1996 temporal and spatial study of thunderstorm rainfall

University of WollongongResearch Online

University of Wollongong Thesis Collection University of Wollongong Thesis Collections

1996

Temporal and spatial study of thunderstorm rainfallin the Greater Sydney regionAli Akbar RasulyUniversity of Wollongong

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Recommended CitationRasuly, Ali Akbar, Temporal and spatial study of thunderstorm rainfall in the Greater Sydney region, Doctor of Philosophy thesis,School of Geosciences, University of Wollongong, 1996. http://ro.uow.edu.au/theses/1986

http://ro.uow.edu.au/


http://ro.uow.edu.au

http://ro.uow.edu.au/theses

http://ro.uow.edu.au/thesesuow



TEMPORAL AND SPATIAL STUDY OF

THUNDERSTORM RAINFALL

IN THE GREATER SYDNEY REGION

A thesis submitted in fulfilment of the requirements

for the award of the degree

UNIVERSITY O*

DOCTOR OF PHILOSOPHY

from

UNIVERSITY OF WOLLONGONG

by

ALIAKBAR RASULY

B.Sc. & M.Sc. (IRAN, TABRIZ University)

SCHOOL OF GEOSCIENCES

1996

CERTIFICATION

The work presented herein has not been submitted to any

other university or institution for a higher degree and,

unless acknowledged, is m y own original work.

A. A. Rasuly

February 1996

i

ABSTRACT

Thunderstorm rainfall is considered as a very vital climatic factor because of its significant effects and often disastrous consequences upon people and the natural environment in the Greater Sydney Region. Thus, this study investigates the following aspects of thunderstorm rainfall climatology of the region between 1960 to 1993.

In detail, it was found that thunderstorm rainfalls in Sydney have marked diurnal and seasonal variations. They are most frequent in the spring and summer and during the late afternoon and early evening. Thunderstorms occur primarily over the coastal areas and mountains, and less frequently over the lowland interior of the Sydney basin. Environmental factors, such as the local climatic factors and physiographic parameters may control thunderstorm occurrence and its associated rainfall distribution. More detailed associations, possible causal relationships, using stepwise regression indicate that thunderstorm rainfall frequency could partially be affected by air and sea temperatures, and air humidity.

Accordingly, specific attention was paid to the patterns of the spatial variation of thunderstorm rainfall during the warm months (October to March) over a long time-span (34 years), using data from 191 rainfall stations. Mathematically, the g a m m a functions (beta and alpha values) describe and summarise the probability distribution of daily thunderstorm rainfall across the Sydney region. The findings reveal the interplay of topographic, coastal and urban effects in controlling the amount of thunderstorm rainfall in both spring and summer.

A "climatologically oriented GIS" (including a Digital Elevation Model (DEM), a proximity map, and a landuse model) together with regression procedures were used to assess the relative importance of physiographic and environmental variables for six of the largest thunderstorm rainfall events. Three patterns emerged. The first is an increase in thunderstorm rainfall occurring toward the coast. The second is an increase in thunderstorm rainfall as elevation increases. Finally, the more compact the urban residential and commercialised areas the greater the amount of thunderstorm rainfall. These variables account for 70 per cent of thunderstorm rainfall variations throughout the Sydney region.

ii

ACKNOWLEDGMENTS

I would like express my very special gratitude to Associate Professor Edward Bryant, m y supervisor, who gave m e encouragement and support throughout the study with ideas, literature, computer programs, proof reading and much more assistance. I am as well grateful to staff and academic members of the School of Geosciences, University of Wollongong for their suggestions and support throughout m y period of study. Very special thanks should be given to Professor A. Chivas, Professor M . Wilson, Associate Professor B. Young, Associate Professor G. Nanson, Associate Professor C. Woodroffe, Dr. A. Young, Dr. A. O'Neill, Dr. L. Brown, Dr. L. Head, Dr. J. Formby, Dr. G. Waitt, Dr. R. Wray, Mr. D. Price, Mr. G. Black and Ms. J. Shaw, very kind people, who gave m e so much encouragement and provided much more valuable materials during m y study. I also wish to thank Mr. J. Marthick for the use of his computing skills, particularly in GIS and Mr. R. Miller and Mr. D. Martin are thanked for advice on cartography. M y fellow postgrads were all very helpful and understanding. Thanks must be given to all these friendly people.

Grateful acknowledgment is made also to the people - at the Bureau of Meteorology Sydney Regional Office; the National Climate Centre in Melbourne; Sydney Water; the Australian Oceanographic Data Centre; the Australian Surveying and Land Information Group; and Infomaster Australia (SPANS GIS) - who kindly provided sources of data for this research.

I am also grateful to my parents, relatives, friends and academic members of the Department of Geography in Tabriz University, who consistently encouraged m e to finish this study in many letters. Without doubt, I am indebted to m y wife and children's infinite understanding, my supporters who shared probably the entire range of emotional states with me in producing this thesis. Appreciation is offered to all.

Finally, I would like to appreciate The Ministry of Culture and Higher Education of Islamic Republic of IRAN, for awarding m e a scholarship and providing financial support to make this thesis possible. Yet, I deeply believe that the greatest acknowledgment is for God's support and guidance.

iii

TABLE OF CONTENTS

PAGE

A B S T R A C T i A C K N O W L E D G M E N T S ii T A B L E O F C O N T E N T S iii LIST O F T A B L E S vii LIST O F FIGURES « LIST O F PLATES xi

CHAPTER 1 INTRODUCTION

1.1 Thunderstorm Rainfall in the Sydney Region 1 1.2 Topo-Climatic Characteristics of the Sydney Region 3

1.2.1 Topography of the Region 3 1.2.2 Climate of the Study Area 3

1.2.2.1 Rainfall Characteristics 5 1.2.2.2 Temperature Patterns 8

1.3 Objectives of This Study 9 1.4 Research Significance 10 1.5 Data Management and Modelling Techniques Applied 12 1.6 Thesis Outline by Chapters 15

CHAPTER 2 LITERATURE REVIEW ON T H U N D E R S T O R M RAINFALL

2.1 Introduction 17 2.2 Thunderstorm Characteristics 17

2.2.1 Life-Cycle of a Single Thunderstorm 18 2.2.2 Complex Thunderstorm Systems 19

2.3 Synoptic Weather Patterns Creating Thunderstorms 22 2.4 Climatic Variables and Thunderstorms 24

2.4.1 Air Temperature 24 2.4.2 Sea-Surface Temperature 25 2.4.3 El Nino / Southern Oscillation 28

2.5 Physiographic Parameters and Thunderstorm Rainfall 28 2.5.1 Topography and Thunderstorm Rainfall 29 2.5.2 Effects of Proximity to the Sea upon Thunderstorm Rainfall 33 2.5.3 Impacts of Urban Areas on Thunderstorm Rainfall Distribution 35

2.6 Distribution of Thunderstorms in Australia 38 2.7 Synoptic Patterns Associated with Thunderstorm Activity in Australia 40

2.7.1 Weather Systems and Thunderstorm Activity in N S W 44

iv

2.7.2 Thunderstorm Development in the Sydney Region 48 2.8 Sydney's Physiographic Parameters and Thunderstorm Rainfall 57 2.9 Conclusions 64

CHAPTER 3 TEMPORAL DISTRIBUTION OF THUNDERSTORM RAINFALL IN T H E S Y D N E Y REGION

3.1 Introduction 67 3.2 Data Used 67 3.3 Methods Applied 68 3.4 Yearly Distribution of Thunderstorm Rainfall 71 3.5 Seasonal and Monthly Distributions 76 3.6 Diurnal Variation 79 3.7 Discussion 81

3.7.1 The Role of Synoptic Weather Patterns 82 3.7.2 The Effect of Climatic Factors 83 3.7.3 The Impact of Physiographic Parameters 84

3.8 Summary and Conclusion 85

CHAPTER 4 THUNDERSTORM RAINFALL A N D CLIMATIC VARIABLES

4.1 Introduction 86 4.2 Data Sources and Analysis Techniques 86 4.3 Description of Variables 87

4.3.1 Air Temperature 88 4.3.2 Sea Surface Temperature 89 4.3.3 Air Humidity 91

4.4 Correlations Matrices of Variables 92 4.5 Multiple Associations Between Variables 95 4.6 Discussion 97

4.6.1 Effects of Sea-surface Temperature 97 4.6.2 Associations Between Air Temperature and Thunderstorms 98 4.6.3 The Role of Air Humidity 100


CHAPTER 5 A REVIEW ON GIS TECHNIQUES

5.1 Introduction 103 5.2 What is a GIS? 103 5.3 Purpose of GIS 104 5.4 H o w GIS Operates 106

5.4.1 Data Structures in GIS 107 5.4.2 Functionality of Data in GIS 108

5.5 Implications of GIS Techniques in Climatology 110

v

5.6 Application of the GIS in Resolving Problems in Rainfall Analysis 112 5.7 Data Sources on GIS System 115

5.8 Methods Used in a SPANS GIS n 5

5.8.1 Data Input 116

5.8.2 Model Building I17

5.8.3 Model Analysing I18

5.9 GIS Potential Errors n 8


CHAPTER 6 THE SPATIAL VARIATION AND DISTRIBUTION OF THUNDERSTORM RAINFALL

6.1 Introduction 121

6.2 Data Selection 121

6.3 Techniques Used 126

6.4 Thunderstorm Rainfall Selection Criteria I28

6.5 Spatial Variability of Thunderstorm Rainfall 131 6.6 Spatial Distribution of Thunderstorm Rainfall I35

6.6.1 Average Event Values I3" 6.6.2 The Biggest Events I39

6.7 Discussion *49 6.8 Summary and Conclusion I52

CHAPTER 7 RELATIONSHIPS BETWEEN THUNDERSTORM RAINFALL A N D PHYSIOGRAPHIC PARAMETERS

7.1 Introduction I54

7.2 Data Used 154

7.3 Techniques Employed I57

7.3.1 GIS Techniques Applied I57

7.3.1.1 Landuse Map of the Sydney Region 161 7.3.1.2 Advanced SPANS GIS Functions Used 168

7.3.2 Statistical Techniques Used 172 7.4 Topography and Rainfall from Thunderstorms 172

7.4.1 Description of Major Topographic Units 172 7.4.2 Association Between Elevation and Thunderstorm Rainfall 174 7.4.3 Association Between Aspect Classes and Rainfall 176

7.5 Proximity to the Sea and Thunderstorm Rainfall Distribution 178 7.6 Landuse Patterns and Thunderstorm Rainfall 180 7.7 Overlay Modelling / Multiple Relations 183

7.7.1 GIS Overlay Modelling 184 7.7.2 Multiple Relations Among Variables 185

7.7.2.1 Stepwise Multi-Regression Technique 185 7.7.2.2 Spatial Distribution of Z Scores Over Sydney 188

7.8 Discussion 191

7.8.1 The Role of Coastal Area 7.8.2 Impact of Topographic Factors 192 7.8.3 Effect of Landuse on Rainfall Distribution 194


CHAPTER 8 CONCLUSIONS

8.1 Introduction 198 8.2 Major Conclusions of the Thesis 198 8.3 Limitations of the Study 200

8.3.1 Limitations of Data Used 200 8.3.2 Limitations of Techniques Applied 201

8.4 Advantages and Implications of the Study 201 8.4.1 Advantages of the Study 202 8.4.2 Implications of the Study 203

8.5 Suggestions for Future Studies 204 8.6 Concluding Remarks 204

REFERENCES 206

APPENDIX A LIST OF COMPUTER PROGRAMS

A. 1 Computer Program Number 1 230 A.2 Computer Program Number 2 233 A. 3 Computer Program Number 3 234 A. 4 Computer Program Number 4 236 A. 5 Computer Program Number 5 238

APPENDIX B THUNDERSTORM RAINFALL DATA

B. 1 Common Thunderstorm-days in Sydney Region 240 B.2 Monthly Thunderstorm Rainfall Data at Richmond 249 B.3 Monthly Thunderstorm Rainfall Data at Sydney R.O. 250 B.4 Monthly Thunderstorm Rainfall Data at Sydney Airport 251 B.5 List of Rainfall Stations 252

APPENDIX C SYNOPTIC WEATHER CHARTS

Synoptic charts 6.1 from 23th to 25th October, 1987 256 Synoptic charts 6.2 5th to 12th November, 1984 257 Synoptic charts 6.3 9th to 11th December, 1988 258 Synoptic charts 6.4 19th to 22th January, 1991 259 Synoptic charts 6.5 7th to 11th February, 1990 260 Synoptic charts 6.6 10th to 11th March, 1975 261

vii

APPENDIX D DATA USED FOR GIS A N D STATISTICAL MODELS

Geographical Location of Rainfall Stations and their Attributes

APPENDIX E EQUATIONS Equations Used in SPANS GIS

LIST OF TABLES

TABLE PAGE

See

1.1 Examples of thunderstorm rainfall events causing flash floods

1.2 Gives examples in using thunderstorm data in the region

3.1 Represents a detailed description of the codes of present and past

weather used in thunderstorm observations

3.2 C o m m o n thunderstorm-days in the Sydney region

3.3 General geographic characteristics of the seven selected stations

3.4 Locality of the seven selected stations

3.5 Yearly variation of thunder-days frequency and thunderstorm rainfall

amounts at 7 thunder-recording stations in the Sydney region

3.6 Summary descriptive statistics for yearly thunderstorm rainfall frequency,

in the Sydney region

3.7 Summary descriptive statistics for yearly thunderstorm rainfall amounts,

in the Sydney region

3.8 Average seasonal thunderstorm rainfall for selected stations

3.9 The percentage of average thunderstorm rainfall to mean monthly

rainfall in different stations, in the Sydney region

4.1 Monthly thunderstorm rainfall frequency at Richmond

4.2 Monthly thunderstorm rainfall amount at Richmond

4.3 Monthly thunderstorm frequency at Sydney R. 0.

4.4 Monthly thunderstorm rainfall amount at Sydney R. 0.

4.5 Monthly thunderstorm rainfall frequency at Sydney A.

4.6 Monthly thunderstorm rainfall amount at Sydney A.

4.7 Description of thunderstorm data

4.8 Means and extremes of temperature at three selected stations

4.9 Monthly and yearly sea-surface temperature data at Port Hacking

4.10 Simple statistics of the relative humidity in the Sydney region

4.11 The correlation matrix between dependent variables

4.12 Correlation matrix for independent variables

4.13 Linear regression coefficients of dependent variables by independent

variables

4.14(a) Results of stepwise multiple regression analysis of thunderstorm

rainfall frequency at the Sydney Airport station

4.14(b) Results of stepwise multiple regression analysis of thunderstorm rainfall

frequency at Richmond station

6.1 Difference between two sets of stations (the Sydney Water and the Bureau

of Meteorology) according to their rainfall means

6.2 List of stations and the periods from which data were used See

6.3 Thunderstorm rainfall values extracted from the intersection of two

See

See

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10

14

68

Appendices

69

71

72

73

74

77

78

Appendices

Appendices

Appendices

Appendices

Appendices

Appendices

88

89

90

92

93

93

94

96

96

123

Appendices

ix

populations using probability excellence graphs

6.4 General descriptions for the 6 biggest thunderstorm rainfall events

in the region

7.1 Origin of the data that used in Chapter 7

7.2 Geographical location of rainfall stations and their attributes

7.3 Limits of the study area / database

7.4 Aspect classes derived from the D E M model, using S P A N S GIS

7.5 Description of landuse types in the Sydney region.

7.6 Equations which were written in S P A N S environment

7.7 Area cross tabulation results between the topography map of the region

and thunderstorm rainfall map.

7.8 The areal distribution of thunderstorm rainfall by topographic classes

7.9 A linear regression analysis between thunderstorm rainfall amount and

elevation of rainfall stations located in the region

7.10 Area cross tabulation results between the aspect map of the region and

thunderstorm rainfall map

7.11 A multiple regression analysis between aspect classes and thunderstorm

rainfall amount

7.12 Area cross tabulation results between the proximity to sea m a p of the

region and thunderstorm rainfall map

7.13 The areal distribution of thunderstorm rainfall by proximity classes

7.14 Correlation coefficients between the proximity to the sea and

thunderstorm rainfall

7.15 Area cross tabulation results between the landuse map of the region and

thunderstorm rainfall map

7.16 The areal distribution of thunderstorm rainfall by landuse classes

7.17 The result of a t-test for rainfall distribution in different landuse classes

7.18 A multiple regression analysis between landuse classes and


7.19 Interrelations matrix among physiographic parameters and


7.20 Presents the result of stepwise multiple regression analysis for the

average of the biggest thunderstorm rainfall amounts

130

139

157

See Appendices

158

161

162

See Appendices

174

175

176

177

178

179

179

180

181

181

182

183

186

187

LIST OF FIGURES

x

FIGURE PAGE

1.1 Study area - the Sydney region, NSW, Australia 2

1.2 Topographic and Location M a p of Sydney region 4

1.3 Illustrates inter-annual precipitation variation at Observatory Hill, Sydney 5

1.4 Median annual rainfall of T H E Sydney region 6

1.5 Average monthly rainfall at three stations in the Sydney region 7

1.6 Average monthly maximum and minimum temperature at Sydney 8

1.7 Percentiles of maximum temperature at Observatory Hill 9

2.1 Schematic representation of a thunderstorm cell 18

2.2 Three stages in the development of a thunderstorm 19

2.3 Schematic visual appearance of a supercell thunderstorm 21

2.4 Average annual thunder-days in Australia 39

2.5 Basic elements in (a) the pattern of pressure distribution and of

associated (b) airmasses over Australia in summer 40

2.6 Represent a pre-frontal trough (a), and a line storm associated with an

eastward moving trough (b) over south-eastern Australia 41

2.7 Shows a sample of cut-off low in the region 45

2.8 Schematic of the life cycle of the precipitation area of a M C C s 46

2.9 Presentation of the anomaly maps using the Terminal Area Severe

Turbulence (TAST) radar data in the Greater Sydney region 50

2.10 Diurnal distribution of thunderstorm occurrence for the different time

periods (local time) in the Sydney region 51

2.11 Presents examples of six meso-scale synoptic weather systems causing

thunderstorm activity in the Sydney region 53

2.12 Selected M S L P charts illustrating the six synoptic classes over Sydney 54

2.13 Thunderstorm density model based on new radar echoes in Sydney 55

2.14 Lightning density for single thunderstorm events based on data from

the N S W lightning detection network 56

2.15 The intensity of annual energy use in the Sydney region 61

2.16 Spatial distribution of nitrogen oxides emissions from all sources

in the Sydney 62

3.1 A dendrogram shows the result of the N N A technique 71


at Sydney Regional Office 74


at Richmond 75

3.4 Normalised Residual Mass curves of annual thunderstorm rainfall

in the Sydney region 75

3.5 Seasonal distribution of thunderstorm rainfall in different stations 76

XI

3.6 Monthly distribution of thunderstorm rainfall in the Sydney region for

different stations 78

3.7 Diurnal variation of thunderstorm rainfall frequency for three thunder

seasons at Katoomba 80


seasons at Richmond 80


seasons at Sydney Regional Office 81

4.1 Average monthly variation of the sea surface temperature 90

4.2 Monthly distribution of the mean relative humidity at three stations

in the Sydney region 92

5.1 Schematically represents different data structures used in a GIS 108

6.1 Relation between correlation coefficient (r) and interstation-distance 124

6.2 Sydney region - rainfall stations networks 125

6.3 The g a m m a density function for a and /? values 127

6.4 Probability of exceedence diagrams for 7 selected thunder-recording stations 129

6.5 Geographical distribution of alpha value, Spring (Oct to Dec) 132

6.6 Geographical distribution of beta value, Spring (Oct to Dec) 133

6.7 Geographical distribution of alpha value, Summer (Jan to Mar) 134

6.8 Geographical distribution of beta value, Summer (Jan to Mar) 135

6.9 Average thunderstorm rainfall per event (Oct to Dec) 137

6.10 Average thunderstorm rainfall per event (Jan to Mar) 138

6.11 Thunderstorm rain - Sydney region (23-25 October 1987) 140

6.12 Thunderstorm rain - Sydney region (5-12 November 1984) 141

6.13 Thunderstorm rain - Sydney region (9-11 December 1988) 143

6.14 Thunderstorm rain - Sydney region (19-22 January 1991) 144

6.15 Thunderstorm rain - Sydney region (7-11 February 1990) 147

6.16 Thunderstorm rain - Sydney region (10 and 11 March 1975) 148

7.1 Sydney region - thunderstorm rainfall, the average of the 6 biggest

daily thunderstorm rainfall events 156

7.2 Proximity map from average coast-line 159

7.3 Aspect map of the Sydney region 160

7.4 Landuse map of the Sydney region 163

7.5(a-d) Physiographic parameters of the Sydney region subject to the highest

daily thunderstorm rainfall amounts 171

7.6 Major topographic units of the Sydney region 173

7.7 The distribution of thunderstorm rainfall in the Sydney region based

upon aspect classes 177

7.8 Distribution of thunderstorm rainfall in the Sydney region based upon

landuse classes 182

7.9 Spatial Distribution of Z scores over Sydney region 190

xii

LIST OF PLATES

PLATE PAGE

1.1 Gives examples of some extensive and serious damage caused by

thunderstorm rainfall in the Sydney region 11

2.1 Shows a cold front off the South Coast of N e w South Wales 42

2.2 Displays thunderstorm development over the Sydney region 49

2.3 Shows smog over central Sydney 63

7.1 Closeup view of heavy commercial landuse showing the part of C B D 164

7.2 Closeup view of heavy industrial landuse 165

7.3 View of compact residential landuse in the Sydney region 165

7.4 View of light-moderate residential landuse 166

7.5 View of normal rural /semi-urban area 167

7.6 Shows example of rural / open areas 167

7.7 Closeup view of compact vegetated land cover in the Sydney region 168

CHAPTER ONE Introduction 1

CHAPTER 1

INTRODUCTION

1.1 Thunderstorm Rainfall in the Sydney Region

Sydney, with its sprawling suburban area, and a population of approximately 3.5 million, is

Australia's largest city. It is located between the South Pacific Ocean and the mountain

ranges in the west. During the last three decades, meteorologists (Williams, 1984;

Colquhoun and Shepherd, 1985) and climatologists (Hobbs, 1972; Sumner, 1983b and

Linacre, 1992) have indicated that thunderstorm activity is a characteristic feature of the

warm summer months in this region and that rainfall from thunderstorms is a major source

of moisture for most parts of the study area. Flash floods are usually associated with the

type of thunderstorm that produces localised, but very intense rainfall, which damages

property and even results in a loss of life (Shanahan, 1968; Riley, 1980; Riley, et al., 1985;

Speer and Geerts, 1994). It seems that thunderstorm rainfalls, and occasionally their

associated flash floods, are a natural part of Sydney's climatic environment (Bryant, 1991;

Johnson et al., 1995).

Information on the variation and distribution of thunderstorm rainfall over time and within

the region is, therefore, crucially important in a variety of applications. However, in the

Sydney region, the knowledge of the temporal and spatial distribution of thunderstorm

rainfall is limited to some case studies of the specific thunderstorm rainfall events which

have been considered over a short period of time (Armstrong and Colquhoun, 1976;

Morgan, 1979a; Nanson and Hean, 1985; Shepherd and Colquhoun, 1985). Consequently,

this study's investigation will involve defining some important aspects of the thunderstorm

rainfall climatology of the region, which has not received a similar level of attention. This

will be done by analysing the available data over a longer period - from 1960 to 1993 -

using appropriate sets of techniques.

There are two main aims of this thesis. The first is to focus attention on the patterns of the

temporal and spatial variation and distribution of thunderstorm rainfalls during the warm

months (October to March) over the time-span of 34 years. The second is to examine the

thunderstorm rainfall patterns in relation to some of the primary climatic variables (air and

sea temperatures, for instance) as well as physiographic parameters such as topography,

proximity to the ocean, and the landuse of the Sydney region.


In the following section, the study area and the important topo-climatic characteristics of

the Sydney region are described. Section 3 outlines the aims of this study. Section 4 offers

some important reasons indicating that the anticipated information from the current

research will be of value for future investigations in the fields of meteorology and

climatology. Section 5 gives a framework for all available data and the techniques which

will be used in this study. Finally, in section 6, the whole thesis is outlined.

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1.2 Topo-CIimatic Characteristics of the Sydney Region

The Sydney region located on the south-east coast of Australia, in New South Wales

( N S W ) includes the Sydney Metropolitan area, which is expanding rapidly inland and

contains highly industrialised pockets. The study area, as part of the Greater Sydney

Region, is bounded in the north by 33° 30* latitude, extending to 150° 15* longitude in the

west, and to the south-east of Wollongong as far as Bowral at 34° 30 latitude south.

Figure 1.1 indicates the geographical location of the study area within Australia as well as

an enlargement of the Sydney region, including the location of all selected thunder-

recording stations (used in Chapter 3) in the area.

1.2.1 Topography of the Region

The Sydney region is bowl-shaped with a low plain in the middle of which is effectively

walled in on three sides by bills and mountains. In the centre of the region there is the

Cumberland Plain opening to the Pacific Ocean from the east. To the north of the plain, the

rise is about 450 m to the top of a ridge lying eastward from the Great Dividing Range

towards the coast. To the south, the rise in elevation on average is over 350 m. In the

south-east of the study area the coastal range rises from 150 m to 500 m, just in the North

west of Wollongong. To the north of Sydney the land rises from about 150 m near Broken

Bay to 450 m on the northern boundary. However, westward, the region rises sharply to

over 1200 m at the top of the Blue Mountains, the eastern section of the Central

Tablelands. The elevation map of the Sydney region (Figure 1.2) illustrates the topographic

features of the region, including the location of the main suburbs. For a more detailed

topographic map of the region, refer to Figure 7.6.

1.2.2 Climate of the Study Area

The Sydney region enjoys a temperate climate and generally the broad-scale wind pattern is

westerly in the winter, and easterly in the summer. This climate can be classified as being

temperate with cool to cold winters and warm to hot summers (Bureau of Meteorology,

1991a). Generally the climate of this region arises from a complex interaction of broad

scale, regional and local controls. O n the broad scale the region is under the influence of

mainly drier westerly airstreams in the winter, and predominantly moist, easterly air

streams in the summer months (Linacre and Hobbs, 1977).

On the regional scale, the major influences are physiographic features (for example,

topography) in and around the region, sea surface temperature off the coast and the

orientation of the coastline (Bureau of Meteorology, 1979). In a such region, local

variation in climate may be caused mainly by the topography (exposure to wind direction,

elevation), proximity to the sea and other local factors (Cox, 1983). Within this region,

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which extends to about 100 k m inland with some parts reaching elevations of over 1200 m,

most climatic elements vary significantly. Therefore, it is not surprising that Sydney's

climate has been considered as a complex of many local climates (Paine et al., 1988).

1.2.2.1 Rainfall Characteristics

In Sydney's climate, the primary rain-producing mechanisms are: major storms, cold fronts

and thunderstorms. Major storms, which are mainly dependent on the deep low pressure

systems in the Tasman Sea, can produce strong winds and heavy rainfall along the N S W

coast (Bureau of Meteorology, 1991a). These systems can be classified by their origin into

several types which occur at different times of the year. In contrast, cold fronts produce

comparatively little rain in the Sydney region, especially in summer, when the flow behind a

cold front is most often from the south (Colquhoun et al., 1985). Little moisture is

provided unless a following upper-level low pressure trough provides additional instability.

Generally, Wilson and Ryan (1987) found that Sydney appears to be a location where only

a small amount of the total precipitation is due to mechanisms associated with fronts.

Thunderstorm activity is also acknowledged as an important rain-producing system in the

region, particularly in late spring and the summer months. Thunderstorms can produce

heavy rainfalls resulting in a considerable contribution to the annual precipitation over the

Sydney region.

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Although Sydney's annual average precipitation is estimated to be about 1200 millimetres,

it tends to be erratic and unreliable. This means that annual rainfall over the Sydney region

is extremely variable spatially and temporally, and this may reflect the occurrence of the


large thunderstorm events in the warmer seasons. To gain an appreciation of longer term

variability of rainfall, examination of the historical record is useful. The station with the

longest record of rainfall in the region was the Observatory Hill station (it has been closed).

Figure 1.3 shows the annual totals from 1900 to 1990. Considerable variability is evident,

the highest fall recorded being 2194 m m (1950) and the lowest recorded, 625 m m (1976).

The totals during the period from about 1990 to 1950 exhibit less inter-annual fluctuation

than after 1950 and more recent times.

It is likely that the greatest spatial variation in rainfall can also be associated with changes

in topography and distance from the sea, both of which can cause considerable variations in

the annual precipitation in the Sydney region (Figure 1.4).

THE SYDNEY REGION

Figure 1.4 Median annual rainfall (in m m ) of the Sydney region. The isohyets are derived from all available data from rainfall stations with at least 20 (1832-1986) years of record. The number of years of record varies for different stations in the region (Bureau of Meteorology).

It is evident from the annual rainfall distribution map, for instance, that an area which is

located west of Wollongong, over the Illawarra Plateau, receives more than 1600 m m rain

annually. While the highest rainfall in Sydney of more than 1300 m m occurs over the more

elevated parts of the northern suburbs that form the Hornsby Plateau. The lowest rainfall

occurs in low-lying pockets of the western Cumberland Plain. For example, both Windsor

and Campbelltown, which are located in lowland areas, receive less than 750 millimetres

N

S

TASMAN

SEA


per annum. The Blue Mountains, located in the west of the study area, in contrast to the

low relief of the coastal plain, receives much more rainfall (more than 1300 m m ) . This

major topographic unit, with more than 1200 m elevation, receives some of its intence

rainfalls in the summer months. In addition, in the south-east of the Sydney region, the

Ulawarra Escarpment appears to be partly responsible for inducing a minor rain shadow

effect over the central part of the Sydney region, as well as increasing local rainfall quite

significantly (Bryant, 1982).

It has been suggested that rainfall in the Sydney region falls uniformly throughout the year

when compared to other parts of Australia (Bureau of Meteorology, 1991a). As it shown

in Figure 1.5, there are, however, considerable variations in monthly average rainfall

amounts over the year for the three selected stations located in different parts of the

region. These stations are: the Sydney Regional Office located in the east of the region

near the coast; Richmond station located in the North-west of the study area (inland); and

Katoomba which is located in the Blue Mountains (see Figure 1.2).

Figure 1.5 Average monthly rainfall at three stations in the Sydney region (1960-93).

Figure 1.5 gives the average monthly rainfall amounts during January to December at these

three stations from 1960 to 1993. The wettest month, the month in which the average

rainfall is highest, is different for the three stations. Generally, during the warm months

(November to March) when thunderstorms and easterly airstreams prevail, the monthly

rainfall is high. This was noted by Fitzpatrick and Armstrong (1973) w h o found that in the

Sydney region there are clearly steeper gradients of rainfall in summer, a reflection of the

influence of prevalent thunderstorm rainfall. Also, there is a secondary maximum in June

affecting the region in the cooler months, which may be attributed, in part, to the frequency

of east coast lows (Holland et al., 1987). As stated in 1968 by Gentilli, the rainfall of


Sydney can be seen as a complicated regime, with two or three major peaks during the

year, and with the driest period in late winter or early spring.

1.2.2.2 Temperature Patterns

Temperatures in the Sydney region vary widely from place to place. As the Sydney region's

location is adjacent to a water mass in the east and relatively high ranges in the west, it

escapes extremes of temperature. However, within the region the range of temperatures is

relatively high. It has been shown by Fitzpatrick and Armstrong (1973) that the variation in

temperature can be caused by differences in elevation and distance from the coast, as well

as by other factors, such as aspect and slope of a particular site and the surrounding

terrain. In Sydney, temperatures are mildest near the coast with a few extremely hot or

cold spells during the year. Inland on the plains, temperatures in excess of 35 degrees (°C)

occur regularly during the summer. Generally, the highest average temperatures occur in

the low-lying central parts of the region, while the lowest occur over the Blue Mountains

in the winter. The range of average maximum temperatures across the region is about 7 °C.

Figure 1.6 shows the variation of the average monthly temperatures (maximum and

minimum) at the three selected stations in the Sydney region throughout the year. More

details about temperature patterns of the Sydney region are given in Chapter 4, where a

close relationship amongst thunderstorm rainfall and temperature data will be considered.

35

30 f •

9

ft. 15

8

io --

5 --

\ N A.

* . -A

•Sydnry R. 0 Max

H 1 1 1- 4 \ h

Sydnry R. 0 Min

• Richmond Max

Richmond Min

A Katoomba Max

Katoomba Min

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 1.6 Average monthly maximum and minimum temperatures (°C) at Sydney.

The variability of maximum temperatures from day to day is also greatest in the summer

and increases with distance from the coast. During the summer the difference between the

90th and 10th percentiles (which is a measure of variability) ranges from about 8 °C at


Observatory Hill, Sydney (Figure 1.7) near the coast, to 14 °C at Richmond, about 55 k m

inland on the plain (elevation = 19 m ) and at Katoomba (not shown), about 90 k m inland

on the Central Tablelands, with 1030 m elevation. Distance from the sea is particularly

prominent in respect of maximum temperatures in summer.

During the winter the variability of maximum temperatures is around 6 °C and is fairly

uniform across the region, but the difference between the maxima at the three locations

varies throughout the year. While in winter the maximum diurnal temperature is quite

definite and occurs around 3 pm, in summer it occurs between 11 a m to 2 pm, with a slight

maximum at 2 pm..

35

5 -:

-•—Percentile 10

- 0 — Percentile 50

-A—Percentile 90

Jan Feb Mar Apr May Jun Jul

Months

Aug Sep Oct Nov Dec

Figure 1.7 Percentiles of maximum temperature at Observatory Hill, Sydney (1900-1990).

1.3 Objectives of This Study

The main aim of this study is the organisation of thunderstorm rainfall data in time and

space within the Sydney region. The second aim is to find any relationships among

thunderstorm rainfall and several environmental factors (climatic and physiographic

parameters of the region). In particular this study addresses several questions:

1) Are there temporal distribution patterns for yearly, seasonal, monthly and diurnal

thunderstorm rainfall in the Sydney region?

2) Are there some possible causal relationships amongst climatic factors of the

region and thunderstorm rainfall?

3) Are there recognisable spatial variations in thunderstorm rainfall?


4) What are the average daily thunderstorm rainfall distribution patterns for spring

and summer?

5) D o the physiographic parameters of the Sydney region significantly control the

spatial distribution of the largest thunderstorm rainfall events?

1.4 Research Significance

The study of thunderstorm rainfall in the Sydney region is important for many reasons.

Although the climatology of severe thunderstorms activity in the region has been studied

by Griffiths et al. (1993), several features of thunderstorm rainfall climatology in the

Sydney region have not yet received a similar level of attention.

Firstly, the temporal distribution of thunderstorm rainfall in the Sydney region, using daily

thunderstorm rainfall data, has not been, in detail, studied over long time periods by

previous investigators. Secondly, the spatial variation and distribution of thunderstorm

rainfall, throughout the Sydney region, has not been analysed completely, for example on a

monthly or seasonal bases.

Table 1.1 Examples of thunderstorm rainfall events causing flash floods in the region.

Location Subject of Floods and Estimated

Date Damage by Thunderstorms Damage, m$

18 Jul 1965 North and South of Sydney unknown

10-11 Mar 1975 Eastern Parts of Sydney and Illawarra District >20

10 Nov 1976 Metropolitan Area* >40

29 Dec 1980 Sydney and Suburbs >50

5-9 Nov 1984 Most Parts of Sydney Region > 100

18 -19 Feb 1984 Illawarra District (Dapto) >6

25 Oct 1987 Illawarra and Metropolitan Area unknown

3 Feb 1990 Metropolitan Area >30

18 Mar 1990 Liverpool, Ryde and City areas 313

21 Jan 1991 Northern Suburbs of Sydney 560

* The Metropolitan area refers to the entire contiguous built up area in the Sydney region,

including suburbs such as Campbelltown, but not separate urban areas such as Wollongong.

** The 'City' is a term often used loosely. Some writers use it in the same sense as 'Metropolitan

area', but here it refers to the CBD (Central Business District) and older suburbs.

In addition, previous studies have not emphasised the importance of the effects of some

climatic factors and the physiographic parameters upon thunderstorm rainfalls. Some

researchers, for example Williams (1991), view an understanding of the physical

environment as essential for an understanding of the weather. Therefore, a good


knowledge of the locations of towns, mountains and details of landscape features is very

useful when dealing with rainfall modelling. Advantages can be obtained by the studying

thunderstorm rainfalls in relation to these parameters.

(a) Roadway at Point Piper

(Eastern Suburbs, Rose Bay)

(b) Flood and erosion

at Scots College

(Eastern Suburbs, Rose Bay)

i _<y v

Plate 1.1 (a and b) Gives examples of some extensive and serious damage caused by thunderstorm rainfall in the Sydney region.

Finally, it is well known that the greatest proportion of the summer rainfall of the Sydney

area comes from thunderstorm activities, occasionally causing flash floods in the region

(Colls, 1991; Egger, 1991). Flash flooding occurs when the intense sudden rainfall from

thunderstorms cannot be absorbed or drained away quickly enough. City areas can also

3 0009 03201123 6


experience flash flooding when the rainfall is too intense for the thunderstorm water

drainage systems to cope with it (Bufill, 1989; Weeks, 1992). Such thunderstorms are

most c o m m o n in the warm months, but little is known about the relative contribution and

importance of temporal and the spatial variability and distribution of these thunderstorm

rainfalls over a long time-span. Table 1.1 summarises the examples of thunderstorm rainfall

events causing flash floods in the Sydney region (Bureau of Meteorology, 1965 to 1991).

Some researchers, such as Colquhoun and Shepherd (1985); Eagle and Geary (1985);

White (1985) and Bryant (1991) described the Sydney thunderstorms of November 1984 in

which insurance losses from flash flooding exceeded $100m. The damage cost in Sydney

from flooding events is particularly high when urban areas are involved. For example, as a

result of the flood that affected the Sydney area in August 1986, damage totalling

approximately $ 100 million occurred, and six lives were lost. In 1990 and 1991 (Spark

and Casinader, 1995), two other thunderstorms caused damage to properties totalling $

599 million. Rural flooding can result in significant crop and stock losses and increased

erosion (see Plate 1.1).

Joy (1991b) has estimated that the total annual cost of flooding to Australia is $ 380

million, and thunderstorms, it is estimated, cause more than 1 6 % of the annual average

costs of natural disasters. More recently, Ryan (1993) calculated that since 1967-91

insurance payouts were more than $ 1808 million for severe thunderstorms in Australia.

Surprisingly, existing records show that in the Sydney region insurance costs have been

more than $ 1100 million in the same period. Most of the damage has been caused by the

direct or indirect effects of flash floods, with considerable loss of lives. Urban areas,

particularly residential and commercial properties, were the most affected by these severe

storm events (detailed inBlong, 1991; Smith, 1993; and Joy, 1993).

Therefore, it is important to obtain temporal and spatial distribution models of this

thunderstorm rainfall. Such information is especially relevant, for instance, in city designing

programs, channel network, the location of rain-gauge networks (Davidson, 1981), for

disaster management, and to the insurance industry (Hobbs and Littlejohns, 1991). As a

result, this research may be considered to lie within the bounds of applied climatology

1.5 Data Management and Modelling Techniques Applied

A variety of data with different scales and origins were used to assess the distribution of

thunderstorm rainfall distribution in time and space for the Sydney region. These data

originated from the National Climate Centres; the Sydney Water Board, and the Sydney

Regional Office of the Bureau of Meteorology. Rainfall data, used to investigate rainfall

variability, either spatially or temporally, should, as far as possible, be homogenous. Like


most rainfall data collected over time in Australia, that for the Sydney region suffers from

inconsistencies and errors in measurement that weaken the absolute confidence that can be

placed in the observed data (Lavery et al., 1992; Griffiths et al., 1993). Specifically,

inhomogeneities in rainfall records include:

Changes in observing practices;

Changes in exposure of rain-gauge;

Changes in station location (both in altitude and position);

Changes in the type of gauge used; and

Missing data (Nicholls, 1995; Karl, 1993).

In addition, data used in the present study were collected by different agencies. Some

investigators note that thunderstorm reports are probably the most 'noisy' and biased of all

meteorological data (Batt et al., 1995), and that for many types of extreme events, such as

thunderstorms, the maintenance of long-term homogeneity in observations is most difficult

(Nicholls, 1995).

