EFFECT OF URBANIZATION ON RAINFALL OVER NAIROBI REGION OURU DISMAS I10/1282/2012

Transcription

1 EFFECT OF URBANIZATION ON RAINFALL OVER NAIROBI REGION By OURU DISMAS I1/1282/212 A Report submitted for partial fulfillment of the requirements for an Undergraduate degree in Meteorology Department of meteorology University of Nairobi May, 216.

2 DECLARATION I hereby declare that this research work is my original work and has not been presented in any other learning institution for academic award. Signature... Date. Ouru Dismas I1/1282/212 This research work was submitted to my supervisors for approval Signature... Date. Prof. John Ng ang a Signature... Date. Dr. F, J. Opijah Department of meteorology University of Nairobi P.O.Box 3197 Nairobi, Kenya

3 ACKNOWLEDGEMENT First and foremost, I would like to thank my Almighty God for granting me good health and strength to complete this work. I would also express my deep gratitude to Prof. John Ng ang a and Dr. Franlin Opija for their valuable suggestions. I would also like to thank my uncle Pst. Joseph Onditi and my cousin Jared Ouru for their endless love and support. Finally, my gratitude goes to Kenya Meteorological Department and Kenya National Bureau of Statistics for providing the data used for this research work.

4 Table of Contents DECLARATION... 2 ACKNOWLEDGEMENT... 3 List of Figures... 6 List of Tables... 7 ABSTRACT... 8 CHAPTER ONE INTRODUCTION OBJECTIVES OF THE STUDY PROBLEM STATEMENT JUSTIFICATION OF THE STUDY AREA OF STUDY Nairobi Region Climatology... 1 CHAPTER TWO LITERATURE REVIEW CHAPTER THREE DATA AND METHODOLOGY Source of Data Estimation of Missing Data Data Quality Control Methodology Time Series Analysis Graphical Analysis Correlation Analysis Regression Analysis CHAPTER FOUR Results and Discussion Data Quality Control Estimation of Missing Data Urbanization Indicator Homogeneity Test... 16

5 4.3 Space and time variability of rainfall amount and number of rainy days over Nairobi region The Relationship between Urbanization and Rainfall amount and Number of Rainy days Regression Analysis Correlation Analysis CHAPTER FIVE Summary, Conclusion and Recommendation SUMMARY CONCLUSION RECOMMENDATION REFERENCES... 36

6 List of Figures Figure 1: Map of Nairobi City Figure 2. Demographic growth of Nairobi Figure 3. Single mass curve for cumulated rainfall over Moi Air-Base Figure 4. Single mass curve for cumulated rainfall over Wilson Airport Figure 5. Single mass curve for cumulated rainfall over Dagoretti Figure 6. Single mass curve for cumulated rainfall over JKIA Figure 7. Single mass curve for cumulated rainfall over Kabete Figure 8. Single mass curve for cumulated number of rainy days over Moi Air-Base Figure 9. Single mass curve for cumulated number of rainy days over Wilson Airport Figure 1. Single mass curve for cumulated number of rainy days over Dagoretti Figure 11. Single mass curve for cumulated number of rainy days over JKIA... 2 Figure 12. Single mass curve for cumulated number of rainy days over Kabete... 2 Figure 13. A graph of annual variation of rainfall amount over Moi Air-Base Figure 14. A graph of annual variation of rainfall amount over Wilson Airport Figure 15. A graph of annual variation of rainfall amount over Dagoretti Figure 16. A graph of annual variation of rainfall amount over JKIA Figure 17. A graph of annual variation of rainfall amount over Kabete Figure 18. A graph of annual variation of rainfall over Nairobi region Figure 19. A time series for annual number of rainfall days over Moi Air-Base Figure 2. A time series for annual number of rainfall days over Wilson Airport Figure 21. A time series for annual number of rainfall days over Dagoretti Figure 22. A time series for annual number of rainfall days over JKIA Figure 23. A time series for number of rainfall days over Kabete region Figure 24. Annual number of rainfall days over Nairobi region Figure 25. Regression between population and number of rainy days over Moi Air-Base Figure 26. Regression between population and number of rainy days Wilson Airport Figure 27. Regression between population and number of rainy days Dagoretti Figure 28. Regression between population and number of rainy days over JKIA Figure 29. Regression between population and number of rainy days over Kabete Figure 3. Regression between population and number of rainy days over Nairobi region Figure 31.Regression between population and rainfall amount over Moi Air-Base Figure 32.Regression between population and rainfall amount over Wilson Airport... 3 Figure 33.Regression between population and rainfall amount over Dagoretti Figure 34.Regression between population and rainfall amount over JKIA Figure 35.Regression between population and rainfall amount over Kabete Figure 36.Regresssion between population and rainfall amount over Nairobi region

