ASSESSING THE CHARACTERISTICS OF RAINFALL DURING THE DECEMBER TO FEBRUARY SEASON CASE STUDY: WESTERN UGANDA SSEMBAJJWE ROMANO 13/U/13935/PS

Transcription

1 MAKERERE UNIVERSITY ASSESSING THE CHARACTERISTICS OF RAINFALL DURING THE DECEMBER TO FEBRUARY SEASON CASE STUDY: WESTERN UGANDA BY SSEMBAJJWE ROMANO 13/U/13935/PS A DISSERTATION SUBMITTED TO THE SCHOOL OF FORESTRY, ENVIRONMENT AND GEOGRAPHICAL SCIENCES IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF A BACHELOR S OF SCIENCE DEGREE IN METEOROLOGY JUNE, 2016

2 DECLARATION I SSEMBAJJWE ROMANO declare to the best of my knowledge that this is an original copy of my work and it has never been presented at any academic institution for the award of any academic qualification and I can assure that any material listed here has been fully cited and referenced. Signature:... Date:.../ /... i

3 APPROVAL This is to certify that this research is the original work of Ssembajjwe Romano and has been done under my supervision. Signature... Date.../.../... Mr. Isaac Mugume Assistant Lecturer Department of Geography, Geo-Informatics and Climatic Sciences. Makerere University. P.O.BOX 7062, Kampala. ii

4 DEDICATION I dedicate this works to my family members and friends especially my mother Ms. Rebecca Nassuna for her great support in my studies financially and morally. iii

5 ACKNOWLEDEMENT The success of this hard work is a result of various people and institution who really gave me well guided support. I would like to send my sincere thanks and appreciation to my lecturers for their support and the assistance, am also grateful to the Meteorology unit at Makerere University and the Department of Geography, Geo-informatics and Climate Science for their great support that they provided to me and other students. I would like to also thank UNMA (Uganda National Meteorological Authority) for having availed the data that I have used for my research. I am grateful to my fellow students for the discussions and the team work that always existed especially when it came to sharing ideas, this was an encouragement to me. Am also grateful to my supervisor, Mr. Isaac Mugume for the guidance and correction he offered to me as I worked on this project. Above all, I thank the almighty God who has guided me and given a good health up to this successful moment. iv

6 TABLE OF CONTENTS DECLARATION... i APPROVAL... ii DEDICATION... iii ACKNOWLEDEMENT... iv LIST OF FIGURES... vii LIST OF TABLES... 1 LIST OF ABREVIATIONS... 2 ABSTRACT... 3 CHAPTER ONE INTRODUCTION BACKGROUND OF THE STUDY STATEMENT OF THE PROBLEM OBJECTIVES OF THE RESEARCH HYPOTHESIS SIGNIFICANCE OF THE STUDY... 6 CHAPTER TWO LITERATURE REVIEW... 8 CHAPTER THREE METHODOLOGY AND DATA STUDY AREA DATA SOURCE METHODS USED IN THE STUDY Data quality control Time Series Analysis CHAPTER 4: RESULTS AND DISCUSSIONS Results on data homogeneity test and quality control Data homogeneity test and quality control Results on temporal variations of rainfall Trends in the December-February (DJF) Rainfall Anomaly Indices Trends in the December-February (DJF) rainfall anomalies for the four stations Mann Kendall Test v

7 Linear Regression Results on the spatial pattern of the December-January (DJF) rainfall Spatial patterns for the December-February (DJF) rainfall totals over the Western Uganda region Spatial patterns for the December-February (DJF) rainfall anomalies over the Western Uganda region Spatial patterns for the monthly December-February (DJF) rainfall anomalies over the Western Uganda region CHAPTER 5: CONCLUSION AND RECOMMENDATIONS Conclusion Recommendations REFERENCES APPENDIX Appendix A: Results of rainfall anomalies for the DJF season over Western Uganda Table A: Computed rainfall anomalies over Kabale district for the DJF season from Table B: Computed rainfall anomalies over Masindi district for the DJF season from Table C: Computed rainfall anomalies over Mbarara district for the DJF season from Table D: Computed rainfall anomalies over Kasese district for the DJF season from vi

8 LIST OF FIGURES Figure 1: Climatologically Homogenous rainfall zones of Uganda Figure 2: A Map showing selected districts with weather stations from the western region Figure 3: Trend of rainfall anomalies for a 35 year period (1980 to 2015) for the four stations.. 21 Figure 4: Trend of the DJF rainfall anomalies for Kabale in a 35 year period (1980 to 2015) Figure 5: Trend of the DJF rainfall anomalies for Masindi in a 35 year period (1980 to 2015).. 22 Figure 6: Trend of the DJF rainfall anomalies for Mbarara in a 35 year period (1980 to 2015).. 23 Figure 7: Trend of the DJF rainfall anomalies for Kasese in a 35 year period (1980 to 2015) Figure 8: Trend of the DJF rainfall anomalies during the El-Nino years ( ) Figure 9: Trend of the DJF rainfall anomalies during the La-Nina years ( ) Figure 10: Trend of the DJF rainfall anomalies during the Normal years ( ) Figure 11: A map of Western Uganda showing the rainfall totals received during DJF ( ) Figure 12: A map of Western Uganda showing the rainfall received during DJF expressed as Rainfall anomalies ( ) Figure 13: A map of Western Uganda showing the monthly rainfall received during DJF expressed as Rainfall anomalies ( ) vii