Few of these limitations could be assessed in detail in the present study because such

factors are poorly documented for Australian rainfall stations. As the present study is event-

based, some steps could be taken to ensure spatial data continuity for the larger

thunderstorm events. First, the raw data on thunderstorm days and associated rainfall

amounts were extracted from data tapes provided by the National Climate Centre. This

source contained all thunderstorm days from 1960 to 1993 for 30 thunderstorm recording

stations. The timing of these events were corroborated using other data sets provided by

the Sydney Regional Office of the Bureau of Meteorology. Next, all stations with less than

a seven year record and having more than three years of missing data were excluded. This

left 15 stations that record thunderstorm events.

Then, rainfall data was extracted from the more that 400 meteorological stations recording

daily rainfall in the Sydney region. This data was provided by the Bureau of Meteorology

(288) and the Sydney Water Board (112). These stations covered the period from 1960 to

1993 inclusive. To restrict these data, stations with less than a 10 year record were

excluded. In addition, stations which had not contributed sufficient thunderstorm rainfall

observations, according a criterion (at least 100 observations), were also excluded (see

Chapter 6). This left 191 rainfall stations in the data set, 134 from the Bureau of

Meteorology and 57 from the Sydney Water Board.

Similar data sets have been used successfully by numerous researchers to provide

significant insights into rainfall in the Sydney region. These studies are summarised in Table

Introduction

1.2. Further criteria for limiting the data are described in Chapter 3 for thunderstorm

observations, and in Chapter 6 for the amount of rain falling at stations during

thunderstorm events. These criteria have ensured that only the largest of thunderstorms

(1584 events for the study of temporal variation, and 347 events for spatial analysis) were

considered in this study.

Table 1.2 Examples of studies that have used thunderstorm data in the region.

The Main Aim of the Study Author(s) Period of Data Used

Spatial and temporal distribution of Williams, A. 5-9 Nov. 1985


Climatology of flash-floods in the Sydney Speer, M., Geerts, B. 1957-1990

Metropolitan area

Thunderstorm distribution in the Sydney Matthews, C., Geerts, B. 1965-1989

area

Climatology of severe local storms in N S W Batt, K., Hobbs, J. 1991-1995

Following the considerations described above, data were summarised and reduced to

managable proportions by writing several specific computer programs (presented in

Appendix A ) . The process of data reduction also involved a number of other steps, some

of which, such as data transformation, editing, coding and the generation of new variables,

have been done by using a variety of commonly used computer programs. For example, the

Microsoft Excel program (Apple Macintosh and P C computers) was widely used for

manipulating, summarising and analysing data as well as for graphing purposes. Other

computer application programs such as Ms-Dos (for editing data) Clarisworks (as a data

base) were likewise used extensively in this study.

In the second stage, a major objective was to reduce the complexity of the subject to

clearly define the climatological relationships. Because of the data complexity, a set of

tools, including statistical, mathematical, and the Geographic Information Systems (GIS)

techniques were applied to the available data.

Accordingly, two statistical computer programs, the JMP (SAS Institute Inc, 1989) and

SPSS (Norusis, 1994) have been used for statistical analysis. These statistical techniques

consist of descriptive and inferential statistics (according to the nature of the variables) are

applied to find possible associations between those variables. Both the descriptive statistics

and inferential statistics are frequently used in this thesis. Detailed descriptions of these

analytical methods are ? in numerous text books.

Using the above-mentioned statistical programs, a wide range of descriptive statistics were

applied to the data sets. In most types of climatic analyses these statistics are commonly


used, firstly, to organise large data sets, and secondly to summarise such data either by

measuring the central tendency or data dispersion, by using inferential statistical techniques

to find bi-variate relationships. For example, the relationship between thunderstorm rainfall

and elevation of the study area was considered using a simple regression technique. Other

inferential statistical techniques which were used in this study are: the chi-square method,

t-tests, and some simple to multiple regression methods.

Each chapter details the data-analysis procedures used. For example, in Chapter 3 the

Nearest Neighbour Analysis ( N N A ) technique was used to establish some possible

relationships amongst thunder-recording stations in the Sydney region. The aim was to

select the best possible thunder-recording stations as they could represent thunderstorm

activity in the region. This technique was again used, in Chapter 6, to determine whether

the two networks of rainfall stations are compatible in the Sydney region.

The gamma functions (beta and alpha values) are mathematically used to find the

probability distribution of thunderstorm rainfall amounts at each rainfall station in the

Sydney region. The purpose of using g a m m a distribution is to analyse the spatial variation

of thunderstorm rainfall on a seasonal basis. Because of its importance, this technique is

described in detail in Chapter 6.

Also, in Chapter 6, GIS techniques are used to model the spatial distribution of

thunderstorm rainfall over the study area. They are then utilised to find some expected and

initial associations among rainfall patterns and physiographic parameters of the Sydney

region (see Chapter 7).

1.6 Thesis Outline by Chapters

All relevant material which has been collected using a set of appropriate methods will be

presented in seven chapters. Chapter 2 will bring together a general relevant literature

review on thunderstorm rainfall. This literature review will consider research questions

which can be related to those thunderstorm activities which cause rainfall. The strategy of

this literature review is to categorise material which will lead to the development of each

question to be tested using various methods.

In this thesis, a methodology chapter was not included because each of the chapters where

results are presented (3, 4, 6 and 7), have their o w n method section, describing a variety of

techniques which were applied for different sets of data. Since the use of GIS in rainfall

studies, at least in Australia, is a relatively new notion, sources of data and techniques in

GIS, particularly its purposes and applications in climatology, are explained in Chapter 5.

The relevance of this chapter to the overall thesis can be found specifically in chapters 6


and 7, where a set of GIS methods were applied to illustrate thunderstorm rainfall data in

space in relation to the Sydney's physiographic parameters.

Chapters 3, 4, 6 and 7 will present the various results of this research. The main aim of

chapter 3 is to characterise the average temporal distribution of thunderstorm rainfall in the

Sydney region. In this chapter the temporal distribution of thunderstorm rainfall, for

different time-scales, will be described, while in Chapter 4 the possible causal relationships

between climatic variables and thunderstorm rainfall data will be examined on a monthly-

basis. The overall goal of this chapter is, therefore, to determine the significant levels of

associations among the variables.

In Chapter 6, the spatial variation and distribution of thunderstorm rainfall patterns will be

analysed using the g a m m a distribution technique at each rainfall station. T o compare

g a m m a values with actual rainfalls, a GIS method will also be used to visualise the spatial

distribution of thunderstorm rainfall amounts over the Sydney region. Then, it will be

argued in Chapter 7 that the spatial distribution of thunderstorm rainfall over the Sydney

region is largely a function of the interplay and interaction between different physiographic

factors such as elevation, proximity to sea, and urban landuse. In order to examine the

possible associations between these parameters and rainfalls, a GIS technique and some

statistical procedures will be employed to assess the strength and significance of the

relationships between variables.

The final chapter, Chapter 8, will, discuss the results obtained and their relationship to

those research questions regarding thunderstorm rainfall amounts and distribution in the

study area. This will help in returning to the original research questions and unifying the

aims of this thesis. This concluding chapter re-states and aggregates all the information

from the preceding chapters in terms of the aims of this thesis. Then, based on the results

obtained, advantages and disadvantages of all techniques used will be outlined. The last

part of the chapter will offer some suggestions for future research.

All computer programs produced in this study to extract data or for other purposes, will be

located in the Appendix A. Data that are referenced to different parts of the thesis or

presented in summarised form in the text, will also be shown in the associated appendices

B to E, in a complete form.

CHAPTER TWO Literature Review on Thunderstorm Rainfall 17

CHAPTER 2

LITERATURE REVIEW ON THUNDERSTORM RAINFALL

2.1 Introduction

Chapter 2 brings together a general relevant literature review on thunderstorms. It considers

those assumptions which can be related to the thunderstorm activities causing rainfall. The

strategy of this literature review is, therefore, to assemble and categorise material which will

lead to the development of each question to be tested using various applicable methods. All

of the material introduced in the literature review has the purpose, either to develop

arguments for use in the analysis to be described later in the following result chapters or to

unify these arguments. This chapter is divided into the following sections, beginning with the

more general concepts of thunderstorm activities and leading on to the special goals of the

thesis.

2) Thunderstorm Characteristics

3) Synoptic Weather Patterns Creating Thunderstorms

4) Climatic Variables and Thunderstorms

5) Physiographic Parameters and Thunderstorm Rainfall

6) Distribution of Thunderstorms in Australia

7) Synoptic Patterns Associated with Thunderstorm Activity in Australia

8) Sydney's Physiographic Parameters and Thunderstorm Rainfall

2.2 Thunderstorm Characteristics

A thunderstorm is defined as a convective cloud or a collection of clouds in which electrical

discharges, visible as lighting or heard as thunder, is observed by a person on the ground

(Houghton, 1985). Convection systems, which may frequently develop through a

considerable depth within the troposphere, are characterised by cumulonimbus clouds and

considerable moisture (Lutgens and Tarbuck, 1982; Moran and Morgan, 1991). These are

all products of a huge convection system in the atmosphere which can be identified by the

towering cumulonimbus cloud (Figure 2.1). Although a thunderstorm cell is defined as a unit

of convection circulation, thunderstorms may be composed of single or multiple cells (Oliver

and Fairbridge, 1987).


Basically, vertical motion in the atmosphere is the key to many of the characteristics of a

convection system. Upward motion results in expansion, cooling, and eventual condensation

of the water vapour in a stream of air (Wood, 1985). The release of latent heat is often an

important factor in accelerating the convection by increasing the buoyancy (instability) of the

air (Wallace and Hobbs, 1977). Therefore, the prime prerequisites leading to the formation

of thunderstorms are high humidity, high temperatures, an unstable atmosphere, suitable

upper wind structure and a lifting mechanism to initiate convective activity.

Figure 2.1 Schematic representation of a thunderstorm cell (Based upon Bryant, 1991).

2.2.1 Life-Cycle of a Single Thunderstorm

Fairbridge (1967) suggested that often a thunderstorm is a small-scale system which affects a

relatively small area and is short-lived. The life-cycle of such a thunderstorm cell was

summarised by following the three stages that a typical thunderstorm undergoes in its life-

cycle.

In the first stage - the developing stage or growing stage - the rising air may cause small

cumulus clouds to appear, under daytime conditions of unequal heating, particularly during

summer, when convection currents can develop fast. During this stage, strong vertical

updrafts (upward moving air) occur throughout the cloud and consequently no precipitation

reaches the ground (Tapper and Hurry, 1993).

In the second stage or mature stage, the most important changes take place inside the

convection system. Some water droplets begin to freeze, which sets off important drop-

growing processes. In this stage, both upward and downward motions occur in the


convection cell which reaches its maximum development. The mature stage of the

thunderstorm cell may, therefore, be accompanied by violent effects near the earth's surface.

These include squalls, often hail and torrential rainfall (Neiburger et al., 1982). Precipitation

from the mature thunderstorm is intense and composed of large raindrops, literally a

cloudburst (Critchfield, 1983).

In such a situation, the updrafts of a mature thunderstorm produces rain drops through the

condensation of moist air which cools as it rises. W h e n rain drops become too large to be

supported, they fall, however intense updrafts of a severe storm can suspend huge amounts

of rain before releasing a deluge onto the ground. Such rain can reach an intensity of more

than 200 millimetres per hour, provided the environment is humid enough to feed sufficient

moisture to the storm. Occasionally, these thunderstorms become storehouses for

precipitation leading to flash floods (Lilly, 1986, 1990).

(a) Cumulus stage (b) Mature stage (c) Dissipating stage

Figure 2.2 Three stages in the development of a thunderstorm (After Lutgens and Tarbuck, 1982).

In the final stage - the decaying or dissipating stage - the thunderstorm enters the dissipating

stage and the updraught currents disappear entirely and the air motion in the convective

cloud becomes mainly downwards. During the dissipating stage the thunderstorm cell loses

its supply of moisture and energy and disintegrates, but the thunderstorm will continue to

exist if new cells are added at its margins (Bradshaw and Weaver, 1993). W h e n the

downdraft weakens, the intensity of the rainfall decreases and finally stops. Surface weather

conditions soon revert to their pre-thunderstorm stage. Figure 2. 2 illustrates the three stages

in the life-cycle of a thunderstorm.

2.2.2 Complex Thunderstorm Systems

According to the above suggested mechanisms, the life history of a thunderstorm cell used to

be classified into the three stages, however, within the atmosphere, in the three-dimensional

flow fields associated with a thunderstorm, it is not always possible to distinguish one stage


from the other. The complexity occurs when all three stages of development take place in

close proximity and nearly simultaneously. In such circumstances, thunderstorms organise

themselves into a group of cells where each one is at a different development stage at a

specific instant. This multicell stage of a thunderstorm can become severe when it causes

surface damages (Oliver and Fairbridge, 1987).

Supercell thunderstorms are also large thunderstorm systems comprising a number of cells

probably each at different stages of development (Doswell and Brooks, 1993) which can

produce violent and severe weather conditions. The term 'supercell' was first used for such

thunderstorms by Browning (1962). Later, the use of radar, scanning the thunderstorms in

both the horizontal and vertical planes, has allowed a greater understanding of their structure

both in terms of two-dimensional cross-sections and more recently of three-dimensional

models. Generally, the supercell thunderstorm is defined as a large and violent storm

dominated by one huge cell or supercell in a mature stage of development, which may persist

in a steady state for hours, emphasising the fact that such thunderstorms are frequently

asymmetric both in shape and in the distribution of their weather elements. These supercell

thunderstorms are more highly organised, larger, more persistent and more severe than all

other types of thunderstorms (Musk, 1988). The structure of a supercell thunderstorm (a)

and its an idealized plan view (b) is shown by Figure 2.3.


-OVERSHOOTING TOP

BACK-SHEARED, AHV1V

FLANKING LINE

1

MAMMATUS \' '

STORM MOTION

(a) Schematic visual appearance of a supercell thunderstorm

Anvil edge

Light rain Hill Moderate - heavy rain

| H I Small hail | Large hail

T Tornado

Flanking line

Overshooting top

Ob) Idealized plan view of a supercell thunderstorm

Figure 2.3 Schematic visual appearance and an idealized plan view of a supercell thunderstorm (Based on the Australian Bureau of Meteorology and The U.S. National Severe Storms Laboratory publications).

In Austraia, the supercell thunderstorm - another basic type of thunderstorm is far rarer and

much more violent. Recently, much research has been undertaken in understanding and

modelling much more complex thunderstorms, so called 'supercell thunderstorms' (Bureau

of Meteorology, 1995). They could be the subject of much study in the future because of

their severe weather characteristics, which are notorious for producing damaging hail and

tornadoes (Mitchell and Griffiths, 1993). In case of the Sydney region, occasionally the

supercell and multi-cell thunderstorms can be introduced by some of the synoptic weather

patterns, generally advancing from the south-east and north-east (Armstrong and

Colquhoun, 1976). While these kinds of thunderstorms are rare in the region, they tend to be


more severe than air-mass thunderstorms, and they can persist for a longer time, up to

several days (Bureau of Meteorology, 1995).

These widespread thunderstorms, introduced by supercell systems, are almost always

associated with unstable weather systems (for example, lows and troughs) where they may

cause rainfall to develop along the region. Perhaps, the severe thunderstorms over Sydney

Metropolitan area on 21st August 1971 (Bahr et al, 1973) or thunderstorm cells which

advanced to Dapto in February 1985 (Shepherd and Colquhoun, 1985) are specific examples

of such thunderstorms causing damaging flash floods. T w o similar episodes took place at

18th March 1990 and on 21 January 1991 producing considerable intense rainfall from

thunderstorms (Armstrong and Colquhoun, 1976; Mitchell and Griffiths, 1993). There were

some evidence that these storms were supercell thunderstorms. Radar and other

meteorological data were supportive of the conclusion that the damages were caused by high

precipitation supercell thunderstorms (Spark and Casinader, 1995). It appears certain that in

warm seasons (spring and summer) the increased influence of such thunderstorm activity is

responsible for some of the greatest and widespread severe events by producing intense and

high rainfall amounts.

Severe thunderstorms also impose dramatic environment impacts (Dargie, 1994). These

thunderstorms produce hailstones with a diameter of 2 cm or more, wind gusts of 90 km/h

or greater, tornadoes, or any combination of the above (Bureau of Meteorology, 1993b and

Johnson et al., 1995). Severe thunderstorms are also able to produce very high intensity

rainfall causing flash-floods (Elliott, 1994). Such floods from thunderstorms, are exacerbated

when the storm moves slowly, so that one small area receives most of the rain. However, the

largest amount of rain occurs when organised lines of thunderstorms form and move in such

a way that several mature thunderstorms pass over the same location within a short period of

time. In such instances, record rainfalls and, thus floods are the result (Bureau of

Meteorology, 1993 a).

2.3 Synoptic Weather Patterns Creating Thunderstorms

A great deal of knowledge has been added in the past 40 years to our understanding of the

initiation of convective systems and their association with different synoptic-scale

circulations (Campbell 1906 and Kessler, 1983). It was found that the distribution of

thunderstorms world-wide varies from year to year and reflects the overall synoptic patterns

and other affecting factors such as: moist warm waters over oceans, and mountains barriers

during the main thunder-producing months. Further studies in thunderstorm distribution

have emphasised that the synoptic patterns provide suitable conditions in which

thunderstorms can develop easily (Atkinson, 1981 and Mortimore, 1990).


Barnes and Newton (1986) suggest that large circulation systems, for example migratory

cyclones and anticyclones in temperate latitudes, provide the general conditions necessary

for thunderstorm occurrence. Such synoptic-scale systems, whose lives range from days to a

week, are very important because their winds transport moist air into the continental regions,

where the main thunderstorm activity takes place. In addition to the role played by these

circulations in carrying heat and moisture horizontally over long distances, their associated

regions of organised ascending and descending motions contribute to thunderstorm

development. These motions also affect the vertical stratification of temperature and water

vapour in ways that lead to selective occurrence of convective storms in restricted regions.

Therefore, most thunderstorms are controlled by the broad-scale circulation systems

(anticyclones and cyclones) and their systematic rising motions, particularly over land areas

adjacent to the western sides of the oceans in subtropical and temperate latitudes.

In the USA, early investigators (for example, Carpenter, 1913) found that there is an

association between thunderstorms and synoptic weather patterns. Later, Blake (1933) noted

that two synoptic patterns are associated with thunderstorms. One pattern consists of air

approaching from the south and east and it is traditionally called the 'Sonara storm'. The

other pattern brings in tropical air from the south and west and is due to a dissipating

'Chubasco' that has penetrated far enough northward to affect southern California.

Regardless of large-scale synoptic weather patterns, the resulting thunderstorms are also

associated with air-mass systems.

Over the European continent, the origin of sever thunderstorms is correlated to the slow

moving low pressure systems and troughs. For example, in the relatively warm summer of

1992 there were several occasions of heavy convective systems with hazardous weather in

central Europe (Kurz, 1993). O n many of these occasions, synoptic weather patterns such

as: the upper troughs moving slowly eastwards, and shallow depressions corresponding to

the troughs and associated fronts were responsible for the development of many

thunderstorm activities (Andersson et al., 1989 and Prezerakos, 1989). In England, Prichard

(1990) found that thunderstorms which occur during summer nights, after a hot day, may be

triggered by cold moving fronts driven by fairly sharp upper troughs. These troughs draw

hot continental air into a zone where there is sufficient moisture to fuel thunderstorms.

However, in Spain Liasat and Ramis (1989) indicated that most convectional generated

rainfalls can be controlled by upper cut-off lows. Over this region, thunderstorms can also be

encouraged by warm air advection from the south or south-east at low levels.

These studies are only a few examples from different parts of the world, suggesting that

within the broad-scale weather systems, thunderstorm activity can take place. Other synoptic

weather patterns on a regional-scale such as fronts, lows, troughs and extreme instability in

the free upper atmosphere are also favoured systems for the introduction and creation of

CHAPTER TWO Literature Review on Thunderstorm Rainfall

many thunderstorms. However, they are probably not necessary nor a sufficient reason for

the occurrence of thunderstorms. Other trigger mechanisms for thunderstorm initiation are

required.

2.4 Climatic Variables and Thunderstorms

It has been suggested that some climatic variables (for example, both air and sea

temperatures) are important in creating or affecting a convection system and, as a result,

explain thunderstorm variation, in time and space (Willet and Sanders, 1959; Golde, 1977).

For example Lutgens and Tarbuck (1982 p:237) suggest:

'All thunderstorms require warm, moist air, which, when lifted, will release sufficient latent heat to provide the buoyancy to maintain its upward flight. Although this instability and associated buoyancy are triggered by a number of different processes, all thunderstorms need an unstable atmospheric environment in which the instability can be enhanced by high surface temperatures'.

2.4.1 Air Temperature

Generally, the air temperature which was primarily assumed to be a function of the amount

of solar radiation received on the ground, has also been known to be one of the main

climatic variables causing convection activity (Critchfield, 1983). It has been already shown

that thunderstorms generally occur within moist, warm air-masses that have become unstable

through surface heating. Because instability is enhanced by high surface temperature,

thunderstorms are most common in the afternoon and early evening, particularly in summer

months when uneven heating generates vigorous convection which can lead to the growth of

storms in a matter of hours.

More recently, Laudet et al. (1994) used lightning data from the Lightning Position Tracking

System (LPATS) to derive a preliminary climatology of lightning in N S W . In contrast with

the traditional thunderstorm observation (lightning seen and thunder heard by observers), the

L P A T S system gives real-time lightning and it is becoming an important tool for

thunderstorm observation and forecasting. Using this system, it was found that the spatial

distribution of lightning (thunderstorms), is seen to be a temperature-related variable more

dominant during summer than during the rest of the year. Over the land in summer the

diurnal variation in thunderstorm occurrence closely follows the diurnal temperature

variation. Therefore, most thunderstorms develop around midday in the spring and summer

months when the potential for convection is usually the greatest and adequate water vapour

is available.

Areas of high elevation facing the sun - which obtain much more solar radiation (Benjamin,

1983) and, as a result, have high surface temperatures, have been suggested, affect


thunderstorm activity, particularly in summer months (see section 2.6.1). However, surface

heating is generally not sufficient in itself to cause thunderstorm activity and, therefore, any

another climatic or non climatic factor that can destabilise the air, aids in generating a

thunderstorm. This simply means that the air temperature should be considered as one of the

factors which is able to create or enhance thunderstorm activity.

2.4.2 Sea-Surface Temperature

Surface heating is another climatic factore that may enhance thunderstorm activity by

supplying moisture to feed convection (Ramage, 1972). Studies by Rodewald (1963) and

Bjerknes (1963) indicate the importance of variations in sea-surface temperature and its

associations with other climatic variables over certain regions, particularly in the North

Atlantic. Bartzokas and Metaxas (1994) suggest sea-surface temperature is a fundamental

parameter in meteorology and climatology of the Mediterranean region. Research has also

been focused on sea-surface temperature patterns and their relationship to rainfall in the

tropics by Ichiye and Paterson (1963). Their results indicated positive relationships between

sea-surface temperature and rainfall amount. Hastenrath (1984) compared selected dry and

wet years for the Sahel in Africa, revealing that wet years are associated with warmer than

normal surface waters and indicated that sea-surface temperatures can modify rainfall

distribution in time and space considerably. In many circumstances a certain relationship

between sea-surface temperature - as a fundamental factor in climatology - and rainfall was

therefore settled.

In Australia, the possible association between sea-surface temperature and rainfall, but not

necessarily thunderstorms, has been well established. For example, Streten (1981 and 1983)

demonstrated that wet years over the Australian continent are associated with warmer than

normal sea-surface temperature. Whetton (1990), by correlating the Victorian rainfall and

patterns of sea-surface temperature anomalies concluded that increased rainfall in Victoria

can be related to warm sea-surface temperature off the north-west coast of the continent.

Also Nicholls (1984) documented the exitence of a relationship amongst the SST anomalies,

the Southern Oscillation, and interannual fluctuations in the Austalian Tropical Cyclone, in a

broad band from south-east Australia through the centre of the continent to the north-west

coast (Nicholls and Kariko, 1993).

It was also found that the sea surface temperature can affect the rainfall distribution in time

and space. For example, in the Sydney region, O'Mahoney (1961) noted evidence of a

correlation between Sydney monthly rainfall and sea temperature at Port Hacking. Later,

Priestley (1964) showed that there is a positive association amongst monthly anomalies in

rainfall, air and sea temperatures along the N S W coast. Subsequent publications by Priestley

and Troup (1966) and Priestley (1970) stressed that some of Sydney's rain comes from


onshore winds, the moisture content and instability of which is increased by the warm

Tasman Sea. However, they indicated that the correlation coefficient was not high (r = 0.2).

Hirst and Linacre (1978) examined connections amongst sea-surface temperature, rainfall

and prevailing winds. Their evidence indicated that rainfall and sea-surface temperature are

positively connected and also that the incidence of onshore winds can increase rainfall

amounts. Correlation coefficients were however low (less than 0.24). Hirst and Linacre

(1978) concluded that sea-surface temperature and onshore winds individually can control

the rainfall distribution in the region. They also suggested that a warmer sea surface and

onshore winds may cause more instability and, as a result, enhance orographic rainfall in the

coastal hills or convective rainfall by bringing more moist and warm air to the region.

Generally, these studies found that the association between sea-surface temperature and

rainfall is small for places which are far from the coast.

Later, Fandry and Leslie (1984) found that easterly flows which are located just off the coast

and parallel to the coast can move west over the Sydney area and appear as meso-scale

phenomena. Leslie et al. (1987) show that these systems can be enhanced by the topography

of the region and by the meridional gradient of sea-surface temperature toward the coast.

More recently, Hopkins and Holland (1994) found that the combination of the Great

Dividing Range, the cooler coastal land mass, and sea surface temperature gradient provides

high zonal baroclinicity favourable for formation and intensification of the Australian east-

coast cyclones. For example, a 24-hour rainfall event on the 1 st August 1990 was simulated

by Golding and Leslie (1993). They showed that falls of over 100 m m were confined to

coast facing slopes of the Great Dividing Ranges from Newcastle in the north to Jervis Bay,

south of the Sydney region. The output of the model simulations indicated that the

precipitation was enhanced about fourfold, with maxima of 90 m m over the sea, and 120 m m

over the mountains. It was also found that over half of the precipitation was of a convective

nature. Sea-surface temperature data in the region, were already correlated to the rainfall by

Bryant (1983a, 1985a, 1988) who suggested significant relationships between monthly sea

level, sea-surface temperature and rainfall at Stanwell Park beach just south of Sydney.

In addition, the effect of the difference between sea and air temperatures on the instability of

the atmosphere is a very important factor. Such instability, particularly over the coastal

areas, can cause thunderstorm activity. Linacre and Hobbs (1977) supposed that in those

conditions, when a warm sea makes the air less stable, a free convection in low winds can be

expected. This mechanism, which is a result of the instability in the atmosphere, can lead to

the growth of tall clouds which are conditionally unstable. Sometimes convection on a vast

scale leads to strong updraughts and turbulence within the cloud, which can cause rain,

lightning and thunderstorms.


The difference between land and sea temperatures may lead to instability and hence influence

the growth of thunderstorms in various ways. For instance, when the daytime heating of the

ground along the coastal areas causes high temperatures, especially in summer, cold air-mass

flows from a cold to warmer surface, from sea to the land (Gentilli, 1971). This mechanism

may enhance the afternoon convection activity. In contrast, when the cold air-mass comes

from the cold land and the sea is warm, nocturnal thunderstorms may occur over the sea

because of oceanic warmth and thus the presence of moisture in the lower layers of the air.

Occasionally, thunderstorms can also be expected, because of the passing of cold air over

warm sea or warm air. This is relatively c o m m o n off the coast of N S W in autumn (Linacre

and Hobbs, 1977).

All of these studies have generally shown an association between rainfall and sea-surface

temperature or air temperature. Although none have established an association directly

between thunderstorm rainfall and sea-surface temperature data, Hirst and Linacre (1978

p. 327) announced, 'a high incidence of onshore winds would enhance convective rainfall, by

bringing the moist air together with a warmer sea-surface temperature'. Both moist air and a

warmer sea can cause greater instability in the coastal atmosphere and, as a result, increase

the tendency for convective rainfall. According to this idea Linacre (1992 p:262) suggests,

'It seems plausible that surface conditions which influence evaporation thereby affect the subsequent rainfall, especially in the case of meso-scale convective precipitation'.

He also assumed that in some circumstances, where high temperatures occur with little wind

there is more possibility of convective rainfall.

On the other hand, there are some arguments (Linacre, 1992) that surface conditions have

little effect on rainfall, except in special circumstances, for at least two reasons. First,

variations in surface conditions are usually not felt beyond a few hundred metres from the

ground, which is much below the level at which rain is formed. Secondly, the sequence of

evaporation, advection, condensation and precipitation normally takes several days. B y that

time the airflow has separated rainfall from its source by some hundreds or thousands of

kilometres. However, it must be noted that in case of convective activity, the precipitation

procedure takes place in a matter of hours or within a day.


2.4.3 El Nino / Southern Oscillation

In the past, the El Nino / Southern Oscillation (ENSO) phenomenon was correlated to the

rainfall variability. The influnce of E N S O on rainfall has been a matter of investigations by

many meteorologiests and climatologists in Australia (Allan, 1985). Recent studies

questioned the stability of relationships between the Southern Oscilation Index (SOI) and the

summer rainfall during the last century (Allan, 1988, 1989; Suppiah, 1992). The instability in

correlation patterns is not only apparent in the Australian region, but it is a c o m m o n feature

in the global scale. Certainly, over the Australian continent, the rainfall data indicate long-

term variations. For example, Pittock (1975) demonstrated a dry phase between 1913 and

1945 and a wet phase from 1946 to 1978 over the Australian region. In another example, the

summer monsoon circulation features were closely linked to various cycles that include the

30-50 day oscillation and E N S O phenomenon over the region. Suppiah (1992) suggested

that the SOI could have a strong influnce on local rainfall. A n inspection of Bureau of

Meteorology (1988a) data - the average number of annual thunder-days in northern

Australia - reveals that the areas having large numbers of thunderstorms, show significant

correlation between summer rainfall and SOI. Interestingly, these areas indicate a greater

number of thunderstorms during the summer season. In the Sydney metropolitan area,

Griffiths et al. (1993) correlated the number of severe thunderstorms with SOI. However,

coefficients calculated were mostly very small (0.214). This has a probability of occurrence

by chance of 0.007, which indicates that the relationship has litle predictive value. Therefore,

in the future, studies on the influnces of phenomenon such E N S O on various thunderstorm

rainfall systems would be useful for providing further information.

Despite these arguments, it can be concluded that climatic variables on a regional scale have

a major influence on rainfall distribution. Ocean waters adjacent to the coast can provide

atmospheric moisture and, as a result, affect the rainfall distribution patterns. Thunderstorms

can also be enhanced by warm and moist air from the ocean and high temperature of the

earth's surface, largely as a result of differential heating.

2.5 Physiographic Parameters and Thunderstorm Rainfall

Many climatologists (Browning and Hill, 1981; Atkinson, 1983) proposed that distribution

of precipitation - over a region in a specific time-scale - is largely a function of the interplay

and interaction between synoptic air patterns (at several scales), cloud physics and

physiographic factors. The influence of each of these factors upon the distribution, amount

and variation of rainfall has been explored both temporally and spatially throughout this

century (Fogel and Hyun, 1990; Bonell and Sumner 1992). These studies have generally

suggested that topography, proximity to sea, and more recently, urban areas are the most

important controlling factors in rainfall distribution and its characteristics.


However, it has been suggested that the effect of these parameters in a wide variety of

latitudes, climates and weather conditions is not the same (Smith, 1982, 1989). Moreover, in

contrast with normal precipitation, which is suggested to be of more modest intensity but of

longer duration and covering a large area (Oladipo and Mornu, 1985), convectional

precipitation has extremely high intensity, a short-duration nature and affects a

comparatively small area with considerable spatial variation at the ground surface.

Therefore, it is logical in a new region to correlate each physiographic parameter with

thunderstorm rainfall distribution rather than rely upon previously defined relationships.

2.5.1 Topography and Thunderstorm Rainfall

Clear relationships between precipitation amount and elevation are now generally established

(Bader and Roach, 1977; Browning, 1980; Hill et al., 1981). The effects of topography on

annual and seasonal rainfall distribution have long been recognised by Salter and Cale (1921)

and Bergeron (1965). Several objective attempts have been conducted to assess statistically

the influence of altitude and other topographic parameters on the distribution of

precipitation. In the most general terms, the orographic impact on cloud and precipitation

enhancement or inhibition is well known (Pedgley, 1970 and Wheeler, 1990).

Orographic precipitation enhancement occurs in a wide variety of latitudes, climates and

weather conditions near terrains of differing size and shape (Schermenhom, 1967; Griffiths

and Saveney, 1983; Storr and Ferguson, 1983). Over the long term, areas of high relief

experience generally greater precipitation amounts and intensities on their windward sides

and near the summits, but often produce a rain shadow on their lee side (Craig, 1980;

Atkinson, 1983). Topographic features such as spot altitude, rise, orientation and exposure

on rain bringing wind, have been suggested to be important topographic factors influencing

rainfall amounts and distribution (Balchin and Pye, 1948). Various authors (for example,

Browning et al., 1974; Atkinson and Smithson, 1974) have discussed the nature of

orographic rainfall in different geographical areas. All these researchers looked at the effects

of topographic features upon precipitation distribution. They found that, generally

orographical precipitation occurs over, and occasionally immediately downwind of the relief,

in a close association with the origin of weather systems.

There is, however, little discussion in the literature dealing with thunderstorm rainfall related

to topographic factors. In contrast with normal precipitation, thunderstorm rainfall was

suggested to be spatially heterogeneous and highly time dependent (Sumner, 1988). O n a

global scale, it was reported that the overall distribution pattern of thunderstorms is

influenced by three primary elements; the intertropical convergence zone, the solar heating

of land masses and finally warm ocean currents (Brooks, 1925). But, on a regional basis, it


has been suggested that the geographical features which encourage convective cloud

formation, and as a result thunderstorm development, are: the land-water boundary, land

heating, particularly on summer days, and mountainous terrain particularly north-east facing

slopes in the Southern Hemispheric (Fuquay, 1962).

Mountains act as a high level heat and moisture source and as a barrier to prevailing air flow

that can enhance convective cloud formation (Chacon and Fernandez, 1985). Because the air

near the mountain slope is heated more intensely than air at the same elevation over the

adjacent lowlands, this may cause a general upward movement during the daytime and the

development of thunderstorm cells. For example, mountainous regions such as the Rockies

and Appalachians in the U S A , experience a greater number of air-mass thunderstorms than

the Plains States. (Lutgens and Tarbuck, 1982). Generally, precipitation systems develop

easily in mountainous areas which can also be subject to local thunderstorm development

during heatwaves. The forced uplift mechanism can provide the final push which causes

atmospheric instability. This itself may release massive potential energy. Such a condition

can trigger-off damaging thunderstorms, whilst most parts of the region may remain sunny

and cloudless (Mortimore, 1990).

In the past, mountain-generated thunderstorms have been studied in several locations. For

example: K u o and Orville (1973) in the Black Hills; Holroyd (1982) in the northern Great

Plains; Klitch et al. (1985) in Colorado; and Banta (1984) in northern N e w Mexico. All these

studies have illustrated that convective activity usually occurs first in mountainous areas. For

example, in a study of a mountain-generated precipitation system in Northern Taiwan, a

radar system was used to investigate the effect of terrain on precipitation systems (Chen et

al., 1991). They found that mountains can obtain high heat and keep moisture which both are

important climatic factors in the producing of thunderstorm activity. The influence of

topography and exposure to moisture sources emerges as the major controlling factor of the

thunderstorm rainfall amount and its distribution.

In addition to the complex interaction between local winds, topography was hypothesised

through the use of many case studies, to be important for generating localised precipitation.

For example, in the U S A the important influence of topographic features on the distribution

of convective rain has long been recognised (Tubbs, 1972). It has been suggested that

orographic features such as hills and aspects to the wind direction, contribute to the

development of convective clouds for one or more of the following reasons: (1) topographic

features may encourage the instability of a conditionally unstable air-mass; (2) the roughness

of the terrain results in a series of vertical perturbations, some of which may trigger the

formation of cumulus clouds in a conditionally unstable air-mass; (3) the hills and mountains

can act as high-level heat sources due to the differential heating of their tops and of the free

air at the same altitudes (Byers and Braham, 1949).

CHAPTER TWO Literature Review on Thunderstorm Rainfall Jl

Again, in the United States, the effect of mountainous barriers on the distribution and diurnal

variations of thunderstorm rainfall was well stressed by Schermenhom (1967), Wallace

(1975) and Mass (1982). These studies found that some thunderstorm cells tend to originate

in the same place because of topographic effects, and then may follow lower surrounding

topography. For example, Astling (1984) studied a relationship between diurnal mesoscale

circulations and precipitation in a mountain valley (Utah in U S A ) . H e found that the

mountainous terrain of Utah is a region where diurnal signatures are present in precipitation

occurrences and in local wind fields. This study indicated that summer diurnal precipitation

modulations are dependent on elevation, with maximum frequencies of measurable events

peaking in the early afternoon at high elevations above 2100 m and nearly three hours later

in mountain valleys below 1500 m.

The summer thunderstorms over southern California, which were studied by Tubbs (1972),

primarily occur over the mountains. It was also evident that the mountains to the south and

east receive many more thunderstorms than those ranges to the north and west. Over the

years, summer thunderstorms have also hit parts of the Rocky Mountains harder than any

other areas of the United States. A study by Easterling and Robinson (1988) and Easterling

(1991) showed that parts of the Rocky Mountains have had the highest number of summer

thunderstorms. Furthermore, thunderstorm tracks can be frequently guided by topographic

features which may locally enhance precipitation when larger weather systems dominate

(Berndtsson, 1989).

In other parts of world, for example, over Nigeria, Balogun (1981) found that the

orientation of the maximum thunderstorm activity lines along the south-eastern part of

Nigeria follows the orientation of mountain ranges. Therefore, he concluded, that even on a

localised scale, the degree of instability, and as a result, the intensity of thunderstorm

activity, can be largely dependant on the topographic features.

In the United Kingdom, a considerable amount of summer precipitation has been attributed

to localised convectional thunderstorms (Mortimore, 1990). In 1962, Shaw indicated that at

some rainfall stations more than 90 per cent of summer rainfall comes under the category of

'thunderstorm'. In this region, topographical features play an important part in the more

local nature of thunderstorm development. It was suggested that mountain ranges can set-off

thunderstorms in potentially unstable airflows and this development can, in some situations,

drift away and further develop and affect large areas of lowland Britain. Fogel and Hyun

(1990 p:126) have generally suggested that for summer thunderstorms,

'The elevation effect may be caused by: (1) an increase in the rate of arrival of events or equivalent decrease of expected inter-arrival time; (2) an increase in the mean rainfall per event which in turn

TWO Literature Review on Thunderstorm Rainfall

may originate from a change in event structure such as changes in event duration or the dependence between duration and rainfall amount. (3) and a combination of (1) and (2)'.

The incidence of thunderstorms in Australia is also higher in the higher parts of the country.

Over much of the Australian continent relatively low relief dictates that weather systems are

comparactively unaffected by local topographic factors, but where considerable relief is

present, such as along the Great Dividing Range and the tablelands of N S W and Queensland,

there can be considerable modifications (Sumner, 1983a). As it was indicated by Linacre and

Hobbs (1977), in N S W there is a clear correlation between the frequency of thunder-days

and elevation. In such area, the occurrence of thunderstorms and rainfall is very much

affected by topographic factors (Batt, et al., 1995). This is because the effect of local

topography is a major factor in the triggering action. The spatial distributions of lightning in

N S W also support the concept that topography is very significant factor in controlling

thunderstorms. This effect is evident in areas surronding the Great Dividing Range and over

the nearby ranges (Laudet et al., 1994).

On the other hand, there is some argument in the literature illustrating the fact that the

enhancement of thunderstorm rainfall is not exactly associated with areas of pronounced or

extreme relief. For example, Castro et al., (1992) have attempted to determine whether the

topography of the area where storm formation takes place has an effect on the behaviour of

storms. The results obtained from an area located in the north east of the Iberian Peninsula,

namely Middle Ebro Valley (in Spain), showed h o w different storms with different internal

structure (unicellular or multicellular) and behaviours were differentially affected by

topography.

In the USA, (in the Santa Catalina Mountains near Tucson, Arizona) Duckstein et al. (1973)

compared precipitation amounts deduced from winter frontal systems with summertime air-

mass thunderstorm rainfalls. They noted that winter precipitation was increased more than

four-fold at 2100 metres as compared to that found at 1200 metres. For the same elevation

(2100 metres), summer rainfall was not quite doubled.