7 List of Tables Table 1: Meteorological Stations (Source: Kenya Meteorological Department) Table 3: Correlation between rainfall amount and population Table 2: Correlation between number of rainy days and population... 34

8 ABSTRACT Various studies on city climate have shown the process of urbanization is accompanied by modification of local climate. The modification of local climate in urban areas could be caused by the following factors; turbulent effect of urban built landscape on air flow, increased particulate matter in the urban atmosphere, increased in the number of condensation nuclei in the urban atmosphere, increased in thermal convection in urban areas and increased water vapor from industrial release, thus urban areas could receive more rainfall amount and because of urban air is more buoyant and contains more condensation nuclei. The objective of this study is to investigate the effect of urbanization on rainfall received over Nairobi city basing on rainfall amount and number of rainy days for five stations within Nairobi city. The results indicates that both the rainfall amount and the number of rainy days increases. Nairobi which is the Kenya s capital city is one of the fastest growing cities in the world. There is a necessity of investigating the effect of urbanization on rainfall in our city. This is arrived at through studying trends in rainfall amount and the number of rainy days. The data used were from JKIA, Kabete, Dagoretti, Wilson Airport and Moi Airbase. The data comprised of observed monthly rainfall data and number rainy days for the period of 44 years that is from The data was tested for homogeneity and found to be homogeneous. Trend analysis, regression analysis and graphical were perfomed. The results showed that the trend for rainfall amount and number of rainy days are increasing with time. The regression analysis performed by plotting scattered diagram of Nairobi s population against both rainfall amount number of rainy days showed an increasing trend. The result showed a significant change in rainfall amount and number of rainy days with increase population of Nairobi. The environmental implications such as urban flood will also increase. Since Nairobi population will get bigger and bigger, the impact of urbanization will also be greater. Therefore, it is important that investigation on urban climatology be initiated soon.

9 CHAPTER ONE 1. INTRODUCTION Urbanization is increasing in parallel with the increase in population. As a result of increase in population and urbanization, the energy consumption has also been increasing due to heating, traffic and industrialization. These are causing remarkable change in meteorological conditions and the climate of the cities and the neighboring regions. The most salient impact of urbanization on climate can be observed on temperature. Urban areas affect rainfall by mechanic turbulence caused by the increasing surface roughness, additional sensible heat caused by urban heat island (UHI) and plenty of condensation nuclei in the atmosphere of cities. Surface roughnesses like tarmac roads, buildings absorbs solar radiation and store the heat because they are made with materials with different heat capacity. The materials later emits the stored energy in the form of long waves which warms the atmosphere of the cities thus creating the urban heat island. Rough surfaces like tarmac roads also prevents water from infiltrating into the ground thus the water forms the surface runoff which causes flood in urban areas. Plenty of condensation nuclei in the atmosphere of cities accelerates the rate of condensation by providing the surface for water vapor to attach and accumulates to form water droplets which falls as rain when the size cannot be suspended in the atmosphere. 1.1 OBJECTIVES OF THE STUDY The main objective of the study is to assess the influence of urbanization on rainfall in Nairobi region. To achieve this main objective, three specific objectives are to be pursued. These include; i) To determine the time and space variation of rainfall amount over Nairobi region. ii) To determine the time and space variation of the number of rain day in each year over Nairobi. iii) To determine the relationship between population and rainfall over Nairobi region. 1.2 PROBLEM STATEMENT Urbanization growth is expected to modify the boundary layer processes through the creation of surface roughness and the contribution of pollution increase the number of cloud condensation nuclei over urban regions, with complex series of feedback to cloud formation and precipitation Extensive urbanization influences weather and local climate through complex urban use and development activities. Change in rainfall pattern in both space and time caused by urbanization is one of the challenges that are faced in major cities. Increase in the amount of rainfall and increase in the number of rainy days over Nairobi city leads to urban flooding which has negative impacts on city residents.