9 LIST OF TABLES Table 1: The description of the climatologically homogenous rainfall zones in the Western part of Uganda Table 2:Weather stations used in the study Table 3:Homogenity test results for Kabale rainfall station data Table 4: Homogenity test results for Masindi rainfall Station data Table 5: Homogeneity test results of Mbarara rainfall station data Table 6: Homogeneity test results of Kasese rainfall station data Table 7: Summary information of the December-February (DJF) rainfall anomalies for the four stations showing Mann-Kendall test results Table 8: Summary information of the December-February (DJF) rainfall anomalies for the four stations showing Linear Regression test results

10 LIST OF ABREVIATIONS UNMA : Uganda National Meteorological Authority WMO : World Meteorological Organisation DJF : December-January-February SON : September-October-November MAM : March-April-May UBOS : Uganda Bureau Of Statistics GOU : Government Of Uganda SNHT : Standard Normal Homogeneity Test VNR : Von Neumann Ratio ENSO : El-Nino Southern Oscillation IOD : Indian Ocean Dipole NGOs : Non-Governmental Organisations GPS : Global Positioning System OND : October-November-December SOND : September-October-November-December ITCZ : Inter-Tropical Convergence Zone GDP : Gross Domestic Product 2

11 ABSTRACT This study is aimed at assessing the characteristics of rainfall during the months of December, January and February over Western Uganda, it employed the use of statistical analysis of historical rainfall data trends in Western Uganda based on time data series. The study analyzed the monthly rainfall totals from Kabale, Kasese, Mbarara and Masindi synoptic - meteorological stations for a period of 35 years. The meteorological data used for this research was obtained at the Uganda National Meteorological Authority. A homogeneity test was carried out on the data and the data was later expressed inform of normalized values basing on the long term means so as to establish rainfall anomalies. A trends were analysed and their significance was tested basing on the Mann Kendall test and linear regression test and the spatial patterns were visualized using interpolation methods. The seasonal (DJF) trend showed a negative trend for Mbarara (MK(τ) = ) while a positive trend for Kabale (MK(τ) = 0.043), Kasese (MK(τ) = 0.016) and Masindi (MK(τ) = 0.048), however there were fluctuations in trends along the time series though it was not significant. Basing on the above deductions and conclusions drawn from this research, it was not fully ascertaining that there was no trend in the rainfall that occurs during the DJF season in Western Uganda, but there is need for more research to be carried out on other phenomenon that have been identified to have a large influence on the occurrence of rains in the DJF season for example the El Nino Southern Oscillation, Indian Ocean Dipole, Wind patterns and other factors. The results from this study are useful in planning and managing the risks and disasters associated with Climate Change in order to enhance community resilience against the unusual rainfall shortages or floods, for purposes of sustaining National Development of the Country. 3

12 CHAPTER ONE 1.0. INTRODUCTION This chapter presents the background and idea of the study. Section 1.1 gives the background of the study, section 1.2 describes the statement of the problem, section 1.3 lists the objectives of the study, section 1.4 states the hypothesis of the study and section 1.5 describes the significance of the study. 1.1 BACKGROUND OF THE STUDY Historically, the December-February period is known to be the driest season in Uganda (Nimusiima et al., 2013; Basalirwa, 1995; Felix, 2015; Koech, 2014; Monaghan et al., 2012; Nicholson, 1996; Nsubuga et al., 2014; Waiswa, 2015). However, observations indicate more rainfall is received in this season, during certain years. Such unseasonal rains often have far reaching impacts since they occur when least expected. Ntale et al. (2003) noted that during the December-February season, the Inter Tropical Convergence Zone is far to the south outside the East African region and any rainfall in Uganda is associated with regional features, for the March April May season, it is the main rainy season due to the presence of the moist southeast monsoons from the Indian Ocean converging into the Inter Tropical Convergence Zone, the June-August season is relatively dry except in northern Uganda that experiences rains that are as a result of the moist westerly Congo air mass influence (Basalirwa, 1995), the September-November season is associated with the convergence into the Inter Tropical Convergence Zone of the northeast monsoons controlled by subtropical anticyclones over the Azores and Arabian Peninsula. The ITCZ moves from about 15 S to 15 N following the sun with a time lag of about 30 days, and along the line where well-defined winds from the two hemispheres converge they generally form a belt marked by rainfall (Mutai & Ward, 2000). The agricultural sector in Uganda is greatly affected by rainfall, this is one of the main contributors to the livelihoods of the people especially in terms of food and financial income though it contributes about 22% to the Gross Domestic Product (GDP) (UBOS, 2013). Therefore, any drastic differences in any of the rainfall seasons, to which the agriculture sector heavily depends on, affects the country s economy, for example reports on the 1997/98 El Nino rains caused damages and loses to the country at a tune of about US$ 20m (Kaggwa et al., 2009). These 4