Also, along the Appalachian region, Easterling (1989 and 1990), showed orography plays a

substantial role in determining the thunderstorm rainfall regime at a station. This effect was

acknowledged by the U.S. Weather Bureau (1947), where a decrease was noted in daily

precipitation intensities for the summer months. Also, Easterling and Robinson (1988) have

suggested that the mountain areas of the Rockies is a region with a relatively high probability

of receiving small rainfall amounts from thunderstorms. In addition, it has been found that in

mountainous areas, because of the complex topography, the spatial distribution of

thunderstorm rainfall varies greatly (Fuquay, 1962). Therefore, Smith (1989), suggested that


the thunderstorm activity in a mountainous area varies not only with elevation, but also with

slope angle, orientation and micro topography.

Some researchers, for example Osborn (1982), think that the importance of topography in

enhancing the variations in thunderstorm rainfall distribution for each of the weather types

such as, frontal systems or troughs, is not the same. Generally, the distribution of

thunderstorm rain in mountainous regions reflects variations in wind direction as different

slopes and land surfaces induce thunderstorm activity (Smith, 1975). The final role of

topography, as a generator of thunderstorms in mountainous regions, is as an initiator of

different convective activity between opposing wind streams in different directions. The

relative importance of each of these topographic factors in generating thunderstorms may

clearly change from day to day, as weather conditions change. For instance, in a study in the

Greater Athens area, Amanatidis et al. (1991) found that during summer months the

thunderstorm activity is influenced less by the local topographic features.

Worldwide, the positive correlation of increased thunderstorm activity with altitude is well

documented, especially on the windward side of mountains (Spreen, 1947; Reid, 1973). In

many locations, the effect of mountains on thunderstorm activity is clearly seen on maps

showing the correspondence between patterns of thunder-days and terrain height. The

results from many parts of the world indicate that thunderstorm rainfall-relief relationships

are also positive because topography plays a large part in the formation of heavy showers

and thunderstorms in association with advancing airmasses. Therefore, over long-time

periods 'classic orographic' enhancement of thunderstorm activity should provide the most

suitable explanation for permanent spatial variations in thunderstorm rainfall amounts.

On the other hand, some investigators have pointed out that because of the localised nature

of thunderstorms, topography does not always appear to be an important factor affecting

thunderstorm rainfall amounts. Thus, attempts should be made by climatologists to look at

the thunderstorm rainfall-elevation relationships over both short and long-term periods, for

each individual geographic location.

2.5.2 Effects of Proximity to the Sea upon Thunderstorm Rainfall

Proximity to the sea is known to be a very important factor in producing rainfall as well as

influencing rainfall patterns (Merva et al., 1976; Berndtsson and Niemczynowicz, 1988). In

the literature there have been only few studies that have concentrated on the details of

thunderstorm rainfall mechanisms along the coastal areas. However, theoretical studies have

generally verified that, in many places, wind circulation may enhance the effects of surface

heating and, as a result, initiate a convection system over land near the sea.


Estoque (1962) and Findlater (1963), for example, suggested that mechanisms such as

surface, upper winds and surface heating together, may produce convection activities

adjacent to water bodies, leading to thunderstorms and perhaps rains. Sumner (1983b) found

that these meso-scale circulations in the lower troposphere may develop in response to

differential surface heating in particular, between the land and the adjacent sea. These

mechanisms may cause convectional activity in response to differential solar radiation during

the day, depending on the geographic characteristics and weather conditions of each place.

Such conditions may produce thunderstorm activity and showers. Simplified and idealised

models have been successfully constructed by, for example, Simpson (1964) and Simpson et

al. (1977). Further detailed information on the dynamics and theory can be found in

Atkinson (1981).

The effect of the distance from the sea on rainfall patterns can be seen on Florida's coastal

areas. Perhaps this region provides an ideal example, in which the thunderstorm / local wind

systems develop parallel to coast-lines (Byers and Rodehurst, 1948). The close association

between the time of occurrence of rainfall and thunderstorms over the region was studied by

Gentry and Moore (1954) and L'hermitte (1974). All these studies have emphasised the

importance of coastal areas in which the thunderstorm activity can easily be developed.

In Tanzania, in Dar es Salaam, coastal influence on rainfall generation has been illustrated by

Sumner (1984) using the spatial correlation of daily data. H e found that, because of the

development of rainstorms along the coast, rainfall distribution patterns paralleled the coastal

area. In other places, locally intense thunderstorm activity, reflecting coastal and orographic

influences, have also been highlighted. For example, in Catalonia (in Spain), it was suggested

that complex relief and morphology can create unique precipitation areas. One such area is

characterised by its vicinity to the sea (Periago et al., 1991). Also, in Israel, a relatively high

incidence of convectional rainfall was recognised in the coastal areas (Sharon and Kutiel,

1986). In the western Mediterranean basin, Sumner et al. (1993) found that the heaviest

rainfall that had contributed to severe localised flooding, was convectionally generated by

upper cut-off lows which were often controlled by proximity to warm Mediterranean waters.

More recently, in a study in South Carolina U S A , Changnon (1994) indicated that the sea-

breeze circulation, which influences convection near the coast, exhibits its strongest

influence on heavy rainfalls during the summer months.

On the other hand, it appears almost certain that enhancement mechanisms in coastal areas

vary considerably and they may change from winter to summer (Smith, 1985). The effect

also depends on the direction of the prevailing wind. In particular, heavy rainfall can occur

on steep windward slopes facing the sea, as the hills may trigger thunderstorms and anchor

them in the lowlands (Tubbs, 1972). In the U S A , Easterbrook and Rogers (1974)

concentrated on sea-breeze front thunderstorms along the Georgia coast. They found that


generally, thunderstorms occurred in a preferred zone within 50 km of the coast and not

exactly near the coastal areas. In this region it was also found that winds blowing parallel to

the coast, were seen as important in the generation of thunderstorms.

The proximity factor seems to influence the thunderstorm rainfall pattern, particularly in

coastal areas. In these areas, thunderstorms occur in a preferred zone, because in many cases

the local winds were thought to be able to increase the instability of the atmosphere. Also

the existence of moist winds blowing parallel to the coast had been supposed as important in

the enhancing or generation of thunderstorms. In many places decreases may be seen in

precipitation as the distance from the coast increases.

By contrast, along the coastal areas, on a smaller scale, the general trend may be spatially

reversed particularly during the summer months in which the thunderstorm rainfall patterns

display larger variability. Therefore, along the coastal area because of the complex

topography and the juxtaposition of land and water over short distances, different

thunderstorms rainfall patterns may be experienced in the various seasons.

2.5.3 Impacts of Urban Areas on Thunderstorm Rainfall Distribution

Several climatic studies during the past 100 years have shown that cities develop their own

special internal climate, being warmer and less windy than rural areas (Chandler, 1965; Oke,

1977). It has also been established in numerous empirical studies, for example Landsberg

(1962) and Oke (1979), that city meso-climates are markedly different from those over

surrounding, more natural areas. Moreover, a few key climatic studies in the past 30 years

such as Smith (1975), Huff and Vogel (1978) and Lee (1984) found that cities may also

produce effects on clouds and precipitation that extend several kilometres out from the city.

For several years, the reality of urban effects on precipitation was the subject of considerable

debate (Changnon, 1969; Atkinson, 1968, 1971 and Landsberg, 1981). The possible effects

of urban areas on precipitation have recently received more attention, particularly in regard

to the incidence of short-duration heavy rainstorms of convective origin (Bradshaw and

Weaver, 1993). A great deal has been written about the influence of urban areas on summer

rainfall distribution and thunderstorm activity in the last decades. For convenience, this

section will review the findings of some investigators throughout the world.

A review of research in the USA concerning the modification of rainfall by urban areas

reveals that research was quite poor before Changnon (1968). The 1968 reporting of the

LaPorte precipitation anomaly by Changnon (1968) drew a great deal of attention and

focused national interest in the U S A on the subject of urban effects on precipitation. Results

of this study showed about 31 per cent increases in warm season rainfall, more days with

moderate to heavy rainfall, 38 per cent more thunderstorms, and even 246 per cent more hail

days on downwind of Chicago compared to the surrounding areas.


The interest initiated by this study led to intensive climatic studies of other American cities

by Changnon (1969), Huff and Changnon (1972 and 1973). In general, relatively strong

evidence of urban effects was found in the precipitation distributions for St. Louis, Chicago,

Detroit, and Washington (Harnack and Landsberg, 1975). Although in some cities (for

example, Indianapolis and Tulsa) evidence was weak, the urban effect appeared to be more

pronounced in summer than in winter and usually maximised 50 to 35 miles downwind of the

city centre. However, effects were identified within the city also, at Chicago, Detroit,

Washington, and N e w Orleans (Changnon, 1978).

In the USA, concern over the complex problem of urban effects on clouds, precipitation, and

related severe weather phenomenon finally led to a major investigation of the prior research

of the Metropolitan Meteorological Experiment ( M E T R O M E X ) at St. Louis (Changnon et

al., 1971). This project was the world's first major field-research program with a 5-year field

experiment, and it was sponsored by several government agencies, universities and institutes.

This study attempted to clearly establish how, when, and where an urban area affects

atmospheric behaviour, especially convective rainfall. As a result, summer thunderstorms and

associated rainfall have been found to be 25 per cent higher in urban areas.

Other studies such as Huff (1975); Huff and Vogel (1978); and Changnon (1973 and 1978)

embraced a variety of cities in different climates of the U S A . They mostly reported 5 to 30

per cent local increases in rainfall amounts. These findings were supported by Landsberg

(1981) and Changnon and Huff (1986). These studies generally found the urban effects on

summer weather conditions included greater convective activity, more thunderstorms, 10 to

30 percent more precipitation, and a greater incidence of hail along storm paths to the lee of

the urban area, than over adjacent rural land. According to Huff and Changnon (1986) in

urban areas, not only the probability of stormy rainfall is high, but also very heavy rainfall

can occur in the late afternoons or nights because of additional urban heating of the lower

troposphere.

In the United Kingdom, the possible effects of urban areas on precipitation have been

attracting increasing attention in the past years, especially with regards to the incidence of

short-duration heavy thunderstorms. Parry (1956) and Barnes (1960) attempted to study

single storms over Reading and the Midlands areas, respectively. These studies were the first

two examples of the climatological approach to the urban rainfall problems in U K . Although,

there was a lack of sufficient data because of less dense rain gauge network, their results

indicated the effect of urban areas on the distribution of thunder rain.

Later, Atkinson (1968 and 1969) showed the maximum in thunder rainfall over the central

part of London in summer in a feature associated with warm frontal storms. H e strongly


attributed this pattern to higher daytime temperatures and increased air turbulence over the

city centre. In 1971, Atkinson studied a storm formed about 20 miles west of London. H e

found that when this storm passed over the city, there was rapid cloud growth which

resulted in a maximum of precipitation over the local urban area. This case study indicated

that moving storms can be influenced by the warm moist air of urban areas.

Atkinson (1977) further demonstrated that convective rainfall over London was enhanced by

the presence of the urban heat island. H e found that urban-producing heat can increase the

incidence of thunder rainfall and thunder itself, especially in the hilly areas of the city. This

study suggested that cities which develop an urban heat island also attract thunderstorms,

because the heat island tends to initiate updrafts over the city, which then draws in any

thunderstorms developing in the area.

In the other parts of the world, the study of urban effects upon rainfall distribution have been

followed by several researchers, using available climatological data. For instance, in India,

Khemani and Murty (1973) used the rainfall data of three stations in the region downwind of

the urban industrial complex at Bombay, and of two stations in the nearby non-urban region.

They found that, with respect to the non-urban region, the region downwind of the urban

industrial complex recorded an increase of rainfall by about 15 per cent. They attributed this

increase in rainfall amount to the high level of industrialisation.

On the other hand, the findings of a number of studies - which were about the relationship

between precipitation and urban-dome - have indicated divergent viewpoints. For example,

in a study of three Japanese cities (Tokyo, Osaka, and Nagoya), Sekiguti and Tamiya (1970)

have noted that it often happens that no rain has been observed in the big cities, but in their

outskirts, fairly good amounts of rain (have been) measured. Also, Tabony (1980) in a study

of rainfall trends over London, indicated that any feature of rainfall patterns could not be

attributed to urbanisation. However, he has not rejected the effect of urban areas upon the

frequency of high-intensity, short-duration rainstorms during summer. O n the basis of the

above-mentioned evidence, the following points appear worthy of emphasis as concluding

remarks:

1) Generally, it was found that urban areas can affect incoming solar radiation changing

albedo rates and heating processes, so that in the day, there is a greater take-up of solar

radiation in the city than in its surroundings (Auer, 1978). Some climatologists such as

Vogel and Huff (1978) think that cities decrease wind speeds and, humidity rates but

increase cloudiness and precipitation amounts.

2) The materials used in buildings, paved surfaces and the multi-faced nature of the rough

urban surface, not only make for increased opportunity for absorption of heat, but also


increase heat storage (Henry et al., 1985). The result is that urban areas are appreciably

warmer that their surrounding rural areas during the day. This produces the so-called 'heat

island' phenomenon which leads to rising vertical motion over the cities and subsequent

convectional activities (Hane, 1978). Therefore, cities as warmer locations are often also

areas of enhanced thermal process. The magnitude of the urban 'heat island' has been shown

to be proportional to city population size for European and North American cities (Oke,

1979).

3) Atmospheric pollution may also increase rainfall (Landsberg, 1962). As a result, it was

also suggested that thunderstorm activity may increase, if it is reinforced by increases in the

amount of atmospheric aerosols such as smoke from bush fires, pollution or thermonuclear

devices (Changnon and Huff, 1973).

4) Finally, there is considerable evidence that the thermodynamic effects of urban

environment upon precipitation processes is a very important factor in the initiation of

convection cells, and over large cities, aerodynamic roughness of urban structure may

enhance the development of severe weather systems such as thunderstorm activity

(Landsberg, 1981).

Briefly, the findings of the above-mentioned researchers established the reality of urban

impacts upon anomalies of most climatic elements such as temperature, humidity, and

precipitation amount. In urban areas, air temperatures are generally warmer than the

surrounding areas, so rural cooler air may be drawn inwards to feed the enhanced convection

near the urban centre. Although the relative humidity in the city may be lower than that of

surrounding areas, but the absolute humidity, which shows the actual moisture in the

atmosphere is often higher. This would lead to higher moisture availability for thunderstorms

(Lee, et al., 1991). Interactions between thermal conditions and the availability of

condensation nuclei sometimes can also trigger convection and the development of

thunderstorm clouds. In this situation, there is a slight increase of cloudiness observed and,

consequently, more rainfall over or downwind of a city.

2.6 Distribution of Thunderstorms in Australia

In Australia, the distribution of thunderstorms is shown in Figure 2.4 (Bryant, 1991). The

greatest intensity of thunderstorms occurs in the tropics and along the Eastern Divide. The

highest incidence of thunderstorms occurs in the north, and, not surprisingly, the tropical

north of Australia experiences the highest number of thunderstorms. The number of average

annual thunder-days increase to above 80 thunderstorm days per year near Darwin (Barkley,

1934 and Crowder, 1995). A similar situation exists over south-east Queensland, where a

combination of summer tropical air and the proximity of the Great Dividing Range provide

ideal conditions for the breeding of thunderstorms (Colls and Whitaker, 1990). In the eastern


part of Australia, along and over the Great Divide the number of thunderstorm-days is more

than 40 days per year (Oliver, 1986). A generally low incidence of thunder days, between 10

and 15 thunderstorm per year, can be expected over the southern parts of Australia (Bureau

of Meteorology, 1989). It must be noted that 'as currently understood, there are still

considerable gaps in our knowledge of the occurrence of such events.

D UNOEH «0

OATS

• 0-^9

OATS

£ OATS

60-69

OATS

70-79

OATS

MORE THAN

60 DATS

Figure 2.4 Average annual thunder-days in Australia (After Bryant, 1991).

In Australia, as thunderstorms represent localised areas of instability, their distribution and

intensity are dependent upon factors which increase this instability and, as a result,

thunderstorm activity. Globally, the overall distribution pattern is influenced by three primary

elements: (1) the intertropical convergence zone; (2) solar heating of land masses and (3)

warm ocean currents. These conditions provide a favourable environment for thunderstorm

development during the entire year (Oliver and Fairbridge, 1987). O n a large scale, the

common processes that take place from the large scale synoptic weather patterns, for

example, a trough line, a low pressure area, or the passage of a cold front, can initiate

convection activity and thus thunderstorm rainfall (Kessler, 1986). O n a localised scale, the

initiation of thunderstorms may be caused by the local physical environment. For example,

conditions of instability may be reached when air is forced over a mountain, or the unequal

heating of the earth's surface which can appear as a 'trigger action'. In the following

sections the role of each the synoptic weather systems, and physiographic parameters in the

initiation of thunderstorms will be examined. Thus, the main aim is the understanding of

situations that create thunderstorms.


2.7 Synoptic Patterns Associated with Thunderstorm Activity in Australia

This section concerns the large circulation systems that bring about the general conditions

necessary for thunderstorm occurrence in Australia. Over the Australian continent the

anticyclones which move eastwards across the continent and dominate the weather pattern

of the whole of country, are a predominating influence (Linacre and Hobbs, 1977).

Generally, the seasonal movement of pressure cells - anticyclonic highs and cyclonic lows

and associated troughs and fronts - determines the types of air that are drawn towards the

region (Tapper and Hurry, 1993). However, this movement is also related to rain-bearing

tropical or sub-tropical maritime air-masses and polar maritime airs which dominate over the

continent, in summer and winter respectively. Basic elements in the pattern of pressure

distribution and associated airmasses over Australia in summer are shown in Figure 2.5.

Figure 2.5 Basic elements in (a) the pattern of pressure distribution and of associated (b) airmasses over Australia in summer (Tapper and Hurry, 1993).

Thunderstorm formation which has been associated with instability of air-masses - in relation

to various physical environment (for example, topography) impacts - can also be related to

the atmospheric triggering mechanisms (Ludlam, 1962, 1963). Investigation of the

respective roles that may have been played by each atmospheric process in the initiation of

thunderstorm activity is a very complex task. Meanwhile, in Australia, literature relating the

meteorological situation (synoptic patterns) directly to the occurrence of thunderstorm

activity may be categorised into four main classes: 1) Tropical cyclones and monsoon

depressions of northern Australia, 2) Eastward moving troughs and lows, 3) Frontal activity,

4) Upper atmospheric activities.

1) Tropical cyclones and synoptic-scale depressions are known as major weather-related

causes of thunderstorms along the northern coast of Australia. They are generally small

intense low pressure cells, often less than 100 k m in diameter, associated with stormy

CHAPTER TWO Literature Review on Thunderstorm Rainfall 4J_

weather conditions, extremely strong winds and heavy rainfall (Tapper and Hurry, 1993).

These tropical cyclones are defined as intense cyclonic storms that originate over warm

tropical seas and develop from December to M a y in this region. They bring intense rainfall to

much of northern Australia during this time.

Barkley (1934) mentioned that a majority of storms in the tropics occur just in those

portions of the north-east and north-west coasts that are subject to the incidence of tropical

cyclones. For example, Willis Island off the North Queensland coast, records many

thunderstorms per annum. Also, between Cooktown and Mackay in Queensland, and from

Condon to Onslow in Western Australia severe thunderstorms are recorded each year.

Occasionally these cyclones can move to the south or south-east of Australia and cause

severe widespread rainfall (Whetton, 1988). In western Australia, the cyclones which reach

these coasts are usually almost circular storms with well marked discontinuities of pressure

in the air streams such as would produce thunderstorms.

(a) (b)

Figure 2.6 Represents a pre-frontal trough (a), and a line storm associated with an eastward moving trough (b) over south-eastern Australia (Tapper and Hurry, 1993).

It was also suggested that some of the thunderstorms over northern Australia may be

associated with the monsoon depressions which are responsible for a significant proportion

of the seasonal rainfall (Tapper and Hurry, 1993). These systems are normally located over

land, but may be intensified when they move over warm ocean waters. Although monsoon

depressions are less organised than tropical cyclones, they can often be strong enough to

dominate in the region. According to Barkley (1934) along the north and north-west coasts,

where monsoons dominate, the maximum number of thunderstorms can be expected.


2) Eastward moving troughs are depressions which form in a low pressure trough. They are

not a type of thunderstorm, but the most frequent type of thunderstorms which experienced

over Australia (Bureau of Meteorology, 1989) may occur in them. Occasionally, ahead of a

trough line or east of a low pressure area, there are areas of uplift. This uplift in itself may be

sufficient to trigger a thunderstorm. A trough line moving eastward divides the cooler, drier

southern maritime air from the warm, moist tropical maritime air-masses (see Figure 2.6).

This trough line can enhance the vertical motions just ahead of the trough which leads to

thunderstorm development.

Plate 2.1 Shows a cold front off the South Coast of New South Wales.

In summer time, when the troughs exist between high and low pressures, the moist onshore

trade winds, orographically uplifted, bring substantial cloud and rainfall to the coastal

regions. For example, on Sunday, 11 January 1981, several population centres in Western

Australia experienced destructive squalls associated with one or more severe thunderstorms.

According to press reports, thunderstorms were associated with an eastward moving trough.

Such a trough is a familiar feature of Australian summers (Kingwell, 1982). Intense

downpours were experienced with 24-hour total falls of 34 m m reported during

thunderstorm activity (Bureau of Meteorology, Perth, 1981). Also, analysis of thunderstorm

activity over Tasmania on Monday, 9th, December, 1985, showed that a trough line over


Tasmania was responsible for widespread thunderstorm activity with high rainfall rates of

greater than 120 m m per hour (Jessup and Hughes, 1991).

3) Cold fronts are found at the boundary between adjacent air-masses. The advancing edge

of cold fronts sharply undercuts warmer air, forcing it to rise. This lifting mechanism can

cause rapid instability resulting in thunderstorm activity with considerable rainfall along the

cold front (Hutchinson, 1970; Linacre and Hobbs, 1977). For example, on 22nd December

1990, the study of synoptic patterns showed that thunderstorms which affected Melbourne,

were formed in warm moist air, ahead of the approaching cold front moving eastward

(Treloar, 1991). Thunderstorms may develop if the air ahead of the front is conditionally

unstable and sufficiently moist (Plate 2.1). These thunderstorms tend to occur in long lines

parallel to the front. Sometimes, a continuous wall of thunderstorms may stretch over

hundreds of kilometres (Tapper and Hurry, 1993). Generally, fronts can introduce

thunderstorm activity, particularly over the south-west of Australia where they are c o m m o n

synoptic weather systems. However, it was suggested by Bryant (1991) that the passage of

cold fronts plays a minor role in Australia, compared to the United States, in forcing


4) Upper atmospheric activity has also been associated with thunderstorm development over

Australia. In the upper atmosphere moving air may somehow affect the lower air currents

because of complex dynamic process or in response to the thermodynamic activities into the

atmosphere. For example, when an area of upper air divergence forms, surface air may be

drawn upwards to replace the diverging upper air. Mclnnes et al. (1992) found that upper-

level cold pool systems are usually associated with intense thunderstorm activity. For

instance, a widespread thunderstorm activity over the south-east of Australia on the 1st

March, 1967 and a localised thunderstorm with more than 50 m m rainfall along the east

coast, on 9 February, 1990 were both related to upper shear line and cut-off low

development. In these situations, warm air advection, in conjunction with the splitting of the

jet stream, were also found to be the most significant factors in thunderstorm development

(Mottram, 1967). Occasionally, thunderstorm activity can also be initiated by divergence in

the upper troposphere causing local convection and representing a localised area of

instability (Bryant, 1991).

In short, during the past decades, it has been stressed that, in Australia, thunderstorms can

be introduced by a number of above-mentioned synoptic weather systems which are known

to be primarily responsible for many thunderstorm developments. These synoptic weather

systems also occasionally provide favourable conditions for the widespread development of

severe thunderstorms and associated rainfalls. Essentially, the initiation of thunderstorms in

conditionally unstable air requires some initial uplift - referred to as 'trigger action' - which


may be caused by one or by the accompanying of these synoptic weather systems,

synchronously.

2.7.1 Weather Systems and Thunderstorm Activity in NSW

Weather conditions over south-eastern Australia are usually dominated by the generally

eastward movement of successive high pressure systems. Troughs or cold fronts form

between the highs and are usually associated with low pressure systems over the southern

ocean (Bureau of Meteorology, 1991a). However, these normal weather patterns can also be

substituted by other atmospheric conditions leading to the development of thunderstorms. In

the past, thunderstorm development over N S W has been associated with the following

synoptic weather patterns.

Firstly, it was suggested by Williams (1991) that the above-mentioned common synoptic

weather situations can introduce thunderstorm activity over N S W . For example, in summer,

when the sub-tropical ridge is generally to the south of N S W with centres of high pressure in

the Tasman Sea and the lower pressures over the continent, the predominant humid winds

from the Tasman or Coral Sea can lead to afternoon and evening thunderstorms, particularly

along the ranges or near the trough. At this time the inland trough - sometimes is known as a

'dry-line' - is a boundary between humid air from the east and dry air from the west. This

dry-line advances and retreats with heating and cooling of day and night and is known as a

line of cumulus cells (as a meso-scale convection system). In this situation, high

temperatures and humid air can enhance thunderstorm activity. O n average, a large

proportion of N S W summer rain comes from these thunderstorms which are occasionally

widespread, if there is some upper disturbance present.

Secondly, in the Australian east coast, including the NSW, thunderstorms can be created by

cut-off lows which are described as synoptic-scale cyclonic low-pressure systems (Mclnnes

et al., 1992). These cut-off lows have been long recognised as major sources of severe

weather in south-east Australia (Holland et al., 1987). They are formed when a low pressure

system - normally in the upper air - becomes isolated from the main low pressre system by a

high pressure system that is ridging rapidly east-wards. This process frequently occurs in the

south-east of Australia, particularly along the east coast. The included figure shows a sample

of cut-off low in the region (Figure 2.7).


Figure 2.7 Shows a sample of cut-off low in the region which reaches maximum cyclonic development off the southern N S W coast on 11 July 1987 (From Tapper and Hurry)..

Although there is no accepted general terminology to describe these systems, for

convenience cut-off lows were classified into two groups namely: coastal lows and blocking

lows. Generally they are synoptic-scale systems extending from the surface to the lower

stratosphere. Such systems have been recognised as a major source of severe weather in

south-east Australia. W h e n the cut-off has no manifestation at the surface, it is referred to by

forecasters as an 'upper-level cold pool'. Such systems are usually accompanied with intense

thunderstorm activity and often could be associated with widespread rainfall and flooding

(Hopkins and Holland, 1994).

The southerly Buster is also an unusual synoptic situation which is the name given to an

intense form of cold front that occure along the coast of N S W (Williams, 1991). These

fronts, which often herald the 'cool change' for Sydney, occur mainly in the spring and

summer months also. The most spectacular casses occur at the of a hot summer day. The

wind turns suddenly from the north to the south and blows with some force, with a change in

air temperature of 10 degrees or more. There is not a great deal of cloud associated with the

initial onset. The intensity of southerly busters and their special characteristics appear to be

closely linked with topographic features (Reeder and Smith, 1992). However, the influnces

of these unusual synoptic phenomenan on the nature of thunderstorms have been not

documanted.


(d) DISTANCE FROM RADAR (km)

Figure 2.8 Schematic of the life cycle of the precipitation area of a M C C s as it would appear on rader in horizontal and vertical cross sections during (a) formative, (b) intensifying, (c) mature, and (d) dissipating stages (From Leary and Houze, 1987).

In NSW, it has also been found that meso-scale convective complexes (MCCs) can

introduce thunderstorm activities. This phenomenon was first identified in the United States

(Maddox, 1980). M C C s are a particular class of meso scale (with length scale between 250 -

2500 k m with duration > 6 hours) convective weather systems that occur over the central

plains of the U S A . These systems are generaly much larger than the individual thnderstorms

and lines. In fact, they represent the largest member of the family of convective clouds which

produce a large proportion of the earth's precipitation and thus are important from a

climatological standpoint. M C C s occur in a variety of forms, however, they have several

features in common. For example, they all exhibit a large, contiguous area of precipitation,

which may be partly stratiform and partly convective (Houze Jr., 1993). The precipitation


area of a MCCs exhibits a characteristic life cycle, which is illustrated schematically in Figure

2.8.

In the regional scale a few cases, which occurred over NSW (for December, 1979,

December, 1983 and January, 1990) have been documented by James (1992). These

thunderstorm-producing systems occur when an instability over a large region is released.

Such a self-sustaining system can introduce a heavy rain event or can develop other severe

weather systems. A relatively heavy thunderstorm rainfall can develop, lasting for a

considerable time (more than 6 hrs) and often reaching a peak overnight (James, 1992).

However, it appears that the N S W M C C s are less frequent and generally on average smaller,

have a shorter duration and are less efficient in producing precipitation than those

documented over the United States. Other conditions necessary for M C C s development

include a broad area of high humidity and instability, and the presence of convergence in the

lower atmosphere (Hicks, 1984 and Wilson and Ryan, 1987).

In addition, squall lines were suggested by a number of authors (for example, Williams,

1991) to be an important, but infrequent phenomena in causing thunderstorm activities in

N S W . Williams (1948) and Tepper (1950) have discussed in detail the most notable features

of a squall line. They have attributed to it the following meteorological characteristics: a line

of active thunderstorms moving rapidly; brief but sharp pressure changes; very strong wind

gusts; brief wind shifts, and abrupt temperature falls. Williams (1991) noted that a squall line

is frequently parallel, or nearly so, to a surface front. W h e n thunderstorms develop ahead of

a cold front or a trough-line, conditions are often such that the storms develop along a line

and propagate as a linear system. The line of storms can be several hundred kilometres long.

These conditions were all relevant to the situation on the 21st January, 1977, and the general'

weather conditions appeared to have been favourable for squall line formation.

Consequently, the line of storms developed ahead of a cold front moving slowly across

south-eastern Australia. It was first observed on Sydney Airport radar 55 k m to the south

west of the airport moving rapidly north eastwards. These thunderstorms did extensive

damage to property, one person was killed, and twenty three injured (Morgan, 1979b).

Finally, tropical cyclone and other associated weather features, for example troughs, are also

responsible for a few thunderstorms in the region. From time to time, across the coasts of

N S W , these systems move southwards and cause widespread thunderstorm activity in the

Sydney region. For example, in February 1990, considerable thunderstorm activities were

introduced by the influence of a tropical cyclone "Nancy" and related troughs over the N S W

and Sydney region especially from 7th to 11th. Associated thunderstorms from this activity

produced widespread heavy rainfall over the Sydney region causing flooding in the

Metropolitan areas. Occasionally, Tasman Sea lows can move to the south-west toward the

Sydney region and cause severe thunderstorm activity. For example, between 5th and 12th


November, 1984, Sydney had more than 550 mm of rain from thunderstorms. Earlier, in

February 1984, under similar synoptic conditions to November, 1984, a centre, in the coastal

ranges 60 kilometre south of Sydney, recorded 515 m m of rain in just six hrs and 706 m m in

12 hours, setting new Australian records (Bureau of Meteorology, 1984).

In summary, in producing large thunderstorm activity throughout the State, it could be

suggested that the above-mentioned regional synoptic systems are able to make the

atmosphere unstable if there is enough humidity and there is a suitable upper wind structure,

and finally, if there is a lifting mechanism to initiate convection activities. Generally,

positions of highs and lows over N S W , and also moving troughs and fronts across the State

can introduce thunderstorm activity, and these thunderstorms are occasionally widespread, if

there is some upper disturbance present.

2.7.2 Thunderstorm Development in the Sydney Region

Thunderstorms in the Sydney region can be initiated by local disturbances which occur as

warm, moist air is lifted several kilometres into the atmosphere, producing lightning and, at

times, strong winds, hail, and torrential rain (Colquhoun, 1972; Mitchell and Griffiths, 1993)

(see Plate 2.2).

Nearly all the synoptic weather systems which can introduce thunderstorm activity over

N S W , could probably also trigger more widespread thunderstorms over the Sydney region

by several mechanisms such as fronts or troughs. More recently, investigations into the

occurrence and distribution of thunderstorms within the Greater Sydney Region were

undertaken, in relation to the meso-scale synoptic weather patterns. Three major research

works which appeared are worthy of emphasis here. Therefore, this part of the literature is

fundamentally based upon the following most recent investigations.

The first research was done by Matthews (1993) who examined the spatial distribution and

movement of thunderstorms (from 1965 to 1989) within the region using radar facilities.

Thunderstorms were found to occur in a number of synoptic situations which have been

classified into both frontal and non-frontal systems. In the frontal category were pre-frontal

troughs, pre-frontal and post-frontal systems, while in the non-frontal category were the

inland trough, inland low, offshore low and high in the Tasman Sea. The study of seasonal

variation of thunderstorms distribution has indicated about 75 per cent of thunderstorms

occurred in warm months - spring (September - November) with 30 per cent, and summer

(December - February) with 45 per cent. This study also found that spatial thunderstorm

distributions for the above-mentioned synoptic classes were significantly distinct, physically

plausible and to some extent internally consistent.

CHAPTER TWO Literature Review on Thunderstorm Rainfall 49.

Plate 2.2 Displays thunderstorm development over the Sydney region.

Figure 2.9 presents the anomaly maps of thunderstorm occurrence in the Sydney region,

from Autumn to Summer seasons. Generally, in winter, thunderstorms occur in the east of

the region, particularly over the Tasman Sea. In contrast, in summer they mainly occur over

the land, especially over the elevated parts in the west of the region.

In addition, it was investigated by the above-mentioned study that thunderstorms tracks

within the Sydney area are almost always from a westerly direction - starting mostly in early

afternoon over higher terrain in the west and reaching over the east of the Sydney region

(near and over the City and the Tasman Sea) in the late evening (Figure 2.10). Although

there is a general eastwards movement from the early afternoon to the early evening in the

diurnal distribution of thunderstorms, there are still isolated thunderstorm occurrences that

are independent of time.


Anomaly plot [or autumn Anomaly plot lor winter

Anomaly plot for spring Jl rTTrTWTfTnfTrT

Anomaly plot for summer

Figure 2.9 Presentation of the anomaly maps - using the Terminal Area Severe Turbulence (TAST) radar data - from Autumn, Winter, Spring, and Summer in the Greater Sydney Region. Note that the shaded areas represent areas of low occurrence of thunderstorms with respect to the all years distributions (from 1965-89), while the unshaded areas represent greater occurrence than the all years distribution (After Matthews, 1993).

Another point of interest was that thunderstorms tracks were found to be largely

independent of the synoptic systems (the surface pressure patterns). Matthews (1993) has,

therefore, assumed that thunderstorm movement may be related to the dynamics of the

atmosphere at all levels, particularly the upper-level atmosphere. Thunderstorms occurrences

are also assumed to be related to main topographic factors of the region. This latter point

was again emphasised by M a y (1995, personal conversation). In spite of the topographic

controls upon the distributions and movement of thunderstorms, figures 2.9 and 2.10

suggest that thunderstorms also follow the sources of heat and moisture in the region in their

seasonal and diurnal distributions.

CHAPTER TWO Literature Review on Thunderstorm Rainfall 5/

Figure 2.10 Diurnal distribution of thunderstorm occurrence for the different time periods (local time) in the Greater Sydney Region. Shaded areas represents areas of low occurrence with respect to the all years distribution (1965-89) (After Matthews, 1993).

In the Sydney region, Speer and Geerts (1994) have more specifically presented a synoptic

climatology of flash-floods-producing storms for the period 1957 to 1990. 94 'flash flood'

events - many of which were associated with thunderstorms - have subjectively been

classified into four synoptic-meso-scale groups: 1) Easterly troughs; 2) Pre-frontal systems;

3) Lows; and 4) Post-frontal systems.

1) Easterly troughs events are known to be the most common (39 per cent), and usually the

longest in duration due to the quasi-stationary nature of the surface trough and upper-level

trough. T w o obvious synoptic types within this category are known. The first type occurs

when the surface trough moves east, from a quasi-stationary position mostly on the western

side of the ranges. The second type, referred to as the offshore trough, occurs when a quasi-


stationary surface trough in the easterlies is located just off and parallel to, the coast.

Shanahan (1968) found that associated thunderstorms with these systems can move over the

Sydney region and they can produce heavy rainfalls causing flash floods. Figure 2.11 gives

examples of easterly troughs: a) an onshore trough occurred on 25 March 1968, one and a

half hours before flash flood rainfalls, and b) an offshore trough occurred on April 1985 two

and a half hours before the flash-flood rainfall.

2) Pre-frontal systems (31 per cent) occur typically in a north-westerly flow with a mid-

tropospheric short wave to the west. They are typical of late spring or summer and are more

common in the afternoon. These systems are usually associated with intense though brief

bursts of precipitation. Speer and Geerts (1994) distinguished two types in the pre-frontal

category. The first is characterised by a meso-scale surface low ahead of the main front. The

second type, with a pre-frontal surface trough through Sydney is more common. Associated

thunderstorms with these systems develop in a convergence zone between north-east and

north-west winds at the surface and low levels. They are generally common features in

eastern N S W over the warmer months producing thunderstorms when high pressure in the

Tasman Sea has been advancing moist north east winds. These systems may persist for up to

several days introducing thunderstorm activity with heavy rainfalls and, as a result, flash

floods in the region. Figure 2.11 shows two examples of pre-frontal systems: c) 14

December 1971, two hours before the flash-flood rainfall, and d) 9 March 1989, before

flash-flood rainfall.

3) Lows - which developed from low pressure systems - the majority of events (17 per cent)

classified as lows, evolved from a depression developing on an old front in the Tasman Sea.

The coastal south easterly winds provided low-level moisture flux. Based on location of the

low with respect to the Sydney region, and as for easterly troughs, two types were known.

The first type is characterised by a slow-moving single or complex system of lows to the

west of Sydney over south-east of Australia, and was referred to as an onshore low which

causes 3 per cent of flash floods. The second type occurs with a single or complex low

pressure system over the adjacent Tasman Sea with a ridge along the coast, and was referred

to as the offshore low (14 per cent). For example, on the 23nd November, 1979, such

systems passed over Sydney and the flash-flood was produced from thunderstorms as the

surface low moved across Sydney. Generally, lows exhibit the weakest diurnal and seasonal

variation. Figure 2.11 gives an example (e) of the offshore low, case of 25 October 1960.

4) Finally, Post-frontal systems are rare (13 per cent) and strongly modulated diurnally and

seasonally, towards the warmer periods. They are associated with a southerly change,

aligned roughly parallel to the Great Dividing Range. These synoptic categories were also

known to be important in producing some of thunderstorms, and as result, introducing flash


floods in the Sydney Metropolitan area. Such thunderstorms are the most common

phenomenon, especially in spring and summer. A n associated synoptic chart for this class is

given in Figure 2.11 which shows an example of a post-frontal system (f) causing flash-flood

on 18 March 1990.

Figure 2.11 Presents examples of six meso-scale synoptic weather systems causing thunderstorm activity in the Sydney region. The thick broken line indicates the trough on the mean sea level pressure chart (After Speer and Geerts, 1994). See text for details.

Most recently, the spatial distribution of deep convection in the Greater Sydney area was

examined by Matthews and Geerts (1995), using an archive of 25 years of radar data located

at Sydney's Mascot Airport. This research studied a sets of different characteristic

thunderstorm distributions in Sydney under different synoptic conditions. It was found that

thunderstorms occur in a number of distinctively different synoptic settings, both frontal and

non-frontal. Three types of frontal settings (pre-frontal trough, pre-frontal and post-frontal),

and also three types of non-frontal situations (inland trough, offshore low and offshore high)

were distinguished. Figure 2.12 gives six examples of selected mean sea-level pressure

( M S L P ) patterns in which thunderstorms occurred.


Figure 2.12 Selected M S L P charts illustrating the six synoptic classes: (a) pre-frontal trough (6/5/85 at 0200 U T C ) ; (b) pre-frontal (16/12/85 at 0100); (c) post-frontal (9/1/87 at 1800); (d) inland trough (8/1/85); (e) offshore low (20/6/85 at 2300); (f) offshore high (14/1/86 at 0700). Note that only the last two digits of M S L P in hPa are shown (After Matthews and Geerts, 1995).

Although there are some distinguishing differences between the two above-mentioned

classifications, the differences are of little importance, it can be accepted that all synoptic

weather systems are able to introduce thunderstorm activity and therefore introduce their

associated rainfalls over the region. In one step further, Matthews and Geerts (1995)

compared the findings with data obtained from much more recent thunderstorm detecting

facilities, such as, a lightning network and a new automatically radar system, as independent

sources. They constructed a thunderstorm density model based upon the new radar data for

above-mentioned synoptic conditions. Because of their importance, the thunderstorm density

and lightning density maps are given in Figure 2.13 and Figure 2.14 respectively.