10 1.3 JUSTIFICATION OF THE STUDY Rainfall over a particular area is caused by the climate of area. Over urban areas, rainfall variability is attributed to the prevailing local conditions. Some of these local conditions include modified land surface parameters such as roughness lengths, heat storage and anthropogenic heat emissions (Kanda, 27) which is caused by buildings, industries and number of vehicles which contribute to an increased aerosol concentration. If weather forecasting would target specific cities especially urban areas then the forecasting would be more helpful to people by giving more accurate weather forecasting and warnings for the potential of flood producing rains on agriculture, urban planning, disaster management etc. Improved understanding of the influence of urban centers on local climate will be of significant benefit to decision makers dealing with climate-sensitive issues. This information would provide insight of the contribution of urbanization to rainfall modifications and thus improves the accuracy of models used in weather forecasting and warnings for potential reduction of economic and social costs of extreme events such as flush floods. 1.4 AREA OF STUDY This describe the physical characteristics and climatology of the study area Nairobi Region Nairobi is the Kenya s capital city. Kenya is situated on the Eastern coast of Africa and lies astride the equator. Nairobi lies between latitudes 5.5ᵒ North and 5ᵒ South of the equator and region between longitudes 34ᵒand 42ᵒ East on the eastern side of African continent. It is located at 1ᵒ 18 S and 36ᵒ 45 E and has a land area of 696 square Kilometers (269 square miles). It is situated 1661 meters (545 feet) above sea level Climatology Nairobi enjoys a moderate climate. Under Koppen climate classification, Nairobi has a subtropical highland climate. Nairobi has a bi-modal rainy seasons, the long rains and short rains. The long rains start mid-march to May whereas short rains are in October to December.

11 Figure 1: Map of Nairobi City Table 1: Meteorological Stations (Source: Kenya Meteorological Department) Station Name Station Number Latitude Longitude Altitude (meters) J.K.I.A º 19 9 S 36º E 1624 Dagoretti º 18 S 36º 45 E 1798 Moi Air Base º 16 S 36º 52 E 1637 Kabete º 15 S 36º 44 E 1941 Wilson Airport º 19 S 36º 49 E 1676

12 CHAPTER TWO 2. LITERATURE REVIEW In various investigations, it was found that both, the number of precipitation days and the intensity of precipitation have changed due to urbanization especially in the mid-latitude countries (Atkinson, 1971; Lowry and Probald, 1978; Yonetani, 1982; Shafir and Alpert, 199; Changnon et al., 1991;Bornstein and Lin, 2; Chongnon, 21) The study carried out by Chongnon et al (1976) in the Metropolitan Meteorological Experiment Project (METROMEX) observed that there is a statistically meaningful increase in the summer precipitation in the St. Louis (heavy showers, thunderstorms). The experiment also observed that the number of days with shower in the summer increased by 45% due to the urban effect. Kadioglu (1997) investigated the influence of urbanization on precipitation in the Marmara region and found that there was an increase in the number of precipitation days because of the increase in the number of condensation nuclei in cities. It was also observed that the number of heavy precipitation days (>3 mm per day) decreased and explain this as a result of the fact that with the increase in the number precipitation days, water droplets falls down more often and its concentration in the atmosphere does not reach a point that is enough to feed heavy rainfall. Shepherd (2) examine the impact of urbanization on precipitation total in six regions (Atlanta;Montgomery,Alabana;Nash-Ville,Tennessee;San Antonio ) hypothesize that the urban heat island is responsible for precipitation enhancement.