13 experiences emphasize the need for timely and accurate climate prediction as well as early warning services to the farmers so as to mitigate against likely disasters that are climate related. It is important to have good understanding of the space-time characteristics of rainfall over Uganda. Western Uganda being one of the four regions in Uganda with a population of 6,298,075 people (UBOS, 2002), 8,874,600 people (UBOS, 2014) and an area of 55,276.6 km 2 was considered in the study. The increased variability of the rains over Uganda have increased the importance and demand for accurate and timely climate prediction and early warning services for protecting human life, infrastructure, ensure food security and household incomes (Magunda et al., 2010). Rainfall patterns in Western Uganda is said to be changing over the past decades and to some extent this rainfall is contributing to frequent occurrence of floods and landslides in this region for example River Nyamwamba has often busted its river banks contributing to floods in Kasese district. 1.2 STATEMENT OF THE PROBLEM Over the time the rainfall pattern has become hard to determine, the initially well know rainfall seasons seemed to be shifting, the known dry months seemed to be getting a little wetter making it hard to determine whether they are still dry or not. Agriculture especially crop farming is dominant especially in the Western Uganda due to the reliability of the rainfall, but also the people here are well adapted to coping with a single rain failure in a particular area, but when successive rainy seasons fail there is simply insufficient water for the proper growth of crops. Communities in Uganda report that the long rains that used to occur between March - August are now beginning as late as May ( Kirkbride & Grahn, 2008). In Uganda most of the studies like those by Nandozi et al. (2012); Bowden & Semazzi (2007); Mutai & Ward (2000); have concentrated on two main rainfall seasons of March-May (MAM) and September/October-November/December (SOND) and very few studies for example Waiswa (2015); Koech (2014) have investigated rainfall characteristics for the dry season of December- February (DJF), yet observations show that some of the heaviest rainfall events have been observed during this season in the recent years. Little is known on the mechanisms associated to the rainfall variability during the months of June- August and December-February. Some studies have link these events with El Nino Southern 5

14 Oscillation (ENSO), while only a few have documented the relationships with other oceanic or atmospheric signals. Meteorologically, forecasting Uganda s weather is complex since it is largely influenced by large-scale tropical controls, which include several major convergence zones, are superimposed upon regional factors associated with lakes, topography and Indian Ocean influence, as a result the climatic patterns are markedly complex and change rapidly over short distances significantly seen with the changing significant changes in seasons (Nicholson, 1996). It is also significant that most of the rains that happen within the dry months of January and July are a result of the Inter-Tropical Convergence Zone (ITCZ). Therefore, this study investigated the spatial and temporal characteristics of the December- February rains over Western Uganda to increase knowledge on the trends and shifts in rainfall so as to determine whether rainfall amounts are shifting or not. 1.3 OBJECTIVES OF THE RESEARCH General objective The general objective of this study is to assess the spatial and temporal characteristics of the rains during the December to February season over Western Uganda. Specific objectives i. To investigate the temporal patterns of rainfall during December to February season over Western Uganda from ii. To establish the spatial variability of rainfall during December to February season over Western Uganda from HYPOTHESIS There was no trend in the rainfall during December to February season over Western Uganda from SIGNIFICANCE OF THE STUDY It is important to understand the space-time characteristics of natural resources like rains, since Uganda normally experiences dry conditions during the December-February season, there are cases when this period gets extreme wet conditions like in the year 1997/98 and 2009/2010 (Koech, 2014; Waiswa, 2015). Advance climate information to farmers (Oludhe et al., 2013) 6

15 is therefore a priority intervention in facilitating farmers to adapt to such scenarios of weather variability, this is because most of the socio-economic activities in Uganda are rain dependent and the occurrences of droughts and floods have been associated with many socio-economic miseries including loss of life, property, animals, crops and house hold incomes. Rainfall influences the performance of agriculture sector that employs many people in the country especially in the Western Uganda. For that reason, studying the characteristics of rainfall especially during the known dry months of December-February will be of importance in both policy planning and implementation of early warning systems as well as developing and managing the agricultural sector, water resources and other rainfall-dependent sectors of the economy, this will enable farmers to grow short term crops like vegetables and harvest water for irrigation and home consumption (Mwale & Gan, 2005). Making use of these waters supports food security in the country hence fostering sustainable development. For example Mubiru et al. (2012) observed that through increased frequency of floods and droughts, the rainfall seasons become more erratic in terms of onset and cessation, causing crop failure and hunger in many households. The increased variability of the rains over Uganda have Magunda et al. (2010) increased the importance and demand for accurate and timely climate prediction and early warning services for protecting human life, infrastructure, ensure food security and household incomes. 7

16 CHAPTER TWO 2.0. LITERATURE REVIEW This chapter focuses on review of what other researchers and scholars have written or published that relates to the study. Uganda s social economic activities are closely influenced by the seasonal rains since the Country is heavily rainfall dependent. Uganda experiences two main rainfall seasons namely March-April-May (MAM) and September-October-December (SON), the Country also experiences two dry seasons that of June-July-August (JJA) and December-January-February (DJF) (Sabiiti, 2008). However the dry season of DJF is the longest and driest over most parts of the Country. Analysis of temporal rainfall indicates that the peak of rainfall occurs mainly in the month of April for the MAM season while for the SON season it is November. The studies also indicate that more rainfall is received during MAM compared to SON (Basalirwa, 1995), severe droughts occur approximately every 3 4 years during the short rains (September-November), every 7 8 years during the hot dry season (December, January, and February), and every 5 8 years during the long rainy season (March-May) (Awange et al., 2008). A study in Nakasongola and Nakaseke by (Nimusima et al., 2013) showed that in the crop, mixed and pastoral communities had noted in the past, the first dry season would fall from mid/end of December - February or early March at most, the study also showed that the second dry season (December to February) is intensifying. According to (Ntale et al., 2003), during the DJF season the ITCZ is far to the south outside the East African region and any rainfall in Uganda and Kenya are associated with regional features. Between December and March, the northeast monsoon brings dry continental air into East Africa leading to low rainfall (Black, 2003), according to (Funk, 2009) the shift in climate has tended to draw moisture away from Africa hence reducing December January rains. Hulme et al., 2001, suggested that under intermediate warming scenarios, parts of equatorial East Africa will likely experience 5-20% increased rainfall from December-February hence climatic changes of this magnitude will have far-reaching, negative impacts on the availability of water resources, food and agricultural security, human health and tourism. 8

