A TIME SERIES ANALYSIS OF THE MONTHLY DISTRIBUTION OF RAINFALL IN ENUGU METROPOLIS.

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ABSTRACT
It is widely accepted that water supply will be a pressing issue in this century. Thus, position of adequate rainfall in the development of human and natural resources is a worthwhile research work. The data used in this project work was monthly amount of rainfall in Enugu city within the period of (2000- 2012). A preliminary inspection on the data revealed that the data has no trend but consist of multiplicative seasonal movements. Furthermore, the monthly data was also found to be stationary and serially uncorrelated by the Augmented Dickey Fuller test of unit root and the Autocorrelation test for serial correlation of the error term respectively.

The exponential smoothing procedures were adopted for the construction of the best fit model for the prediction of future rainfall pattern in Enugu. This was achieved by algorithms aimed at smoothing out all irregular components inherent in the series. The best fit model parameters were used to predict monthly rainfall distribution for 2013. The result suggested heavy rainfall in general for the year in question with its amplitude in the month of October.

CHAPTER ONE

INTRODUCTION

BACKGROUND

A time series is a set of observations obtained by measuring a single variable regularly over period of time most forecasting problems involve the use of time series data. Or a time series is a time-oriented or chronological sequence of observations on a variable of interest (Akaike, H. (1974)). In a series of inventory data, for example, the observations might represent daily inventory levels for several months. A series showing the market share of a product might consist of weekly market share taken over a few years. A series of total sales figures might consist of one observation per month for many years. What each of these examples has in common is that some variable was observed at regular, known intervals over a certain length of time.

  1. 1.2 Background of the problem

World book Encyclopedia (2011) defined rain as a precipitation that consists of drops of water, almost all precipitation is rain. But in the highland area Antarctica all precipitation is snow. Interactions between the various components of the climate system such as the oceans, land and atmosphere have brought about climate change. This is characterized by rainfall variability which brings with it negative impacts to the countries’ economies. Forecasting of severe weather and extreme climate events is one of the major challenges facingMeteorological services worldwide especially inAfrica, example Kenya where we have been experiencing severe droughts and Floods in 1991-2011. This has led to negative social-cultural, physical, economic and environmental effects. Researchers have made enormous efforts in addressing the issue of accurate rainfall predictability including the use of numerical and statistical methods (Brockwell, P.J. and Davis, R.A. 1991). Time series analysis provides great opportunities for describing, explaining, modeling and predicting climatic variability and impacts. To understand the meteorology information   and integrate it into planning and decision making process, it is important to study the temporal characteristics and predict, this calls for a statistical Time Series model of rainfall data which will be used to accurately predict extreme rainfall patterns. For Enugu in the previous studies various objectives have been achieved, where by temporal and spatial variability of rainfall was determined. Results from the temporal characteristic of Enugu rainfall can be classified into unimodal and bimodal. Moreover results from trend analysis have shown that there were decreasing trends for all stations except in Mwanza, Sumbawanga, and Nsukka, but trends are not statistically significant except in two stations i.e. Pemba and Mmaku

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