13 CHAPTER THREE 3. DATA AND METHODOLOGY The chapter describe the data used and also includes the methodology used to achieve the objectives. 3.1 Source of Data The data used in the study comprised of two categories: i) The observed rainfall data collected from Kenya Meteorological Department headquarter for the following stations: Dagoretti corner, Jomo Kenyatta International Airport, Kabete, Wilson Airport and Moi Airbase. The data runs from 1971 to 214. ii) Urbanization indicator: Nairobi city population that was obtained from Kenya National Bureau of Statistics (Statistical Abstract 25 and 21 edition) Estimation of Missing Data The missing data were estimated using the Arithmetic Mean Method. This involved replacing the missing data with the mean value of a given station. n X = 1 N x i i=1 Where: X is the Arithmetic mean of the station N is the sample size x i is the individual observation Data Quality Control In this study, the quality of the estimated data were examined using single mass curve which involved plotting the cumulated rainfall data against time. In this method, straight lines indicates the homogeneous record. 3.2 Methodology This outlines the methods used to analyze data and they includes the following: Time Series Analysis Trend analysis as one of the component of the Time series analysis was used to study variation on number of rainfall days over Nairobi region. This involved plotting the graphs of total annual number of rainfall days against time to investigate the trend of number of rainfall days over Nairobi region.

14 3.2.2 Graphical Analysis Graphical method was used to show the demographic growth of Nairobi city. This involves plotting a graph of population of Nairobi against time. In addition, it was used to show the variation of rainfall amount over Nairobi Correlation Analysis This was done through correlation method to establish the degree of relationships between rainfall amount and number of rainy days and population. The correlation coefficient was computed using the formula below. r xy 1 n x x y y n 1 i i i 2 1 n 1 n 2 x x. y y n i i i 1 n i Where: r xy is the correlation coefficient N is the sample size x and y are the mean value of variables beingcorrelated x i and y i are the variables being correlated r xy For = -1, this implies perfect negative linear relationship. For = 1, it implies perfect positive linear relationship while for variables r xy =, it means there is no linear relationship between the The significance of correlation coefficients was tested by using the student-t test at significance level of 5%. The significance of the correlation was tested using the t-test formula given by; r xy r n r r ( n 2) r tn Where; n is number of observations r is computed correlation coefficient n-2 is degree of freedom

15 Population Regression Analysis This was done by plotting the scattered diagram between population against the annual amount of rainfall and annual number of rainy days over Nairobi CHAPTER FOUR 4. Results and Discussion This chapter discusses the results obtained from various analyses which were done. 4.1.Data Quality Control Estimation of Missing Data In this study, there were some missing data for rainfall amount and the number of rainy days for the five stations; Moi Air-base, Wilson Airport, Dagoretti, JKIA and Kabete. Estimation of missing data was therefore necessary and hence arithmetic mean method was applied where the missing data were replaced by the long term mean Urbanization Indicator POPUPLATION Linear (POPUPLATION) Figure 2 Demographic growth of Nairobi The plot above indicates the growth of Nairobi s population with time. The trend line shows an increasing trend indicating that the population of Nairobi is increasing with time. According to Kenya National Bureau of Statistics, the growth rate of Nairobi is currently at 6.9%. It is estimated that half of the population live in slums hence the growth of informal structures, slums