17 The convective activity over the Western Uganda region may be attributed to the strong westerly wind which bring moisture from Congo air mass, convective activity over this region may also be attributed to easterly winds which inject moisture from Lake Victoria and Indian Ocean and the highlands of these region also enhance convective development through orographic lifting. A research study carried out by (Stampone et al., 2011) in areas around Kibale National Park, in Western Uganda on trends and variability in localized precipitation for the period , results from investigations on the magnitude, direction and significance of trends of the annual and seasonal rains in the region showed no direction and magnitude of a trend in the annual trends. However there was a significant negative trend in March-May and June-August rains in a number of stations while others had no significant trends. Positive trends in September- November rains occurred at a number of stations. The season December-February had no significant trends. However the data used was of a short periods of 24 years and of long base period of A study at Namulonge Station in Uganda that investigated trends of rains (Nsubuga et al., 2011) for the period 1947 to 2009 showed that there was an increasing trend in monthly rainfall amounts during the September, December and January especially in the recent years of , the DJF season also registered a positive trend. In a study carried out in South Western Uganda for the period analyzing rainfall trends (Nsubuga et al., 2014) especially for stations Kabale, Masindi, and Mbarara. Results from the study revealed that that some stations like 6.8% of the 58 stations under experienced positive significant trends in DJF rain. However based on a regional rainfall index, there is a positive increase in the rainfall amounts for the DJF period. 9

18 CHAPTER THREE 3.0. METHODOLOGY AND DATA This chapter presents the methods and source of data used to carry out the statistical analysis of the DJF rainfall characteristics over Western Uganda. 3.1 STUDY AREA The study was carried out over Western Uganda, with reference to Kabale, Kasese, Masindi, and Mbarara Districts, these districts were chosen because they have weather stations that are representative of these different climatic zones especially according to the climatologically homogenous rainfall zones of Uganda (Basalirwa, 1995). Figure 1: Climatologically Homogenous rainfall zones of Uganda according to Basalirwa (1995) 10

19 Below is a description of the climatological rainfall zones in the study area, Table 1: The description of the climatologically homogenous rainfall zones in the Western part of Uganda Climatologically homogenous rainfall zones CW MW ME L Representative station Kabale Kasese Mbarara Masindi The study focused on DJF rainfall over Western Uganda for a period of 35 years. Masindi district is located in the mid-west part of Uganda approximately 217 km from Kampalawest at coordinates 1 o N, 31 o E with a population size of 459,490 in 2002 (UBOS, 2002) and 292,951 in 2014 (UBOS, 2014b), and it borders Buliisa district in the Northwest, Nakasongola, Kiboga and Nakaseke district in the Southwest, Hoima district in the Southwest, Apac district in the East, Oyam district in the North East, Amuru district in the North and the Democratic Republic of Congo in the West. The district comprises of a total area of about 5,000 sq.km. Masindi has a favourable climate and its rainfall pattern is bimodal. The district receives an annual long-term average rainfall of 1,304 mm, with area of Budongo, Pakanyi, Karujubu and Nyangahya sub- counties receiving more than 1000 mm per annum, Kigumba, Kiryandongo and Miirya subcounties receive mm per annum and Masindi Port, and Kimengo sub-counties receive less than 800 mm per annum. 11

20 Kabale district is located in the Southwestern part of Uganda at coordinates 1 o S, 30 o E with a population size of 458,318 in 2002 (UBOS, 2002) and 534,160 in 2014 (UBOS, 2014b) and it boarders Kisoro district in the West, Bushenyi in the North and Rwanda in the South. Kable district mean annual rainfall varies from mm. Kasese district is located in the Western part of Uganda at coordinates 0 o S, 30 o E with a population size of 523,033 in 2002 (UBOS, 2002) and 702,029 in 2014 (UBOS, 2014b) and it boarders Kabarole district to the North, Kamwenge district to the East, Rubirizi district to the South and Democratic Republic of Congo to the West. Its mean annual rainfall is 1,475 mm. Mbarara district is located in the Western part of Uganda at coordinates 0 o S, 30 o E with a population size of 1,088,356 in 2002 (UBOS, 2002) and 474,144 in 2014 (UBOS, 2014b) and it boarders Ibanda district to the North, Kiruhura district to the East, Sheema district to the west and Isingiro district to the south. Its mean annual rainfall is 1,200 mm. 12