The results (normalised storm probability and density maps) indicate that the distribution of

thunderstorm activity is not the same for all synoptic situations. Although there are distinct

differences in synoptic conditions by which thunderstorm occur, general patterns can be

understood in terms of low-level flow, topography and land-sea differences.


Figure 2.13 Thunderstorm density model based on new radar echoes, under the following synoptic situations: (a) pre-frontal trough; (b) pre-frontal; (c) post-frontal; (d) inland trough; (e) offshore; (f) offshore high (After Matthews and Geerts, 1995).

The more general findings of these investigations indicate that thunderstorm activity is

stronger near the coastline, especially the southern coastline and the northern beaches, due

to the coastal topography. Places such as the Hornsby Plateau, Illawarra Plateau and the

Blue Mountains are also subject to the highest number of thunderstorms. In winter,

thunderstorm cells are generally more c o m m o n offshore (over the coastal areas and over the

Tasman Sea). In summer, on the other hand, thunderstorms are relatively more c o m m o n

over land, except over the Southern Tablelands. Matthews and Geerts (1995 p:133) suggest:

'In many cases for example offshore high, topographically controlled, thermally forced convergence is a primary trigger of convection systems in the region.'

In the Sydney region, although thunderstorms are known to be important components of

many weather systems such as active fronts, troughs or squall-lines (Morgan, 1979b), in turn

thunderstorms can also be initiated by local disturbances mainly caused by climatic-

environmental factors (Linacre and Hobbs, 1977). Broadly speaking, thunderstorms may be,


with caution, classified into two general categories in terms of their origination and

organisation; 1 - the air-mass thunderstorms (thermal) which are more likely to have an

environmental origin, and 2 - big and multi-cell or supercell (Dickins, 1994) thunderstorms

(dynamically) which are largely of synoptic derivation (Alford, 1994). Simply, it can be

viewed that the region can be overwhelmed by both systems during the thundery seasons of

the year.

Figure 2.14 Lightning density for single thunderstorm events based on data from the N S W lightning

detection network, in units of number of strikes per km^ per event, under the following synoptic situations: (a) pre-frontal trough (22/12/92); (b) pre-frontal (09/11/92); (c) post-frontal (23/2/93); (d) inland trough (06/01/93); (e) offshore low (06/12/92); (f) offshore high (23/12/92). After Matthews and Geerts (1995).

In brief, the data from Sydney thunderstorms during past decades may indicate that

thunderstorm development is the result either of the larger synoptic weather systems or,

more specifically, synoptic-meso-scale systems. Both systems are potentially able to provide

an unstable environment in which thunderstorms occur and track. Although, development of

thunderstorms over the Sydney region varies from month to month and reflects the overall

impact of above-mentioned synoptic weather patterns, they may also reflect the effects of

climatic factors (discussed in Section 4) and physical environment parameters by which


thunderstorms may be developed or controlled. In the next section, the role of physiographic

parameters will be examined literally .

2.8 Sydney's Physiographic Parameters and Thunderstorm Rainfall

In the Sydney region there is some evidence indicating the fact that spatial distribution of

rainfall can be influenced by some physiographic parameters such as elevation and distance

from the sea. For example, Linacre (1992) stressed the effect of landform on rainfall

distribution patterns in the Sydney region by showing that isohyets closely paralleled the

height contours. However, in terms of convectional rainfall, little, if any work has been done

relating thunderstorm rainfall to physiographic parameters such as topography, proximity to

the sea and urban centres.

Topography has been suggested to be an important factor in affecting thunderstorm

development in the N S W coastal areas (Sumner, 1983a). In terms of topographic effects,

Williams (1991) mentioned that places along the Illawarra Escarpment experience very high

orographic rainfall, illustrating the effect of local topography and exposure on rainfall. For

example, in an area of more rugged terrain, such as the Illawarra escarpment, just south of

Sydney, intense rainfalls with much longer duration causes flash floods which may be

correlated with thunderstorms (Foreman and Rigby, 1990). In 1983, Cox supposed that in

the Illawarra there are pronounced differences in rainfall totals between wet and dry years,

but the distribution pattern remains quite stable because of the topographic effects.

In the north-west of the Sydney region, it was suggested that, one of the most regular and

predictable types of orographic rain may occur, particularly in warm season conditions, near,

over and adjacent to the Blue Mountains (Gentilli, 1971). This happens, because the general

meridional alignment of this relief along the N S W eastern margins causes an orographic

uplift of the moist air streams which is clearly reflected in the amount of rain. The amount is

sharply increased on the windward side, and gradually decreased on the leeward side of

mountains. Also, it may happen because the daily heating of the hillsides generates warm

upslope winds which continue rising after reaching the mountain ridge-top and trigger deep

convection. These convective clouds can produce thunderstorm rainfall in the afternoons

over the peaks, or downwind if there is cloud drift. This behaviour is shown clearly in the

results of some research (Morgan, 1979a) and the daily thunderstorm patterns of the Sydney

region.

However, these mechanisms may also produce statistically verifiable night-time

thunderstorm activity over the City and the coast line. It can be supposed that thunderstorms

build over the mountains and then travel to lowland areas. For example, during the late

afternoon and evening of the 10th November 1976, thunderstorms developed over elevated

terrain to the south-west of Sydney and then moved over the urban area (Morgan, 1979a).


Rainfall from the thunderstorms was very heavy in some parts of the Metropolitan area.

Both the highest rainfall and rainfall rate were reported from Observatory Hill, where 30 m m

of rain fell in 11 minutes, a rate of 164 m m per hour (Bureau of Meteorology, 1976).

The topography of the coastal region also plays an important role in enhancing surface

convergence near the coast. Holton (1992) found that if orography slopes locally upward in

a downstream direction, vortex lines in the lower layer are compressed and the flow in the

lower layer must move towards the equator to conserve potential vorticity. Also, Leslie et

al., (1987), in modelling east coast cyclogenesis, suggested that without topography no such

convergence and convective concentration occurs and the cyclone development is retarded.

In addition, Speer and Geerts (1994) using radar data, presented examples in which quasi-

stationary thunderstorm cells developed over relatively high topography in the Sydney

region. They concluded that topography would, therefore, aid convective systems to

produce higher rainfall totals over higher ground. More recently, using data recorded by the

L P A T S system (Laudet et al., 1994) it was found that the spatial distribution of lightning

(associated with thunderstorms) is closely related to the topography of the region. The effect

of topography was pronounced as flash data showed marked concentration over the

mountains of the region particularly on and east of the central part of Range. The results

support the concept that topography is a very important physiographic parameter in

controlling thunderstorm occurrence.

At the same time, there is some evidence which shows that coastal areas - proximity to sea

as an important physiographic parameter -can affect the distribution of thunderstorm rainfall

in the Sydney region. Speer and Geerts. (1994) found that the south-easterly winds, oriented

by the coastal ridging, can enhance the low inflow of moisture to the storm. Also, in some

postfrontal cases, the coastal ridging is known to be responsible for the stationary

convergence zone causing thunderstorms to propagate over the coastal areas (Speer and

Leslie, 1994). It was found that, the interaction of fronts with coastal dividing ranges in the

presence of more humid air to the east of the ranges, can lead to severe thunderstorms along

the south coast of N e w South Wales (Reeder and Smith, 1992). Another important point is

that thunderstorm rainfall of longer duration than 6 hours was recorded in areas close to the

coast. (James, 1992). For example, during a series of thunderstorms on 10th and 11th

March, 1975, thunderstorms were extended over the coastal areas of the Metropolitan and

Illawarra districts and intense rainfalls were recorded in costal areas (Armstrong and

Colquhoun, 1976). Meanwhile, some researchers such as Abbs and Physick (1992) believe

that the topography of the coastline has an extra important controlling effect upon

thunderstorm activity in the region.


Finally, it is more likely that, in the region, urban areas affect the distribution of

thunderstorm rainfall to some degree. Although there is no data correlating the thunderstorm

rainfall patterns to Sydney's environment directly, Linacre (1992) mentioned that there is

more rainfall over the City. Investigation of thunderstorms, over Sydney during the past

years, has shown that a sequence of thunderstorm cells originating over the elevated terrain

may, subsequently, enter the severe stage prior to moving (tracking) over the City

(Matthews, 1993). The resultant heavy rainfall, large hail and strong wind gusts can cause

extensive damage throughout the Sydney Metropolitan area. Speer and Geerts (1994)

studied some of the heavy rainfall from thunderstorms during the period 1957 to 1990. They

showed that the slow movement of storms over Sydney causing flash floods, can produce

much more rainfall over the Metropolitan areas.

An important question is why the City is a preferred (favoured) area for more

thunderstorms. In recent years, Tapp and Skinner (1990) and Tapper and Hurry (1993)

examined some aspects of urban climatology and have suggested that in many large cities in

Australia, noticeable heating of air over the urban centre occurs relative to adjacent suburban

and rural areas at night, particularly in winter. Schwerdtfeger (1982) gave evidence (a map)

showing the heat island effect over central Melbourne on a winter night. The warmest part

(8.9°), took in the north-eastern end of the C B D , the western part of Fitzory, and much of

the suburb of Carlton. More recently, Crowder (1995) in comparing rainfall distributions

over Sydney and Melbourne, says that rainfall over the Sydney Metropolitan area varies

from less than 700 m m near Campbelltown to more than 1600 m m on the coast just north of

Stanwell Park. Also, there are wet spots over 1400 m m near Katoomba and southeast of

Hornsby and another one over the centre of the Sydney ( C B D ) (see Figure 14). H e

concluded that Sydney is prone to severe thunderstorms and associated very heavy rainfalls.

Although in Sydney there is no measured data for the urban heat island over a long time

span, the effect of such phenomena upon rainfall distribution is highly reasonable. Several

authors have over the past years approached the modification of Sydney's atmospheric

boundary layer by thermal and mechanical turbulence. For example, Fitzpatrick and

Armstrong (1973 p:18) in a study of effect of the urbanisation on climate in the Sydney area

wrote:

'Although the maps of mean maximum and minimum temperature do not reveal any clearly identified effect of the urbanisation, this cannot be taken to indicate that such effect must be small. All investigations of temperature have shown that one heat island effect over cities is best developed under calm, clear sky conditions that favour maximal daytime heating and the development of strong temperature inversions at night'.

The detailed spatial pattern of temperature within the Sydney area was carried out by

McGrath (1971) using mobile temperature recording equipment at about 1.00 am. on April


24th, 1971. He found a difference of about 5 °C between the City and the outlying rural

areas. Although he did not attribute the difference found to a true heat island, a steep

temperature gradient was observed between closely built-up areas and those having

extensive parkland and open spaces.

In another study, Kemp and Armstrong (1972) examined temperature trends at Observatory,

Hill, Sydney for the period from 1859 to 1971. They concluded that there has been no

change in maximum temperatures over the past years. However over the same period they

indicated that minimum temperatures increased by about 0.6 C - an increase which may

possibly be explained as a 'heat island' effect resulting from increasing industrialisation of

the City.

A study has also been made by Kalma et al. (1973) in spatial and temporal aspects of energy

use by domestic, industrial and commercial sectors and in transport, in the Sydney Statistical

Division (SSD), in 1970. They found that there is a great spatial variation in energy use, and

it was estimated that intensity of energy use ranged from 1 * 1 0 1 0 BTU/sq mi/yr in rural

areas such as Camden and Windsor to 143 * 10*0 in Parramatta and 382 * 1010 in the City

of Sydney. This study indicated that energy use on average days in July was about 20 per

cent greater than on average days in January. In this research they also gave evidence

showing the intensity of annual energy use across the Sydney region. In areas such as the

C B D and surrounding sectors, energy use ranged between 300 and 400 * 10*0 B T U

ye/sq/mile. In contrast, in suburban areas further out, the rate was a minimum (1 * 10 ^

B T U ye/sq/mile) as shown in Figure 2.15. This high spatial variation in artificial heat

generation of Sydney region may be correlated to the urban heat island phenomena.

Examples of estimates of artificial heat released from various urban areas (in 1970s) were

given in Bridgmam (1990). It was expected that artificial heat released would be increased

with the growth of population and density in many cities around the world, including Sydney

specifically in central urban area. Identification of the magnitude of these artificial energy use

is obviously of fundamental importance in understanding of heat island progress over the

Sydney region (Kalma and Byrne, 1976).

CHAPTER TWO Literature Review on Thunderstorm Rainfall M.

Figure 2.15 The intensity of annual energy use in the Sydney region (After Kalma,, et al., 1973).

In addition, Linacre and Edgar (1972) reported evidence (visibility and suspended particulate

as atmospheric pollution) on the surface configuration of Sydney's heat island. Their work

has given improved evidence of the influence of urban development on the climate of the

Sydney area. For example, they gave a typical isotherm map showing the temperature

difference within the City. In another work by Kalma (1974) it was again shown that, in the

Sydney region, considerable spatial and temporal variation in energy use exists. These

studies provided evidence that artificial heat generation is a significant factor in energy

exchange processes over the urban environment when compared with the surrounding less

build-up areas. These studies also discussed the primary processes involved in the formation

of the urban heat islands and they generally concluded that artificial heat generation in the

Sydney region was largely responsible for the downwind temperature increase over the City.

This effect may be maximised by high density building within City centres and may create a

heat island (Davey, 1976) with greater cloudiness and, as a result much more rainfall.

Undoubtedly under specific conditions, for example, calm weather conditions, especially in

summer, the influence of urban aerosols on cloud condensation nuclei or ice nuclei also

appears to be an important factor in the modification of the City's atmosphere. In an

extensive study of the climatology of air pollution by Carras and Johnson (1982) and

Literature Review on Thunderstorm Rainfall

Leighton and Spark (1995), it was found that the Sydney's climate is subjected to serious

pollution events and there are different sources of pollutants emitted to Sydney's atmosphere

(Moss, 1965). Industrial and commercial activities, including the usual forms of transport,

are amongst the important man-made local sources of emission (Linacre, 1970). Although

trends of air pollution levels in Sydney since 1950 showed a general decrease in traditional

pollutants, such as dust and sulfur dioxide, because of less use of coal as a fuel in large

boilers and for transport, new emerging pollutants including lead, hydrocarbons, and oxides

of nitrogen have become of increasing concern in the Sydney region (Paine et al., 1988) (see

Figure 2.16).

HYDROCARBONS EMITTED PER 3-2km GRID SQUARE |kg/h|

Figure 2.16 Spatial distribution of nitrogen oxides emissions from all sources in the Sydney region (After Carras, et al., 1982).

All above-mentioned studies showed a great spatial variation in the distribution of some

pollutants, such as nitrogen oxides emissions from all sources in the atmosphere of areas

located within the City, which has much more pollution than the suburbs. The areas such as

C B D , and south of Parramatta river were among the worst. In 1992 once again, the problem

associated with the polluted atmospheric environment was highlighted by Taylor. H e found

that as the Sydney region expands, the air pollution levels increases, and smog moves west


as Sydney grows due to the increasing concentration of chemical materials such as nitrogen

oxides and hydrocarbons in the atmosphere of the Sydney (see Plate 2.3). Also, Cohen, et

al., (1994) indicated that spatial and temporal concentrations of particles such as lead in

Sydney is higher than other areas. This study emphasised the important role of motor

vehicles in generation of lead in the region.

All aerosol particles, may contribute in formation of cloud condensation nuclei and, as a

result, rainfall occurrence. The pollution products may contribute to cloud formation and to

changes in the drop-size spectra. As such they can either promote or inhibit thunderstorm

rainfall. In case of ice nuclei, particulate matter, especially hygroscope particulates, might

initiate the precipitation process in supercooled clouds. A study of rainfall in Melbourne has

shown that the average rainfall on Sundays is 1.7 mm/d, but during the week the average

rainfall is 2.2. So, weekends are significantly drier than weekdays, presumably because of air

pollution volume changes (Linacre, 1992). Despite intensive efforts devoted to the

understanding of Sydney's atmospheric environment in the past (State Pollution Control

Commission of N S W , 1974 and 1975), the role of pollutants upon the urban precipitation

process has so far remained difficult to identify.

Plate 2.3 Shows smog over central Sydney.

Although, in the Sydney region, there has been some progress in understanding

meteorological processes in the urban boundary layer in the past years, there are still large


areas of uncertainty. Experimental studies of radiation and energy balance at both meso-scale

and micro-scale are limited to the few above-mentioned studies, largely as a result of the

complexities of the urban atmosphere and different landuse patterns with dissimilar surface

materials. M o r e importantly, the relative importance of the urban heat island and surface

roughness in modifying the thunderstorm rainfall distribution is still unclear.

In summary, in the Sydney region, the coastline and nearby hills and high elevated areas, it

has been suggested, have sufficient encouraging physiographic factors in the development of

convective clouds. They may also be supposed to be important parameters in the triggering

of thunderstorm activity and, as a result, more thunderstorm rainfall. It is also more likely

that the urban area (heat island) may affect the special distribution of thunderstorm rainfall.

Because, urban heat island impacts are most noticeable on cool, clear, stable spring and

summer evenings, when most convection cells advance over the urban environment and the

moist coastal margins.

2.9 Conclusions

The thunderstorm is a much more familiar weather event worldwide. It is a combination of

many things all occurring together. Usually, strong gusty winds, vertical currents at higher

levels, heavy precipitation with thunder and lighting, even more spectacular scenes and

occasionally with distractive consequences, are products of thunderstorm activity. Although

there is wide agreement that all thunderstorms require warm and moist air (as the prime

gradients leading to the formation of thunderstorms) in the atmosphere, other suitable

conditions are needed to increase instability and, as a result, to initiate a convective activity.

The roles that may be played by atmospheric instability are very important factors in


Synoptic weather patterns such as fronts, lows, troughs and extreme currents in the free

upper atmosphere are among conditions which are the main factors responsible for the

introduction of the instability in the atmosphere and, thus, the creation of many

thunderstorms. However, synoptic weather systems are probably not always necessary nor

sufficient conditions for the occurrence of thunderstorms. Other trigger mechanisms for

thunderstorm initiation are also important. Over the last few decades, it has widely been

stressed that the development, occurrence and distribution of thunderstorms over a region,

also largely depends upon some climatic factors (air and sea surface temperatures, for

example) and physiographic parameters such as topography and proximity to sea.

As with other places in the world, thunderstorms in Australia can be introduced by the

presence of larger-scale synoptic weather systems and the nature of the prevailing air

masses, for example, lows and fronts. In addition, thunderstorms can be enhanced by


physiographic parameters which have been suggested to be more important parameters in

the occurrence and the controlling of thunderstorms.

On a regional scale, over the NSW and the Sydney region in particular, researchers have

found that the seasonal movement of pressure cells - anticyclonic highs, cyclonic lows and

troughs, determine the type and nature of air that is drawn towards the region and, as a

result, also in part determine thunderstorm occurrence. Meanwhile, when these air-masses

encounter the physical environment with various physiographic parameters, uneven spatial

distribution of thunderstorm patterns can be expected.

While synoptic weather systems control the availability of moisture and other gradients

needed for thunderstorm occurrence, and thus the actual amount of thunderstorm rain that

can fall, site and physiographic characteristics of each specific geographical location could

encourage and determine the spatial distribution of thunderstorm rainfall. Both climatologists

and meteorologists have emphasised that, in the Sydney region, climatic and physiographic

features play an important part in the more local nature of thunderstorm development in

different ways as follows:

Firstly, these investigations suppose an interaction between surface heating and source of

moisture and its subsequent impact on the thunderstorm activities. This may occur in

response to the combination of solar heating of surface layers to a critical temperature, with

the forces of air motions associated with synoptic weather systems and climatic factors of

the region. In Chapter 4, the close relationship between thunderstorm rainfall and climatic

factors will therefore be examined.

Secondly, it was also suggested that hills and mountain ranges can set-off thunderstorms in

potentially unstable airflows and these developments can, in some situations, drift away and

further develop and affect large areas of lowland in Sydney. For example, mountainous

areas, located in the west of the Sydney region, can be subjected to local thunderstorm

development during the warm seasons (late spring and summer months). These areas can

introduce convection systems and release massive potential energy, triggering-off

thunderstorms with intense rainfalls, while most parts of the region remain sunny and

cloudless.

In addition, coastal areas, particularly the high ground near windward coasts, are also

subject to thunderstorms. Areas near the coast may play a large part in the attraction of

thunderstorms deducing heavy rainfalls in association with advancing moist winds from the

ocean.


Finally, it may be proposed that heat-island effects and other physical and thermodynamic

characteristics of the Sydney region, such as pollution and roughness of the City itself, also

help in the development of thunderstorm rainfalls over and near the Sydney Metropolitan

area or over the City with large industrialised and commercial-residential areas. There is

some evidence that thunderstorm rainfall over the City is greater than for nearby suburban

areas. Therefore, in Chapter 6, the spatial variations of thunderstorm rainfall in the Sydney

region will be studied. Then, existing associations amongst above-mentioned physiographic

parameters and thunderstorm rainfall will be examined in Chapter 7.

To sum up, a complex interaction and relationship between synoptic weather systems,

climatic factors and the physiographic environment could be responsible for much of the

thunderstorm activity in the Sydney region. At times that the larger or meso-scale synoptic

weather systems are dominant, widespread thunderstorm activity is pronounced and,

consequently, high rainfalls can be expected. Although local climatic and physiographic

factors, which influence rainfall distribution, may tend to be masked by the nature of such

widespread thunderstorm activity, even in this situation, some degree of regionalization of

thunderstorm rainfall patterns can be climatologically distinguished.

CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 62

CHAPTER 3

TEMPORAL DISTRIBUTION OF THUNDERSTORM

RAINFALL IN THE SYDNEY REGION

3.1 Introduction

The main aim of this chapter is to characterise the general behaviour of thunderstorm

rainfall in the Sydney region, over time. Thunderstorm activity can be addressed using a

variety of time scales. The pattern of thunderstorm frequency and rainfall amounts in the

study area are examined at yearly, seasonal, monthly and diurnal levels using measures of

central tendency or dispersion of data. As the distribution of thunder-recording stations

reflects the distribution of major cities, suburbs and dams, as a first approximation, spatially

the sampling network of thunderstorms is uneven. This chapter attempts to understand the

behaviour of thunderstorms using the better thunder-recording stations (with longer more

complete records) in the Sydney region. The N N A technique, defining significant

thunderstorms in the Sydney region, was therefore applied to thunderstorm data.

The sources of the data and the choice of data analysis techniques will be explained in

sections 2 and 3 respectively. Section 4 identifies the distribution of thunderstorms on a

yearly basis. In section 5, the seasonal and monthly distribution of thunderstorm rainfall is

analysed. In section 6 the diurnal variation of the thunderstorm rainfall frequency will be

determined. In the final section, findings of the temporal distribution of thunderstorms in

the Sydney region, can be discussed.

3.2 Data Used

The National Climate Centre (Bureau of Meteorology, Melbourne) provided the raw data

on thunderstorm activity in the Sydney region. Firstly, thunder activities data, which have

been recorded on three magnetic tapes were loaded on a P C computer system. In the

second stage, the University of Wollongong's main frame computer system was utilised to

extract the thunderstorm observations for all thundery days in all thunder-recording

stations. They were summarised for different time-scales - such as diurnal, monthly,

seasonal and yearly time spans. For each thunder-recording station a thunderstorm day was

considered to be a day for which at least one thunderstorm observation was reported.

For this study, according to the World Meteorological Organization (WMO, 1975 and

1988) a thunder observation is defined as the occurrence of a thunderstorm when one or


more sudden electrical discharges are manifested by a flash of light and a rumbling sound.

The traditional method of recording thunderstorm occurrence is to simply note whether

thunder is heard during the day. All thunderstorms observed during the last hour (past

weather) and also during the observation period (present weather) were counted in this

study, according to the present guide of the Bureau of Meteorology, Australia (Table 3.1).

Table 3.1 Represents a detailed description of the codes of present and past weather used in thunderstorm observations.

Past Weather Code

(Thunder was heard) 9

13

29

91

92

93 and 94

Description Thunderstorm with or without precipitation Lightning seen, no thunder is heard Thunderstorm (thunder is heard) Slight rain at the time of observation Moderate or heavy rain at the time of observation Slight, moderate or heavy hail at the time of observation

Present Weather Code

(Thunder is heard) 17

95

96

97

98

99

Description Thunderstorm without precipitation Slight or moderate thunderstorm with rain Slight or moderate thunderstorm with hail Heavy thunderstorm with rain Thunderstorm with dust and sand storm Heavy thunderstorm with hail

Therefore, for each thunder-recording station a thunderstorm day was considered to be a

day for which at least one thunderstorm observation was reported. A thunderstorm may or

may not be accompanied by precipitation. In this case, code 17 refers to instances where

thunder was heard at the station but no precipitation occurred. Again, if lightning is

observed (code 13) without thunder being heard at the station, the event is not considered

in this research to be a thunderstorm because this can occur at distances remote from the

station, especially at coastal locations with flat topography. Using these codes the daily

thunderstorm rainfall amount (more than 0.1 m m ) and its frequency data were collected for

the period 1960-1993 for each individual thunder-recording station. From these data sets,

the mean monthly, seasonal and annual time-series have been derived. It must be noted that

rainfall on a thunderday is not necessarily produced all or in part by a thunderstorm. This

was addresed by selecting the better thunder-recording stations (see Section 3) and defining

a thunderstorm-day for the Sydney region, using data from a set of selected stations. At

least three thunder-recording stations had to record a thunderstorm on a thunderstorm-day

throughout the region (see Chapter 6).

3.3 Methods Applied

It has been found that many thunderstorms seem to occur in an independent manner in time

and space (Duckstein et al., 1973). Also, the results of researchers such as Sharon and

Kutiel (1986) and Sharma (1987) have indicated that thunderstorm rainfall values are

typically strongly skewed in time and space, occasionally with extremely intense localised


rainfalls. Therefore, the estimation of the temporal and spatial distribution of thunderstorm

rainfall can be biased by these cases, particularly where there is missing data.

In the case of the Sydney region, this bias was assessed by examining the similarities

(associations) amongst different stations of thunderstorm rainfall. Figure 1.2 shows the

location of the 15 thunder-recording stations in the Sydney region. T o find the relative

interdependence and associations among all existing thunder-recording stations, the

following steps were undertaken.

In the first stage, a computer program was written to find common thunderstorm-days

between 15 key stations listed in Table 3.3 (the computer program number 1 is located in

Appendix A ) . These stations were selected because they had data for at least seven years

continuously and the fewest missing values for the period 1960 to 1993. The specific days,

totalling about 1584 thunderstorm-days, are listed in Table 3.2 (see Appendix B) .

Table 3.3 General geographical characteristics of the thunder-recording stations. N o of stations

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Stations names

Katoomba Richmond Camden A. Bankstown Sydney Airport Sydney R. Office Wollongong Prospect D a m Liverpool Lucas Heights Bowral Parramatta Penrith Campbelltown Picton

Latitude

33.72 33.60 34.05 33.93 33.93 33.87 34.40 33.82 33.92 34.05 34.48 33.80 33.75 34.08 34.18

Longitude

150.30 150.78 150.68 150.98 151.17 151.20 150.88 150.92 150.92 150.98 150.40 151.02 150.68 150.52 150.62

Distance from sea in K m 93 54.5 47 26 8 7.3 3.4 29.3 29.3 16 47 24 56 31 37.3

Altitude inm

1030 19 70 9 6 42 30 61 21 140 690 60 25 75 171

Period of data used** 1987-93 1960-93 1972-93 1969-93 1960-93 1960-93 1972-93 1965-92 1962-92 1962-82 1975-92 1967-92 1967-85 1962-84 1965-75

* The nearest distance from the average coastal line. ** See Table 3.2 (Appendix B).

In the second stage, a clustering analysis technique based on the Nearest Neighbourhood

Algorithm (Tversky, 1983) was used to group similiar thunder-recording stations. The

technique of N N A is discussed at some length by many geographers including, for example,

Cliff, et al. (1975), Cliff and Ord (1981) and Unwin, (1981). The technique has been used

specifically in regionalising climatic variables and clustering point observations of rainfall

(Theakstone and Harrison, 1971). In nearest neighbour clustering, the optimality condition

is for the generated clusters to give the least possible distance amongst all possible cluster

combinations (Dasarathy, 1991). The computer program for the N N A technique uses an

iterative procedure, where at each iteration each data sample is compared to all other sets

of randomly chosen seeds. After each iteration the set of seeds with the minimum distance

is grouped and the central (centroied) of each cluster is calculated. The iterations are

CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall W

stopped when the ratio of overall improvement compared with the previous step, is less

than a predefined value. The criterion for clustering is based on Euclidean Distance defined

by the magnitude of cluster members for a particular event for each thunder-recording

station relative to the cluster centre, where N is the number of stations which could be

clustered into K groups. The distance can be written as:

1=1

where D shows the overall distance and D[ shows the distance in cluster /'. Distance in each

cluster can be calculated by summing the distance of all the cluster members from the

cluster centre. The definition of distance depends on data characteristics. A simple

definition is the Euclidean Distance is defined as:

Euclidean Distance = •yj(x1 -x2)2 + (yx -y2)

2 +(*, ~x„)2 +(yf-y„)2

where jq and y\ are the data components. In clustering all thunder-recording stations, all

possible physiographic components of each station such as latitude, longitude, distance

from the sea, and altitude, were used (see Table 3.3). These parameters were selected

because they have been linked to the distribution and variation of thunderstorms over the

study area (see Chapter 2).

Using the NNA technique, seven main clusters (A to E) were found representing distinct

areas of thunderstorm activity in the Sydney region. As it can be seen from a dendrogram

(Figure 3.1), for example, group six includes the Sydney Regional Office, Sydney Airport

and Parramatta stations. Katoomba and Bowral stations which are located in mountainous

areas, have been classified as two separate groups, as has Wollongong located in the south

east of the study area.

Locations such as coastal strips or tablelands in the region, with different geographic and

physiographic characteristics have already been suggested by Williams (1991) to affect the

occurrence of thunderstorm activity. The N N A clustering results support this assumption.

For instance three stations, Camden, Campbelltown and Picton, close to each other in the

southwest of the Sydney basin, group together, as do Sydney airport, Sydney Regional

Office and Parramatta in the centre of the study area. The stations with the longest and

most complete record of data collection in each group were selected for further analysis.

These stations are listed in Table 3.4.


Index Of Similarity

(Euclidecen Distance Score)

L_

L_

- Katoomba

Bowral

— Parramatta

i

"Sydney Airport

1— Sydney R.Offiee

Liverpool

i

i

I

Bankstown

- Lucas Heigths

- Camden

Picton

CampbeUtoum

- Richmond

• Penrith

Prospect Dam

Wollongong L

Figure 3.1 A dendrogram shows the result of the N N A technique in grouping thunder-recording stations.

Table 3.4 Locality of the seven selected stations.

Selected Stations Groups Locality Katoomba Bowral Richmond Camden Airport Bankstown Sydney R.O. Wollongong

C A D B E G F

Mountain Mountain Near-mountain Far-inland Near-inland Coastal Coastal

3.4 Yearly Distribution of Thunderstorm Rainfall

The broadest time scale over which thunderstorm rainfall varies is the year-to-year

variation in total amounts. In many respects this is also the most important as it represents

the changes of thunderstorm rainfall frequency over the 34 year period from 1960 to 1993.


Table 3.5 Yearly variation of thunder-days frequency and thunderstorm rainfall amounts (rainfall is in m m ) at 7 thunder-Stations

Year 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 Total

Average

Katoomba

TDF

13 24 22 29 31 30 25 174 25

Rain

190 205 139 350 212 300 163 155S 223

recording stations in

Bowral

TDF

21 25 21 23 24 12 16 19 18 21 15 12 17 21 19 26 27 25

I 362 20.1

Rain

170 102 110 130 101 68 105 67 98 204 102 79 160 188 122 201 175 140

2322 129

the Sydney region.

Richmond

TDF 11 23 15 22 10 15 32 12 16 34 33 20 32 28 14 25 21 29 29 25 18 23 14 26 26 33 11 22 25 22 34 26 34 41 801 24

Rain 118 100 86 125 133 167 90 58 96 312 264 128 161 172 76 207 105 193 186 87 70 135 111 115 126 254 63.2 138 186 136 369 170 183 199 5119

157

Camden A.

TDF Rain

8 14 7 14 9 9 9 14 6 12 6 11 10 14 11 13 20 3 1 12 7 2 212 10

48.4 182 67 115 55 85 40 147 34 78 13 87 75 93 30 73 157 51 9 86 38

1562 74.4

Bankstown

TDF

7 9 6 7 12 6 10 17 12 9 11 10 12 3 20 28 25 16 14 22 18 25 16 8

323 13.5

Rain

20 42 70 124 69 111 129 97 132 114 140 93 10 180 399 221 195 249 215 196 297 146

3249

135

Sydney R.O.

TDF 22 22 19 24 17 17 22 13 14 21 26 14 20 17 9 16 28 21 20 25 11 13 7 15 23 21 15 15 23 20 27 21 23 18 639 19

Rain 136 216 112 231 196 109 133 229 71 227 310 185 158 271 87 322 273 120 113 115 113 110 46 125 487 116 77 196 274 217 251 261 114 213 6214 183

Wollongong

TDF Rain

6 4 3

5 10 9 9 4 5 3

14 9 5 6 8 7 14 12 9 13 155 8

54 48

83 75 210 52 43 49 10

311 98 40 115 142 381 202 235 43 161 2352

125

Table 3.5 summarises the total annual thunder-days over this time-span. T D F represents

Thunder-Days Frequency, and missing years are left as blank cells. The Sydney Regional

office and Richmond stations, recorded data continuously over this period. Annual

thunderstorm rainfall amounts over the same period are also shown in Table 3.5. The

highest mean number of thunderstorm days were observed at Katoomba, Richmond with 25

and 24 thundery-days per year respectively while the minimum average frequency occurred

at Wollongong and Camden stations with 8 and 10 thundery days per year respectively.

To see the variations of thunderstorm rainfall over the study area, the coefficient of

variation, c v = (s /x )*ioo was used.

Where x = the average annual thunderstorm rainfall,

and s = standard deviation.


This technique was not used to find one-to-one links between thunderstorms and their

associated rainfalls at each thunder-recording station. Thus, it could not be assumed that

the rainfall falling at a station comes from the storm where thunder in heard. It should be

regarded as a descriptive statistical technique, simply representing means and the coefficient

of variations of the yearly thunderstorm frequency and rainfall values in different stations

(given in Tables 3.6 and 3.7 respectively). The greatest coefficient of variation in yearly

thunderstorm frequency (50.2 % ) was observed at Bankstown station about 26 K m far

from the coast. In contrast, the lowest coefficient of variation in thunderstorm frequency

(25 % ) occurred at Katoomba located in the Blue Mountains. Also, Sydney Regional Office

showed a low coefficient of variation with 27 %, located in the east of the study area about

8 k m distance from the Tasman Sea. The highest number of thunderstorm days was

observed at Katoomba and Richmond stations located in the north-west of the Sydney

region, 93 and 55 k m respectively from the ocean.

Table 3.6 Summary descriptive statistics for yearly thunderstorm rainfall frequency, in the Sydney region, from 1960 to 1993.

No. Station Name No. of Mean Max. Min. Range Coefficient

Years of Variation (%)

1

2

3

4

5

6

7

Katoomba

Bowral

Richmond

Camden Airport

Bankstown

Sydney R.O.

Wollongong

7

18

34

22

24

34

20

25

20.1

23.6

9.6

13.5

18.8

7.8

31

27

41

20

28

28

14

13

12

10

1

3

7

3

18

25

31

19

25

21

11

25

28

34.1

47

50.2

27

45.6

The highest thunderstorm rainfall values occurred at Katoomba (223 m m per year) and 183

m m at Sydney Regional Office. Also later station recorded the highest annual rainfall values

(see Table 3.7).


Table 3.7 Summary descriptive statistics for yearly thunderstorm rainfall amounts, in the Sydney region, from 1960 to 1993.

No. Station Name No. of Mean Max. Min. Range Coefficient

Years of Variation (%)

1

2

3

4

5

6

7

Katoomba

Bowral

Richmond

Camden Airport

Bankstown

Sydney R.O.

Wollongong

7

18

34

21

22

34

19

223

129

150.5

74.4

147.7

182.8

123.8

350

204

369

182

399

487

381

139

67

58

9

10

46

10

211

137

311

173

389

441

371

34

29

46.8

61.5

62.5

50

82.6

The annual variation of thunderstorm frequency and rainfall for the two stations, with a

complete 34 year record, Sydney Regional Office (Figure 3.2) and Richmond (Figure 3.3)

were graphed in more detail. It should be noted that these stations are not representative of

the thunderstorm variation over the entire Sydney Region.

Sydney Regional Office

Rainfall • Thurxfer-days

Figure 3.2 Yearly variation of thunder-days frequency and thunderstorm rainfall at Sydney Regional Office station (1960-93).

Comparison of the number of years above and below average in all stations shows that

there is a fluctuating pattern with high and low years. It was found, on average, for

example that in 1969, 1975 and more significantly in 1984, there were some considerable

thunderstorm rainfalls in the Sydney region.

Distribution of Thunderstorm

Richmond

a «

&

O)

g

Figure 3.3 Yearly variation of thunder-days frequency and thunderstorm rainfall at Richmond station (1960-93).

To gain an appreciation of the longer term variability of thunder-day frequency and rainfall,

the data in Figures 3.2 and 3.3 were analysed using Normalised Residual Mass curves

( N R M ) , used by the Bureau of Meteorology (1991a). The N R M can be defined as the

accumulated difference between the actual annual thunderstorm rainfall for each year and

the mean annual thunderstorm rainfall over total years of the record, divided by the mean of

these factors. The N R M for the Sydney region (the average of two above-mentioned

stations) from 1960 to 1993 is shown in Figure 3.4. This graph clearly shows sequences of

wet or dry thunderstorm rainfall years.

•o s

.a •a

s

•NRM Rainfall NRM'Thunder-day

100.0 T

80.0 -•

-40.0 -•

-60.0

it H Ol w .9 •a

I

Figure 3.4 Normalised Residual Mass curves of annual thunderstorm rainfall for three important thunder-recording stations (1960-93).


The results of this section indicate, the average frequency of thunder-days and

thunderstorm rainfall amounts over the mountainous and urban areas adjacent to the coast

are much greater than over the inland areas in the Sydney region. This result confirms the

work of Griffiths, et al.(1993) who found that the number of severe thunderstorms in N e w

South Wales is increasing. They also give evidence showing a pronounced maxima in the

distribution of thunderstorms in recent decades, particularly in the Sydney region.

However, these results are not yet valid indications of thunderstorm rainfall variations over

the study area, because the increase might be a function of greater population densities and

better of data reporting (see Chapter 6 for spatial variations). Overall, the pattern for yearly

variation of thunderstorms at different stations are not similar. Comparison of the number

of years above and below average in all stations show that there is a fluctuating pattern

with high and low years.

3.5 Seasonal and Monthly Distributions

In the Sydney region there is a considerable seasonal variation in thunderstorm rainfall

throughout the year as shown in Figure 3.5.

Figure 3.5 Seasonal distribution of thunderstorm rainfall in different stations in the Sydney region.

As expected, in response to the warm environment and unstable atmosphere, thunderstorm

activity is greatest during late spring (October to November) and summer (December to

February) and weakest during autumn (March to May) and winter (June to August). For

the whole region the maximum falls are in late spring and summer, however there are

considerable differences. Some stations, for example Wollongong, show a maximum in

spring rather than in summer. The graphs for the Sydney Regional Office and for

Bankstown are very similar with a peak in summer and a secondary peak in spring. In

contrast, Katoomba shows a peak in summer and a much smaller secondary peak in


Spring. Camden and Richmond stations which are located in inland areas, experience a

summer maximum with little thunderstorm activity in autumn and winter.

The range of seasonal values are generally considerable at all stations. Average seasonal

thunderstorm rainfall amounts are given in Table 3.8 for select stations. Both Richmond

and Katoomba, which are located in the west of the Sydney region receive a high

percentage of rainfall in summer rather than in other seasons. In contrast, stations which are

located in the east of the Sydney region near the coast, for example Wollongong and

Sydney Regional Office, have considerable thunderstorm rainfall (on average 32 per cent)

in both autumn and winter seasons.

Table 3.8 Average seasonal thunderstorm rainfall (in m m and % ) for selected stations.

Stations

Katoomba

Bowral

Richmond

Camden

Bankstown

Sydney R.O.