16 Cummulative rainfall (mm) Cummulative rainfall which covers just over 5% of the city area. The growth of these slums is a result of urbanization following increase in rural-urban migration in search of employment. 4.2 Homogeneity Test The test for homogeneity for rainfall amount and number of rainy days were done using single mass curve method where cumulated data for rainfall amount and number of rainy days for Moi Air-base, Wilson Airport, Dagoretti, JKIA and Kabete were plotted against time. The results are as shown below 5 4 R² = Figure 3 Single mass curve for cumulated rainfall over Moi Air-Base 45 4 R² = Figure 4 Single mass curve for cumulated rainfall over Wilson Airport

17 Cummulative rainfall (mm) Cummulative rainfall 6 5 R² = Figure 5 Single mass curve for cumulated rainfall over Dagoretti 35 3 R² = Figure 6 Single mass curve for cumulated rainfall over JKIA

18 Cummulative number of rainy days Cummulative rainfall (mm) R² = Figure 7 Single mass curve for cumulated rainfall over Kabete 4 35 R² = Figure 8 Single mass curve for cumulated number of rainy days over Moi Air-Base

19 Cummulative number of rainy days Cummulative number of rainy days 4 35 R² = Figure 9 Single mass curve for cumulated number of rainy days over Wilson Airport 45 4 R² = Figure 1 Single mass curve for cumulated number of rainy days over Dagoretti

20 Cummulative number of rainy days Cummulative number of rainy days 35 3 R² = Figure 11 Single mass curve for cumulated number of rainy days over JKIA 45 4 R² = Figure 12 Single mass curve for cumulated number of rainy days over Kabete The graphs of cumulated rainfall and the number of rainy days for all the five stations had a coefficient of determination (R 2 ) values all above.99. This implies that more than 99% of the data were within the line of best fit. Therefore, both rainfall amount and the number of rainy days data set available were consistent and valid and thus homogeneous and considered useful for further analysis. 4.3 Space and time variability of rainfall amount and number of rainy days over Nairobi region The graphs of total annual rainfall and the number of rainy days for different stations within Nairobi region were plotted to investigate the trend of rainfall amount and the number of rainy

21 Rainfall amount (mm) Rainfall amount (mm) days. The rainfall amount over the five stations were averaged to get the annual amount of rainfall received over Nairobi region. In addition, the number of rainy days over the five stations were also averaged to get the annual number of rainy days experienced over Nairobi region. The results are as shown below y = x Figure 13 A graph of annual variation of rainfall amount over Moi Air-Base y = x Figure 14 A graph of annual variation of rainfall amount over Wilson Airport

22 Rainfall amount (mm) Rainfall amount (mm) y = 1.652x Figure 15 A graph of annual variation of rainfall amount over Dagoretti y =.41x Figure 16 A graph of annual variation of rainfall amount over JKIA

23 Rainfall amount (mm) Rainfall amount (mm) y = x Figure 17 A graph of annual variation of rainfall amount over Kabete y = 7.196x Figure 18 A graph of annual variation of rainfall over Nairobi region

24 Number of rainy days Number of rainy days y =.1481x Figure 19 A time series for annual number of rainfall days over Moi Air-Base y =.14x Figure 2 A time series for annual number of rainfall days over Wilson Airport

25 Number of rainy days Number of rainy days y =.548x Figure 21 A time series for annual number of rainfall days over Dagoretti y = -.459x Figure 22 A time series for annual number of rainfall days over JKIA

26 Number of rainy days Number of rainy days y =.16x Figure 23 A time series for number of rainfall days over Kabete region 12 1 y =.518x Figure 24 Annual number of rainfall days over Nairobi region From the graphs presented above, it is observed that the annual rainfall amount over the stations within Nairobi that is Moi Air-Base, Wilson Airport, Dagoretti, JKIA and Kabete, the annual amount of rainfall received have an increasing trend indicating that the rainfall amount over those stations increases yearly. On averaging the rainfall received over the stations to represent the rainfall amount received over Nairobi, the graph also show an increasing trend indicating that rainfall amount over Nairobi is increasing yearly. In addition, the plots of the number of rainy days experienced over the five stations within Nairobi show the increasing trend indicating that the number the rainy days over the stations increases yearly. Averaging the number of rainy days experienced at the five stations to represent the number of rainy days experienced over Nairobi region also shows the increasing trend