21 Figure 2: A Map showing selected districts with weather stations from the western region. 3.2 DATA SOURCE Secondary data of rainfall amounts will be used in this study to investigate the spatial and temporal characteristics of the December to February rains over Western Uganda. The data used in the study included the monthly rainfall records of 4 stations distributed over the Western Uganda region within the period of Rainfall data was obtained from the Uganda National Meteorological Authority (UNMA). Monthly totals of rainfall data recorded at 4 synopticmeteorological stations were used covering the period Average record length of the stations is 35 years. Table 2: Weather stations used in the study Station No. Stations Latitude ( ) Longitude Elevation Period N/S ( ) E (m) (years) 1 Masindi Kabale Kasese Mbarara The Climatology of the Western Uganda region The Western area is located to the Northwest and west of Lake Victoria in Uganda. The area is traversed by the equator, has a number of water bodies, and has varying topography. It is also characterized by a dense network of rivers, which draw its water from the different water catchment areas into the main water bodies. The major climate parameter which has the highest space-time variation over Western Uganda is rainfall. Rainfall determines the spatial patterns of the natural resources and land use activities. Basalirwa. (1995) outlined the major systems which control the space-time characteristics of rainfall in Uganda. These systems include the intertropical convergence zone (ITCZ), subtropical anticyclones, monsoonal winds, moist 13

22 westerlies from Congo, regional features like the large water bodies, and topographic features. These features introduce significant modification in the general wind patterns over the region, coupled with convection processes that generate a climatic pattern, which is complex and changes rapidly over short distances (Nicholson, 1996). These climatic processes result into a bimodal pattern of rainfall, with the primary rainy season occurring in March-May and a shorter secondary rainy season occurring in September/October-November/December (Phillips & McIntyre, 2000). 3.3 METHODS USED IN THE STUDY This section describes the various methods were used to achieve the specific objectives. They include data quality control, time series analysis and graphical displays. These methods are further described in the subsequent sections. The study employed quantitative methods and the tools used in the study are presented in the sections that follow: Data quality control In order to make valid inferences from the analysis of observed data, its important to first ascertain its quality before its subjected to further analysis. Data quality control is the careful scrutiny of the completeness and consistency of observed data sets. Since there is a possibility of finding missing records within the rainfall dataset, estimation of missing data and homogeneity test were therefore used as data quality control checks, this was done using the Standard Normal Homogeneity Test (SHNT), this test is performed on a ratio or difference series between the candidate station and a reference series (Jaagus & Ahas, 2000) and Von Neumann ratio test (VNR), this assumes that data series are not randomly distributed (Sahin & Cigizoglu, 2010) Time Series Analysis A time series is an orderly set of observations that are collected at equal time intervals (Mörchen & Ultsch, 2007). It should be continuous in time so that representative characteristics of its past are defined therefore any data gaps must be filled. A time series analysis consists of a stochastic trend and seasonal variations (Cleveland & Tiao, 1976). The present study employed the trend analysis. 14

23 Trend analysis A trend refers to a long term movement of time series (Easterling et al., 2000). In this study, trend analysis will be carried out using the representative stations within the rainfall homogenous zones over Western Uganda. Several methods are available to describe trends in climatological data. These methods include; graphical and statistical methods (Helsel & Hirsch, 1992). The graphical methods were employed and involved plotting of rainfall data series using the RStudio-software, XLSTAT and MATLAB R2015a software. The time series were be smoothed using a long term means which were later used in calculating the rainfall anomalies. Calculation of monthly DJF seasonal rainfall anomalies equations were adopted from (Mugume et al., 2016; Shao-e & Bing-fang, 1997) Where R a are the Rainfall anomalies R a = R R R where n R = 1 n R i i=1 Where R is the Long Term Mean of the period, n is 1 to 35 years, i = 1, 2,..,n. The period in this study is December, January and February, and the term is from 1980 to 2015, so n = 35. Using rainfall data computed from equations put forward by Awange et al. (2008), seasonal means were obtained by averaging the monthly totals for three months of a particular season, i.e. 3 S m = 1 3 M t m=1 Where S m is the seasonal mean and M t are the total monthly rainfall anomalies Mann-Kendall trend test method The Mann-Kendall test was performed to evaluate the trend of rainfall totals. Approaches used for detecting trend in the time series can be either parametric or non-parametric. The most popularly used non-parametric test is the Mann Kendall (MK) test (Mann 1945; Kendall 1955). Several 15

24 studies like (Kizza et al., 2009; Ngongondo et al., 2011) have widely used it for different climatic variables in various trend studies since it does not necessarily require data to be normally distributed, it also has low sensitivity to abrupt breaks due to in-homogeneous time series and is not affected by outliers. The Mann Kendall test at a significance level of 5% was applied to study the temporal trends of the following: a) Seasonal (December-January-February) total rainfall b) Seasonal (December-January-February) average rainfall anomalies. For the purpose of cross-verification, a linear regression test was carried out to identify whether there was a linear trend by examining the relationship between time and rainfall. Khambhammettu (2005) defined Mann-Kendall statistic (S) as; Let x 1, x 2. x n represent n data points where x j represents the data point at timej. Then the Mann-Kendall statistic (S) is given by. n 1 i=0 n j=i+1 ) S = sign(x j x i 1 if x j x i > 0 where; sign(x j x i ) = { 0 if x j x i = 0 1 if x j x i < 0 Where x j and x i are the annual values in years j and i respectively. A very high positive value of S is an indicator of an increasing trend, and a very low negative value indicates a decreasing trend. However, it is necessary to compute the probability associated with S and the sample size, n, to statistically quantify the significance of the trend. A normal-approximation test that could be used for datasets with more than 10 values, provided there are not many tied values within the data set. The test procedure is by calculating the variance of S, VAR(S), using this equation: VAR(S), σ 2 = 1 18 [n(n 1)(2n + 5) t p(t p 1)(2t p + 5)] g p 1 16

