 and 2015 were downloaded from USGS website (USGS Earth Explorer) under the following link http://earthexplorer.usgs.gov. The image specifications before processing are illustrated in the table below:

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TABLE 1: Landsat Images specifications

In addition, an Aster Image (30m Resolution) of the study area was downloaded from the USGS website to facilitate the extraction of slope information in the study area.

Stream discharge data

Stream discharge data of 1985 to 2015 was acquired from the Water Resource Management Authority, Upper Ewaso Ngiro North Regional Office. The acquired stream discharge data was measured at one river gauge station RGS_5BE01 along Nanyuki River.

Rainfall data

Annual rainfall data of 1985 to 2015 for Nanyuki River watershed was acquired from Kangaita, Mow Nanyuki, Likii Farm, Laikipia Airbase Monitoring stations. Among the four monitoring stations Laikipia Airbase station had complete data for the period 1985 to 2015 . As such, rainfall analysis was based on this data.

Software

The study utilized a number of software in analyzing the data above. These software included ArcGIS 10.1, Google Earth Pro, TREND and Ms. Excel. ArcGIS was used for both vector and raster analysis. Google Earth Pro complemented ArcGIS in the process of accuracy assessment. To study the nature of the trend in both stream discharge and rainfall data the TREND water-toolkit was used. For the purpose of establishing the cause and effect relationship between land use change and stream discharge Ms. Excel was used.

4.3 METHODS OF DATA COLLECTION

Ground truth information collection

This involved visiting the area of study so as to confirm whether the results obtained from the unsupervised classification method were correct. Also it involved collecting the coordinates of three points, i.e., Mwea, Huku and Maka.This was done using a 4m accuracy hand held GPS. Additionally, the current land uses were identified.

Photography

To supplement the ground truth information collection photos were taken.

4.4 METHODS OF DATA ANALYSIS AND PRESENTATION

4.4.1 Vector and Raster Image Processing and Analysis

Vector data capture and processing

This involved georeferencing the topographic map sheets that cover Nanyuki River watershed 121/1 and 107/3 (scale 1:50000) in Arc 1960 datum and Clarke 1880 spheroid modified datum and creating a geodatabase in the same coordinate system with the following feature classes to be digitized such as the main river (Nanyuki River), streams and the major town. This was then followed by data capture from topographic maps for the various feature classes through on-screen digitizing. Undershoots and overshoots were cleaned in Arcmap.

Harmonising the vector data to the same coordinate system as the raster data

This was done through re-projecting the entire vector data created into the same coordinate system as that of the raster data. As such, the geodatabase with its features classes transform from Arc 1960 datum and Clarke 1880 spheroid modified datum to WGS84 datum and WGS84 spheroid zone 37 North.

Raster image data processing and analysis

This involved three major operations: image restoration, image enhancement and image classification.

i. Image restoration

This operation entailed undertaking calibrations and correction so that the image may be a replica of the earth surface. Some of the process involved in this operation included resampling and sub-setting of satellite image. In the process of resampling entailed making the satellite images to be used in analysis to be in the same spatial resolution. In this case, the bands to be used in the land cover/land use analysis i.e band 4, 3, 2 of the Landsat 7ETM+ sensor were at spatial resolution 28.5m which was higher than for bands of the images acquired by Landsat 5TM which has a spatial resolution of 30m. This therefore called for resampling the bands with 28.5m spatial resolution to that of Landsat 5TM bands which had a spatial resolution of 30m.This was achieved using the resample tool in the image analysis window in ArcMap 10.1. Subsetting the image involved clipping the satellite image so that the output would be the area of interest. This was achieved by using the clip tool under Raster Processing in the ArcToolbox window.

ii. Image enhancement

Image enhancement operation involved modifying the satellite images to make them visually suited for interpretation. Image enhancement was performed through creating a composite. The composite was generated using the composite tool in the image analysis window in ArcMap 10.1. Landsat TM band 4, 3, 2 which corresponds to Landsat OLI_TIRS Bands 5, 4, 3 were used to generate a color composite for the Nanyuki River Catchment. This is a false color combination where vegetation appears in shades of red, urban area are represented by cyan blue color, ice, snow and clouds appears white or light cyan while soils vary from dark to light browns. The composites generated were bands for the year 1985,1995,2000 and 2015. The following is a summary of characteristic of Landsat TM and Landsat OLI_TIRS.