Wollongong

Average

Spring (SON)

Rain m m

43.5

32

46.9

27

43.5

58.5

39.5

41.5

%

19.1

24.1

31.1

36.3

29.4

32

37.5

28.7

Summer (DJF)

Rain m m

141

60 4

65.1

33.3

60.5

62.5

46.5

67.03

%

61.5

45.6

43.1

44.8

41

33.6

32

46.32

Autumn (MAM)

Rain m m

32.3

30.2

25.8

11.7

32.8

40

21.5

24.07

%

14.2

22.7

17.1

15.7

22.1

22

18

16.6

Winter (JJA)

Rain m m

11.4

10

13.1

2.4

11

21.5

15.5

12.12

%

5.6

7.5

8.7

3.2

7.4

12.4

12.7

8.4

In light of these comparisons, it appears that the seasonal response of thunderstorms varies

across the region. Despite this variation, in general, late spring and summer are the peak

seasons of the year for thunderstorm activity, 72 per cent of all thunderstorms occurred at

these times. Thunderstorms also tend to account for a higher percentage of rain-days

toward the end of the thunderstorm season.

The monthly distribution of thunderstorm rainfall at different stations is shown in Figure 3.6

in more detail. Thunderstorms occur most frequently in November and December, and least

frequently in May, June and July. Generally, the warm summer months, October to March,

clearly dominate. However, the peak months for thunderstorm rainfall differs amongst

stations. For example, stations such as the Sydney Regional Office, Camden and

Bankstown, have peak thunderstorm rainfall in November. Unlike these stations,

Katoomba, because it is a mountain station, receives more thunder rainfall in January and

February. Both coastal and inland stations exhibit two maxima, in November and February

during the course of the year.


Bankstown -A Camden

Katoomba

Bowral Sydney R.O

•Richmond

• Wollongong

Oct Nov Dec

Figure 3.6 Monthly distribution of thunderstorm rainfall in the Sydney region for different stations.

The percentages of the average monthly rainfalls due to thunderstorms are shown in Table

3.9. The highest proportion occurs, on average, in the Sydney region in November with

31.4 per cent, but this rate varies for different stations.

Table 3.9 The percentage of average thunderstorm rainfall to mean monthly rainfall in different stations, in the Sydney region.

Stations Katoomba Bowral Richmond Camden Bankstown Sydney R.O Wollongong Average

Month (1987-93) (1975-92) (1960-93) (1972-92) (1970-91) (1960-93) (1973-93)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Yearly

32.2 29.7

13.3

4.4 1.5 3.3 0.3 7.9 9.1 6.5 26.3

27.8

15.3

30 28 13 5 1.2 3 0.3 7 8.2 7 25 26 12.8

23.5

18.2

17.3 9.0 4.9 7.1 12.9

9.6 22.5

18.9

31.6

29.9

18.0

14.4

11.4 4.5 7.5 2.4 0.0 4.6 1.4 5.3 13.5

18.8

16.7 9.0

18.3

20.1

15.1 11.3 5.9 4.7 5.4 8.3 10.9

13.4

36.7

28.2

15.6

17.7

21.6

18.6

7.5 4.7 8.2 2.3 11.9

13.1

18.9

42.3

22.3

14.9

8.0 6.0 8.0 1.2 4.1 8.4 1.1 5.4 12.4

4.7 27.4

15.0

8.4

20.6

19.3 12.8

6.5 3.5 5 3.8 7.4 11.6

11.8

29.7

23.7

13.42

For example, in November, in the Sydney Regional Office about 42 percent of monthly

rainfall is obtained from thunderstorms. In contrast, Katoomba receives only 26.3 per cent

of its rainfall from thunderstorms at this time of the year. In the winter months, M a y to

July, all stations obtain less than 6 per cent of their rainfall from thunderstorms.

Distribution of Thunderstorm Rainfall

3.6 Diurnal Variation

The importance of the diurnal cycle in the Sydney region is clear, and this work is in line

with that of earlier investigators. During the past decades, this most regular and predictable

diurnal distribution of thunderstorm activity has produced studies all over the world by

Brooks (1925), Tubbs (1972), Wallace (1975) and Tucker (1993) for U S A , Oladipo and

Mornu (1985) in Zaire, Barkley (1934) for Australia, Grace et al. (1989) for South

Australia, Treloar (1991) for Melbourne and Williams (1991) and Griffiths et al., (1993) for

the Sydney region. Generally, all these investigators indicated that in many parts of the

world, during the summer months, convectional processes predominate and can produce a

distinct diurnal distribution and variation in thunderstorm activity and an induced

precipitation amount mainly in the afternoon or early evening.

This section will describe the diurnal variation of thunderstorm rainfall at various parts of

the study area during the spring, summer and autumn seasons. In the Sydney region, there

are some problems with the data (provided by the Bureau of Meteorology) as they relate to

thunderstorm observations. Firstly, there are insufficient thunderstorms to define a clear

pattern for winter. Second, for some diurnal time spans there are not enough data for all

seven stations considered. Also, some stations report every 3 hours, some twice and others

only once a day. In data-set, there are no thunderstorm observations for 1, 4, 7, 10, 13, 16,

19, 22 and 24 hours, based on the N S W Local Standard Time (LST). These problems with

data caused some spatial and temporal gaps in illustrating the diurnal variation of

thunderstorm rainfall throughout the Sydney region.

Therefore diurnal patterns are only, shown for Katoomba, Richmond and Sydney Regional

Office stations. These are representative of three different geographical locations located in

the Sydney region, including mountainous, inland and coastal areas respectively. They also

have sufficient data for the purposes of this study in comparison with other thunder-

recording stations. Moving eastward across the region, there is a gradual transition to a late

afternoon and early evening maximum over the City and coastal areas. This diurnal pattern

is shown by data for the three stations located in the region (figures 3.7-3.9).

CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall EQ

katoomba (1987 -93)

40

co co un to oo oi *-•

Figure 3.7 Diumal variation of thunderstorm rainfall frequency for three thunder seasons at Katoomba station.

At Katoomba, over the mountains, the highest thunderstorm activity occurs in the

afternoon and a second highest in late afternoon (see Figure 3.7). However, this station has

the fewest number of thunderstorms at midnight and early morning.

Richmond 0960-93)

BSj?ig03SD •SbnrH?(DaF) S AiiumflVlAM)

40

Figure 3.8 Diumal variation of thunderstorm rainfall frequency for three thunder seasons at Richmond station.

The Richmond station, which is located near the base of the mountains, has a maximum

thunderstorms between 1700 and 1800 LST, but there is an additional one in the late

afternoon or early evening, between 1400 and 1500 (see Figure 3.8).


Finally the Sydney Regional Office station (Figure 3.9) has one maxima in the late

afternoon (1700-1800 LST). However, it is clear from the results, that a considerable

number of thunderstorms have a nocturnal nature in the western and in the eastern parts of

the study area. There are also some differences in diurnal patterns regarding different

seasons.

Figure 3.9 Diumal variation of thunderstorm rainfall frequency for three thunder seasons at Sydney Regional Office station.

It can be seen that in all cases at Sydney Regional Office, there is an afternoon-evening

diurnal pattern between 17 and 21 LST. In contrast, Katoomba shows a distinct afternoon

pattern (14-17) during the summer months. January and February, in particular at

Katoomba, seem to be more characterised by an afternoon (12-18) pattern than the other

months.

On average, the results of the diurnal variation analysis indicate that in most of the region

to the west and central parts of the Sydney region, thunderstorms exhibit a strong late

afternoon maximum, particularly during summer months. In the eastern part, thunderstorms

show marked diurnal distribution with maxima during the late afternoon and earlier

evening, however, thunderstorms may also occur at any time of the day or night, in the

Sydney region.

3.4 Discussion

This chapter has presented a temporal distribution of thunderstorm frequency and rainfall

amount in the Sydney region for the period 1960 to 1993 using data from the seven

selected thunder-recording stations. It is clear from the results that the temporal variation

of thunderstorm frequency and rainfall amount over the Sydney region varies from year to


year. During a calendar year, two seasonal periods with different thunderstorm rainfall

characteristics were distinguished: September-March and April-August. It was found that

in the September-March period thunderstorm activity predominates, particularly during

November through to February.

Thunderstorms are most common in the spring and summer during the afternoon or

evening hours. Generally, thunderstorm weather begins in late October and increases quite

abruptly in November as was shown by Ryan (1992) who found that in N S W , both severe

and less severe thunderstorms are most common in the summer months. There are,

however, alternating periods of high and low thunderstorm rainfall amounts during the

spring and summer seasons. The results of the diurnal variation analysis indicated that the

thunderstorm regime in the Sydney region is an 'afternoon/early evening' type. This result

agrees with the work of Griffiths et al. (1993) and Batt (1994).

In the summer months, the maximum values of thunderstorm frequency are observed west

of the study area over the mountains. It is evident from the data that the mountains to the

west of the Sydney region receive many more storms than lowland areas between the

mountains and the coastal areas. Thunderstorms are least frequent in these lowland areas.

The areas near the coast and City, however, generally received greater thunderstorm

rainfall amounts on average in the same period (1960 to 1993). Explanations for the

temporal distribution and variable nature of thunderstorms over the Sydney region are

complex. Various mechanisms, which can introduce or enhance thunderstorm activity, have

been proposed by different authors for the observed thunderstorm patterns in the region.

These may reflect the overall impact of three important controlling factors that include: 1)

the synoptic weather patterns, 2) the local climatic factors and 3) physiographic parameters

of the Sydney region.

3.7.1 The Role of Synoptic Weather Patterns

In the past, atmospheric conditions leading to the creation of thunderstorms have been the

subject of detailed investigation by different authors (Hales, 1978; Winkler, 1988). They

have suggested that convection rainfall often develops when synoptic weather patterns and

meso-scale mechanisms promote instability in the atmosphere or enhance present unstable

conditions. More recently, Konrad and Meentemeyer (1994) found that various synoptic

scale features such as lows and fronts, can be connected with heavy rainfall from

thunderstorms over the Appalachian region. These synoptic features provide a supportive

environment on the meso-scale (10-100 km) for the development of convective cells which

produce heavy rainfall as they move and interact with one another (for more details see

Chapter 2).

CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 8J

In the Sydney region, while local convection as a result of maximum solar heating occurs

mainly in the afternoon or evening, the timing of thundery weather still depends upon

timing of the passage of synoptic weather systems which may occur during the day or

night. These close relationships between thunderstorm activity and weather patterns have

been studied in the past by several researchers such as Morgan (1979b); Griffiths et al.

(1993); Matthews (1993); Speer and Geerts (1994). These studies indicate that there are

some synoptic conditions which favour thunderstorm development in the Sydney region.

Firstly, occasional lows, formed over the north of the Tasman sea, move close to the NSW

coast and produce considerable thunderstorm activity, causing very heavy rainfall in the

region. Secondly, it was found that in some cases thunderstorms can occur with, or ahead

of an active front leading to thundery showers and that these thunderstorms are

occasionally widespread if there is some upper air disturbance present (Williams, 1991)

Finally, investigators such as Matthews (1993) and Speer and Geerts (1994) - who more

recently studied the formation and structure of thunderstorm events in the Sydney region -

have found that the occurrence of thunderstorms in the Sydney region can also be

correlated to the synoptic-meso-scale weather systems such as: easterly troughs; pre-frontal

systems lows which developed from low pressure systems, and post-frontal systems. In

such situations, thunderstorms can move over the region, particularly in the spring and

summer months, when the predominant winds are humid north-east or south-east

originating from the Tasman or Coral Seas. To relate some specific thunderstorm events to

type of synoptic conditions, several examples are given in Chapter 6 in Table 6.4 and

Appendix C.

It is also possible that the effectiveness of night thunderstorms in generating rainfall is

probably different from daytime thunderstorms, due to the expected differences in humidity

and temperature patterns produced by the above mentioned types of synoptic systems.

Although the synoptic weather systems are very important in the production and

enhancement of thunderstorms, this study will not analyse the effects of these weather

patterns upon the thunderstorm activity in the region. The association between synoptic

weather patterns and the development of thunderstorms in the Sydney region was discussed

in more detail in Chapter 2.

3.7.2 The Effect of Climatic Factors

Synoptic scale weather patterns allow for a broad understanding of thunderstorm rainfall

occurrence over the study area. However, the fact that all stations in the Sydney region

have at least a diurnal maximum in the afternoon or early evening, suggests that the diurnal

distribution of thunderstorms may be controlled by local climatic factors such as air and

sea-surface temperatures and air humidity. Explanations for the prevalence of afternoon-

CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 8J

evening thunderstorms over a region such as the Sydney area (with variable physiography)

are complex. Various mechanisms can be proposed for the observed diurnal thunderstorm

patterns.

One such group of mechanisms are those based on thermodynamic actions (for example,

solar radiation). It seems that most thunderstorms are due to local convection induced by

diurnal surface heating particularly in summer months. This occurs because high

temperatures are available from the heating of ground surfaces and there is a great deal of

moisture in the air, originating from a warm, nearby ocean.

This mechanism can be applied to the Sydney region as a simple explanation for the

maximum thunderstorm activity in the late spring and summer. That is 1) a diurnal heating

over the land and 2) an abundant supply of moisture from the Tasman Sea, which not only

increases the amount of precipitation produced, but also the degree of conditional

instability in the atmosphere. This may suggest an investigation of the possible relationships

between thunderstorm rainfall and some climatic factors such as, the sea-surface and air

temperatures and relative humidity in the region.

3.7.3 The Impact of Physiographic Parameters

More likely, as was discussed in Chapter 2, it is also possible that some types of

thunderstorms could be controlled by physiographic parameters such as topographic

features, proximity to the ocean and landuse patterns. These parameters play an important

part in the more local nature of thunderstorm development in the Sydney region. Previous

studies such as Astling (1984), and Smith (1979 and 1985) have already shown that hills

and mountain ranges can set off thunderstorms in potentially unstable airflows and these

developments can, in some situations, drift away and further develop and affect large areas

of lowland. The Sydney region is walled by a mountain range to the west, so, it is possible

that the most regular and predictable types of orographic thunderstorm activity may occur

in the warm season conditions to the west of Sydney. In this area, the daily heating of the

hillsides may generate warm up-slope winds which continue rising after reaching the

mountain top. In this situation, the heated air rises to form convective clouds which can

trigger deep convective systems. Then, this diurnally forced convection may produce more

thunderstorm activity when the thunderstorm is forced to travel some distance away from

the mountains towards the coastal areas (Morgan, 1979a).

Alternately the combination of mountainous terrain and moist, warm and unstable air

masses may provide the most favourable conditions for thunderstorm development. This is

why they are more common over mountains and about the coastal areas particularly in

summer. In this case, the violent thunderstorms which can be triggered by, for example, the


physical environment also may be enhanced by convergence, a high atmospheric advection

or an active front.

The resulting thunderstorms which occur primarily over the mountains and coastal areas,

are most important evidence signalling that topography and proximity to the sea can

control the temporal thunderstorm occurrence and also associated rainfall distribution. This

may reflect a tendency for some of thunderstorms to develop over the mountains, then

move eastward towards the coastal area. It seems quite likely that the afternoon maximum

in summer thunderstorms along mountainous parts of the study area and the late evening

and night-time maximum on coastal areas can be explained by this mechanism.

3.8 Summary and Conclusion

Careful study of thunderstorm rainfall amounts and frequency at the different stations

located in the Sydney region, indicated that thunderstorms show marked diurnal and

seasonal variation. They are most frequent in the summer months and during the late

afternoon and early evening, but there are some recognisable differences between stations

in the region. Thunderstorms are most frequent over the west of the region where they may

be initiated by the air rising over the mountains, and less frequent over the lowland interior

of the Sydney region. However, it is evident from the above results that the stations which

are located in coastal areas, near the ocean, receive more thunderstorm rainfall than those

located inland or in the nearby mountains. This result does not hold, however, for

thunderstorm frequency, because the periods of monthly maxima of thunderstorm activity

do not necessarily coincide with the periods of rainfall maxima in all of the study areas.

It is obvious from the evidence that there is no single hypothesis that is capable of

explaining the nature of temporal variation of thunderstorm activity in different parts of the

Sydney region. As the results of this study indicated, the distribution of thunderstorms over

the Sydney region varies over time and space. O n one hand, these results may reflect the

overall effects of some of the synoptic scale weather patterns upon thunderstorm activity

which have been widely studied by many researchers in the region.

On the other hand, because the temporal and spatial variation of thunderstorms in the

Sydney region is relatively high, they may be affected by the above-mentioned climatic

factors and physiographic parameters of the region. Therefore, in Chapter 4, the more

detailed associations among additional climatic factors and thunderstorm rainfall will be

analysed. Chapters 6 and 7, will then address the spatial variation and distribution of

thunderstorm rainfall with an emphasis on the possible associations between thunderstorm

rainfall and physiographic parameters of the Sydney region. This will open a new approach

for future studies on a regional scale.

CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables 86

CHAPTER 4

THUNDERSTORM RAINFALL AND CLIMATIC VARIABLES

4.1 Introduction

In Chapter 3, it was argued that the distribution of thunderstorm rainfall in the Sydney

region may be affected, to some degree, by some important climatic variables such as air

and sea-surface temperatures. The overall goal of this chapter is, therefore, to determine

the association of three main climatic background variables, sea-surface temperature, daily

air temperatures (maximum and minimum), and mean relative humidity with thunderstorm

rainfall amount and its frequency. This will be done using monthly data mainly from

Richmond, Sydney Airport and Sydney Regional Office. These stations, having the longest

data in the region.

In section 2 the data sources and analytical techniques are given. In the first stage of data

analysis, in section 3, descriptive statistics describe and summarise single variables in order

to demonstrate the general characteristics of these variables. In the second stage of the data

analysis, in section 4, some types of statistical techniques, such as simple correlation

techniques, determine the significant levels of associations amongst variables. Section 5

examines the effect of independent variables separately upon the dependent variables, using

a stepwise multiple regression technique. Finally the reasons, for statistically significant

comparisons, are outlined in section 6.

4.2 Data Sources and Analysis Techniques

To find the possible associations between thunderstorms data and climatic factors, data

were obtained from the Sydney Regional Office on a monthly basis. Sydney's air

temperature and humidity records for three synoptic stations, namely: Sydney Airport;

Sydney Regional Office, and Richmond, were available (this is the most appropriate range

of stations with data available for this analysis). The data were first described on a monthly

basis, and were then calculated using mean daily maximum and minimum values (one-half

of mean daily maximum plus minimum) from 1960 to 1990.

Tables 4.1 to 4.6 (see Appendix B) show the monthly thunderstorm rainfall frequency and

thunderstorm rainfall amounts for the three above-mentioned stations. These stations were

selected because: (1) they are the main synoptic stations recording thunderstorm events;

(2) they have the longest time-span records; and (3) they have complete records of the

CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables -XI

necessary data. These data were restricted to the period 1960 to 1990 because sea-surface

temperature was available only for this period.

The sea-surface temperature data was obtained from the Australian Oceanographic Section

(Sydney Division). The data was collected at a station off of Port Hacking (34° 05' S, and

151° 12' E ) which performs measurement on a semi-regular basis. Most, but not all the

years contain observations for each month of the year. Sea-surface temperature

observations are usually taken weekly. Therefore, monthly means were calculated by

aggregating the weekly data and then averaging these values. Where a single month was

not observed, its value was assessed by a linear interpolation and then used as part of the

time series.

To analysis the data and to find the possible associations among these data sets, some

simple to complex statistical techniques were used. The following methods form the basis

for all analysis in the current chapter:

1) A simple correlation technique was employed in order to determine the possible

causal relationships between dependent (thunderstorm rainfall) and independent variables

(air and sea-surface temperatures) and the extent to which the variables are interrelated.

This statistical technique is used when both the independent variables and the dependent

variables are measured on a ratio scale.

2) A correlation coefficient test was undertaken for preliminary hypothesis testing.

This technique was also employed for the purpose of determining the reliability of the

variables.

3) For the last stage of analysis, a stepwise regression was used. By using this

technique, independent variables were entered into the equation separately according to the

strength as predictors of the dependent variables. Consequently, the regression coefficients

provided estimates of the effect of each of the independent variables on thunderstorm

rainfall frequency, holding statistically constant the effects of the other variables included in

the equation.

4.3 Description of Variables

In this chapter, both thunderstorm rainfall amount and its frequency have been assumed to

be dependent variables affected by above-mentioned climatic variables. Addressing this

assumption, and to find possible associations between these independent and dependent

variables, it is first necessary to describe each of these variables using monthly-based

averages. This will facilitate analysing, comparing, and measuring the correlations between


the different variables. Table 4.7 gives simple descriptive statistics for the monthly

distribution of thunderstorm data.

Table 4.7 Description of thunderstorm data.

Stations

Statistics

Minimum

Maximum

Range

Mean

Std. Dev.

Variance

Sum

N

Sydney Airport

TRF

1

9

8

2.1

1.4

1.9

531

252

TRA

0.1

265.5

265.3

21.9

29.5

868.9

5533

252

Sydney R.O.

TRF

1

9

8

2.1

1.4

1.9

501

237

TRA

0.1

334

333.8

23.4

34.5

1187.4

5626

237

Richmond

TRF

1

8

7

2.5

1.7

2.9

561

227

TRA

0.1

126

125.8

20.1

20

405

4568

227 N = The number of months with thunderstorm rainfall (> 0.1 m m ) in the sample T R A = Thunderstorm Rainfall Amount, TRF = Thunderstorm Rainfall Frequency

It is evident from Table 4.7 that the total number of thunderstorm rainfalls recorded (1960-

90) at the Richmond station with 561 thunderstorms is greater than the Sydney Regional

Office and Sydney Airort stations with 501 and 531 thunderstorms respectively. In

contrast, the total amount of thunderstorm rain at the Sydney Regional Office (5626 m m )

and Sydney Airport (5533 m m ) is higher than at the Richmond station (4568 m m ) .

4.3.1 Air Temperature

Air temperature was primarily assumed as a function of the amount of solar radiation

received on the ground which can be an important climatic factor affecting thunderstorm

activity (Critchfield, 1987). Table 4.8 summarises air temperature data on a monthly basis

between 1960 and 1990 only for those months having thunderstorms with at least more

than 0.1 m m rainfall. The data are measured for the three selected stations.

Table 4.8 lists average maximum and minimum temperatures and extremes for three

stations in the Sydney region. It is evident that there are considerable differences in

temperature between these three stations. Generally, the highest extremes and variance

occur at Richmond, while stations, which are located in the east of Sydney, show less

variability. Figure 1.5 shows the average daily temperature for different months in the study

area.


Table 4.8 Means and extremes of temperature at three selected stations.

Line 1: Mean Daily Max. Temperature Line 2: Mean Daily Min. Temperature Line 3: Average Daily Temperature N= Number of months

Stations

Sydney Airport N=252

Sydney R.O. N=237

Richmond N=227

1 2 3

1 2 3

1 2 3

Mean

22.91

14.10

18.51

22.88

15.15

19.01

25.33

13.18

19.26

Min.

16 5.2 10.6

16.2 7.1 11.65

16.6

2.8 9.95

Max.

29.2

21 24.6

28.6

21.1

24.4

33.3

19.5 25.65

Range

13.2 15.8

14

12.4

14 12.75

16.7

16.6

15.7

Std. Dev.

3.43

4.22 3.78

3.17

3.73

3.43

3.94

4.33

4.04

Variance

11.8

17.82

14.33

10.07

13.94

11.77

15.56

18.83 16.36

4.3.2 Sea Surface Temperature

In the Sydney area, it has been assumed that the sea-surface off the coast has a major

influence on rainfall on a regional scale (Priestley, 1964, 1970; Hopkins and Holland,

1994). This hypathesis has never been tested for thunderstorms alone. Also, it was assumed

that the ocean waters adjacent to the coast can provide atmospheric moisture, and as a

result, affect the temperature patterns in the region. Table 4.9 gives the average monthly

sea-surface temperature.

CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables _20

Table 4.9 Monthly and yearly sea-surface temperature data (°C) at Port Hacking. Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Yearly 1960

1961

1962

1963 1964

1965

1966 1967

1968

1969

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982 1983

1984

1985

1986

1987

1988

1989

1990

Mean

21.4

20.8

21.6

19.3 21.5

20.5 21.1

20.8

22.2

21.7

21.9

21.9

19.9

21.9

21.4

20.7 21.7

20.6

21.6 21.0

21.4

21.8

21.6

21.0

18.6

21.7

22.5

21.3

20.4

20.5

21.0

21.1

22.0

21.7

22.0

22.1 22.3

21.8

21.4 21.7

20.5

19.9

21.4 22.8

20.4

21.6

22.4

21.5

23.1

20.6

22.3 21.5

22.1

23.0

21.3 22.3

22.5

20.6

22.9

23.8

22.5

22.1 22.3

21.9

21.7

22.1

21.8

22.2

20.8

21.0 20.9

21.4

22.7

19.9

22.4

20.4

21.4

22.4

21.5

21.9

23.4

21.4

21.3

21.4

22.3

22.6

22.0 19.7

21.9

20.3

21.3

21.0

21.4

21.1

24.1

21.6

20.3

18.2 20.6

21.7

20.5

19.3 21.1

21.0

21.4

20.1

21.1

20.3

20.4

20.9

22.4

20.0

20.9

20.9

21.4

20.2

21.1

21.6

20.9 20.4

19.9

21.3

20.4

19.8

19.9

21.0

23.1

20.7

18.3

18.5 19.2

19.1 19.2

19.3 19.8

19.4

18.9

19.3

18.3

18.9

19.0

19.4

19.5

19.7

20.6

19.0

20.1 18.8

19.0

20.4

20.9 18.9

18.9

19.2

20.4 19.0

19.5

20.6

19.9

19.4

17.0 17.1

17.6

18.2

18.7

18.4 19.0 17.5

17.4

17.4

16.6

17.2 17.8

17.4

17.7 17.8

19.5

16.9

17.6 17.9

18.1

18.1

18.7

17.1

17.9

18.0

17.8

17.7

18.0

18.5

18.2

17.8

17.8

15.9

16.7

17.3

16.9

17.7 16.6

16.0

16.0

17.3

15.1

15.9

17.4

17.3

16.7

16.3

18.8

15.4

16.9

17.0

16.2

16.7

17.4 15.5

15.9

16.7

17.2

18.4

18.0

17.3

16.6

16.8

17.1

15.4

15.5

15.9 16.9

17.2 16.7

16.4 16.1

16.2 14.8

16.5

16.9

18.3

16.8 17.0

18.6

15.2

16.3 15.7

16.8

16.0 16.6

16.3

15.4

16.2

16.3

16.4

16.0

15.7

16.0

16.4

16.9

15.4

16.0

16.5

16.5

16.7 17.6

16.5

16.5

16.0

16.5

16.0

16.6

16.8

16.3

17.7

18.3

17.7

16.2 16.1

16.4

17.1

17.2 16.1

16.7

18.0

16.9

17.0

16.5

16.0

16.8

16.7

18.1

18.1

16.9

16.5

17.3

17.5 18.2

17.1

16.1

16.5

17.6

16.4

17.3 18.1

17.3 17.7

17.8 17.8

17.4 16.4

17.0

17.2

18.0 18.3

16.8

18.3

18.4

18.5

19.1

19.4

16.4

17.5

19.1

18.0 19.0

18.3

18.8

18.9

19.1 18.3

17.3 20.1

18.5

16.9

19.1

19.4

18.7

18.8

19.6

17.2

18.5

18.5

18.0

18.3

18.4 19.4

20.8

19.3

19.0

18.9

19.6

18.2

16.2

18.6

19.0

19.5

19.5

19.8

19.3

19.7

20.1

19.6

20.4

20.3

20.2

19.9 21.8

21.0

20.4

19.7

20.9

19.6

19.6

20.1

19.7 20.6

21.6 20.1

20.5

21.9

19.3

21.1

20.0

18.9

22.1

20.2

19.1

18.4

18.9

18.9

19.1

19.0

19.3 18.8

18.8

18.7

18.7

18.6

19.0

19.5

19.3

19.1

20.3

18.5

19.1

18.7

19.0

19.5

19.5 18.8

18.8

19.3 19.4

19.4

19.2

19.1

19.4

19.1

0 U

tn

SST - at Port Hacking

24T

22± 20+ 18--16--14--12--

+ + + + + + Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Average Monthly Variation of SST (1960-1990)

Figure 4.1 Average monthly variation of the sea surface temperature in °C.


Figure 4.1 plots the average monthly variation of sea-surface by calender month over the

period 1960 to 1990 (372 months), at Port Hacking, with 34°05 S and 151°12 E for 0

depth. It is clear that sea-surface temperature had a maximum of 22 °C in February and a

minimum of 16.40 °C in August.

4.3.3 Air Humidity

Humidity, or the water vapour content of the air, it is suggested, is an important

meteorological element both in terms of the development of weather patterns and in terms

of the efficiency of living systems such as convection cells (Lutgens and Tarbuck, 1982).

Both the absolute humidity and relative humidity are known as important indicators in

computing atmospheric moisture amounts in creating or affecting convection systems (see

Chapter 2). M a n y climatologists recommend that the absolute humidity, as measure of

independent variable, should be used in correlating air humidity to thunderstorm variations.

They regard the relative humidity as more or less meaningless. Unfortunately, the absolute

humidity, as an important climatic variable, was not availble in the Bureau of Meteorology

sources. Thus, for estimation of absolute humidity, there is a need to have other humidity

parameters such as: dry-bulb and wet-bulb temperatures, atmospheric pressure or

saturation vapour pressure at the wet-bulb temperature (Abbott and Tabony, 1985). Still,

the calculation of absolute humidity is a complex task and there is a variety of methods,

using a set of different equations. Without such data, this is very costly in terms of

computing time, with difficulty in evaluating of results (Sargent, 1980).

Therefore, in this study the relative humidity was used, which is the maximum amount of

water vapour that the atmosphere can hold. The relative humidity of the air at a given

temperature is the ratio (expressed as a percentage) of the actual vapour pressure to the

saturation vapour pressure. Relative humidity is defined (World Meteorological

Organization, 1988) by

U= 100 (e/ed) per cent,

where e = ambient vapour pressure in millibars, and

ed = saturation vapour pressure in millibars with respect to water at the same

pressure and temperature.

Generally, this is dependent on the temperature of the air and increases with increasing

temperature. Consequently, in this study, if temperature is to be used as a measure of

convection activity, relative humidity must also be taken into consideration.

According to the report of the Sydney's Bureau of Meteorology (1991a), the maximum

relative humidity nearly always occurs near dawn and the minimum at about noon during


summer, and at 2 pm or 3 pm during winter, at about the time of maximum temperature.

The mean relative humidity at 3 p m in the three stations in the Sydney region is given in

Table 4.8. This time of day was adjusted in the analysis because many of the convection

activities take place in the afternoons. This matter was discussed in Chapter 3.

Table 4.10 Simple statistics of the relative humidity (in per cent) in the Sydney region, from 1960 to 1990 (372 months).

Station Sydney Airport Sydney

RO. Richmond

Mean 55.22

56.5

47

Min. 30

33

21

Max. 78

72

71

Range 48

39

50

Std. Dev. 7.7

7

8.7

Variance 59.4

48.9

75.3

Also, Figure 4.2 shows the average monthly variation of the relative humidity at 3 p m by

the calender month over the period of 31 years. The period of analysis is 1960-1990.

Figure 4.2 Monthly distribution of the mean relative humidity at three stations in the Sydney region (1960-90).

4.4 Correlations Matrices of Variables

To find possible associations between the different variables some analytical procedures

have been established. First, in order to determine the associations between thunderstorm

rainfall frequency and thunderstorm rainfall amount as dependent variables, and air and sea-

surface temperatures and air humidity as independent variables. A n initial correlation has

been separately introduced for each group of variables. The correlation found does not

depend on data distribution normality. Because of the nature of thunderstorm data no

CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables 9J

transformation procedure was applied. Table 4.11 summarises the correlation matrix of

thunderstorm items in the sample for the period beginning from 1960 and ending to 1990.

Table 4.11 The correlation matrix (associations) between dependent variables.

Variables

T R A Sydney Airport

T R A Sydney R.O.

T R A Richmond

T R F Sydney Airport

T R F Sydney R.O.

T R F Richmond

Yi

Y 2

Y 3

Y 4

Y 5

Y 6

Y i

1

0.82

0.40

0.64

0.57

0.45

Y 2

1

0.43

0.56

0.61

0.46

Y 3

1

0.44

0.48

0.72

Y 4 Y 5 Y 6

1

0.80 1

0.64 0.67 1

All associations are at 0.05 significant level T R A = Thunderstorm Rainfall Amount, TRF = Thunderstorm Rainfall Frequency

In this correlation matrix, each of the thunderstorm items was correlated against the others.

They were interrelated in the range of approximately 0.4 to 0.82. These correlation

coefficients m a y not show, on average, high correlation, but they indicate that all

dependant variables are positively associated, and all associations are significant at 0.05

level. Also, an attempt was made to see if there are any associations between air, sea-

surface temperatures and humidity among the three above-mentioned stations. Therefore,

again, a simple correlation method has been employed in order to estimate the associations

between independent variables.

Table 4.12 Correlation matrix for independent variables.

Variables

SST at Port Hacking

Sydney Airport

Sydney R.O.

Richmond

Max. Tem.

Min. Tem.

Rel. Hum.

Max. Tem.

Min. Tem.

Rel. Hum.

Max. Tem.

Min. Tem.

Rel. Hum.

* i

x2

x3

x4

x5

x6

x7

x8

x9

Xio

*i

1

.75

.80

.50

.77

.81

.56

.44

.78

.30

x2

1

.95

.22

.99

.96

.40

.60

.94

-.12

x3

1

.44

.95

.99

.56

.58

.98

.13

x4

1

.25

.42

.92

.11

.47

.79

x5

1

.97

.40

.60

.94

-.08*

x5

1

.56

.59

.98

.09*

X 7

1

.20

.60

.70

x8

1

.57

-.13

X 9

1

.14

Xio

1

* Non-significant at 0.05 level

Table 4.12 shows that there are generally high positive correlations among independent

variables. It also may indicate that the association between sea-surface temperature and air

temperatures at Sydney Airport and Sydney Regional Office are higher than the association

CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables JL4

at Richmond station which is the weakest. It must be noted that the relationship between

temperature and air humidity is not entirely independent. According to these correlations, it

can be concluded that all independent variables which have been used in this study have a

com m o n relationship to each other.

In one stage further, to examine the coefficient of correlation between the thunderstorm

data and the independent variables, it was decided to find the possible correlations,

However, a problem which may arise in the study of thunderstorms on the basis of monthly

rainfall is brought about by the inclusion of the data from those months without any

thunderstorm rainfall. If these months are included in the computation they will increase the

correlation coefficient significantly without entailing any increase in information on the

thunderstorm rainfall incidence. This problem has been identified by Cornish, Hill and

Evans (1961) and Sumner and Bonell (1990). For this reason, months with zero values

were excluded from the analysis.

Table 4.13 summarises the regression analysis between the climatic variables and

thunderstorm rainfall values for the 3 stations. This table reveals distinct associations

between independent variables and thunderstorm rainfall frequency and thunderstorm

rainfall amounts in the study area. However, there are two points to consider. Firstly, in

some cases associations are not at significant levels. Secondly, as can be seen from Table

4.13, there is a general decline of relationships between dependent variables and sea surface

temperature with the distance inland. For example a 0.0009 significant level at Sydney

Airport declines to 0.05 at Richmond for thunderstorm rainfall frequency values.

Table 4.13 Linear regression coefficients of dependent variables by independent variables.

Dependent Variables Stations and

Independent Variable

Sydney Airport

SST Max. Air Tem.

Min. Air Tem.

Rel. Hum.

Sydney R.O.

SST Max. Air Tem. Min. Air Tem.

Rel. Hum. Richmond

SST Max. Air Tem. Min. Air Tem.

Rel. Hum.

Thunderstorm

Rainfall

R*

.21

.29

.32

.20

.14

.22

.25

.22

.12

.29

.34

.13

Frequenc p**

.0009

.0001

.0001

.001

.05 .0005 .0001

.0005

.05 .0001

.0001

.04

Thunderstorm

Rainfall Amount

R

.17

.12

.17

.19

.13

.09

.12

.17

.05

.07

.16

.20

P

.008

.05 .008

.002

.05 NS .05 .007

NS NS .01 .002

* Regression coefficient, ** Probability level NS = Non-significant correlation at 0.05 level

CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables £5

4.5 Multiple Associations Between Variables

At this stage of the data analysis, to ensure that each major independent variable makes a

statistically significant contribution to the predictable variance of thunderstorm rainfall

amount (here the focus is on h o w much rather than h o w often), and in order to examine the

relative importance of each climatic variable, a stepwise multiple regression technique was

introduced. This technique was considered a 'stepwise solution' which is c o m m o n

computational procedure in regression analysis having been used in many studies (Ohring,

1972). For example, Bryant (1985a) used this technique for correlating beach erosion with

sea-level rises. Therefore, this technique, as a rank ordering of the total correlations of

major independent variables with thunderstorm rainfall, was separately applied to two

stations (with the longest records), Sydney Airport station (the nearest station to Port

Hacking) and Richmond station (the furthest station from Port Hacking).

Generally, this technique can predict as much variance in the dependent variable as is

possible from the composite of independent variables. This process is complicated by the

fact that the independent variables may be correlated with each other and, consequently,

each predicts the "same part" of the variation in the dependent variable. For example, sea-

surface and air temperatures may correlate with each other (see Table 4.12).

By using a stepwise multiple regression procedure it was therefore possible to evaluate

systematically the relative contributions of important variables in the explanation of

thunderstorm rainfall. This statistical technique was also used in order to measure whether

there is a cumulative effect of several variables on thunderstorm behaviour. Independent

variables in this regression equation were mean daily maximum and minimum temperatures,

sea-surface temperature and mean relative air humidity, with thunderstorm rainfall as the

dependent variable. The independent variables were selected according to the literature

framework of this study, because in Chapter 2, it was hypothesised that all of these four

factors would contribute significantly to the explanation of thunderstorm rainfall.

Each independent variable was entered into the regression equation in order to determine

its unique contribution in relation to the other three. The order in which the independent

variables are entered into the equation had no impact on the outcome because each variable

is treated as though it is the last variable to be entered. The stepwise regression procedure

selected the strongest independent variable in the first stage and at each stage a new

variable was added to the equation. The results of the stepwise regression are presented in

Tables 4.14 (a) and 4.14(b).

The independent variables were introduced into the regression equation, summarised in

Tables 4.14 (a) and 4.14 (b), in the order in which they increased the explained variance in

M

thunderstorm rainfall. In both stations, the rank ordering of variables in terms of their

predictive strength are: mean daily minimum air temperature; sea-surface temperature;

mean relative air humidity; and mean daily maximum air temperature.

The first step identified the minimum air temperature as the best single predictor of

thunderstorm rainfall. Sea-surface temperature is the second strongest predictor of

thunderstorm rainfall ( F[30.8], p< 01). This variable adds 6 per cent to the explained

variance in thunderstorm rainfall at the Sydney Airport station.

Table 4.14 (a) Results of stepwise multiple regression analysis of thunderstorm rainfall at the Sydney Airport station (n=252 ).

Step

Number

1

2

3

4

Total

Predictor Variable

Minimum Air Temperature

Sea-Surface Temperature

Mean Relative Humidity

Maximum Air Temperature

Multiple

R

0.35

0.42

0.44

0.45

R

Square

0.12

0.18

0.19

0.20

Variance

Added in

%

12

6

2

1

21

F ratio

to enter *

30.8

24

18

14

Number of

Variable in the

Equation

1

2

3

4

All F values are significant at 0.01 level

Table 4.14 (b) Results of stepwise multiple regression analysis of thunderstorm rainfall at Richmond station (n= 227).

Step

Number

1

2

3

4

Total

Predictor Variable

Minimum Air Temperature

Sea-Surface Temperature

Mean Relative Humidity

Maximum Air Temperature

Multiple

R

0.32

0.34

0.35

0.36

R

Square

0.11

0.12

0.13

0.137

Variance

Added in

%

11

1

1

1

14

F ratio

to enter*

29.4

16.2

11.4

10.1

Number of

Variable in the

Equation

1

2

3

4

* All F values are significant at 0.01 level

Tables 4.14 (a) and 4.14 (b) also show a significant relationship between the relative air

humidity at each station and thunderstorm rainfall. This variable is recognised as the third

major predictor of thunderstorm rainfall and adds about 2 per cent to the explained

variance in the equation. W h e n the 4 variables are included in the regression equation, the

amount of explained variance in thunderstorm rainfall behaviour increases to about 21 per

cent at the Sydney Airport and about 14 per cent at the Richmond station. The stepwise


relationships are not high, and despite the significance, the variance explanied is very low,

therefore, other factors must be operating.

As the result of the stepwise regression technique indicated, all 4 independent variables

have significant relationships with thunderstorm rainfall. A comparison of these tables

clearly indicates that both sea-surface and air temperatures can affect the thunderstorm

occurrence throughout the Sydney region and they can explain some of the variance

statistically. However, the percentage of variance explained is not high generally, and it is

not the same for the two selected stations which are located in the east, near the coast, and

in the west of the Sydney region.