25 Where n is the number of data points, g is the number of tied groups (a tied group is a set of sample data having the same value), and t p is the number of data points in the pth group. Compute a normalized test statistic Z as follows, the test statistic is then given by the standard Gaussian value, Z defined as: s 1 if s > 0 VAR(S) Z = 0 if s = 0 s+1 { if s < 0 VAR(S) The test statistic Z is used a measure of significance of trend thereby testing the null hypothesis, Ho. If Z is greater thanz, where α represents the chosen significance level (e.g. 5% with Z = 1.96) then the null hypothesis is invalid. If the p-value is equal to the level of significance, then there a small trend and it is less significant. Rejecting Ho indicates that there is a trend in the time series, while accepting Ho indicates no trend was detected. On rejecting the null hypothesis, the result is said to be statistically significant Linear regression Analysis Linear regression was used to show the relationship between the years and rainfall anomalies. Linear regression describes the linear relationship between two variables, say x and y. Conventionally the symbol x is used for the independent, or predictor variable, and the symbol y is used for the dependent variable (Wilks, 2006). Linear regression estimates the regression coefficients β o and β 1 in the equation, Y j = β o + β 1 X j + ε j where X is the independent variable Y is the dependent variable, β o is the Y intercept β 1 is the slope, ε is the error A trend is a gradual increasing or decreasing change over time and is usually associated with cumulative natural phenomenon such as rainfall amounts received over an area during a particular period of time (Huntington et al., 2009). This was done by analyzing changes in rainfall with 17

26 reference to years at a given district in which the station is located. Linear rainfall trend and the coefficient of determination (r²) were computed from the regression model Spatial pattern Analysis Inverse Distance Weighting (IDW) The Inverse Distance Weighted tool uses a method of interpolation that estimates cell values by averaging the values of sample data points in the neighborhood of each processing cell (Snell et al., 2000; Daly et al., 2008; Di Luzio et al., 2008). The closer a point is to the center of the cell being estimated, the more influence, or weight, it has in the averaging process (Liu, 2008), the different rainfall datasets were used as a basis of surface averaging of rainfall over Western Uganda using the Arc GIS 10.1 software. 18

27 CHAPTER 4: RESULTS AND DISCUSSIONS This chapter presents the findings and their discussions from the analysis. This chapter explains the temporal patterns and spatial variations in western Uganda for the four stations individually that is to say Kabale, Masindi, Mbarara and Kasese Results on data homogeneity test and quality control. This section presents homogeneity results from two homogeneity tests that is the SNHT and VNR tests. Table 3: Homogeneity test results for Kabale rainfall station data. Station Test variables SNHT VNR Kabale Value Time of change (t) 1983 Does not give time of change p-value (twotailed) Result Data are homogeneous Data are homogeneous Table 4: Homogeneity test results for Masindi rainfall Station data. Station Test variables SNHT VNR Masindi Value Time of change 1985 Does not give time of change (t) P-value (twotailed) Result Data are homogeneous Data are homogeneous 19

28 Table 5: Homogeneity test results of Mbarara rainfall station data. Station test variables SNHT VNR Mbarara Value Time of change 2013 Does not give time of change (t) P-value (twotailed) Result Data are homogeneous Data are homogeneous Table 6: Homogeneity test results of Kasese rainfall station data. Station test variables SNHT VNR Kasese Value Time of change 1986 Does not give time of change (t) P-value (twotailed) Result Data are homogeneous Data are homogeneous Data homogeneity test and quality control. The monthly rainfall data was sorted and arranged in XLSTAT and was found to be fully complete without missing data. A homogeneity test was carried out on it using the Standard Normal Homogeneity Test and Von Neumann ratio test. DJF rainfall anomalies averages for each of the four stations were used to test for homogeneity. These tests were examined at significance level of 5 % (alpha=0.05) and tables 3, 4, 5 and 6, illustrate results from these homogeneity tests. From the tables it is observed that data series from these four stations that were used in this study are homogeneous. 20

29 4.2. Results on temporal variations of rainfall. The main objective of time series plots are to detect the trends in time series so as to establish whether there is statistically significant evidence of increasing or decreasing trend at 0.05 confidence level Trends in the December-February (DJF) Rainfall Anomaly Indices. Figure 3: Trend of rainfall anomalies for a 35 year period (1980 to 2015) for the four stations. Figure 3 shows fluctuations in increase and decrease in rainfall, however, there is no significant change in the trend. The high rainfall peaks were generally in 1989/90, 1990/91, 1992/93, 1997/98, 2009/10, 2012/13 all these years had rainfall anomalies above /98 received the highest rainfall anomaly of 5. The DJF season of the region does not seem to be getting wetter nor is it getting drier. 21