27 Population Population indicating that the number of rainy days over Nairobi increases. The increase in the number of rainy days could as a result of increase in the number of condensation nuclei over Nairobi produced by industries, motor vehicles and probably burning of fossil fuels by city occupants. 4.4 The Relationship between Urbanization and Rainfall amount and Number of Rainy days The effects of population on rainfall over Nairobi region were investigated using both regression and correlation analysis. The results are presented as shown below Regression Analysis Results for regression analysis are presented in the figures below y = 14575x R² = Number of rainfall days Figure 25. Regression between population and number of rainy days over Moi Air-Base y = x + 1E+6 R² = Number of rainfall days Figure 26. Regression between population and number of rainy days Wilson Airport

28 Population Population y = 457.4x + 1E+6 R² = Number of rainfall days Figure 27. Regression between population and number of rainy days Dagoretti y = -9222x + 2E+6 R² = Number of rainfall days Figure 28. Regression between population and number of rainy days over JKIA.

29 Population Population y = x + 2E+6 R² = 2E Number of rainfall days Figure 29. Regression between population and number of rainy days over Kabete y = x + 1E+6 R² = Number of rainfall days Figure 3. Regression between population and number of rainy days over Nairobi region.

30 Population Population y = x + 1E+6 R² = Rainfall amount (mm) Figure 31.Regression between population and rainfall amount over Moi Air-Base y = x R² = Rainfall amount (mm) Figure 32.Regression between population and rainfall amount over Wilson Airport

31 Population Population y = 97.14x R² = Rainfall amount 9 Figure 33.Regression between population and rainfall amount over Dagoretti y = x + 2E+6 R² = Rainfall amount (mm) Figure 34.Regression between population and rainfall amount over JKIA.

32 Population Population y = x R² = Rainfall amount (mm) Figure 35.Regression between population and rainfall amount over Kabete y = 139x R² = Rainfall amount (mm) Figure 36.Regresssion between population and rainfall amount over Nairobi region.

33 Based on regression analysis, it is shown that the there is a positive relation between population and number of rainy days over Moi Air-Base, Wilson Airport, Dagoretti and Kabete as reflected by increasing trend and positive coefficient of determination. For JKIA, the trend is negative and the coefficient of determination is very small which indicates that the increasing population had little influence on rainfall amount received over JKIA. But on averaging, the regression between population and rainfall amount over Nairobi have a positive trend and coefficient of determination is also positive indicating positive relation between population and rainfall amount over Nairobi. Considering the number of rainy days, it is shown that there is a positive relation between population and number of rainy days over Moi Air-Base, Wilson Airport, Dagoretti and Kabete as reflected by increasing trend and positive coefficient of determination. However; there is a negative trend and coefficient of determination is also small indicating that the increasing population had little influence on the number of rainy days over JKIA. But on averaging, the regression between population and number of rainy days over Nairobi have a positive trend and coefficient of determination is also positive indicating positive relation between population and rainfall amount over Nairobi Correlation Analysis The results of correlation analysis between population and rainfall amount and the number of rainy days are as presented in the tables below. Table 2: Correlation between rainfall amount and population. Stations Correlation coefficients T-test tcomputed ttabulated Moi Air-Base Wilson Airport Dagoretti JKIA Kabete Nairobi Region