4.6 Discussion

In this chapter - using the available data taken from the limited of stations - first, some

descriptive statistic techniques have been used to summarise, present and compare the

distribution of thunderstorm data on a monthly basis. Then, to find the possible

associations among some of the climatic variables which may affect thunderstorms,

different kinds of statistical procedures were considered. Some simple correlation methods

and a stepwise multiple technique were used to find the percentage contribution of each

independent variable upon thunderstorm rainfall. Results indicated that there are possible

causal relationships between the above-mentioned climatic variables and thunderstorm data

specially for coastal stations. This relationship becomes weaker further inland.

The evidence involves relatively low correlation coefficients, similar to those reported by

Priestley (1964) and Hirst and Linacre (1978) for monthly rainfall values (see Chapter 2).

The results of the stepwise regression technique indicated that thunderstorm rainfall

amount (explaining about 21 per cent of the variance at the Sydney Airport station and 14

per cent at Richmond) is associated with three main climatic factors; air, sea-surface

temperatures and air humidity. Three distinct effects come to mind as likely to cause these

associations between variables in the region.

4.6.1 Effects of Sea-Surface Temperature

The direct effect of sea-surface temperature upon the rainfall process was shown by

Priestley and Troup in 1966. The ocean waters adjacent to the coast can provide

atmospheric moisture and moderate temperatures and therefore affect rainfall patterns in

the region (Rochford, 1977). Apparently the importance of sea-surface temperature to the

climate of the Sydney region has been recognised for a long time. For example,

investigation has revealed that the east coast current which carries w a r m tropical water

southwards along the N e w South Wales coast can affect the climate of the region (Lough,

1992). The positive correlations between rainfall in eastern Victoria and the w a r m sea-


surface temperatures from the Coral Sea, which was found by Whetton (1989), also

supports this positive correlation. Therefore, although the ocean temperature varies in both

space and time, it can also significantly influence the distribution of rainfall patterns in the

region.

Supposing other affecting factors to be equal, the warmer the water, the warmer and more

moist will be the lower layers of air reaching the coast. In addition, there will probably be a

greater tendency for convection activity. In 1978, Hirst and Linacre indicated that the

onshore winds, which can control the sea-surface temperature, may also enhance

convective rainfall by bringing in moist warm air to coastal areas. In this case, a warmer sea

surface would cause instability of the coastal atmosphere, increasing the tendency to

convective rainfall.

Although it is evident from the result of this study that the association between sea-surface

temperature and thunderstorm rainfall is positive during the calendar year, it seems this

association is stronger in autumn/winter than the spring/summer seasons. This was

emphasised by Colquhoun and Batt, in a personal conversation (Bureau of Meteorology,

N S W Regional Office, 1994). This is when the land-sea temperature difference is greatest

(Holland et al. 1987). Also, Hopkins and Holland (1994), found that the East-Coast

Cyclones show a preference for formation in the autumn/winter months which occasionally

create very heavy rainfalls along the east coast ranges.

On the other hand, the results of this study indicate that the association between coastal

sea-surface temperature and thunderstorm rainfall at the Richmond station (which is more

than 55 K m inland) is less or even non-significant. A simple explanation may be that in the

west of the Sydney region, because there is less moisture and because it is further from the

warm easterly winds off the ocean, the chance of thunderstorm occurrence with intense

rainfall is lower than in the coastal areas.

4.6.2 Associations Between Air Temperature and Thunderstorms

A relatively high association between air temperatures (minimum and maximum) and

thunderstorm rainfall amount probably indicates that the incidence of high air temperature

can cause high thunderstorm activity in the region. This simply means that the air

temperature should also be considered as one of the factors which is able to create or

enhance thunderstorm activity.

Many researchers in the field of thunderstorm activity have found that air temperature is an

important climatic factor in creating or enhancing a convection system. For example,

Lutgens and Tarbuck (1982 p:237) wrote:


"All thunderstorms require warm, moist air, which, when lifted, will release sufficient latent heat to provide the buoyancy to maintain its upward flight. Although this instability and associated buoyancy are triggered by a number of different processes, all thunderstorms need an unstable atmospheric environment in which the instability can be enhanced by high surface temperatures."

To these features can be added the effects of unequal heating of the land surfaces,

particularly in summer months when the clear areas can be warmed rapidly by solar

radiation (Baines, 1990). Probably this uneven heating can generate vigorous convection

which leads to the growth of storms in a matter of hours. It has been shown in chapter 3

that most thunderstorms develop in the afternoons in the spring and summer months, when

the potential for convection is usually the greatest and adequate high air temperatures are

available.

In the USA, Benjamin (1983) found that some severe thunderstorms were the result of

differential heating, differential advection and local topography. In addition to these

factors, Golde (1977) has shown that vigorous thunderstorms can occur along an active

cold front or in squall lines in the warm air ahead, at any time of the day or night.

It may be supposed that surface heating is generally not sufficient, in itself, to cause

thunderstorm activity, and any factor that can destabilise the air, aids in generating a

thunderstorm. A s Smith (1975 p:13) mentioned:

"The importance of surface features increases markedly as the scale

of climatic reference clirninishes and it is only at the very lowest

levels of the atmospheric boundary layer that surface influences

become strong enough to create really special phenomena".

A high air temperature may indirectly cause, or enhance, other associated atmospheric

phenomena which should be considered as important factors in introducing or causing a

convection activity in the region. M a n y investigators (for example, Simpson, 1964 and

Atkinson, 1981) have highlighted the importance of unequal heating in coastal plains. They

linked these phenomena to both local convection and to the role of sea breeze fronts in the

generation and enhancement of meso-scale systems such as thunderstorms.

In Australia, sea-breezes have been studied extensively by some researchers who have

found that the summer months are times of the greatest sea breeze development (Lyons,

1977). Hobbs (1971) noted that for the N S W coast the incidence of sea breezes increases

as the summer progresses. Clarke (1955 and 1960) and Drake (1982) suggested that sea

breeze penetration is greatest in southern of N e w South Wales. For example, at Nowra,

CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables 1M

about 15 km north-west of Jervis Bay, Mathews (1982) found that the magnitude of the

sea-breeze component depends on the land-sea temperature difference. The occurrence of

temperature differentials leads directly to pressure variations that give rise to air movement.

At the mesoscale level, this effect may cause local sea-breezes and formation of thermal

lows over land masses in summer. Also, Sumner (1983b) proposed that local winds and

proximity to the sea may cause storm development which is dependent largely on the

presence of an escarpment. These studies generally indicate that the diurnal variation of

land and sea heating is the cause of sea-breezes.

In the Sydney coastal areas, because of the apparent difference between the temperatures

of the land and of the adjacent ocean, sea-breezes are generated by cool air from the ocean

replacing warmer air rising over the land. In 1974, Linacre and Barrero showed the

positions of the sea-breeze front at various times in the Sydney region. They concluded

that, although the sea-breeze front moves inland to a distance which depends on the day-

length and the speed of the front, it is stronger in the mid and early-afternoon, particularly

in summer months. Therefore, the sea breeze is generally most pronounced in the late

spring and summer, and during the early afternoon hours which have the highest daily

temperatures. This is an atmospheric phenomenon which may contribute, in a general way,

to the convection mechanisem and as a result to thunderstorm enhancement (Abbs and

Physick, 1992). This may be one of the reasons thunderstorms are most common in the

afternoon and warm months of the year.

4.6.3 The Role of Air Humidity

Finally, the role of the moist air available in the surrounding atmosphere can also be

statistically seen to be an important factor in initiating convection activity, because it makes

the atmospheric environment more unstable. As Moran and Morgan (1991) indicated,

thunderstorms usually develop in unstable atmospheric environments as a consequence of

uplift caused by one or more of the following: (1) frontal activity, (2) orographic effects,

(3) surface convergence, or (4) intense solar heating of the land surface.

According to these mechanisms, it was suggested that the available moisture in the air can

help a convection development when dense cold air overlies warm, moist air which is less

dense. Therefore, many thunderstorms require warm, moist air which will release sufficient

latent heat to provide the buoyancy necessary to maintain its upward flight.

Although this instability and associated buoyancy are triggered by a number of different

processes, all thunderstorms need a moist atmosphere to keep their life cycle. A trigger

such as solar heating, a front, or a trough-line can then begin the development of a

thunderstorm. Thus, high heat energy and water vapour stored in the air can be converted

CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables Ml

into wind and electrical energy introducing much more instability into a convection system,

which may then organise and produce much rain.

Eagle and Geary (1985 p:2) both point to the importance of moist air in the increasing of

rainfalls from well-organised and widespread thunderstorms in the region. They suggested

that:

'The atmosphere is modified by the ocean surface over which it moves. Given a sufficient transport time over the sea, air in the lower layer will achieve a balance with the temperature and vapour pressure at the underlying surface. With favourable conditions the water vapour may be adverted vertically through the air column. Ocean conditions have no obvious immediate influence to the coastal location but if winds are onshore a short term effect may ensue, as is the case with coastal shower situations,.

They found that during early November, 1984 a north easterly airstream originating in low

latitudes, was moving coastward across the W a r m East Australian Current. At this time the

waters of this current were one to two degrees above average for the time of year. As a

result, the temperature and humidity of the airstream which affected the coastal areas, was

largely sustained.

In coastal locations, this positive temperature anomaly was very favourable to the

maintenance of a w a r m moist air mass and then conducive to thunderstorm rainfall with the

presence of uplift mechanisms. All previous evidence has indicated that, on average, coastal

areas in the Sydney region experience many more gradient winds which are both stronger

during the day or night, and which have a higher percentage of humidity.

As the results of the statistical analysis have indicated, there is a considerable amount of

unexplained variance which may suggest other independent variables need to be

incorporated into the regression model. It seems certain that suggested climatic variables

are not the only factors which explain all the variation of thunderstorm rainfall in the

region. This occurs because, in a complex three dimensional atmospheric environment, in

which convection activities take place, there must be several independent variables

affecting the development of a thunderstorm system.

More importantly, it is well known that the occurrence of a thunderstorm also depends

upon the vertical distribution of temperature and humidity in the atmosphere. Most often,

moving convection systems track from places where they create and affect the other

surrounding low land areas. Therefore, it is clear that accurate atmospheric information on

the convective motions within, about and beneath a thunderstorm system, is necessary to

CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables M2

understand the nature of convective activity and, as a result, to explain the associated

rainfall amount from thunderstorms for a specific location.

While the quality of data and the number of stations used in this study may not be precise

enough to confidently take the calculated coefficients as predictive values of thunderstorm

activity in the region, the results obtained statistically, indicate that the air and sea-surface

temperatures, and air humidity can be linked to thunderstorm development and amount of

precipitation. These factors seem to be more effective climatic factors, particularly in the

coastal areas where moist air currents and warmer sea-surface temperatures can cause

instability of the atmosphere, increasing the tendency to convective rainfall. This last factor

is probably less important further away from the coast where the moist local winds arrive

later or not at all.


Results obtained statistically demonstrate that the associations between some of the

environmental-climatic variables; such as air and sea temperatures and relative air humidity

and thunderstorm rainfall, are mainly positive. There are some possible causal correlations

among independent and dependent variables, particularly in the east of the study area, near

the ocean. They may reflect the combined effects of all these environmental variables upon

thunderstorm rainfall. It appears that the correlation between sea-surface temperatures and

thunderstorm rainfall roughly decreases with distance from the coast. In contrast, the

correlation between air temperature and thunderstorm rainfall amount becomes stronger

with increasing distance from the coastline.

In summary, it is more likely that the effects of above-mentioned climatic factors upon

thunderstorm rainfall patterns result from a complex climatic interaction. It is clear from

the total variance discovered that there are definitely other independent variables which are

also responsible for spatial variation of thunderstorm rainfall amounts in the region.

Therefore, to visualise these variations in space, the variation and distribution of

thunderstorm rainfall will be examined in Chapter 6.

CHAPTER FIVE A Review on GIS Systems Ml

CHAPTER 5

A REVIEW ON GIS SYSTEMS

5.1 Introduction

GIS can be used to visualise the spatial pattern of thunderstorm rainfalls by emphasisng

the physiographic features of the Sydney region. The current chapter reviews an overal

GIS methodology which could be applied for data with a spatial nature, and follows a

progression of topics, becoming more specialised in the following chapters. Chapters 6

and 7 will, thus, deal with analysis and modelling of thunderstorm rainfall data and

physiographic parameters of the study area.

The literature on GIS is vast and spread over a large number of areas, representative of

many disciplines and covers an enormous number of applications. The most relevant

sources for the material presented in this thesis are, therefore, selected and explained.

Sections 5.2 to 5.6 bring together the relevant conceptual issues of GIS. First, some of the

currently used definitions, the common purposes, the principle mechanisms and operating

systems of GIS are defined. Then, the use of GIS for geographical applications and its use

in climatology are reviewed. The application of GIS techniques in spatial modelling of

thunderstorm rainfall is also examined. Data sources and those technical aspects of the

S P A N S (which is an acronym and stands for SPatial ANalysis System) are explanied in

sections 5.7 and 5.8 respectively. Finally, GIS potential errors are outlined in section 5.9.

5.2 What is a GIS ?

GIS is a computer technology consisting of hardware and software that is used to

produce, organise, and analysis information (Aronoff, 1989). In fact, GISs are computer

software for managing data that are spatially distributed over the Earth (Bonham-Carter,

1994). Maguire (1991) states that GISs are computer systems capable of storing,

analysing, manipulating and displaying spatial data from the real world which can be

represented spatially in a computer environment (Dangermond, 1986).

Accordingly, GIS is able to provide natural resource managers with the tool to merge

spatial data and their attributes into computerised data base systems allowing input,

storage, retrieval and analysis of geographically referenced data (Calkins and Tomlinson,

1977). With this capacity for spatial and temporal modelling of the real world, GIS as a

technology has been developed to accomplish the complicated tasks which are grouped

CHAPTER FIVE A Review on GIS Systems WI

together as 'GIS' functions. Two selected definitions of GIS are as follows; Aronoff (1989

p:39) "any manual or computer based set of procedures used to store and manipulate

geographically referenced data". Koshkariov et al. (1989 p:259) "a system with advanced

geo-modelling capabilities". Many of the definitions are relatively general and cover a wide

range of subjects and activities (Tomlinson et al., 1976; Moore et al., 1981).

GISs have three important components- computer hardware, sets of application software

modules and a proper organisation context (Burrough, 1989). These three components

need to be in balance if the system is to function satisfactorily. Maguire and Dangermond

(1991) believe that four basic elements of GIS, which operate in an institutional context

are: computer hardware, computer software, data and liveware. However, some

researchers think that GISs are the result of linking parallel developments in many separate

spatial data processing disciplines (Cliff and Ord, 1981).

Essentially, all these disciplines are attempting the same sort of operation, mainly to

develop a powerful set of tools for collecting, storing, retrieving, transforming, and finally

displaying spatial data from the real world for a set of particular purposes. These sets of

tools were combined to constitute a GIS environment (Burrough, 1989). In other words,

GIS should be thought of as being very much more than a means of coding, storing, and

retrieving data about aspects of the earth's surface (Goodchild and Kemp, 1990). In fact,

GISs are designed to bring together diverse spatial data sources into a unified framework,

often employing a variety of digital data structures, and representing spatially varying

phenomena as a series of data layers as models from the real world (Prisley, 1986; Rhind,

1988).

5.3 Purpose of GIS

The purpose of using a GIS system for geographical and other applications can be reduced

to about six activities dealing with spatial data: 1) organisation, 2) visualisation, 3)

combination, 4) analysis , 5) modelling, and 6) query (Bonham-Carter et al., 1988;

Burrough, 1989; Goodchild and Kemp, 1990)

1) Organisation is the ordering of information according to logical links (Bonham-

Carter, 1994). Anyone w h o has collected a large mass of data for a particular purpose

knows that data organisation is essential. Data can be arranged in many different ways, but

all the data has to be spatially referenced in GIS. For example, a table of geographic data

may be interesting for viewing relationships between elements, but without knowing the

locations of samples, the interpretation of spatial patterns and relationships with other

spatial data, such as geographic features, cannot be made and understood (Johnston,

1987). A GIS must be concerned not only with location, but must also organise data to

allow the extraction of other types of information (Aronoff, 1989). Because, the GIS can


organise data both by spatial and non-spatial attributes, the efficiency and type of data

organisation effects all the other five activities and is therefore of fundamental importance

(Maguire, 1989).

2) Visualisation is an important technique for analysing, explaining and

understanding the distribution of a phenomenon on the surface of the earth (Buttenfield,

1987). Using new technology capacities, the graphical capabilities of computers are

exploited by GIS for visualisation (Dangermond and Smith, 1988). Generally, visualisation

is the assessing of information through the use of sight which is normally carried out using

the video monitor, but other output devices such as colour printers are used for hard-copy

displays (Intera Tydac, 1992a). Often, visualisation is enhanced in a GIS system by

specialised methods using colour, perspective, shadowing and other means. One of the

immediate benefits of this function of GIS is that visualising data stimulates the mind in

ways which are different from traditional data analysis procedures (Cuff and Mattson,

1982).

3) In a GIS, combination is the bringing together of data sets. Data used in GIS

often come from many different sources, are of many different types (even with different

spatial nature) and are stored in different ways (Flowerdew and Bantin, 1989). GIS

provides the tools and method for combining, or integrating, these data into a format

which allows the data to be compared. This process of creating a c o m m o n form of the

data or the bringing together spatial data from a number of sources, is described as data

integration. The role of the GIS as an 'information integrator' was examined by several

researchers on various approaches. D o E (1987 p:2) states that, 'The benefits of a GIS

depends on linking different data sets together.' Dangermond (1989 p:25) said that:

'A GIS brings information together, it unifies and integrates that information. It makes available information to which no one had access before, and places old information in a new context. It often brings together information which either was not or could not be brought together previously'.

This is one of the really powerful features of GIS in which the ability to link several maps

together provides various kinds of models. The benefits that follow the integration of

diverse information are widely recognised.

4) One of the important stages in the GIS environment is the analysis of the results

of previous stages or the process of inferring meaning from data (Berry, 1986). In fact,

analysis is the interpretation and the study of data and information that have been

collected. With GIS, the relationships between different spatial data and their associated

features can be measured and understood. Spatial analysis in a GIS simply means, the

analysis of spatial data. For instance, the area cross-tabulating of two maps may lead to

A Review on GIS Systems 106

useful conclusions about the relationship between the two map layers. Therefore, with GIS

the relationships between different spatial data and their associated features can be

measured and understood (Samet, 1989).

5) Just as it is possible to analyse spatial information to extract knowledge, it is also

possible to use known relationships to model geographically the outcome of a set of

conditions (Intera Tydac, 1992b). This function of GIS is helpful for assessing models

from patterns in the data. Normally, the final purpose of many GIS studies is often for the

prediction and modelling of data. For example, a number of data layers can indicate new

sets of maps which could be combined to predict the suitability of the final desired model

(map). Such a map may then be used as a basis for making exploration or landuse

decisions (Dickinson and Calkins, 1988). In other words, prediction is sometimes a

research exercise to explore the outcome of making a particular set of assumptions, often

with the purpose of examining the performance of a model (Alberti, 1991).

6) Finally, a strong feature of GIS is the ability to query intellectually the underlying

data simply by moving a pointer around on a map. Since all data in a spatial database are

geographically referenced, a pointer to location means access to all data associated with

that location (Intera Tydac, 1993). Spatial query is a complementary activity to data

visualisation, because it would permit the user to find the special circumstances of each

case, by searching the name and other particulars of characteristics of individual

geographic features in the selected locations of interest. Generally, GIS provides tools for

two types of interactive query: geographical information of a location and its attributes

(Unwin, 1981). This powerful function of GIS allows the user to enjoy the dynamic query

of attributes of up to 19 map layers, simultaneously.

5.4 How GIS Operates

At its most basic level, a GIS can be viewed as a simple input / output process. Data goes

into the GIS (such as collected data), some form of processing occurs (averaging of data

for different areas), and information comes out (perhaps in the form of a map). Regardless

of its complexity the input / output view of GIS is a useful starting point from which to

examine h o w the technology actually works. However, in order to understand the basic

operations in a GIS environment, it is first necessary to understand the main structure and

functionality of the GIS in which the data must be processed.


5.4.1 Data Structures in GIS

There are currently three common data structures used by geographical information

systems; 1) vector, 2) raster, and 3) quadtrees (Ibbs and Stevens, 1989). Each structure

has an associated set of characteristics, some good, some bad (Bonham-Carter, 1993).

1) The Vector format was defined as positional data in the form of co-ordinates of

the ends of line segments in a point, line or polygon format (Intera Tydac, 1993). This is

the most c o m m o n method for representing spatial data in which, 2-D space is assumed to

be continuous and allows very precise representation of locations, lengths, distances and

areas. Locations are described by coordinate pairs, and these pairs are the fundamental

building blocks from which spatial entities such as points, lines, and areas are composed.

In a vector structure, points are represented by a single x, y coordinate pair, while liner

entities and area entities (polygons) are composed of straight line segments joining two

coordinate pairs (vertices). The attribute of the values for point, line, and polygon entities

are typically stored independently of the entity's spatial representation. Generally, the

vector structure is ideal for representing point (rainfall stations) and linear features such as

rivers, and for cartographic map production. This structure is also very useful for

topological relations, but is very limiting for overlay modelling procedures (Cook, 1978)

2) The Raster format is spatial data expressed as a matrix of cells or pixels, with the

spatial position implicit in the ordering of the pixels. The simple raster data structure

represents 2-D space as an array of matrix of square or rectangular grid cells. Each grid

cell represents a square or rectangular portion of the Earth's surface. The resolution of

raster data is determined by the size of the cell on the ground, thus, raster data represent a

discrete space where the locational precision is dependent upon the size of a grid cell

(Brown and Norris, 1988). Each grid cell is assumed to have only one value for any given

attribute. A grid cell attribute value may represent a point measurement (for example,

elevation) or an integrated areal measurement (for example, landuse map). In a raster data

structure, points are represented as individual cells, while lines and areas are represented

as clusters of adjacent pixels. The coordinated precision of raster data is constrained by

cell size. Generally, the raster structure is ideal for representing continuous data, such as

elevation and is excellent for multiple map overlays, but it is poor for certain data

approximation (Knaap, 1992).

3) Finally, the Quadtree format is a data structure for thematic information in a

raster database that seeks to minimise data storage. In fact, this kind of data structure is a

hierarchical grid based data structure which is used to improve the storage efficiency of

either its raster or vector counterparts (Ibbs and Stevens, 1989). A hierarchical spatial data

structure is one which is developed through a process of regularly subdividing the space

A Review on GIS Systems

occupied by geographical entities on a map layer into regular spatial units (Intera Tydac,

1993). This process continues until each unit produced by the subdivision is occupied by

spatial entities with similar attributes (see Figure 3.1).

Each data structure has its merits and its pitfalls (Ibbs and Stevens, 1989). Generally,

vector data structure are used for digitising data and cartographic purposes which use (x,

y) coordinates to describe point, line and area features. In this format, data structure

retains information about the consecutiveness and adjacency of features, but are

computationally more demanding. The raster data structure is, however, useful when

combining satellite imagery, which is already in raster format, into the database and this is

used for analysis (Johnston, 1987). A raster data structure is formed by a matrix of regular

cells, each a specified size and area (Knaap, 1992). M a n y GIS have the capacity to use

both data structures. The quadtree structure is ideal for representing both continuous data

and discrete polygonal data. In other words, it can be thought of as a raster structure with

the ability to have a variable sized grid cell (Webster, 1992).

Figure 5.1 Schematically represents different data structures used in a GIS: (a) raster and quadtree (b) points and lines (vector) and (c) polygons.

5.4.2 Functionality of Data in GIS

In GIS, realistic spatial models of the world, called entities, can be developed using these

structures. Entities are points, lines, areas, surfaces and networks (Martin, 1982). A n

entity has a spatial dimension which identifies its geographical location. GIS data

structures are able to accept both spatial and non-spatial data in any GIS project.

Therefore, identification and collection of relevant structure and data are essential

(Webster, 1990). Data used in GIS often come from many different sources, are of many

types, and are stored in different ways. These mechanisms should be summarised into 6

stages as follows:


1) Geographic data sources which can be imported into the GIS environment

include: paper maps; aerial photographs; satellite images, and digital data from other areas

which can be combined to create new complex maps or tables (O'Neill et al. 1992). This is

another source of geographic information which is not often thought of as being

geographic. These data are mainly tabular databases or files of records such as weather

station observations (rainfall records) or water samples records.(databases) which are

often geographically referenced. If the underlying structure of the geographic reference

system is known (latitude and longitude), it is possible to transform and integrate this

information into thematic data which can then be processed in the GIS environment

(Bonham-Carter, 1994).

2) After the data are collected and integrated, the GIS must provide facilities which

can contain and maintain the data (Brown and Norris, 1988). Effective data management

has many definitions but should at least, include all of the following aspects: data security,

integrity and maintenance abilities. In fact, data management refers to the ability of a GIS

to manage functions efficiently, the ability to link to other data types and transfer data in

compatible formats (Davis, 1991).

3) Data processing operations are those performed on the data to produce

information. In GIS, data on its o w n may be impossible to interpret and data processing is

not an end in itself. It should turn data into a form that is informative, that helps the user

decide what to do next and whether more data processing or qualitative analysis should be

done. Data processing produces images, reports and maps.

4) Data integration and conversion is only part of the input phase of GIS. What is

required next is the ability to interpret and analyse, quantitatively and qualitatively, the

information that has been collected. This ability to analyse and manipulate spatial data that

has led to the use of GIS for both statistical and deterministic modelling (Cressie, 1991).

Analysis is carried out on data organised as maps, and also on data organised as tables.

Using the analysis function of GIS, it is possible to explore existing relationships between

the data sets.

5) The ability to model geo-referenced information is critical in a GIS (Webster,

1992). In the geoscience fields, especially geographical exploration, this type of overlay

modelling has been done for years, typically with several maps and a light table. The main

objective is to create a new map which highlights areas which meet a certain set of criteria

favourable for modelling. The GIS allows geographers to combine maps to produce new

maps, without struggling with the variable scale and projection problems (Rasuly, 1991).

CHAPTER FIVE A Review on GIS Systems LM

6) Finally, one of the most exciting aspects of GIS technology is the variety of

different ways in which information can be presented, once it has been processed by the

GIS. Traditional methods of tabulating and graphing data can be supplemented by maps

and three dimensional images. Also, tables and figures, having results, can be transformed

into maps which reveal spatial or non-spatial entities. The use of GIS technology allows

information to be viewed on the computer screen, plotted, as paper maps, captured as a

image or slide and used to generate a computer file. Generally, visual communication

which is the most important aspect of GIS technology, can be enhanced by the diverse

range of output options (Webster, 1990).

5.5 Implications of GIS Techniques in Climatology

The GIS has been widely used in recent years for natural resource planing and

management (Alberti, 1991 and Davis, 1991) and solving complex problems associated

with multiple-use of land resources (Martin, 1985). Initially, the origins of GIS lie in

environmental management (DoE, 1987), but uses of GIS have expanded to incorporate

private and government planning in areas such as: property and land parcel data; transport,

and distribution networks; civil engineering; defence; industrial site selection; and water

supply application (Tomlinson, 1987; Johnston et al., 1988).

In addition, GISs are used in many environmental spatial analysis and modelling situations.

Technical and applications-oriented workers from many fields (for example, ecology,

hydrology and geography) are interested in the use of GIS (Ferrier and Smith, 1990).

Recent environment applications can be expanded to include: survey design, dynamics and

distribution of soil (Moore et al. 1981), individual species and soil-climate modelling (Duff

and Eamus, 1992), vegetation communities (Head et al., 1992), bushfire patterns (O'Neill

et al., 1993) and habitat modelling (Marthick, 1995). All these studies found that the GISs

can be used in the handling of environmental problems. But these are only a few

applications within the general GIS literature in climatology which is both highly disparate

and complex.

Currently, there is a fast growing interest in using GIS methodology within physical

geography (Rasuly, 1993) and environmental science, which can be characterised as

"Physical-Environmental GIS" (Riddle, 1991). For example, Maguire (1989 p:222) said

that:

"The synthesis of geographical facts relating to the locational properties of spatial entities and their associated attributes is a necessary counterbalance to analytical studies carried out in physical and human geography'.

CHAPTER FIVE A Review on GIS Systems Ul

From 1991 there were rapid increases in further developments in the use of GIS for

research in physical geography and the environmental sciences (Raper, 1993). This interest

is growing fast, because a GIS can store both cartographic data, showing topography or

individual themes, such as soils or rainfall distribution, and attribute data associated with

the spatial entities (points, lines and polygons), that were represented in Figure 3.1.

Therefore, in many respects a set of disparate data can be only linked by GIS techniques.

The methodological problems and applications of this new sub-field have resulted in a

number of publications. For example, an application research was introduced to estimate

crop yield in south western Ethiopia (Simmons, 1986). Using GIS it was possible to

perform a series of map overlays of climatic and soils factors from which predictions of

crop yields were calculated. In this study three input maps (climate zones, elevation and

soil types maps) were used to produce different classes of climatic suitability.

Although, GIS has been used for a variety of projects, many with environmental themes,

there are examples of GIS techniques being used in climatic studies. Recently, its use in

the atmospheric and climatic fields have been concentrated on the modelling of spatial

impacts of climatic events or conditions. For example, Michener (1991) assessed the

ecological disturbances due to hurricane H u g o in 1989 by integrating a large quantity of

data with different sources.

The topic of GIS and climate is very new, but, because of the ideal application of the GIS

technology to environmental subjects, there is already a strong tendency to use the GIS for

climatic purposes. In many circumstances, new technology allows the rapid mapping of

point or polygon climatic variables, the correlation of maps, and the use of maps as

variables in computer models.

In the literature, there are some examples of the use of GIS which can demonstrate its

suitability to Climatology. For example, Johnson and Worobec (1988) used GIS

techniques in the study of spatial analysis of insects in relation to weather conditions. In

this study, the abundance of adult grasshoppers was correlated to monthly rainfall,

monthly hours of sunlight and annual grasshopper counts. The grasshopper distribution

was estimated from the previous year's grasshopper population in a close association with

climatic variables which were successfully constructed using GIS techniques. In another

attempt, in Italy, a GIS application for climatological analysis and productivity estimation

was applied (Ciaramaglia et al., 1992). This paper described the research that was aimed at

developing climatologically based rainfall-landscape planning models, using the GIS

technology.

CHAPTER FIVE A Review on GIS Systems 112

Also, GIS techniques were used in some studies of climate change. For example, in 1990,

Aspinall and Miller described a modelling procedure which mapped climate change

scenarios on a national and regional scale. The procedure was applied through a raster-

based GIS system which allowed integration of land cover data from remotely-sensed

sources with scenarios of climate change for impact assessment. Using climatic data such

as an accumulated growing temperature and the length of the growing season, a variety of

agricultural land-suitability assessments were derived for both current conditions and for a

future scenario of climatic change. Therefore, attempts to assess the possible impact of

climate change on agriculture and natural ecosystems are increasingly drawing upon GIS

in order to gain the regional and national picture required for policy-relevant results

(Brignall et al., 1991).

GIS, as a tool, was similarly used to bring together different elements of the climate of a

region and its physiographic attributes. For instance, Strobl (1992) modelled the spatial

distribution of climatic elements in high-relief terrain using GIS techniques. Various

topographic, atmospheric and surface data are combined in the S P A N S GIS environment

to asses the climate variations in the Alpine Regions. In Australia, a GIS system was also

applied for visualisation and demonstration of some environmental factors such as sea-

surface temperatures and rainfall distribution to estimate drought scenarios (Beswick et

al., 1993). It was generally suggested that ultimately, spatial modelling of climate elements

should necessarily replace the use of old hand-drawn maps (they have been good - but

their contents are non-reproducible) which give limited results.

5.6 Application of the GIS in Resolving Problems in Rainfall Analysis

Clearly, there is no apparent relationship between the thunderstorm rainfall - the main

topic of this thesis - and the GIS techniques discussed here. However, various aspects of

the GIS technology can be orientated towards to solving some problems in the study of

the rainfall distribution. There are some distinct advantages using a GIS in the study of the

spatial distribution of rainfall described below:

1) For a climatologist the understanding of the spatial distribution of a climatic

variable, say rainfall variation, over a specific area, is a very important task (Berry and

Marble, 1968; Rasuly, 1993). For elements such as rainfall maps, GIS produces good and

satisfactory information of the spatial distribution of rainfall, which is of interest not only

from a climatological viewpoint, but also for its importance in different fields such as

agriculture, hydrology, water resources, atmospheric pollution or even in flood control.

The estimation of the spatial distribution of rainfall is a complex and lengthy task. But,

when detailed information concerning the rainfall records is available, the use of GIS in

constructing the distribution maps, it is a matter of only several hours. The contouring


approach is probably the most common and thus the most familiar to climatologists and

hydrologists. During the last two decades, the need for an efficient and rapid method of

contouring and computing areal estimates of rainfall from rain gauge data has been a

demanding task (Chidley and keys, 1970 and 1972). Currently, a GIS system, for example

S P A N S software, creates a Triangular Irregular Network (TIN) between the points

(rainfall stations) and interpolates a surface (rainfall map) model in a very short time

(Intera Tydac, 1993).

In the literature there are a few examples of direct and indirect use of GIS in the study of

rainfall distribution. For example, in 1990, Eklundh and Pilesjo suggested that it is possible

to create a rainfall data base explaining the variation of mean rainfall in Ethiopia, using a

GIS including a digital elevation model. Currently, at the Canadian Climate Centre

preparations are being made for the production and publication of long term monthly

climatic variables such as rainfall data. For example, Sajecki (1991) used S P A N S GIS to

produce a set of sample maps for the temperature, precipitation and sea level pressure

elements so that they may be included in climatic atlases. Finally, Bryceson and Bryant

(1993) created the continental rainfall maps for Australia by interpolating between the

sparse rainfall-recording stations. They suggested that GIS techniques can be used to

mitigate the climatic variables, for example rainfall, by a better supply of information. This

is a significant improvement in the ability to accurately interpolate point rainfall data and

allows a greater confidence in using GIS techniques to create rainfall maps from point

rainfall data for modelling purposes.

2) In all practical spatial analysis of rainfall distribution, climatologists and others

have to estimate the areal distribution of rainfall from point measurement. This can be

done with methods ranging from simple arithmetic averaging to sophisticated

computerised interpolation and extrapolation techniques (Watson, 1992). The

development and spread of personal computers, equipped with GIS software - for example

the S P A N S GIS - provides an ability to maintain and exploit the climatologists ordering of

information in ways never before attempted. In this way, different rainfall maps can be

drawn and compared with each other by computer and summaries provided by whatever

set of areal distributions seems necessary to the investigator.

3) Historically, the study of the spatial organisation and distribution has always been

an important factor to many climatologists, especially when rainfall maps can be correlated

to the main physiographic parameters, for example, the topography or landuse patterns of

a specific area. The area based statistics and a standard overlaying feature of a GIS allows

for the estimation of the areal distribution of rainfall based on physiographic parameters

(Webster, 1992). In this way, a GIS system can be employed to the data due to the

subsequence analysis for integrating, constructing and exploring relationships between the

CHAPTER FIVE A Review on GIS Systems 1L4.

different variables involved. Generally, the functionality of a GIS system allows for the

construction of the elevation, aspect, distance and landuse maps. These maps can be

compared with maps of rainfall distribution based on rain-gauge observations. Therefore,

describing and explaining all possible variables which may contribute in the distribution

and variation of such rainfall, is made possible by GIS.

4) More importantly, overlaying techniques provided by GIS can also be used to

create a set of new maps with specific aims (Bernhardsen, 1992). This should be done in

the GIS environment, according to the rule based combination of maps and some specific

overlay modules, it is possible to evaluate a set of values of maps. Berry (1993:111) states

that, 'in GIS, overlaying maps go beyond traditional procedures of "sandwiching" map

sheets on a light-table'. In a GIS, procedures for point-by-point, regionwide, and mapwide

summaries can be described. Using such overlaying techniques in the GIS environment, for

many climatological purposes, a series of further digital distributed maps of the physical

environment can therefore be correlated to the rainfall distribution models. In GIS it is

possible to specify, analysis and display several raster-base maps simultaneously. For

example, the topographic information, which is referenced as the primary requirement for

rainfall distribution information, is the essential database to be included in the GIS

structure.

5) Finally, all maps provided by GIS can very efficiently convey information about

the earth's surface with an adequate selection of the colour palette. B y the assignment of

colours to values of the variable and gradually varying the number of colours, considerably

interesting effects can be achieved in the displaying and visualising of the topographic and

rainfall maps (Max et al., 1993; Kelly, 1994).

Nowadays, GISs have enormous scientific importance and, more significantly, they are

already being used to make valuable contributions to the understanding and solution of

environmental problems. Currently, interest in GIS is expanding rapidly and it is therefore

reasonable to expect that GIS should also be carefully used to solve some of the problems

in climatology by a better modelling of information. In this way, although the climate will

never be controlled, the use of accurate data and powerful computing technology and

sophisticated software such as S P A N S GIS may allow greater access to methods of

monitoring rainfall distribution patterns spatially.

To sum up, the GIS as a representative of recent technology, can not only be used to

analyse climatic variables, but can also be adapted to examine the spatial aspects of rainfall

distribution. Therefore, some geographers (for example Maguire, 1989) think that there

could be more advantages in using GIS in the study of the climatic variables such as


rainfall distribution, if they can be related to the other climatic or physiographic factors

simultaneously.

5.7 Data Sources on GIS System

Although the learning of a GIS technique seems a difficult task, in the current study only a

short time was spent establishing a database and in converting / translating existing maps

and spatially-referenced data into a S P A N S GIS system. The various types of data created

for this study included elevation data, rainfall data including the geographic locations of

rainfall stations, a basemap, a proximity map and a landuse map. These are outlined below

together with a brief description of their sources.

The Digital Elevation Model (DEM) data was obtained from the Australian

Surveying and Land Information Group (AUSLIG, 1993). These data are basically

produced for mapping and geographic information systems. D E M data were collected by

digitising all spot heights on 1:100 000 maps and selected points from 20 metre contours.

Heights together with location information (on A M G Easting and Northing) were

recorded in ASCII format. Using an excel computer program, the elevation data were first

prepared in a specific text format. Then they were imported to the S P A N S GIS

environment to establish a digital elevation model which represents a continuous property

of the topography in the region.

To model the general distribution of thunderstorm rainfall patterns in the study area,

a sets of point data from rainfall stations (see Chapter 6) were entered into the GIS

environment. Within the GIS, the rainfall data were analysed and integrated with other

physiographic data, in the modelling of thunderstorm rainfall distribution in the Sydney

region.

To evaluate rainfall maps by a number of physiographic parameters, some GIS

internal methods have been used to construct the basemap, the proximity and landuse

maps of the study area. For this purpose, a database was created for the study area and the

digitised data was converted into raster or vector format and entered into the S P A N S GIS

(see Chapter 7 for more details).

5.8 Methods Used in a SPANS GIS

This section will explain the SPANS GIS module and all methods used in which the data

have been analysed to construct GIS models. S P A N S is a microcomputer-based

geographic information system which were developed by Intera Tydac (established in

1982). S P A N S GIS is currently being used and supported world wide by professionals and

decision makers attempting to solve complex spatial problems. Therefore, the S P A N S line


of software products, as a tool, was selected for the organisation, integration and analysis

of the geographic information obtained for the spatial study of thunderstorm rainfall

distribution in the Sydney region.

In this study, the power of the analytic and modelling capabilities of the SPANS GIS

allowed the researcher to work with a climatic phenomenon of spatial nature (for example,

modelling the distribution of rainfall). The S P A N S version 5.3.1 was used for many spatial

analyses in this study. Therefore, the following stages have been proposed as three more

general categories: data entry, model building and model analysing procedures.

5.8.1. Data Input

Two GIS systems have been employed to enter the data and subsequent analysis. First, an

Environmental Resource Mapping System, E - R M S (1989) was used for digitising a

basemap of the Sydney region. The basemap boundary was entered by manually digitising

from a 1:250,000 scale map of the Sydney region. The accuracy of digitising is estimated

to being within 80 m 2 of the indicated location on the map. The E - R M S system was

developed by the National Parks and Wildlife Service of N e w South Wales. Using this

system, a basemap of the Sydney region was entered in digital form, then edited and

converted into a grid cell format to be exported. After that, the data export module of E-

R M S allowed data to be exported to the S P A N S GIS. Because many S P A N S operations

require a basemap, this study first established a basemap to define the boundaries of the

study area in the S P A N S GIS system. The basemap must be a binary map, that is, it could

not contain classes other than 0 and 1.

