Rainfall Trends and Variability in Ibadan, Oyo State

Rainfall Trends and Variability in Ibadan, Oyo State

CHAPTER ONE

AIM AND OBJECTIVES OF THE STUDY

The aim of this study is to investigate the changes in rainfall over Ibadan from 1982 to 2011. The specific objectives include:

To determine the monthly variation in rainfall from 1982 to 2011
To determine the annual variation in rainfall variation from 1982 to 2011.
To determine the biannual variation in rainfall from 1982 to 2011.
To predict the rainfall in Ibadan for the next 30 years.

CHAPTER TWO

CONCEPTUAL AND THEORETICAL FRAMEWORK AND LITERATURE REVIEW

INTRODUCTION

This chapter has been divided into two main sections which include the relevant concept and exiting empirical studies on rainfall trend and rainfall variability. The most relevant concept for this study is the concept of climate change which will be explained in the subsequent section. The literature review focused on studies on rainfall trends, variability and climate change.

CONCEPT OF CLIMATE CHANGE

Climate change is a change in the statistical distribution of weather patterns when that change lasts for an extended period of time (i.e., decades to millions of years). Climate change may refer to a change in average weather conditions, or in the time variation of weather around longer-term average conditions (i.e., more or fewer extreme weather events). Climate change is caused by factors such as biotic processes, variations in solar radiation received by Earth, plate tectonics, and volcanic eruptions. Certain human activities have also been identified as significant causes of recent climate change, often referred to as “global warming. There is now a strong global consensus that climate change presents an urgent challenge to human welfare and sustainable development (Anyadike, 2009 et al). Climate change is seen as statistically significant variations that persist for an extended period, typically decades or longer and includes shifts in the frequency and magnitude of sporadic weather events as well as the slow continuous rise in global mean surface temperature (IPCC, 2001 & 2007). According to Anyadike (2009), there is no such thing as a “normal” or average climate but as the weather changes from day to day, so also climate changes from year to year. Climate change is that change in climate that continues in one direction at a rapid rate and for an unusually long period of time, lasting for several years.

LITERATURE REVIEW

Rainfall Variability And Trends Around The World

The knowledge of climate variability over the period of instrumental records and beyond on different temporal and spatial scale is important to understand the nature of different climate systems and their impact on the environment and society (Oguntunde et al. 2012). Most of the observational and numerical simulation studies on climate are based on the instrumental records of about a century which are aimed at the understanding of the natural variability of climate system and to identify processes and forces that contribute to this variability. This is essential if we are to predict global and regional climate variations, determine the extent of human influence on the climate and make sound projections of human induced climate change. The climate of a location can be understood most easily in terms of annual or seasonal averages of temperature and precipitation. The global climate has changed rapidly with the global mean temperature increasing by 0.7^o C within the last century (IPCC 2007). However, the rates of change are significantly different among regions (IPCC 2007). This is primarily due to the varied types of land surfaces with different surface albedo, evapotranspiration and carbon cycle affecting the climate in different ways (Meissner et al. 2003; Snyder et al. 2004).

CHAPTER THREE

RESEARCH METHODOLOGY

INTRODUCTION

The methodology used for this study consists of all procedures that were used in selection, collection and analysis of the data. This chapter consists of the type of data used for this study along with the data collection and the method of data analysis that was used in carrying out the analysis.

TYPE OF DATA AND DATA COLLECTION

The type of data used in this study is a secondary data. It was gotten from the Nigerian Meteorological Agency (NIMET). The most recent rainfall data available from NIMET was 2011. The secondary data collected for Ibadan’s rainfall data was therefore from 1982 to 2011 (See Table 1).

METHOD OF DATA ANALYSIS

The method of data analysis that was used descriptive statistics and inferential statistics. The descriptive statistics included Mean and Standard Deviation. While the inferential statistics included Linear egression Mean was used to determine the monthly variation in rainfall within the study area. An average rainfall graph was drawn to help explain this monthly variation using monthly mean of the rainfall. Biannual rainfall variation was also determined using a chart to show the variation in rainfall amount by determining the mean rainfall every 2 years then plotting in a map. Furthermore, the Time Series was used to analyze annual rainfall trend over time (Terence, 2006). This study employs the use of the 5-Year Moving Average. The moving average has the characteristics of reducing the amount of variation in a set of data. This property in the time series is used mostly to remove fluctuations that are not needed. The use of moving average result in the formation of new series in which each of the actual value of the original series is replaced by the mean of itself and some of the values immediately preceding it and directly following it Ayoade (2008).

Linear regression Analysis was used to estimate the value of a variable Y (i.e. rainfall), corresponding to a given value of a variable X (i.e. time). This was accomplished by estimating the value of Y from a least-square curve that fits the sample data. The resulting curve is a regression curve. This type of analysis involves predicting a dependent variable (Y) by one independent variable (X).

CHAPTER FOUR

VARIATIONS IN RAINFALL IN IBADAN

RAINFALL VARAITION

Rainfall data of 30 years (1982-2011) for the study area was subjected to various statistical techniques to arrive at valuable results. The Time Series analysis was used to determine the trend in total rainfall over time for the weather station in the study area, and the 5-year moving average was used to smoothen out the variations present in the data sets. The results of this study show that rainfall variations and trends in Ibadan is worthy of close examination, as its variations over time is remarkable. Figure 4.1 shows the trend of annual total rainfall for the periods 1982 – 2011, while figure 4.2 is the 5-Year Moving Average graph for total rainfall. The 5-year moving average aims at smoothing out the sharp unevenness and variability.