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TABLE 2: characteristics of Landsat TM and Landsat OLI_TIRS bands

- Landsat TM Band 2 corresponds to Landsat OLI_TIRS Band 3
- Landsat TM Band 3 corresponds to Landsat OLI_TIRS Band 4
- Landsat TM Band 4 corresponds to Landsat OLI_TIRS Band 5

iii. Image Classification

Unsupervised classification

Image classification entailed the use of computerized system to interpret the remotely sensed images. In this operation, the project utilized both the unsupervised and supervised classification technique. This technique of classification used the ISO-cluster method. The numbers of classes were specified as 16, minimum class size as 10 and the sample interval as 5. The resultant classification signature for the 10 classes was saved for later use.

Supervised classification

This involved reclassifying the number of land cover classes resulting from unsupervised classification to five and assigning them their appropriate colors. The criteria for combining the classes was based on the information from the ground truth exercise and high resolution color (40cm) true color images from geo-eye in Google maps. These classes were:

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TABLE 3 : specified land use/land cover classes

Accuracy assessment

Accuracy assessment is carried out to enable a degree of confidence to be attached to the results with presumably the correct information i.e. ground truth through confusion matrix/error matrix. The confusion matrices for each classification (1985, 1995, 2000 and 2015) were generated in ENVI by using the classified image subset and their corresponding composites (ground truth).

The overall classification accuracy was given by the following formula (Senseman et al., 1995)

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Where ρ is the overall classification, ‘n’ is the number of points correctly classified on the image and ‘N’ is number of points checked in the field.

Kappa coefficient is also used in assessing the accuracy of the classification, as such, it is a potent indicator of the accuracy estimation for the LULC map (Senseman et al., 1995).Abbildung in dieser Leseprobe nicht enthalten

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Total Squared – Sum of all the (Row total*Column total)

Extraction of classification statistics

This involved calculation of areas for the specified land use/land cover classes for the year 1985, 1995, 2000 and 2015. This operation was made possible by field calculator tool in the table of content window in ArcMap. The areas were provided in percentage.

4.4.2 STREAM DISCHARGE DATA ANALYSIS

The analysis of stream discharge data was performed using two approaches. In the first one, stream discharge data (table 4) was analyzed to get the annual mean stream discharge and afterwards the trend of annual stream discharge from 1985-2015. The Mann-Kendall test was employed in studying the nature of the stream discharge trend using the TREND software. The periodicity of the stream flow was examined by decomposing their time series into the frequency space. Per se, the continuous wavelet transform was used.

4.4.3 RAINFALL DATA ANALYSIS

The trend of rainfall was analyzed using the annual rainfall data (table 4) between 1985 and 2015. The characteristic of rainfall trend was analyzed using the Mann-Kendall test provided in the TREND software. In order to study the rainfall periodicity the continuous wavelet transform was used so as to examine the inter-annual variability of the rainfall in the watershed.

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TABLE 4 : stream discharge and rainfall data

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Table 5: METHODOLOGY MATRIX

4.5 CONSTRAINTS OF DATA ANALYSIS AND COLLECTION

The major challenge was experienced in the process of image classification where identifying settlements and classifying them with other classes was difficult. This constrain was because on the ground due to the prevailing poverty, in areas such as Huku and Maka villages, most houses are made of mud and thatched roofs and they are scattered settlement pattern. Additionally, due to the low resolution (30m) of the Landsat imagery, it was difficult to identify and classify settlements in some areas.

The other challenge experienced was lack of comprehensive rainfall data from Kangaita, Mow Nanyuki and Likii Farm monitoring stations. The data from these monitoring stations were not consistent in that they were only available for 3 to 4 years and with no other records made between 1985 and 2015. This led to the use of Laikipia Airbase monitoring station for rainfall analysis.