30 Trends in the December-February (DJF) rainfall anomalies for the four stations Kabale DJF rainfall anomalies Figure 4: Trend of the DJF rainfall anomalies for Kabale in a 35 year period (1980 to 2015). Figure 4, shows that the trend of rainfall is not significant from 1980 to The high rainfall was received during the period 1985/86, 1997/98, 2009/10 all these years had rainfall anomalies above 3. The lowest rainfall was received during the period 1981/82, 2011/12. The DJF season of Kabale does not seem to be getting wetter nor is it getting drier Masindi DJF rainfall anomalies Figure 5: Trend of the DJF rainfall anomalies for Masindi in a 35 year period (1980 to 2015). 22

31 Figure 5, shows that the trend of rainfall is not significant from 1980 to The high rainfall was received during the period 1990/91, 1992/93, 1997/98, 2009/10, 2012/13 all these years had rainfall anomalies above 3. The lowest rainfall was received during the period 1982/84, 1994/95, 1998/99, 2001/02. The DJF season of Masindi does not seem to be getting wetter nor is it getting drier Mbarara DJF rainfall anomalies Figure 6: Trend of the DJF rainfall anomalies for Mbarara in a 35 year period (1980 to 2015). Figure 6, shows that the trend of rainfall is not significant from 1980 to The high rainfall was received during the period 1989/90, 1994/95, 1997/98, 2009/10 all these years had rainfall anomalies above 3. The lowest rainfall was received during the period 1987/88, 1988/89, 1998/99, 2004/05, 2005/06, 2013/14. 23

32 Kasese DJF rainfall anomalies Figure 7: Trend of the DJF rainfall anomalies for Kasese in a 35 year period (1980 to 2015). Figure 7, shows that the trend of rainfall is not significant from 1980 to The high rainfall was received during the period 1989/90, 1992/93, 2012/13, all these years had rainfall anomalies above 3. The lowest rainfall was received during the period 1984/85, 2014/ El-Nino Years DJF rainfall anomalies Figure 8: Trend of the DJF rainfall anomalies during the El-Nino years ( ). 24

33 Figure 8, shows that the trend of rainfall is not significant from 1980 to El-Nino years were identified and categorized into strong, moderate and weak, the strong El-Nino during this season was 1986/87, 1990/91, 1997/98, 2009/10, 2015/16, the moderate El-Nino period was 2003/04 and the weak El-Nino period was 1985/86, 2005/06, 2002/03, 2004/ La-Nina Years DJF rainfall anomalies Figure 9: Trend of the DJF rainfall anomalies during the La-Nina years ( ). Figure 9, shows that the trend of rainfall does not seem significant from 1980 to The La- Nina years were identified and categorized into strong, moderate and weak, the strong La-Nina during this season was 1987/88, 1998/99 the moderate La-Nina period was 1999/00, 2007/08, 2010/11 and the weak La-Nina period was 1983/84, 1984/85, 1995/96, 2000/01, 2011/12. 25

34 Normal Years DJF rainfall anomalies Figure 10: Trend of the DJF rainfall anomalies during the Normal years ( ). Figure 10, shows an increase in rainfall amounts from 1980 to The highest amount were received during the period 1989/90, 1992/93, 2012/13 and the lowest rainfall was received during the period 1980/81. In general decreasing and slow increase in rainfall showed clear evidence of climate change signals over Western Uganda. These changes have negative impacts on the socio-economic sectors such as Forestry, Transportation, Energy, Water Resources, Agriculture and Food Supply. The productivity of forests, agriculture, and wildlife could be affected by precipitation Mann Kendall Test Table 7 below represents a summary of objective rainfall variables which were computed based on observational rainfall data for the four stations. Table 7: Summary information of the December-February (DJF) rainfall anomalies for the four stations showing Mann-Kendall test results. Station Mann Kendall Statistic Kendall's p-value (two Sen's slope (Q) Test (S) Tau tailed test) Interpretation 26

35 Kabale Accept Ho Masindi Accept Ho Mbarara Accept Ho Kasese Accept Ho From Table 7, in this section, Mann-Kendall tests provide the results of the non-parametric Mann- Kendall test at a 5 % confidence level for the DJF rainfall anomalies. These were subjected to the Mann-Kendall test for each station. On running the Mann-Kendall test on rainfall data, the following results in table 7, were obtained for the four stations. If the p -value is less than the significance level α (alpha) = 0.05, Ho is rejected. Rejecting Ho indicates that there is a trend in the time series, while accepting Ho indicates no trend was detected. On rejecting the null hypothesis, the result is said to be statistically significant. From the table it can be observed that the Null Hypothesis was accepted for all the four stations. Table 7 also indicates a negative trend in the DJF rainfall anomalies for Mbarara stations and a positive trend for Kabale, Masindi and Kasese stations. It is observed that there is no significant trend observed for the stations during the DJF rainfall season over the study period. Sen. s estimator of slope, following the Mann Kendall test, was employed to figure out the change per unit time of trends observed for all rainfall time series. Outputs are presented in Table 7, where a positive sign indicates an upward slope and a negative sign represents a downward slope, Sen. s Slope method also indicated the slope magnitude for the four stations Linear Regression Table 8: Summary information of the December-February (DJF) rainfall anomalies for the four stations showing Linear Regression test results. Station R 2 P-value t-stat Test Interpretation Kabale Accept Ho Masindi Accept Ho Mbarara Accept Ho Kasese x Accept Ho 27