34 Table 3: Correlation between number of rainy days and population Stations Correlation coefficients T-test Tcomputed ttabulated Moi Air-Base Wilson Airport Dagoretti JKIA Kabete Nairobi Region Correlation analysis of rainfall amount and population indicates a positive relation over Moi Air- Base, Wilson Airport, Dagoretti and Kabete with correlation coefficient.145,.335,.45 and.459 respectively with only JKIA which has a negative correlation of Taking Nairobi as a whole, the correlation between population and rainfall amount received over Nairobi is positive indicating a positive relation. At 5% significance level, the student t-test showed that the correlation for Wilson, Dagoretti, Kabete and Nairobi region were significantly related to population as the values of computed t were greater than the tabulated t. For Moi Air-base and JKIA, AT 5% significance level, the student t-test showed that correlations were not significantly related to population as the values of computed t were less than the tabulated values of t. Correlation analysis of the number of rainy days and population indicates a positive relation over Moi Air-Base, Wilson Airport and Dagoretti and a negative relation over JKIA and Kabete. Averaging the number of rainy days over the five stations and computing the correlation coefficient between population and number of rainy days over Nairobi region gives a positive relation. At 5% significance level, the student t-test showed that these correlations were not significantly related to population as the values of t computed were less than the tabulated values of t.

35 CHAPTER FIVE 5. Summary, Conclusion and Recommendation 5.1 SUMMARY This study aimed to study the influence of urbanization on rainfall over Nairobi region through determination of space and time variation of rainfall amount and number of rainfall days and their relationship with urbanization indicator (population). The objectives were achieved through graphical, time series, correlation and regression analysis. Based on graphical analysis, the trend of the amount of rainfall over the five stations shows an increasing trend indicating that the rainfall amount is increasing. 5.2 CONCLUSION In the study, the main objective was to investigate the effect of urbanization on rainfall over Nairobi using data set for both rainfall amount and number of rainy days. The findings in this study have established that Nairobi city growing evidenced by an increasing trend when population is plotted against time. The findings also showed that rainfall amount and the number of rainy days over Nairobi is increasing. The population growth is directly proportional to energy (fuel) consumption and increased in energy use implies an increase in the emission of pollutant particles which increases the number of condensation nuclei and increase in the emission of heat that makes the urban air to be more buoyant. Increase in population also leads to increase in the number of buildings and this have an effect on the airflow in urban areas. Therefore, it can be concluded that increase in population that is increase in urbanization has a significant effect on rainfall. 5.3 RECOMMENDATION Since the cities in the future will get bigger as a result of increasing population and economic development, the impact of urbanization on rainfall will also be greater. Therefore, it is important that investigation on the urban climatology should be initiated soon.

36 REFERENCES 1. Aitkinson,B.W.,1971. The effect of urban areas on the precipitation from a moving thunderstorm. J.Appl.Met.1, Bornstein,R. and Q. Lin,2. Urban heat island and summer time convective thunderstorms in Atlanta :Three case studies.atmos Environ.34, Chandler, T.,1965.The climate of London.Hutchinson,London,122P. 4. Changnon,S.A,R.G..Semonin and F.A.Huff,1976.A hypothesis for urban rainfall anomalies.j.appl.met15, Changnon,S.A,1978. Urban effects on severe local storm at St. Louis.J.Appl.Met17, Changnon, S.A.,R.Shealy and R. Scott,1991.Precipitation changes in fall,winter and spring caused by St. Louis.J.Appl.Met.3, Changnon, S.A.,21. Assessment of historical thunderstorm data for urban effects. The Chicago case.climatic change49, Yonetani. T., Increase in the number of days with heavy precipitation in Tokyo urban area. J.Appl. Met. 21, Shafir, H. and P. Alpert,199. On the urban orographic rainfall anomaly in Jerusalem: a numerical study. Atmos. Environ. 24(B), Lowry, W. P., 1998, Urban effects on precipitation amount, Prog Phys Geogr,22, Huff, F.A. &Changnon, S.A., 1973, Precipitation modification by major urban areas, Bull Amer MeteorolSoc, 54,