CHAPTER FIVE DATA ANALYSIS, PRESENTATION AND DISCUSSION

5.1 LAND USE/ LAND COVER CHANGE

In order to detect the change of LULC within Nanyuki River Watershed, an onscreen0 interpretation approach was adopted. This led to the analysis and mapping of the LULC classes. The study used satellite data of the year 1985, 1995, 2000 and 2015 to achieve the project’s objective. False color band combination was employed in this study as it aimed at studying the LULC. As such, for the satellite data for the 1985 1995 and 2000 a 4, 3, 2 band combination was used and that of 2015 a 5, 4, 2 band combination was used to analyze and map the LULC in the watershed. Figures 3 below shows the subsets of the satellite imagery used in the study.

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FIGURE 4: False color subsets used in the study

From the satellite imagery five classes of LULC were delineated. These LULC classes were snowcap, water, forest, agriculture and built up. Hybrid classification method was utilized in the classification process. This was achieved by conducting the unsupervised classification using ISODATA method. A classification signature for the 16 classes was saved for later use. This was followed by supervised classification which involved combining the number of LULC classes that resulted from unsupervised classification signature to five and assigning them their appropriate colors. Figure 5,6,7,8 shows the thematic map of the LULC

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FIGURE 5: Thematic land use/land cover map 1985

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FIGURE 6: Thematic land use/land cover map 1995

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FIGURE 7 : Thematic land use/land cover map 2000

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FIGURE 8: Thematic land use/land cover map 2015

The degrees of confidence attached to the results of the LULC classification of the watershed, for the year 1985, 1995, 2000 and 2015, with presumably the correct information were generated using the confusion matrices. The overall accuracy and the Kappa coefficient were used to estimate the degree of confidence for each year.

From the classification of LULC in the watershed classification statistics were extracted. The classification statistics included the percentages of each class in each classification and the areas of each class in each classification. The following table contains the classification statistics extracted.

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TABLE 6: Class coverage in Percentage

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FIGURE 9: Trends in land cover between 1985 and 2015

From the extracted classification a trend in LULC of the watershed is evident. From 1985 to 2015 the snow cap has reduced from 6% to 2% coverage. This result indicate a significant loss of snow cap in the watershed as it initially covered 4.1167515 Km2 and in 2015 it reduced to 2,744501 Km2. The water coverage has increased by 9% as it was initially 11% and in 2015 it raised to 20%. This is an indication of an increase of water coverage from 7.547378 Km2 to 13.7225 Km2. This is due to the increased impervious surfaces in the watershed. The Forest cover had a significant percentage deviation from 71% to 52%. This values shows a decrease in forest cover as it dropped from 48.7149 Km2 to 35.67852 Km2. The agriculture land use has increased by 5% in terms of coverage. This means that the watershed resources are being used for agricultural purposes in the watershed a phenomenon that has seen the area changing from 6.1751277 Km2 to 9.6057535 Km2. The built up areas have increased between 1985 and 2015 by 8%. This means that there has been an increased development the watershed a fact that has led to the notable change of built-up areas from an initial coverage of 2.058376 Km2 to 8.233504Km2.

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FIGURE 10: Land Use/Land cover change between 1985 and 2015

5.2 RAINFALL AND STREAM DISCHARGE ANALYSIS

5.2.1 Rainfall analysis

Data from Laikipia Airbase monitoring station was used to analyze the rainfall trends. The mean monthly rainfall in figure 7 revealed an annual trend of rainfall with highs in April and November. This shows that the rainfall in the project area is not a reliable source of water.

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FIGURE 11: monthly rainfall variation

Between 1985 and 2015 the mean annual rainfall was characterized by highs and lows. Although it was characterized by these periods of rise and fall, the mean annual rainfall decreased with a slope coefficient of -0.31431.The graph below is an illustration of the trend in mean annual rainfall within Nanyuki River Watershed.

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