36 From table 8, it is observed that there is no significant change in the rainfall anomalies for the four stations from December to February hence no trend detected in the DJF rainfall season, it is also noted that the significant changes in the rainfall anomalies averages are contributing to the alternating increase, decrease and constant trend detected over the four station during the DJF season. These results are in line with those of earlier researchers like Waiswa (2015) Results on the spatial pattern of the December-January (DJF) rainfall. Under this section, results of spatial patterns of rainfall are presented. The spatial characteristics of rainfall have been examined for the period December-February (DJF) rainfall anomalies Spatial patterns for the December-February (DJF) rainfall totals over the Western Uganda region. Figure 11: A map of Western Uganda showing the rainfall totals received during DJF ( ). 28

37 Figures 11 represent the spatial distribution of rainfall received over the Western Uganda region during the DJF season. The results indicate that the rainfall distribution in over Western Uganda varies greatly with time and it varies between slight and heavy. This indicates that spatial patterns of rainfall distribution should be considered when allocating projects like agriculture and farming, mining and industrialization in Western Uganda. The spatial distribution of rainfall totals (Figure 11) have been classified into six classes to group the vast area into few classes for analysis purpose, such as very low, low, moderate, high, higher and highest amount of rainfall. From the figures it can be observed that the southeastern parts of the Western Uganda receive higher rainfall as compared to the western and extreme southwestern areas that receive moderate rainfall. This could be due to the influence of the large water body (Lake Victoria) in the south, the highland areas in the southwestern part of the region that create an orographic effect and the influence of the Congo air mass Spatial patterns for the December-February (DJF) rainfall anomalies over the Western Uganda region. Figure 12: A map of Western Uganda showing the rainfall received during DJF expressed as Rainfall anomalies ( ). 29

38 Figure 12 represent the results for the changes in rainfall patterns expressed as rainfall anomalies over the Western Uganda region. This is essential in determining the change in magnitude of rainfall over Western Uganda. It displays that during this period, the rainfall anomaly in the study area is generally negative, except portions of areas that are next to the weather stations, which shows positive rainfall anomaly. Overall, Kasese, Masindi and Mbarara seemed to be getting moderately wet while Kabale seemed to be getting moderately dry during this season (Fig. 12). 30

39 Spatial patterns for the monthly December-February (DJF) rainfall anomalies over the Western Uganda region. Figure 13: A map of Western Uganda showing the monthly rainfall received during DJF expressed as Rainfall anomalies ( ). 31

40 The monthly rainfall distribution maps show that the major rainfall patterns are generally oriented west to southeast, with maxima and strong rainfall gradients located along the western and southern parts of the basin (Fig. 13). During the month of December and January the area receives higher rainfall than during February. Generally, highest rainfall pattern is observed in the western boundary of the study area. However, in the month of January, the pattern changes from western boundary to the eastern side, then in February the pattern changes back to the Western side. This anomalies shift of rainfall zones could be clearly identified from the maps (Fig. 13). Overall, during the month of December, Kasese seemed to be experiencing wet conditions, Kabale and Mbarara seemed to be experiencing moderately wet conditions while Masindi seemed to be experiencing moderately dry conditions, during the month of January, Mbarara and Masindi seemed to be experiencing more wet conditions while Kasese seemed to be experiencing moderately dry conditions and Kabale seemed to be experiencing moderately dry conditions, during the month of February Kasese seemed to be experiencing more wet conditions, Mbarara and Masindi seemed to be experiencing moderately wet conditions while Kabale seemed to be experiencing moderately dry conditions (Fig. 13). 32

41 CHAPTER 5: CONCLUSION AND RECOMMENDATIONS This Chapter contains the main conclusion made from the results of this study. In addition it also provides some recommendations to facilitate research studies in the future Conclusion. The results obtained show a slow increase in the frequency of rainfall during the DJF seasons over Western Uganda. The highest amounts of DJF rains were found to occur mainly during periods of El Niño years. This also implies that incase of increased intensification of climate change, this is likely increased the frequency of El Nino and hence we are most likely to continue having more rains during this DJF season. In the present study, the spatial and temporal characteristics of the DJF rainfall over Western Uganda have been analyzed. The analysis part was done using several methods and tools that included the Standard Normal Homogeneity Test and Von Neumann ratio for data homogeneity test, graphical linear trend method, Mann-Kendall test and Sen. s slope estimation test for temporal trend detection in rainfall and Linear regression. Further, the spatial distribution and variation in the DJF rainfall anomalies were determined by the Inverse Distance Weighting interpolation method using the ArcGIS 10.1 software. Although DJF season is usually a dry season, this season also experienced some extreme wet spells especially during the periods of 1997/98 and 2009/10. Most parts of the country experienced above normal rains in these year. From the analysis, increasing and decreasing rainfall trends were observed in the DJF season over Western Uganda, variations were also observed in the rainfall received over Western Uganda. These results indicate that spatial variations should also be considered when allocating land for rain-fed projects like agriculture Recommendations. In relation to the findings of the study and the relevance of utilizing these findings so as to enhance sustainable development activities in Western Uganda, the study recommends the following: Government and the relevant development partners like NGOs should set up training programs to different stakeholders that rely generally on rain-fed agriculture for example sensitizing them on how to benefit from the changing dynamics of the DJF rains so as to promote sustainability and 33