In the second stage, a SPANS raster module was used to transform the basemap to a

raster-base format to be imported into the GIS environment. Then, a S P A N S was also

used to enter the D E M data. Generally, this system accepts any A S C I file, this can be data

related to the location of rainfall stations and their associated attributes (rainfall amounts

or other statistic values). A digital elevation data set consisting of approximately 20741

points (Australian M a p Grid coordinates), together with elevation in meters was imported

into a S P A N S GIS. Six 1:100,000 scale maps covered the study area, four of which

extend beyond it. The data approximates a 20 m grid which roughly covers an area of

9170.36 km 2. These data were imported into a GIS coverage system showing point data

and displayed on a computer screen to provide a visual impression of the distribution of

sample elevation points. Imported data were then checked for possible errors or

corrections.


5.8.2 Model Building

Once the data were integrated into SPANS, various techniques were used to analyse the

data sets. A major part of the analysis involved the generation of elevation and rainfall

maps from the point data sets. Several functions of GIS, for example, a contouring

method, were used in S P A N S to convert the data to thematic maps.

Firstly, a set of elevation data (with point structure) was used to create a digital

elevation model by establishing topological relations between the elements using a

rectangular grid (or elevation matrix) with a Triangulated Irregular Network method

(TIN). TIN structures are based on triangular elements, with vertices at the sample points.

Generally, the TIN surface can be constrained to pass through the point data. In this case,

the contouring program was used to convert point data representing spatially continuous

phenomena into classified, trend surface maps such as elevation maps which were used

then for further analysis. After the TIN was created, some classification schemes, for each

specific data, were applied to produce the desired classifications. The accuracy and

reliability of this technique has been computed by Weibel and Heller (1991). They found

that the surface models can be used to create, analyse and display surface information.

Also, the SPANS contouring module which interpolates a surface map from a point

data set through a process of triangulation, was used to generate maps of thunderstorm

rainfall distribution based on rain-gauge station observations. This surface was constrained

to pass through the data points. Generally S P A N S GIS supports both linear and non-linear

implementations and it allows extrapolation outside the convex hull defined by the data

points. In this study, a linear interpolation model which computes a linear interpolation

surface, was applied for the data. During data analysis stages, a query module containing a

query capability was used to verify the final results. The query function of S P A N S GIS

was also used to perform and confirm all geographic information in relation to locations

specified on the map layers.

Secondly, some of the information related to topography such as aspect and

elevation maps were automatically produced in the S P A N S environment. The D E M

quadtree was used to create an aspect map which is measured in azimuth degrees. In

S P A N S GIS, a map of the aspect is computed from a grid elevation map. In fact, the

aspect is the orientation of the steepest slope with respect to north and is computed as an

angle clockwise from north. A slope facing north has an aspect of 0°, facing east, 90°,

facing south 180°. If it is a flat surface (no slope) it has the value 360°. The aspect map

derived was used to analyse and identify the relationship between rainfall distribution and

exposure.

CHAPTER FIVE A Review on GIS Systems JM

The next important specific aim was the production of a proximity map. Proximity

to the sea was suggested to be an important physiographic variable affecting the

distribution of thunderstorm rainfall patterns in the region. A distance map was therefore

generated by creating concentric buffers, with 10 k m distance (a arbitrary classification),

around the average coastline. In this map 10 different classes have then been expressed as

buffer zones which have been used in a proximity analysis of the thunderstorm rainfall

distribution.

A set of satellite images and also hard-copy maps of the Sydney region were used to

create the landuse map of the Sydney region which covers the whole of the study area. All

procedures, which were taken to establish the 'specific landuse' map the Sydney region,

are described in Chapter 7.

5.8.3. Model Analysing

Within a SPANS GIS there are several analytical functions which allow a user to explore

the possible relationships between the data sets and associated map layers. One such

function was, for example, used to determine the average thunderstorm rainfall amounts

for each of the topographic, proximity and landuse classes.

An area-based analysis function of a GIS was also used to analyse single map

characteristics or determine the content of each area covered by different topographic

classes. A statistical report was then produced, for example, to give the average rainfall

for each class.

In addition, using an area cross-tabulation technique it was attempted to find the extent of

the correlations between the digital elevation map, the aspect map, the proximity classes

and the landuse patterns with thunderstorm rainfall distribution. Generally, the results of

the cross tabulation can indicate the possible correlations between the two map layers.

Statistically, chi-square coefficients are used as the measure of the degree of correlation,

association or dependence of a thunderstorm rainfall map to the topographic maps. Some

examples of the GIS functions which have been used for data building or data analysing

have been given in the current chapter, other advanced GIS functions, which could be

used in the analysing of thunderstorm rainfall data, are given in detail in Chapter 7.

5.9 GIS Potential Errors

There are some advantages in using the GIS method in evaluating the spatial distribution

of rainfall from thunderstorms. One of the advantages is the ability to model and display

the results as colour maps which show the spatial pattern of rainfall variation over the

study area. The contouring approach is probably the most used and thus the most familiar


to those who are interested in studying rainfall distribution in space. The most significant

advantage of using a S P A N S GIS, in this study, lies in its modelling capabilities. With

S P A N S , simple to complex models have been generated, modified, and regenerated to be

compared to the originals in a matter of hours.

However, there are two main potential errors or problems with using a SPANS GIS.

Firstly, SPANS's TIN method, as an interpolation technique, was required to approximate

the surface behaviour between sample points. The S P A N S contouring module interpolates

a surface from a point dataset through a process of triangulation which honours the data

points. In the triangulation method, the surface passes exactly through each known data

value, and interpolation is only affected by the heights at the three vertices. Thus, the size

of the zone of influnce of a point is affected by the density of the surrounding points

(rainfall stations). In small triangles (dense points) the effective zone of influnce of a single

observation is correspondingly small, whereas in large triangles (sparse points) the zone of

influnce is large. T I N method is desirable in cases where the values at the data points are

known to have relatively small errors, such as elevation data. However, where the samples

of a surface are associated with errors due to sampling and measurement, such as rainfall

observations, there are relatively large errors as compared with the overall spatial

variation. In such situation, an alternative interpolation method that produce smooth

surfaces, and do not necessarily honour the data points, should be used (Bonham-Carter,

1994).

Secondly, because the interpolation technique in SPANS GIS is based on a raster based

format, the resultant rainfall maps may not be completely smoothed to reduce noise in the

data. It is because, surface modelling of spatially continous field variables (such as rainfall

values) involves interpolation from the irregularly-spaced samples to a raster format. Each

interpolated point is simply a cell over which the variable (rainfall or topographic values) is

constant. The resultant raster map has a relatively continous values dependent upon

classes used rather than discrete values of observed in the field. It must be noted that, in

this study, the data taken from rainfall stations for interpolation purposes had discrete

nature. GIS technique was, however, successfully used (Skidmore 1989 and 1990) to

interpolate the rainfall data, digital terrain data, and to identify terrain position and to

calculate aspect values from a girded digital elevation model.


The SPANS GIS can helpe to visualise, organise, combine, analyse, model and question

the real data from Sydney's climatic environment which has been spatially organised in a

computer environment. In other words, the power of a GIS is in its ability to integrate,

manipulate, and process data from different sources. Data with spatial and non-spatial

CHAPTER FIVE A Review on GIS Systems 120.

nature could be handle to provide information and models which aid in the understanding

of the Sydney's geographic location and its attributes. In Chapter 6, the GIS interpolation

techniques could be used for mapping of thunderstorm rainfall variations throughout the

Sydney region.

The combination and display of map layers in pairs is also an important aspect of GIS,

because it allows the examination of spatial relationships between spatial phenomena such

as a rainfall m a p and a topographic map, for example. Although the ultimate goal of most

GIS studies involves multiple data layers, the relationship between map pairs is often an

exploratory first step, and may determine h o w features of one or both maps are to be

enhanced or extracted for subsequent analysis (see Chapter 7).

Multiple maps could be obtained using overlay techniques in a GIS environment. The

ultimate purpose of this study would be to combine spatial data from diverse sources

together, inorder to describe and analyse interactions, to make models, and to provide

support for decision-makers. Chapter 7 will, therefore, present some models of interst to

climatologists, to show h o w they can be implemented in a GIS environment. Multiple

maps also help to illustrate the models with reference to two applications: selection of

geographic areas which are suspect for the highest rainfall values, and their associations

with physiographic maps of the Sydney region. These techniques could be also supported

using statistical procedures. It may be concluded that although the topic of GIS and the

climate is relatvely a new notion in Australia, some functions of GIS can be applied to the

raw data sets to create new products such as rainfall maps. Other products such as

landuse, aspect and elevation maps could also be suited for climatic applications.

CHAPTER SIX Spatial Variation and Distribution of Thunderstorm Rainfall 121

CHAPTER 6

THE SPATIAL VARIATION AND DISTRIBUTION

OF THUNDERSTORM RAINFALL

6.1 Introduction

Information on the distribution of thunderstorm rainfall in space is very important in a

variety of applications. In the Sydney region, the knowledge of the spatial variation and

distribution of thunderstorm rainfall is limited to a few case studies (Bahr et al., 1973;

Morgan, 1979a; Nanson and Hean, 1985). Although various aspects of the thunderstorm

activity of the Sydney region have been examined over the years, two aspects that have not

been thoroughly examined are: the long term variability of thunderstorm rainfall of the

region over a long period of time, and the relationships between the local physiographic

parameters and the distribution of thunderstorm rainfall patterns for the whole of the

Sydney region. The former is examined in the current chapter. The latter is the subject of

the next chapter.

The purpose of this chapter is to analyse the spatial variation and distribution of

thunderstorm rainfall in the study area. First, in sections 2 and 3 the data and methods used

are described respectively. Section 4 examines the methodology developed for the

generation of reliable data on daily thunderstorm rainfall events, based on specific criteria.

Then, in section 5, the spatial variation of thunderstorm rainfall in the Sydney region, using

gamma distributions, is analysed. The g a m m a distribution is used to find the probability

distribution of thunderstorm rainfall amounts at each rainfall station. Finally, in section 6,

the spatial distribution of thunderstorm rainfall patterns are constructed using a GIS

technique. Thunderstorm rainfall maps are based on average seasonal values and the

biggest thunderstorm rainfall events for each month used in the study.

6.2 Data Selection

Unlike most previous studies that examined single but major thunderstorm rainfall events

over a short period in the region, (for example, Williams, 1984; Colquhoun and Shepherd,

1985), the present study views the distribution of thunderstorm rainfall at two long time-

scales namely Spring (October, November, December) and Summer (January, February,

CHAPTER SLY Spatial Variation and Distribution of Thunderstorm Rainfall 122

March) and over 34 time period. The study also considered the largest thunderstorm

rainfall events for each of the warm months (October to March). The definition of seasons

is not conventional by Bureau of Meteorology standards. There are two reasons for this

choice. Firstly, as it was shown in Chapter 4, three important climatic factors (air, sea

temperature and air humidity) are high during October to March in comparison to the

other months of the year. This seasonality affected the temporal and spatial distribution of

thunderstorms considerably (see Chapter 2). As shown in Chapter 3, the warm months

clearly dominate thunderstorm rainfall with the most thunderstorm activity, and more than

72 per cent of thunderstorm rainfall. Secondly, in the cold months of the year including

September, thunderstorm rainfall is not common enough to give a reliable indication of

thunderstorm distribution throughout the region.

The precipitation data were provided in three data sets supplied by the Bureau of

Meteorology and the Sydney Water Board. The original data set was collected on a daily

basis and was converted to monthly or seasonal records, where needed. Thunderstorm-day

records were extracted from the original 3 data sets, using three different computer

programs written for this purpose (see Appendix A, computer programs 2, 3 and 4).

A prime consideration of the present study was to determine the spatial variation and

distribution of thunderstorm rainfall over a region that extended beyond the Sydney

Metropolitan area for as long a time-span as possible. During the processing of the data

set, it was noted that for some stations there was a considerable amount of missing data.

Stations with less than 10 years of records were excluded from the analysis. Mooley and

Crutcher (1968) in a study of rainfall in India investigated the number of years of record

needed to stabilise the gamma parameters. Although Weisner (1970) indicated that from

25 to 50 observations of precipitation data are needed to give a stable frequency

distribution, Bridges and Haan (1972) estimated that with 100 observations, there is a

negligible 0.6 percent chance of error (see Section 3 for functionality of gamma

distribution). Therefore, in this study, in order to ensure stability in the statistics, stations

with fewer than 100 thunderstorm observations for the entire period were excluded. The

resulting data set consisted of 191 stations (134 from the Bureau of Meteorology and 57

from Sydney Water Board) covering the period 1960-1993.

To show that the two networks of rainfall stations were comparable the data were

subjected to statistical techniques. First, to find any possible difference between the means

of the 2 data sets, an analysis of the variance ( A N O V A ) technique was applied (Webster

and Oliver, 1990). A n F value of 2.36 was calculated showing that there is not a significant

difference between the means of the two sets at the 0.05 level of significance (see Table

6.1).


Table 6.1 Difference between two sets of stations (the Sydney Water and the Bureau of Meteorology) according to their rainfall means. _ _

Number of Mean in F-test Groups Stations m m Std. Dev. (SWs vs. BMs)

Sydney Water stations (SWs) 57 14.57 1.769

Bureau of Meteorology stations 134 14.99 1.761 F = 2.362*

(BMs) T = 1.537

Total 191

* non-significant at 0.05 level

To confirm this result, the NNA technique (see Chapter 3) was applied to the data. This

technique takes into account the range and structure of the data at each rainfall station.

All rainfall stations from the Sydney Water Board and the Bureau of Meteorology

clustered based on 8 optimum clusters. The results indicate that the Sydney Water Board

rainfall stations (57 stations) were randomly interspersed with the Bureau of Meteorology

stations (134 stations) at the 05 level of significance.

Finally, to test the results of the NNA technique,the nearest Bureau of Meteorology station

was found to each of the 57 Sydney Water Board stations (see Table 6.2, Appendix B )

using the S P A N S GIS Spatial Query function (see Chapter 5). For each pair of those

stations, the commonality statistical association in thunderstorm rainfall was then evaluated

using the correlation coefficient (Hutchinson, 1970; Davis, 1973). Only rainfall grater least

0.5 m m at each pair of stations for Spring and Summer thunderstorms from 1960 to 1993

was used in this analysis. These correlations were then plotted against distance and a linear

regression performed on the results (Figure 6.1). This model describes the relation between

the correlation coefficient and the interstation distance of rainfall stations (Stol, 1972).

Figure 6.1 indicates that the correlation coefficient between the pairs of stations decreases

with increasing distance (n = 0.957) in a linear fashion. The best corresponds occurs when

stations from the two data sets lie within 5 K m of each other. Within that distance the

correlation between paired stations approximates 0.9.


Regression Fit Y= 0.956853-1.64E-02X

R-Squared = 0.957 95.0% Confidence Bands 95.0% Prediction Bands

v '-••"fv-™"mwr "• r'"'"1 i r n T ™ m'T 0 5 10 15 20 25 30 35

Distance Figure 6.1 Relation between correlation coefficient (r) and interstation-distance (in K m ) of pairs of thunderstorm rainfalls in the region.

The results of the ANOVA, the NNA and the correlation coefficient techniques make it

clear that there is no significant deifference, at the 0.05 level of significant, between the

two networks of stations, in their recording of thunderstorm rainfall in the region.

Accordingly a dense network (combining the Sydney Water Board and the Buerau of

Meteorology stations) ensures that a reliable spatial distribution of thunderstorm rainfall

over the study area could be constructed. The combined rainfall records covers an area of

9172.21 square kilometres.

It was impossible to separate the precipitation amounts for thunderstorms from the daily

rainfall total received on any thunderstorm-day. As the works of Sharon and Kutiel (1986)

suggest, most rainfall from an individual event containing a thunderstorm is likely to come

from the convection associated with that thunder. Hence in this study, for each individual

thunderstorm-day event which matched the criteria, it was assumed that all the rainfall in

that thunderstorm-day was the result of thunder activity (the validity of this assumption

will be discussed latter in Section 4). Three hundred and forty seven relatively intense and

widespread events between October and March occurred in the Sydney region for the

period 1960-1993.

The list of the rainfall stations used, together with their geographical coordinates and their

elevations above mean sea level, are given in Table 6.2 (see Appendix B ) . The spatial

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distribution of these stations is shown in Figure 6.2. Generally, the distribution of stations

reflects the distribution of major population consentrations, suburbs, dams, post offices and

rail stations. The possibility of spatial bias exists with this type of sampling network, but no

better one was available.

6.3 Techniques Used

T w o sets of techniques were used to analyse the variation and spatial distribution of

thunderstorm rainfall in the Sydney region. Firstly, in order to allow direct inter-station

comparisons and to find the probability distribution of thunderstorm rainfall amounts at

each station, the gamma distribution was used. Thom (1958) introduced the gamma

distribution with two estimators - which are in fact a minimal number of summarising

measures. The mathematical functionality of the gamma distribution has been widely

discussed by, for example, T h o m (1958, 1968) and Shenton and B o w m a n (1970).

In fact, the probability density function is one of the statistical characteristic

measurements of the spatial distribution of thunderstorm rainfall that should be

determined. The gamma distribution with two parameters is therefore the most flexible

class of probability density functions and has extensive applications in the analyse of

rainfall data (Bridgman,1984) and thunderstorm rainfall modelling (Easterling and

Robinson 1988).

This method was first introduced by Thom (1958) as a frequency analysis:

*, ^ l r-i ~XIP P>0 m f(x) = -z^—-xrle ; ' 0)

P r(y) 7 > 0 wherex is thunderstorm rainfall amount, beta (fi) is the shape parameter of the

distribution, g a m m a or alpha (y ) is the slope parameter and r is the gamma function of

? • These parameters were estimated for each station by the maximum likelihood method

(Thom, 1958). In this method the best estimate of gamma is given by

A = Inx--Unxi (2)

n

1 + JTT473A 1 AA

_! + [! + Ajlnx -1 / rillnXi) 13]1/2

0r 7 " Wnz-UnHnXt) (4)


where x is the mean thunderstorm rainfall amount and n is the number of thunderstorm

rainfall occurrences on a daily basis in the data set. The best estimate of beta is then

P=X/Y (5)

Some curves from the family of gamma distributions, and probability density functions,

are graphically illustrated in Figure 6.3 for different values of a and p. a is the shape

parameter which determines the peakedness of the curve. Figure 6.3 (a) shows that the

probability density function can take the shape of an exponentially decaying curve (a = 1)

or the shape of a normal distribution (a = 4). A value below 1 indicates an exponential

decrease in the probability density function from a maximum of infinity at zero x. W h e n a

equals one, the exponentially decreasing curve has a zero x intercept at 1/p. A value

greater than unity produces a curve with zero probability density function at zero x, a

rapid increase to the maximum probability density function and a slow decay thereafter.

As a increases, the peakedness and skewness of the curve decreases, and the curve

approaches that of the normal Gaussian distribution. This implies that as a increases the

range of x commonly occurring also increases. The skewness of the probability density

function increases and the peakedness decreases, as P increases.

0.8

^ 0.4

0.2 •

0

(a)

\ cc = l

/ \ A ~ N ? = 4

5

- ^ ^ a = 8

10

X

15 2 0

0.2 •

*** 0.1

0.05

/Ty=2

0 2

(b)

R = 4

4 6 8 1

X

0

Figure 6.3 The gamma density function for (a) four a values, P = 1 and (b) three P values, a = 4.

To calculate the alpha and beta values, a computer program was thus written using

M A T L A B , the MATrix LABoratory programming language (1994). Both alpha and beta

values were extracted for 191 rainfall stations in the Sydney region. Then alpha and beta

values were contoured over the study area using the SURFER computer program, version

5.01 (1994). This is an objective mapping technique which interpolates to a grid by fitting


a polynomial to a set number of control points surrounding each grid point. Results were

subdivided by season - spring (October to December) and summer (January to March),

representing the two main seasonal thunderstorm rainfall regimes in the region for the

period 1960-93. There are some distinct advantages for using g a m m a function. For

example, it is one of the best probability models which could be fitted to data with a

skewed nature, as it summarises thunderstorm rainfalls data by a minimal number of

measures. Also, using gamma function, it is possible to group all stations by similar alpha

or beta values, with direct comparisons between stations.

In the second stage, the thunderstorm rainfall data were mapped using the SPANS GIS

interpolation module. This is an objective mapping technique which interpolates data with

a spatial nature using a "Triangulated Irregular Network" (TIN) interpolation method

(Intera Tydac, 1993). It is less likely that areas with sparse distribution of rain-gauges can

lead to some overestimated problems using this interpolation technique. For obtaining the

general distribution of thunderstorm rainfall models (and evaluating rainfall by a number of

physiographic parameters in the following chapter) a standard GIS linear interpolation

method was used. In this way, an average depth for two seasons, mainly Spring (October

to December) and Summer (January to March) from 1960 to 1993, were determined. Also,

for each month of the warm season (October to March) the biggest thunderstorm rainfall

events from the record were analysed. These maps are based on data from 191 rainfall

stations (see chapters 5 and 7 for more details about GIS techniques).

6.4 Thunderstorm Rainfall Selection Criteria

Thunderstorms occur randomly in time and space (Duckstein et al., 1973; Fogel and Hyun,

1990). The amount of rainfall shown in chapters 3 and 4, is strongly skewed over time and

space, low rainfall occurs often during thunderstorms while heavy and intense rainfalls are

rare. Most previous work considered thunderstorm rainfall to be convective no matter

what the rate was, or the season of occurrence. For example, the U.S. Weather Bureau

(1947) examined rainfall totals for days with thunder and daily totals for the summer

months in the U S A . In another work Sharon and Kutiel (1986) isolated heavy convective

rainfall by assuming a rainfall rate of 20 mm/hr or more. Both of these studies assumed the

majority of rainfall to be convective in nature by the season of occurrence.

In order to study only significant thunderstorm rainfall events across the whole Sydney

region, the data were constrained using a number of analytic stages and associated criteria

as follows:

In the first step, by using the NNA method, the associations between 15 thunder-recording

stations, in the Sydney region were found for thunder-days in which thunderstorms have

been observed (see Chapter 3 for more details). The results have indicated that in the


Sydney region, there is a definite statistical relationship between many of the stations when

there is a thunderstorm-day. These associations are strong among some stations.

According to these associations, all 15 stations were then clustered into seven groups.

Each similar group may represent an area for the development or occurrence of

thunderstorm activity, in the region. In fact each cluster can be considered to be a

representative of an area for thunderstorm activity based on general geographic

characteristics (for example distance or locality). Associations obtained may indicate a

relative interdependence among different clusters statistically, and as a result, suggest a

c o m m o n thunderstorm activity for the whole of the Sydney region.

•«.»

M.a

M.O

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C a m d e n Airport

1972-93 (172 Events)

V Population 1

^ ^ » w Papulation

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I , I •

14.0 2S.0 42.0

Katoomba

1987-93 (152 Events]

. Population 1

\ ^*^,^ Population 3

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12.0 24.0 38.0

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1905-93 (105 Events)

o •

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D -\ 0 - V

o - vv ° - \ *v . 0 " \ ^ v o - •\-^7*'"v

0 - * ^ ^ ^ ^

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10.0 20.0 30.0

% laaathaji

•

:

56.0 7

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4S.0 •

*. k«" 0i»"

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Thunderstorm Rainfall In m m

0.1

i.e

3.0

10.0

20.0 30.0 40.0 so.o so.o 70.0

so.a

90.0

os.o

so.o

99.0

.0

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99 0

95.0

•0.0

10.0 70.0 10.0 50.0 40.0 30.0 20.0

to.o

5.0

1.0

0.1

Wollongong

1972-93 (139 Events) %L*a»m*n

I Population 1

\ ~ ^ » - — Population 3

% Excaadanea

4 0 . 0 S O . O 120.0 i s o o

Richmond

1960-93 (650 Events) % Lasa than

. , Population 1

• \ \

_ \ ^ ^ - ^ . Population 2

% Eicaaoinca "°~~ —

2o.o 40.0 so.o ao.o

99.9

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. Sydney Regional Office

1960-93 (556 Events) % La«a thu

. Pop ula Hon 1

\ . vy : rv

-\ ^ ^ " ^ , Population 3

— • — ^ ^

%ExcaManca ^ —

40.0 10 0 120.0 150.0

Thunderstorm Rainfall In m m

•

. -. . •

--•

-

2C

"

.

_' "

%

0.1

1.0

3.0

to.o

20.0 30.0 40.0 50.0 GO.O 70.0 M.O

90 0

95.0

99.0

99.9

0.0

0.1

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20.0 10.9 40.0 30.0 eo o 70.0 •0.0

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99.0

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• 9.9

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• 0.0

10.0 70.0 •0.0 30.0 40.0 30.0 20.0

10.0

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0.1

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1969-92 (2S7 Events)

Population 1

- \^"***^V • 1 ^ ^ ^ ^ ^ Population 2

" ' "***^

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14.0 2B.0 42.0

' « l M I Owl

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10.0

20.0

30.0

40.0

50.0

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70.0

•0.0

•0.0

9S.0

9t.O

o*.9

56 0 70.0

Thunderstorm Rainfall in m m

Thunder-recording

Stations

Richmond Katoomba Bowral Sydney R. 0. Bankstown Camden Airport Wollongong

Average Value

Extracted rainfall

Values

11.0 10.0 9.0 17.0 7.5 7.0 16.0

11.0

Figure 6.4 Probability of exceedence diagrams for 7 selected thunder-recording stations located in the Sydney region.

In the second step, daily thunderstorm rainfall amounts were studied in order to determine

a standard value to be known as, a thunderstorm rainfall event, in the region. For this

purpose the 'probability of exceedence' technique, modified by Bryant (1991) for the


Macintosh computer, was used. Probability of exceedence diagrams could be constructed

by the following equation:

Exceedence probability = M(N+1)_1 100 %

where N = the number of ranks

M = the rank of the individual event (highest = 1 )

This technique was applied graphically using the amount of rainfall, recorded during

thunderstorm events, on a special semi-logarithmic paper for each station. Note that the

amount of rainfall values (in m m ) from thunderstorms was plotted along the x- axis, while

the probability of exceedence (in per cent) is plotted along the y-axis.

It is generally assumed that straight line segments plotted on this paper form normal

populations. The intersection of the two straight lines forms the boundary between two

normal sub-populations (see Figure 6.4). All thunderstorm rainfalls plotted in this fashion

show two distinct populations on straight line segments. A n advantage of this technique is

that the probability of both low and high rainfall values from thunderstorms can be

determined. The average value of these line intersections using data from 7 selected

thunder-recording stations was 11 m m (see Table 6.3). Also, the average rainfall for all

3070 thunderstorm events (taken from all thunder-recording stations) was 10.4 m m for the

region. This analysis indicates that there appears to be 2 types of rainfall associated with

thunderstorms in the Sydney region. In the first type, small thunderstorms with less than 11

m m of rainfall are very common, representing 95 per cent of all thunderstorms. In the

second type, rainfall exceeding 11 m m was rare but copious. This latter event represented

5 per cent of all thunderstorms, accompanied with extreme rainfalls.

Table 6.3 Thunderstorm rainfall values extracted from the intersection of two populations using probability exceedence graphs, for the Sydney region.

Row

1 2 3 4 5 6 7 Average

Thunder-recording

Stations

Katoomba

Bowral

Richmond

Camden Airport Bankstown

Sydney RO.

Wollongong

Year

From- To

1987-93

1975-92

1960-93

1972-93

1969-92

1960-93

1972-93

Extracted rainfall

values

10 9 11 7 7.5 17 16 11.0

For this work, a thunderstorm-day event was, therefore, defined as the occurrence of any

storm with at least 11 m m of rain or more in at least one of these 7 stations. Accordingly,

CHAPTER SIX Spatial Variation and Distribution of Thunderstorm Rainfall 13J.

three criteria had to be met before a thunderstorm's rainfall was considered a significant

event.

Criteria 1. Three thunder-recording stations had to record a thunderstorm on the same day

- at least 2 of the clusters of stations, defined in chapter 3, had to be represented.

Criteria 2: At least one of these stations had to be a main station: Richmond and Sydney

Regional Office. The Sydney Airport station could be also the main station, because it does

appear to associate well with the Sydney Regional Office station.

Criteria 3: At least one of the main stations had to have 11 or more millimetres of daily

rainfall on the day of a thunderstorm.

In this way a thunderstorm rainfall event could be characterised by above-mentioned

criteria. Thus, all rainfall data recorded by any rainfall station - on a thunderstorm day

which met this study's criteria - were considered to come from a or more convection

systems which introduced thunderstorm activity for whole of the Sydney region.

Accordingly, three hundred and forty seven common thunderstorm-days were selected for

the period 1960 to 1993 to be used in a spatial analysis.

6.5 Spatial Variability of Thunderstorm Rainfall

The spatial variation of thunderstorm rainfall in the Sydney region is illustrated best by

values of the g a m m a distribution. The g a m m a distribution has been used in several research

works associated with rainfall and thunderstorms (for example, Simpson, 1972; Robinson

and Easterling, 1988). A similar approach, using the two-parameter g a m m a distribution

(the empirical distribution of Thorn's maximum likelihood estimators), was adopted for use

in this study. Estimates for alpha and beta were estimated using only those observations

where measurable rainfall occurred. Hence, the two parameters, alpha and beta, describe

the probability distribution of rainfall amounts only from thunderstorm observations giving

measurable rainfall which match with this study's criteria.

In spring, the spatial distribution of the alpha parameter is shown in Figure 6.5. The lowest

alpha values (which indicate a high probability of rainfall amounts from thunderstorms),

less than 0.8, can occur in the eastern parts of the Sydney region, over the Metropolitan

area and to the north of the City. Alpha values between 0.7 and 0.9 can be also seen over

the two major topographic features of the study area, mainly over the Blue Mountains and

the Illawarra Plateau.

In the central part of the region and to the south-west (south of Bowral), alpha values

increase to greater than 1.1. The geographical distribution of the values for alpha, shown


by Figure 6.5. The lowest values occur south-east of the region, where alphas with less

than 0.70 can be seen. Areas which are located in the southwest of the Richmond and

Camden station appear to have the highest alpha values (alpha > 1.2).

Longitude On decimal)

Figure 6.5 Geographical distribution of alpha value, Spring (Oct to Dec), over the Sydney region. H designates areas of alpha > 1.1, in contrast L represents areas of alpha < 0.8.

The geographical distribution of beta for spring (October to December) is shown in Figure

6.6. Beta values greater than 32 - which indicate a high probability of rainfall amounts

from thunderstorms - can be seen for the topography of the south-east of the study area,

just over the Illawarra Plateau. In comparison with the lowlands of the Sydney region.

Over the Blue Mountains, beta values are also relatively high, more than 22 for Katoomba.

Again, beta values more than 23 can also be seen over the eastern part of the City. The

central part of the Sydney region, for example in the south-west of Richmond, and areas

located in the south-western corner of the study area (at Bowral), show very low beta

values, less than 12 on average.

The summer situation is considerably different, because the alpha values, to some degree,

increase over the Illawarra Plateau, and an unclear pattern dominates the non-coastal

areas. The highest values for alpha occur in the middle portion of the Sydney region, for

example near Campbelltown (alpha > 1.2), and in the south of Camden Airport, where

alpha values exceed 1.4 (see Figure 6.7).


In contrast, along the coast, beta values show an extension of coastal influence, particularly

over the Illawarra Escarpment, over the City and north of Sydney just over the Homsby

Plateau (beta > 27). These values generally decrease moving towards the west and south

west, over the central parts of the study area. The values for beta are generally quite low in

this area, being less than 10 near and south-west of the Camden Airport (Figure 6.8).

Figure 6.6 Geographical distribution of beta value, Spring (Oct to Dec), over the Sydney region. H designates areas of beta > 20, in contrast L represents areas of beta < 11.

Generally, the distribution patterns indicate four main thunderstorm rainfall areas in the

Sydney region, better shown by the alpha and beta values. Over the eastern part of the

study area extremely high beta values can be seen (a high beta value represents a high

probability of rainfall amounts from thunderstorms) indicating that, the coastal location is

very important in the distribution of thunderstorm rainfall amounts.

In contrast with the centre of the study area, which shows a greater amount of spatial

stability for low beta values, the Metropolitan area and the nearby northern suburbs show

sharply increasing or decreasing beta values. For example, over the City there are high beta

(or low alpha) values indicating the significant centres for the occurrence of thunderstorm

rainfalls. Also, the areas most likely to produce high rainfall from a thunderstorm event

during the w a r m months include the northern suburbs of the City. Therefore, considering


both the alpha and beta values, the chance of high rainfall from thunderstorms over an area

located in the centre of the Sydney region (for example, Prospect D a m ) is much less than

thunderstorm rainfalls which could occur over the Metropolitan area or its north-western

suburbs.

Lougltudo (In decimal)

Figure 6.7 Geographical distribution of alpha value, Summer (Jan to Mar), over the Sydney region. H designates areas of alpha > 1.1, in contrast L represents areas of alpha < 0.8.

Over the Blue Mountains, in the north-west of the study area, the probability of the rainfall

amount from thunderstorms is high. O n average, over the Illawarra Plateau, located in the

south-east of the region, the probability of the rainfall amount from thunderstorms is

approximately twice the amount recorded over an area in the central low lands of the

Sydney region. These areas tend to experience the lightest rainfall from thunderstorms.

The rest of the region including the central part of Sydney and the foothills and higher

areas to the south-west comprise the fourth area, on average a low probability in

thunderstorm rainfall. The lowest beta (or the highest alpha) values occur through a large

portion of the study area, in the centre of the Sydney region with a north-east to south

west direction.


Longitude (in decimal)

Figure 6.8 Geographical distribution of beta value, Summer (Jan to Mar), over the Sydney region. H designates areas of beta > 20, in contrast L represents areas of beta < 10.

The probability distributions of thunderstorm rainfall amounts at each station were

summarised by use of the g a m m a distribution for total events for the spring and summer

seasons. These probabilities have established different spatial patterns throughout the study

area. In other words, the curve-fitting process and calculation of alpha and beta values,

allows us to map and describe the probability distributions, but does not reveal h o w much

rain may fall from an individual thunderstorm. To test the reliability of this technique in

modelling thunderstorm rainfall distribution, there is a need to compare results with

surface rainfall maps. Thus, in the following section the 'average thunderstorm rainfall

value' is the object of primary interest, not only for comparison purposes with probability

patterns, but also for mapping its variations in rainfall over the Sydney region. The basic

question, from a climatic view point, is' what is the spatial distribution of average

thunderstorm rainfall patterns, taking into account the associated data which were used for

g a m m a distributions?

6.6 Spatial Distribution of Thunderstorm Rainfall

The study of the spatial organisation and distribution of thunderstorm rainfall has always

been an important factor to many climatologists and meteorologists (Hobbs, 1972; Sharon,

1983; Bryant, 1991 and Batt, et al., 1995). This section generalises the point observations


(rainfall data) into a spatial maps of thunderstorm rainfall (mean area rainfall values) over

the Sydney region. Thunderstorm rainfall maps were constructed using a standard GIS

interpolation method. Constructed GIS maps successfully show the apparent detail of

rainfall variation over the study area described by alpha and beta values. These should

provide a logical and effective approach to the derivation of an area mean. The mean is

obtained by applying an area weighting (TIN) interpolation method to the mean rainfall

between each pair of consecutive isohyets. Spatial distributions of two seasonal averages

and the biggest daily events for each thundery month, from 1960 to 1993, are described in

separate sub-sections.

6.6.1 Average Event Values

The distribution maps obtained from the available data are most effective in showing the

general trends in the distribution of thunderstorm rainfall, over the study area. The average

thunderstorm rainfall map for spring is shown in Figure 6.9. The wettest part of the region

is found in the south-east of the Sydney region at the top of and over the Illawarra Plateau

where the rainfall per event averages between 20 and 23 m m . Just to the west of this

region, in the Burragorang Valley, averages are less than 10 millimetres. Areas with

thunderstorm rainfall in excess of 20 m m are found near and over the City. Generally, the

coastal areas show high thunderstorm rainfall at this time of the year, and the high elevated

areas, such as a section of the Blue Mountains, have more rainfall from thunderstorms.

Figure 6.10 displays the average thunderstorm rainfall per event for the summer season.

During the period, the Blue Mountains (Katoomba) and the Wollongong Escarpment show

distinct locations for thunderstorm rainfall distribution. In the east, over the Sydney

Metropolitan area, there is a high average rainfall (more than 22 m m per event). Another

location for rainfall maxima is the Hornsby Plateau which is located to the north of the

Sydney. The driest areas are located in the low-lying central plain, with a north -south path

in the region between the mountains in the west and the coastal areas. The south-west of

the region shows an average of less than 10 m m rainfall from thunderstorms.

On average, figures 6.9 and 6.10 indicate that the maximum rainfall occurred over the

eastern parts of the Sydney region, and more precisely on the north-west part of the City

near Turramurra, over the Illawarra Plateau and Escarpment, and over a small section of

the Blue Mountains. The minimum rainfall occurred in the south-west of the Sydney region

and over inland areas. Of particular interest, are the low rainfall amounts over the Southern

Tablelands (for example, at Bowral located in the south-west corner of the study area) and

near the Campbelltown basin (just the west of Lucas Heights).

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Although thunderstorm rainfall distribution is especially characterised by an extremely

irregular area pattern, a marked gradient in rainfall from east to west can be observed. At

the same time, these isohyet maps feature a number of cells or centres produced by

thunderstorms over the City, northern suburbs and elevated areas. Generally, it can be seen

that these rainfall distribution maps agree with g a m m a distributions patterns. However,

some researchers, such as Chuan and Lockwood (1974) and Sumner (1988), have already

suggested that individual thunderstorm events analysis may offer the soundest basis for

determining the spatial distribution of thunderstorm rainfall. Therefore, in the second

stage, the individual biggest events for the warm months, October to March respectively,

have also been considered. In this way it was expected that the more intense thunderstorm

rainfall events may be distinguished from the more uniform average thunderstorm rainfall

patterns.

6.6.2 The Biggest Events

The impact of thunderstorm rainfall, from the point of view of flooding processes taking

place in the Sydney region from time to time, is very important (Colquhoun and Shepherd,

1985). Therefore, the purpose of this section is to model the amount of rainfall from

individual thunderstorms likely to be recorded on thunder-days with flash flooding in the

region. Rainfall events, in this study, were defined by the daily occurrence of measurable

precipitation for periods (days) with available data. This definition provided some heavy

thunderstorm rainfall event samples that displayed relatively widespread characteristics.

Table 6.4 General descriptions for the 6 biggest thunderstorm rainfall events in the region.

Main synoptic Areas subject to flash flooding

Date weather patterns

Events by

Month

October

November

December

January

February

March

23-25 1987

5-12 1984

9-11 1988

19-22 1991

7-11 1990

10-11 1975

Fronts and Low

pressure systems

Troughs and Low

pressure systems

High pressure system

and Fronts

Low pressure

system and Fronts

Tropical Cyclone

"Nancy" and Troughs

Tropical Cyclone

'Alison' and Troughs

East of Sydney, Metropolitan area

Georges, Nepean and Hawkesbury Rivers

Rose Bay, Kensington, Metropolitan area

Hawkesbury Rivers and Mawaira area

Sydney Metropolitan area, Illawarra are

north-east of Sydney

Turramurra, Parramatta areas

and north-west of City

Metropolitan area, north-western

suburbs

Dapto, Wollongong City, Metropolitan area

Mount Keira

All thunderstorm events, included in Table 6.4, were not defined according to their

percipitation produced. They have been selected, because they were the biggest

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thunderstorm rainfall events, in the data-base for each month, introducing floods in the

region. The biggest events are, therefore, presented here to characterise thunderstorm

rainfall in a climatologically more meaningful way. This also includes reference to the

synoptic patterns leading to the situations in which these thunderstorm events occurred.

The biggest and most widespread events for each of the 6 months, October to March are

presented in Table 6.4.

October 1987: In this month five cold fronts moved through NSW and caused some

widespread rainfalls (Bureau, of Meteorology, 1987b). However, the major features of the

month were the low pressure systems that intensified some thunderstorm activities,

bringing widespread rain to the region. These occurred most frequently in the eastern half

of the State. O n the 22nd and through to the 25th, a low pressure system moved from the

west of the State to the north coast (see Appendix C, synoptic charts 6.1 from the 23nd to

the 25th October, 1987b) and brought wide-spread thunderstorm rainfalls to the study

area.