CHAPTER FIVE

SUMMARY, RECOMMENDATION AND CONCLUSION

SUMMARY

This study was carried out to investigate rainfall trends and variability in Ibadan, Oyo States from 1982-2011 with the following objectives: (1) to determine the monthly variation in rainfall from 1982 to 2011, (2) to determine the annual variation in rainfall variation from 1982 to 2011, (3) to determine the biannual variation in rainfall from 1982 to 2011, (4) to predict the rainfall in Ibadan for the next 30 years. Hypotheses were also formulated and tested based on the objectives of the study. Secondary rainfall data (1982-2011) gotten from the Nigerian Meteorological Agency (NIMET) was used for the analysis. The main statistical analysis used to test the hypothesis included charts, scatter plot, linear regression, analysis of variance(ANOVA) and the paired simple T-test.

The statistical analysis and findings can be summarized as follows:

The monthly rainfall pattern indicates that the highest average rainfall of 124.83mm was recorded in the month of June. This means that June was the wettest month in study area. This was followed by September, July and August with average rainfall of 114.33mm, 107.67mm and 101.37mm respectively. The lowest average rainfall of 17.52mm was recorded in January and this shows that January was the driest month. The total mean monthly rainfall for the study area was 78.3825mm, which indicates that January, February, March, April and November fall below the total mean monthly rainfall while May, October and December are slightly above total mean monthly rainfall of the study area.
Rainfall in Ibadan has been highly varied. The mean annual rainfall for the area was 1348.563 mm; with a mean minimum of 0.1 mm in 1996 with a mean maximum of 411.3mm in 1987. Total rainfall of the area was characterized by two distinct peaks: one in 2007 (1745.8mm) and the other (highest) peak in 1999 (1814.9mm). This shows that these years were the wettest years within the period under study (1982-2011). In general, total rainfall of the area increased in the period 1984-1997 and declined in 1998. There was a sharp increase after 1999, with a sharp decline in 2000and rose again between 2003-2004, which continued a rise and fall till about 2011, with another sharp decline in 2007. Within this period there was an interesting variation of rainfall as it experienced a rise and fall. The total rainfall continued to increase and decline in the period 1982-2011 with an overwhelming sharp rise in 1999. Rainfall amount in the period of study has shown a slight rise since 1982.
The total rainfall with the study area shows a distinct variation both for the first half and second half of the yea. The graph shows that the second half of the (in red lines) have greater amount of rainfall annually with two distinct peaks of 1109.8mm (highest) in 1999 and 1095.6mm (lowest) in 2010. The first half of the annual rainfall also has an interesting variation with the highest peak experienced in 2003 (801.2mm)
The result of the thirty year prediction shows a constant increase in the annual rainfall from 2012-2014. This result is not significant. This is because the P value is greater than 0.05. The table also shows that b₁ which is 13.990 has a positive value, indicating that the predicted rainfall is rising with time though not significant. We can therefore conclude that the hypothesis which states that there is an upward trend in the rainfall variability for the next 30 years is accepted though this upward trend is not significant
TheAnova was used to test for the first hypothesis which states that there is a significant difference in the monthly variation of rainfall in Ibadan from 1982-2011. Within the various months the sum of squares of all the rainfall amount is 1194026.657 (mean square of 3927.19.180) which is less than the sum of squares between groups (1346120.243) with a mean square of 122374.568. This implies that there is a larger difference in the variation of rainfall between the amount of rainfall in the various months in Ibadan than within the amounts in the different months. In essence it says that the amount of monthly rainfall shows large differences in the study area. This is supported by the significant level (P) of 0.001 (P<0.05), showing that there is a significant difference in the monthly variation of rainfall in Ibadan from 1982-2011.
The Hypothesis which states that there is a significant difference in the monthly variation of rainfall in Ibadan from 1982-2011 is accepted. Therefore H₁ is accepted and H₀ is rejected. It can then be concluded that there is a significant difference in the monthly variation of rainfall in Ibadan from 1982-2011.
In the table showing the descriptive statistics of the paired sample t-test for the fourth hypothesis which states that there is a significant difference in the biannual variation of rainfall in Ibadan from 1982-2011, the Annual rainfall (first half) represents the mean of all rains recorded between January and June whereas Annual Rainfall (2^nd half) represents the mean of rainfall recorded between July and December. The table shows that the mean of the second half (7.4848E) of the year is higher the 1^st half (6.0008E2) of the year. This result agrees with the biannual variation. This result showed whether these differences are significant. The t value is showed a difference of -3.884 at a significant of 0.001.this result is significant because P is < 0.05, indicating that it is statically significant. Therefore, we can conclude that there is a significant difference in the biannual variation of rainfall in Ibadan from 1982-2011.we then accept H₁ and reject.

RECOMMEDATION

We can conclude that the hypothesis which states that there is an upward trend in the rainfall variability for the next 30 years is accepted though this upward trend is not significant. Hence, there would be an increase in the amount of rainfall variability in the next few years and this is going to affect agricultural practices across Ibadan and could eventually lead to increase in cost of food. I recommend that the government helps with agricultural solutions (like irrigation, production of drought resistant seeds, etc) that would help farmers cope within impending rainfall variability.

CONCLUSION

The purpose of this study is to observe the variations in rainfall in Ibadan from 1982 to 2011. After this study, I conclude that the rainfall variability trend is going to occur with an upward trend and it’s better to begin to prepare for it as it looks inevitable now.

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