The rain was often heavy, particularly in the south-east of the study area. The heaviest

rainfall for October occurred on, and adjacent to, the Illawarra Escarpment with Cataract

D a m recording 600 m m rain which was a very high rainfall, well above the monthly

average. The heaviest fall in a one day period during the month was 343 m m at Cataract

D a m in the 24 hours to 9 am on the 25th. Another heavy thunderstorm rainfall occurred in

the Metropolitan area. Severe thunderstorms brought minor to moderate flooding in the

Metropolitan area overnight on the 24th / 25th, and, as a result, a number of deaths were

attributed to the thunderstorms. At the same time, minor to moderate flooding occurred in

the Georges River (north-east of Lucus Heights), Nepean-Hawkesbury River (west of

Sydney Basin). Further details on the spatial distribution of rainfall from these

thunderstorms are shown on Figure 6.11.

November 1984: On the 14th of this month, moving troughs, upper air disturbances, and a

coastal low over the Tasman Sea (Jessup, 1985) together produced some very intense

thunderstorm rainfalls, and as a result, floods in many parts of the Sydney region (synoptic

charts 6.2 from the 5nd to the 12th November, 1984, see Appendix C). During this period

atmospheric soundings showed that the airmass above Sydney was moist and unstable up

to 300 mb. Also the temperature sounding had warmed moticeably. This event was marked

by a period of intense thunderstorm rainfall activity which led to severe flash flooding in

the Sydney Metropolitan area (Bureau of Meteorology, 1985). Flooding was most severe

at Rose Bay and Kensington and other inner City areas. In the period from the 9th to the

12th, major flooding was reported at the Hawkesbury River, to the west of Sydney area.

Rainfall was, however, very much above average in many coastal districts.

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During this time, Sydney received one of its highest rainfalls, more than 500 mm. The

monthly rain totals for this month exceeded 300 m m in the Metropolitan and Illawarra

areas. Extremely heavy rainfalls of over 580 m m occurred in Sydney's City area and the

adjacent eastern suburbs and over the Illawarra Plateau in the south-west of the Sydney

region. At Observatory Hill in the City, 235 m m was recorded in the period from 9 am on

the 8th to 9 a m on the 9th. This was the highest 24 hour November rainfall on record. The

driest part of the Metropolitan area was in the outer western areas with rainfall totals of

less than 150 m m . Sydney's rain was the highest for the State. By contrast the rainfalls in

the western half were mostly well under 120 m m , except for Katoomba which had more

than 300 m m of rain for the same period. Full details of the spatial distribution of

thunderstorm rainfall for this event indicated that in the Sydney Metropolitan area the

rainfall from thunderstorms on the 8th and the 9th gave a record-breaking intense rainfall

for November over the City and near the City areas, particularly the suburbs just east of

the City (see Figure 6.12).

December 1988: This month was dominated by a series of high pressure systems that

moved into the Tasman Sea and directed warm, moist, unstable air across the State

(Bureau, of Meteorology, 1988). By the 8th, cold fronts had moved into the Sydney area

bringing thunderstorm activity (see Appendix C, synoptic charts 6.3 from the 9th to the

11th of December, 1988). Rainfalls from these thunderstorms caused considerable damage

in the Sydney region. For example, on the 9th, a thunderstorm in the Metropolitan area

dropped 35 m m of rain in 30 minutes at the Sydney Regional Office, where flash flooding

resulted. During this event the Metropolitan area and the North-east of Sydney received

more that 110 m m rain. The areas just to the west of Bankstown and the Illawarra

Escarpment were subject to high intense rainfalls from these thunderstorms (see Figure

6.13).

January 1991: Between the 19th and the 22nd of January, 1991, a low pressure system

accompanied by a series of fronts located at the western side of the Sydney region - was

developed over inland N S W bringing widespread thunderstorms over the Sydney region

and causing considerable damage in the Metropolitan area from 19th to 22 January 1991

(Appendix C, synoptic charts 6.4). Some of the meteorological conditions such as low-

level moisture, thermodynamic instability, and a lifting mechanism, reduced the

atmospheric stability throughout the region, and as a result produced some thunderstorms.

It was reported that a severe thunderstorm moved from the south-west of Camden, over

Parramatta and through Turramurra and Palm Beach before moving out to sea (Bureau of

Meteorology, 1991b). Very heavy rain, up to 90 m m , in a severe thunderstorm was

reported in the north-west of the City. The heaviest rainfall occurred to the north-east of

the Turramurra area under the growing thunderstorm cells (Spark and Casinader, 1995).


This caused extensive damage and minor flash flooding in the areas, just north of

Parramatta. Generally, during this time, the central parts of the Sydney region, from the

south-west to the north-east, received a large amount of rain from moving thunderstorms.

Other parts of the study area had, on average, less than 20 m m of rain (see Figure 6.14)

February 1990: Between the 7th and the 11th February, 1990, after tropical cyclone

"Nancy" passed southwards from the Coral Sea (where it had developed) over the Sydney

region, a trough caused unstable conditions bringing thunderstorm activity (see Appendix

C, synoptic charts 6.5 from the 7th to the 11th February 1990). These unstable conditions

caused further thunderstorms and rains, particularly in the Metropolitan area and flash

flooding in the northern and north-west suburbs (Bureau of Meteorology, 1990). Over this

period, other parts of Sydney, specially the southern parts of the Sydney region, had less

than 40 millimetres of rainfall (see Figure 6.15).

March 1975: Tropical cyclone "Alison" and other associated weather features such as

upper level currents and a trough line dominated during the first half of this month (Bureau

of Meteorology, 1975). As a result, thunderstorms developed in the Sydney area on the

10th and the 11th March, 1975. A moist air mass had been advected to the coast by a

tropical cyclone well to the north. This air mass was almost saturated. A n upper level low

moved from the west across the area (see Appendix C, synoptic charts 6.6 from the 10th

to the 11th March, 1975). Temperature and dew-point temperature soundings at Sydney

Airport showed that the instability in the upper atmospheric was high enough to provide

enouph buoyancy for thunderstorm development.

All these weather situations caused torrential rain of more than 440 mm to occur in the

Illawarra district and Sydney Metropolitan areas. The isohyet Figure 6.16 shows March

thunderstorm rainfall as an extremely localised event, with maximum falls over the south

east of the study area. This event was described by Armstrong and Colquhoun (1976).

They showed that the thunderstorm rainfall was concentrated over Kiama and Mount Keira

just west of Wollongong. There was major flooding in Dapto, located south of

Wollongong, and in the Sydney Metropolitan area which resulted in flash flooding. Except

for a small area over the Blue Mountains, much of the western parts of the region,

particularly over the central plains at Richmond and Camden, had rainfalls of under 50

millimetres.

In brief, from the general climatological point of view, thunderstorm rainfall distribution is

meaningful in terms of the synoptic processes leading to such high variations in space.

Spatial variations of the seasonal averages and thunderstorm rainfall values for the biggest

events may, however, suggest that the great spatial variability of thunderstorm rainfall, in

the Sydney region, can not only be attributed to the synoptic weather patterns.

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6.7 Discussion

This chapter has presented details of spatial variation and distribution in thunderstorm

rainfall resulting from the spring and summer averages, and from the biggest thunderstorm

rainfall events for each of the six months (October to March for a 34 year period, from

1960 to 1993). The results described in this chapter, are only representative of the Sydney

region with its prevailing climate type. Because of the arbitrary criterion for selection of

the thunderstorm events , the results can only be generalised for relatively big and

widespread thunderstorms.

The criteria outlined above (in section 6.4) were employed in the determination of the

spatial distribution and variability of thunderstorm rainfall observations using data supplied

by the Sydney Water and the Bureau of Meteorology of the Sydney region. Using these

criteria, it was therefore suggested that over a long period of time there will be a series of

thunderstorms passing over each individual station, on Sydney's thunderstorm-days,

producing various precipitation amounts. Given a sufficient time period (at least 10 years

in this study and more than 100 observations) this will produce general thunderstorm

rainfall statistics. A n immediate benefit of using this criteria is that it is possible to consider

many thunderstorms which developed further, and became larger and lived longer, over the

study area in the past 34 years. Examples of these types of events occurred in March 1975

and November 1984.

In order to allow direct inter-station comparisons, a gamma function was applied to

thunderstorm data. The probability distribution of thunderstorm rainfall amounts at each

station can be expressed by two summarising measures, the alpha and beta values. During

the last decades, this technique has been widely used by many investigators in meteorology

and climatology. For example, Mooley and Crutcher (1968) used it in India for rainfall

analysis, while Simpson (1972) used the g a m m a distribution in single-cloud rainfall analysis

in the south of Florida. This technique was also applied to the study of the spatial

distribution of rainfall in the Hunter Region (located at the north of the N S W ) by

Bridgman (1984). Richardson (1982) modelled the distribution of daily rainfall amounts

from 10 locations in the United States. Later, Easterling (1989) used this technique to

differentiate between different regions in the U S A in terms of thunderstorm rainfall

patterns. Finally, Fogel and Hyun (1990) applied the g a m m a distribution technique to data

to simulate the spatial variation of thunderstorm rainfall in the U S A . They all concluded

that, unless other models can be shown to have a clear advantage over the g a m m a

distribution for a given application, the g a m m a distribution should be the appropriate

choice of models for most applications.


In the present study, the results obtained by using the gamma distributions have established

three main spatial patterns in the Sydney region, indicating topographic, coastal and non-

coastal (inland) areas. The distribution of mean thunderstorm rainfall amounts confirms the

reliability of these patterns. The geographical distribution of beta and alpha values illustrate

that, in total, coastal areas are subject to thunderstorms with a high probability of rainfall.

In the south-west extension of the coastal area, over the Illawarra Escarpment, topography

influences thunderstorm rainfall amounts. The relationship between rainfall and topography

of the Illawarra Escarpment was already detected by several other researchers (Bryant,

1982 and Cox, 1983), where a distinct daily thunderstorm high rainfall amount can be also

seen in both spring and summer season averages and even for some of the biggest event

values. High thunderstorm rainfall totals in the vicinity of elevated topography of the

Illawarra Escarpment suggest orographic enhancement of instability, particularly for sites

facing the east. At these times of the year the prevailing easterly moist winds provide much

of the moisture needed for thunderstorm activity in the region (Sumner, 1983b).

In the area west of Sydney, over the mountains, there appears to be at least two different

patterns to thunderstorm rainfall. The Blue Mountains area, located in the north-west of

the study area has high rainfall, particularly in the summer months. This may be due to

orographic influences. Over the Southern Tablelands located in the south-west corner of

the Sydney region, however, the topographic influence disappears, showing considerably

lower rainfall amounts from thunderstorms. This low annual rainfall was confirmed by the

Sydney's Weather Bureau (1979). A more recent investigation by Matthews and Geerts

(1995) suggested that, in summer, thunderstorms were relatively less likely over the

Southern Tablelands.

Along the coast, in the eastern part of the Sydney region, the greatest proportions of

rainfall from thunderstorms occurs over the Central Business District ( C B D ) and over the

Turramurra area just north of the City. The increased roughness associated with variations

in tall buildings could also affect the spatial distribution of thunderstorm rainfall. The

pronounced highest rainfalls over and nearby City may support the theory of the heat

island, and particularly the mechanical effect of an extended urban area on rainfall

enhancement by promoting atmospheric instability (Goldreich and Manes, 1979). The

surface roughness, caused by many tall buildings mainly located in the Sydney's

Metropolitan area, may increase mechanical turbulence, thus increasing the instability. In a

case study over London it was suggested that the mechanical effect of the urban area may

be of prime importance in the process of urban enhancement of precipitation (Atkinson,

1975).

As discussed in Chapter 2, these areas are also under the influence of sea-breeze

circulations, which work with relatively high surface temperatures and readily available

CHAPTER SIX Spatial Variation and Distribution of Thunderstorm Rainfall Ml

moisture from the nearby ocean to enhance convective rainfall. This influence may extend

northward and southward along the coastlines during the summer months. The effect may

also be due to the effect of urban heating or particles provided by pollution, both factors

can enhance thunderstorm rainfall. These will be discussed in the following chapter.

In addition, the gamma parameters indicated that there was a relatively low incidence of

high-intensity thunderstorm rainfall in the Sydney basin centred on the Hawkesbury -

Nepean river between the Blue Mountains to the west and the coastal areas in the east.

This low can extend over the adjacent mountain region and to the south-eastern parts of

the study area. There, thunderstorm rainfall is predominantly low, especially for the biggest

thunderstorm events. These general patterns of thunderstorm rainfall distributions may be

attributed to the following phenomena.

First of all, the selected events can occur when some kind of low level cyclones, fronts,

troughs or local convection systems are active overhead or in nearby areas. These systems

are known to initiate or enhance thunderstorm activity in the region (Bryant, 1991). This is

of course not surprising, since synoptic systems can potentially produce heavy

thunderstorm rainfall over large areas (Speer and Geerts, 1994). The systems which are

favoured for thunderstorm activities and associated rainfalls, have a maximum frequency in

spring and summer (Matthews and Geerts, 1995). However, during the late spring and

summer months, isolated convective thunderstorms may occur in any part of the study

area. These events have also been known to contribute to the production of a high portion

of total thunderstorm rainfall observations. Thunderstorm development in the Sydney

region which was discussed in Chapter 2 supports this idea.

Secondly, the main rain-bearing depressions moving from the north-east to the south-west,

or from the east to the west, can be influenced by the ocean when they are passing over the

coast. Generally, very moist air over the N S W coast are advected from warm tropical and

sub-tropical waters associated with the moisture of the East Australian current. These

conditions occur predominantly in the period of January to M a y when the sea-surface

temperatures are higher and the prevailing and saturated easterlies winds cause

thunderstorms to move inland to adjacent coastal areas from this convection (Eagle and

Geary, 1985). The passage from the sea to the land, together with the forced ascent due to

physical environment effects, are thought to be responsible for some of the spatial patterns

observed.

Superimposed on the above pattern are the effects of localised topography. It appears that

the coastal area, where 'the Metropolitan area' is located, particularly on the Hornsby

Plateau has experienced much larger thunderstorm rainfall amounts. Also, the uplands to

the west of the Sydney region (Katoomba), and the Illawarra Escarpment located in the

CHAPTER SLY Spatial Variation and Distribution of Thunderstorm Rainfall HI

south-east of the region, show clear topographic enhancement of rainfall during

thunderstorm rainfall events.

Although this work has used data from selected thunderstorm days for the Sydney region

and also the six biggest major thunderstorm rainfall events from each month, the results

obtained could be linked closely to the major synoptic weather patterns and physical

features of the Sydney region. Because of the large number of observations used to

determine thunderstorm rainfall values, if provided the best available mathematical estimate

of the probability of thunderstorm rainfall amounts and, as a result, the true average

amount of rainfall from thunderstorms could be expected. Generally, most of the studies in

the region, cited above or in the literature, have explicitly concentrated upon one or two

short terms, severe thunderstorm events (Batt, et al. 1995), or thunderstorm rainfall

(Armstrong and Colquhoun, 1976). The present chapter, however, has focused on the

study of thunderstorm rainfall in general during the warm seasons over a 34 year period in

order to define more precisely the regional patterns of thunderstorm rainfall distribution

over the longer period in the Sydney region.


The purpose of this chapter was to focus attention on the patterns of the spatial variation

and distribution of thunderstorm rainfall during the thundery months of the year (October

to March). According to the applied criteria, the amount of rainfall recorded during a

thunderstorm-day event was derived for 191 stations in the Sydney region by season for a

34-year period (from 1960 to 1993).

The probability distribution of thunderstorm rainfall amounts was described using the

gamma distribution. This distribution provided two measures which describe the patterns

of thunderstorm rainfall in the Sydney region well. The g a m m a distribution was therefore

found to be a suitable technique for characterising the distribution of thunderstorm rainfall

amounts at individual observation sites (rainfall stations). This method also allows

specification of the probability distribution of rainfall amounts and has the potential to be a

predictive tool. As a result, the g a m m a distribution, as a summarising function, could be

regarded a basis tool for defining a thunderstorm rainfall climatology in the Sydney region.

To compare these values with actual rainfalls, a GIS method was used to characterise the

spatial distribution of thunderstorm rainfall patterns over the Sydney region.

The geographical representation of alpha and beta values, including the mean rainfall

values from thunderstorms, indicated that there is considerable spatial variability in rainfall

related to Sydney's physical environment. It seems that while the spatial distribution of

thunderstorm rainfall follows a gradient between inland and coastal areas, it is also


influenced by the topography of the region. Two major topographic features of the region,

such as the Illawarra Plateau and the Blue Mountains, are seen as areas with high rainfall.

Although the development of thunderstorms over different areas suggests that there are

synoptic-scale processes which cause such thunderstorm rainfalls, these processes are most

likely not solely responsible for the resulting variation and distribution patterns. More

detailed analysis of thunderstorm rainfall amounts are required to explain the relationship

between amounts and such parameters as proximity to sea, topography and landuse

patterns. The next chapter pursues these relationships.

CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters ISA

CHAPTER 7

RELATIONSHIPS BETWEEN THUNDERSTORM

RAINFALL AND PHYSIOGRAPHIC PARAMETERS

7.1 Introduction

The spatial analysis of thunderstorm rainfall has indicated that the distribution of

thunderstorm rainfalls is highly variable over the Sydney region. This should be apparent

from the results of the previous chapter, particularly in the case of widespread

thunderstorm rainfall events. It was also argued that despite the role of the different

synoptic air patterns, the distribution of thunderstorm rainfall in the region would be

largely a function of physiographic parameters such as elevation, aspect, proximity to the

sea and landuse patterns of the study area. The influence of each of these factors upon the

spatial distribution of thunderstorm rainfall is examined in more detail throughout this

chapter.

In sections 2 and 3 respectively, the data and methods used are described. In section 4, the

elevation and aspects throughout the study area are considered first, as they are the most

important physiographic parameters affecting the distribution of the thunderstorm rainfall

event. Then, the relationship between the proximity to the sea and thunderstorm rainfall is

analysed in section 5. After that, section 6 examines the possible spatial relationship

between the landuse patterns and the distribution of rainfall from thunderstorms over the

study area. In section 7, the areas affected by high thunderstorm rainfalls are highlighted

firstly by using an overlay modelling technique of GIS. Then, a 'stepwise multiple

regression' technique is applied to explore the statistical relationships amongst these

variables. Finally, in section 8, the results obtained are discussed.

7.2 Data Used

In this chapter, some of the Geographical Information Systems (GIS) techniques and also

simple-to-complex statistical methods (for example, a t-test or regression techniques) were

used in order to describe the spatial nature of the data, and to explore the possible

associations between thunderstorm rainfall amounts and several of the important

physiographic parameters of the Sydney region.

Firstly, thunderstorm rainfall data sets, from the previous chapter, have been reassembled.

Then, the average thunderstorm rainfall map was constructed by using data from the six

CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 151

largest thunderstorm rainfall events which were taken for each thundery month (October

to March, 1975 to 1993) (see Figure 7.1). These largest thunderstorm rainfall events were

selected because they are quite important in the region (Colquhoun and Shephred, 1985;

Bryant, 1991). There are several reasons for this criterion.

In the literature, the idea has been well established, that the importance of topography in

enhancing the variations in thunderstorm rainfall distribution for each of the weather types

(for example, frontal systems or air masses), is not the same (Osborn, 1982). Some

researchers, for example Amanatidis et al., (1991) think that the relative importance of

each of the topographic factors - such as elevation and aspect to the wind direction, or

facing the sun - in generating thunderstorms may change from day to day, as weather

conditions change. Climatologists such as Chuan and Lockwood (1974) and Passarelli and

Boehme (1983) and Smith (1989) have pointed out that because of the localised nature of

thunderstorms, topography does not always appear to have the same effect upon the

distribution of thunderstorm rainfall over long and short time-spans. Analysis of the

relationships between the average daily thunderstorm rainfall amounts (as they have been

shown in Chapter 6) and physiographic parameters would be helpful in the understanding

of the role of the Sydney's physical environment upon the rainfall distribution pattern.

However, they are not ends in themselves. They are intended to provide a start for the

analysis of individual widespread thunderstorms which are the soundest basis for

determining topographic effects on precipitation deduced from such thunderstorms. This

last aim is within the scope and content of the current chapter.

More importantly, the impact of large thunderstorm rainfall events - from the point of view

of flooding processes which take place within the Sydney region from time to time - is a

very serious concern, because of these disastrous consequences (Riley et al., 1985; Speer

and Geerts, 1994). Therefore, the purpose of this chapter is to discover the relationships

between physiographic parameters and the average of large thunderstorm rainfall events

likely to be recorded on thunder-days with flash-flooding in the region (all thunder-days

and associated floods have been mentioned already in Chapter 6). This procedure will,

therefore, provide opportunities by which the relative effect of each physiographic

parameter upon thunderstorm rainfall data, of a widespread nature, could be characterised.

156

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Other data sets and their sources which were used in this study, such as proximity to sea or

landuse data are shown in Table 7.1.

Table 7.1 Origin of the data that used in Chapter 7.

Variables Data Sources and Origin

Thunderstorm rainfall data a) Maps See Chapter 6 b) Point data-sets See Appendix D (Table 7.2)

Elevation Map Digital Elevation Model (DEM)

Aspect Map Created from D E M

Proximity Map Created by GIS Internal Functions

Landuse Map Using Landsat, Hard-Copy Maps and GIS Functions

7.3 Techniques Employed

To analysis the above-mentioned data sets, a GIS method and a set of statistical techniques

were used in the study of thunderstorm rainfall in the region.

7.3.1 GIS Techniques Applied

Increasingly, GIS systems are currently becoming popular in many academic centres, for

example, in universities, for storing, integrating, analysing and displaying various types of

climatic data (Sajecki, 1991; Brignall, et al., 1991). There is a large amount of GIS

software being used for spatial analysis purposes by geographers. In the first phase of data

analysis, GIS techniques were, therefore, applied to explore the spatial characteristics of

the variables.

The SPatial ANalysis System (SPANS), which is a microcomputer-based geographic

information system, was used to analyse data in relation to the Sydney region. S P A N S GIS

is a software product of Tydac Technologies (now Intera-Tydac). The S P A N S 5.3.1

version, using Operating System 2 (OS2) version Warp, is currently being used by the

Geosciences School at the University of Wollongong. S P A N S works with point, vector,

raster and quadtree files (for more details see Chapter 5). In applying the S P A N S GIS, for

the purposes of this study, several steps had to be taken before using the other advanced

GIS functions.

As a first step, the study area - the boundaries of the Sydney region - had to be

established in a S P A N S directory. This was done by means of a set-up menu in S P A N S

GIS. Using S P A N S projection function, a projection (from the master projection list) for

Thunderstorm Rainfall and Physiographic Parameters

the Sydney region was defined. The geographic location of the Sydney region, located in

the Southern Hemisphere, was assigned according to the Equator and the Prime Meridian.

The term "Study Area" is used in SPANS to define the location and description of the

current project. Both S P A N S and E - R M S , which was used for digitizing the basemap and

landuse maps of the study area, require that the geographic co-ordinates of a square or

rectangular boundary be defined for the study area / database (Table 7.3). These co

ordinates define the rectangle in the projection plane containing the study area / database.

Both systems - S P A N S and E - R M S - however allow for irregular areas to be defined in

the study area / database as a study site for analysis and modelling purposes. These are

called the "Basemaps" and domain in S P A N S and E - R M S respectively.

Table 7.3 Limits of the study area / database.

Geographical Coordinating of the Study Area

Min. Max.

Easting 247529 * Easting

Longitude 150° 15' Longitude

Northing 6289469 Northing

Latitude -33° 30' Latitude

* Easting and Northing in meters

The second step, was the preparing and formatting of data sets on thunderstorm

rainfall in S P A N S formats for the average daily thunderstorm rainfall from the six largest

rainfall events (the biggest event of each thundery month was taken from October to

March, from 1975 to 1993) using data from 152 rainfall stations located by latitudes and

longitudes in Table 7.2 (see Appendix D ) . The results of this phase of data analysis have

already been applied to create the thunderstorm rainfall maps, described in Chapter 6.

Also, the digital elevation data ( D E M ) set was added at this stage to the S P A N S GIS

system. D E M data consisted of height digitised at 20 m contour intervals from 1:100,000

scale maps. Over 20741 points were used to describe the topography over the study area,

with 80 m 2 resolution in S P A N S raster format.

At the third step, the SPANS contour module was used to produce raster-based

maps. A n analysis was made by means of a Triangular Irregular Network (TIN) grid

between sets of data points. Interpolation could be linear or non-linear. A non-linear

interpolation usually provides smoother rounded contours which are more visually

appealing. However, there is a high possibility that with a non-linear interpolation, an

isohyet line, for example, may be projected to an unrealistic value exceeding the observed

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values. For this reason, linear interpolation was used to interpolate rainfall values on a

raster-based format.

At the fourth step, the "proximity to the sea" of each rainfall station was created

using automatic compilation of buffer zones. For this study, 10 kilometre wide zones were

subjectively defined away from the coastline (see Figure 7.2). Also, using the digital

elevation model ( D E M ) , an aspect map was automatically produced in the S P A N S GIS

environment. In S P A N S GIS, a map of the aspect, which is measured in azimuth degrees,

is computed from a grid elevation map. The orientation of the steepest slope clockwise

with respect to North is computed (Figure 7.3). A slope facing the sky has an aspect of 0°,

facing East, 90°, facing South 180°. If it is facing the west, it has the value 270° (see Table

7.4).

Table 7.4 Aspect classes derived from the DEM model, using SPANS GIS

No. of

Classes

1

2

3

4

5

The fifth

Aspect

Flat

North

East

South

West

Class Degree °

(None)

(>337.5 to 22.5)

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(>202.5 to 237.5)

Digital Numbers

Lower Limit

255

239

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112

143

step involved the addition of a landuse m a p of the I

Upper Limit

255

254

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238

Sydney region to tl

GIS model. However, no landuse map exists that is primarily concerned with

climatological aspects.

7.3.1.1 Landuse Map of the Sydney Region

According to the literature (for example, Atkinson, 1969 and 1977) different landuse

patterns create different meso-climates. Large urban areas, with their characteristically

warmer urban climates and other thermodynamic effects, are able to enhance convectional

rainfalls (Changnon, 1978; Landsberg, 1981). Therefore, the following procedures were

taken to establish a 'tailer-made landuse map' for the Sydney region:

1) A set of satellite images were used to find the boundaries of different landuse

patterns. 'Landsat' satellite images of the Sydney region cover the whole of the study area,

and were recorded by U S A (NASA) satellites on December 1972, October 1986 and

November 1990. They use false colour composites of bands having different wavelengths.

Such false colour images indicate variations in vegetation types and vigour and they have a

Rainfall and

ground resolution of 30 metres and measure the reflectance of light from the surface at 7

different wavelengths or band-passes.

2) The impact of man on the natural terrain of the Sydney region is clearly shown on

these images. Urban development which is encroaching upon rural areas and natural

wilderness reserves can easily be recognised using these reflectance colours; water appears

as black to very dark-blue, depending on the amount of sediment and depth of water;

vegetation is highly reflective in the infra-red band and shows as red. Natural bushland, in

and around Sydney, appears red-brown. The areas of cultivation, especially along the

Nepean - Hawkesbury river, are light-red. It was possible to delineate the textural

variations and the fine detail of residential development in contrast to adjacent larger

paddocks and cultivated lands.

Table 7.5 Description of landuse types in the Sydney region.

Description _

Type Landuse Patterns Use / Structure Area

Km2

1 Central Business District Metropolitan natural 7.9 0.09

(CBD) very dense built-up areas

with skyscrapers

2 Industrial areas Airports, factories, 120.17 1.36

(IND) refineries

3 Urban-Residential, Barren Compact residential 807.75 9.13

area (URB) with separated treed areas

4 Urban-Residential, Treed Dispersed residential 784.48 8.87

area (URT) with intense treed area

5 Rural / Semi-Urban area Agricultural rural and 885.57 10.01

(RUS) light urbanisation

6 Rural / Open areas Agricultural rural 1292.87 14.61

(RUO) Grass, trees

7 Treed area, National / Dense vegetated areas 4947.67 55.93

Urban parks (TNP) (forest, grass lands)

Dams, lakes and rivers of the study area have not been regarded as part of the landuse map.

3) Digital data produced for the urban development program by the GIS

cartographic section of the Urban Planning Centre (1993) was used to refine the

boundaries of forests and the existing urban and non-urban areas.

163

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4) The Sydney region outline plans, produced by the State Planning Authority of

N S W for 1970 and 1994, were used to locate the main industrial areas.

5) These landuse maps were combined and digitised as impute data, using the E-

R M S computer program. Then, the landuse data was imported into the S P A N S GIS

program and converted into a raster format. Seven classes, as shown in Table 7.5 and

Figure 7.4, were delineated for this final map.

Using the landuse map which was created for this study, the Central Business District

(CBD) was known as landuse 'type 1'. The central portion of the City is the most

developed, containing most of the major commercial developments. The centre of Sydney,

with very dense built-up areas and a rough surface topography with tall skylines (see Plate

7.1). There are some small C B D nodes, for example Parramatta's C B D . Because of the

scale of model used in this study, these areas could not be shown in the landuse map of the

Sydney region.

Plate 7.1 Closeup view of heavy commercial landuse showing the part of CBD.

All major industries, factories and airports (type 2) have been categorised as 'Industrial

areas (IND). Plate 7.2 gives an example. The term 'Urban-Residential Barren' (URB) area

has been assigned to that area encompassed by the out-lying boundary of the dense

residential areas (type 3) generally with less tree coverage (see Plate 7.3).

CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters Ml

Plate 7.2 Closeup view of heavy industrial landuse (type 2).

Plate 7.3 View of compact residential landuse (type 3) in the Sydney region.

CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters J66

In contrast, the term 'Urban-Residential Treed' (URT) was applied to those areas (Plate

7.4) with less dense residential area and much more tree cover (type 4). It should also be

pointed out that there are a few small recreational parks and cemeteries throughout the

Metropolitan area.

Plate 7.4 View of light-moderate residential landuse (type 4).

CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters J61

Plate 7.5 View of normal rural / semi-urban area (type 5).

Plate 7.6 Shows example of rural / open areas (type 6).


Plate 7.7 Closeup view of compact vegetated land cover (type 7) in the Sydney region.

If rural areas are being encroached upon by urban development, they were categorised as

type 5 and they were simply called 'Rural / Semi-Urban' areas (RUS) (see Plate 7.5). But,

cultivated areas or productive rural areas (type 6) with considerably more natural reserves

were classified as the 'Rural / Open' areas (RUO). A n example of this kind of landuse is

shown by Plate 7.6.

Finally, all areas with considerable natural vegetation, including State forests, National

parks, urban water catchments, and many major parks located in the region (type 7), were

traced and defined as 'Treed National and Urban parks' (TNP) (see Plate 7.7).

As a result, seven landuse types comprise the major portion of the Sydney region which

can be seen in Figure 7.4 and the associated Table 7.5. Also, plates 7.1 to 7.7, as

representative of all landuse classes, illustrate a close-up view of different landuse patterns

from the region

7.3.1.2 Advanced SPANS GIS Functions Used

The following analytical functions of the SPANS GIS were utilised to analysis the data and

to produce the final products.


1) A GIS allows simple statistical comparisons between maps by arranging the data in a contingency table (Bonham-Carter, 1994). In the GIS, a contingency table was,

therefore, first used to test the hypothesis that the area-based distribution of rainfall in the

various categories of one map are independent of, or dependent on, the classes of the

physiographic maps (as independent variables). Statistically, chi-square coefficients are

used to be the measures of the degree of correlation, association or dependence of a

thunderstorm rainfall map to the physiographic maps. In practice, because the contingency table varies in dimension, S P A N S GIS uses three measures of association - such as

Contingency coefficient, Tschuprow's T and Cramer coefficient - to measure the degree of

correlation between two map layers as follows:

First, the contingency coefficient, C , is estimated by

ifo^+ril( GFX2 + n

where <P2 is the goodness-of-fit chi square GFX , and n is the sample size

( o 2 = G F X 2 /n).

Theoretically C lies between 0 and 1 but does not always reach 1, even when the variable

seem completely associated. In square tables (that is, I = J), for instance, its maximum value is (1 -X)lI.

Then, Tschuprow's T can be estimated by SPANS GIS. This estimator varies between 0

(for independence) and 1.0 (dependence), but it can only attain its maximum in square

tables. T is calculated by

• O2 J GFX2

T"^|)(I-IXJ-I)""H(I-IXJ-I)

Also, the Cramer coefficient (V) corrects for some of the deficiencies of the contingency

coefficient C and Tschuprow's T in that it achieves its maximum in asymmetric arrays

(Intera Tydac, 1993). v varies between 0 (no correlation between maps) to a maximum

value of 1 and, then, it is determined by

~&i GFX2

nm (3)

Where m equals the number of classes in column and it is the smaller of (I - 1 or ( J -1),

while n is the number of classes in row.


In fact, the degrees of freedom for a two-sample test are given by the number of rows in

the contingency table minus one multiplied by the number of columns minus one (Ebdon,

1985). In this study, the above-mentioned three measures of association were found to be

effective analytical techniques in comparing the spatial distribution of thunderstorm rainfall

amount (as a dependent variable) by the number of physiographic parameters (as

independent variables).

2) In the second stage, the 'Map Analyse' functions of GIS were used to calculate

the average areal distribution of rainfall from point measurement (rainfall stations). The

averaging technique was found to be extremely useful when averaging rainfall values over

fixed physiographic units such as topographic, the proximity zones or landuse classes. The

results produced by this function display area-based mean thunderstorm rainfall values for

each class of physiographic maps, for example, the landuse map of the Sydney region.

Such procedures can also help to find new statistics (such as attribute means or totals)

derived from the areal distribution of thunderstorm rainfall based on different classes of

each physiographic parameter.

3) In the third stage, GIS internal reclassification methods and a multi-overlaying

technique were used to analyses all maps (rainfall and physiographic maps) in the GIS

environment. B y using GIS reclassification techniques, it was first determined which of

these areas in the Sydney region, had the highest amounts of rainfall distribution, (more

than 120 millimetres). This rate indicates the medium class on the thunderstorm rainfall

map, and it was subjectively selected (see Figure 7.1). This arbitrary rainfall value was

selected because in Chapter 6, the study of the 6 largest thunderstorm rainfall events

indicated that there is not a definite rainfall value for the onset of floods in the region. It is

possible that the conditions, by which these events occur, differ from one weather system

(with different rainfall intensities and associated amounts) to another, and possibly, it also

depends on the ground conditions at the time of flooding. Armstrong and Colquhoun

(1976) defined a daily heavy thunderstorm rainfall with more than 100 m m isohyet

extended over the Sydney region in March 1975. Therefore, the current study supposed

that areas in the region which have rainfall amounts above the average values of 120 m m ,

are more prone to floods and, as a result, they must be visualised.

Then, the SPANS GIS modelling language, as a powerful analytical mapping tool, was

employed to produce new maps which overlayed the rainfall map and all the independent

physiographic models, simultaneously. This was made possible by writing equations to be

understood by the S P A N S modelling language system (see Table 7.6 located in Appendix

E). The final productions are new maps each with a specific aim using the visualisation

capabilities of GIS techniques. By this, it was possible to show areas, for example on a

landuse map, subject to the highest rainfall from thunderstorms (see Figure 7.5 (a-d)).

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Thunderstorm Rainfall and Physiographic Parameters

4) Finally, the 'Visualise' menu of SPANS which is an important tool in GIS, was

used to annotate all maps and slides it produced. Rainfall maps and topographic features

have been annotated with complete captions, titles, legends and coordinates with desired

pallets. Colours were usually considered for each map as an advantage of using S P A N S

GIS. For numerical and interval values the accuracy of the data were tested using a query

module.

7.3.2 Statistical Techniques Used

In the second phase of data analysis, statistical analysis techniques, consisting of the t-test

and simple regression coefficient techniques, were used to find possible associations

between thunderstorm rainfall amounts and physiographic parameters, individually. In

addition, a stepwise multiple regression (Hauser, 1974; Bryant, 1985a) was used to

construct a statistical model explaining the average thunderstorm rainfall as a relative

function of elevation, aspect, proximity to sea and landuse pattern. These analytical

methods were not only applied to find possible associations between physiographic factors

and thunderstorm rainfall, but also to test the results obtained from the GIS overlaying

map techniques.

7.4. Topography and Rainfall from Thunderstorms

To investigate any possible associations between the topography of the region and

thunnderstorm rainfall, it is necessary to describe the main topographic features of the

Sydney region in detail.

7.4.1 Description of Major Topographic Units

Generally, the Sydney region can be characterised by five topographic units. Most of the

Sydney region is spread out along the gently undulating Cumberland Plain, on average,

less than 100 meters above sea level. This basin is surrounded by four other topographic

units rising to elevations up to 1200 m in height. Sydney covers part of the Hornsby

Plateau in the north-east of the study area, rising to nearly 250 meters above sea level. The

Blue Mountains is located to the west and north-west of the region with elevations higher

than 1200 meters. To a lesser extent the Illawarra Plateau to the south-east of the region

has an average elevation of 350 to 450 meters with a relatively sharp escarpment facing the

Tasman Sea. Finally, the south-western part of the study area can be characterised by a

relatively flat landscape which is a part of the Southern Tablelands. This flat relief lies

approximately 700 meters in elevation on average, above sea level. The elevation map for

the study area contains elevations that range from sea level to 1200 meters at the top of

the Blue Mountains (see Figure 7.6). Also Table 7.8 summaries the area based on

topographic characteristics of the Sydney region.

CO +-•

'c Z>

c o 'co CD

tr CD C >> CO H—

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to

c Z> o 1c Q. CO I—

CO

o d

o CO

CD

CD L-

CO

LL

CO 'CD

X

o LO 1 r> l

o o

LD

O LO

1 O

o o OJ

LO

o o CO 1

o CM

o o 1

o CO

o o LO 1

o "<*

o o CO 1

o in

o o 1

o CO

o o o 1

C_)

ou

+

C J o T~

E o OJ

? Cj

D. CO i_

CO

o Q.

o SI CO Q oS

q co" <

oe 1-5L

fm *&?* 91. 091-

co CO

CO <* CO

CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters L7A

The major river network in the region is the Hawkesbury - Nepean River which flows

through the study area south to north. In the east, the coastline is crossed by rivers such as

the Parramatta and George Rivers. At the coast these rivers form significant estuaries such

as Broken Bay in the north and Botany Bay in the south. Zigzag coastline which is a

boundary between the Sydney region and the Tasman Sea, cuts the region in the east.

7.4.2 Association Between Elevation and Thunderstorm Rainfall

In the literature (reviewed in Chapter 2), a positive correlation of increasing thunderstorm

activity with altitude is well documented (Spreen, 1947 and Reid, 1973). The effect of

mountains on increasing thunderstorm activity is most clearly seen on thunder-days and

rainfall maps (Court, 1960; Duckstein, et al., 1973). Results from many parts of the world

also confirm that thunderstorm rainfall increases with relief (Cheong and Tay, 1982). The

association noted here is broadly true for N S W and whole of Australia (Hobbs, 1972). In

instance, for Hunter Valley ( N S W ) Hutchinson and Bischof (1983) used a new method

(Laplacian Smoothing Spline Function) for estimating the spatial distribution of mean

seasonal and annual rainfalls. The rainfall maps show that the areas with higher elevations

(for example, the Barrington Tops) received much more rainfall than to the low-lands (the

Goulburn River). The stronger influnce was evident in summer reflecting the inflow of

warm saturated air of equatorial origin from the norttheast.

The thunderstorm rainfall-elevation relationships over the Sydney region were defined

using an area cross-tabulation technique between the digital elevation map (Figure 7.6) and

the thunderstorm rainfall map (Figure 7.1). The GIS technique indicated that there is a

spatially significant association between the topography of the region and the rainfall

distribution map. Chi-square coefficients are given in Table 7.7.

Table 7.7 Area cross tabulation results between the topography map of the region and thunderstorm rainfall map.

Thunderstorm

Rainfall M a p

Topographic

Map

Contingency

Coefficient

0.429*

Tschuprow's T

value

0.179

Cramer's V

value

0.213*

* significant at 0.05 level.

The chi-square value is at a significant level of 0.05 which can confirm the existence of a

correlation between variables. However, it does not give any information about the effect

of the topography of the region upon the rainfall distribution pattern.


The areal based distribution of mean rainfall which was calculated using a GIS technique,

indicates that the distribution is not the same for all topographic classes and the area

distribution itself varies according to the topographic units. It seems that the highest

amounts of rainfall from thunderstorms (more than 120 millimetres) is limited to the 400 -

500 elevation classes.

Table 7.8 shows the areal distribution of thunderstorm rainfall amounts for each

topographic interval. The advantage of this kind of calculation is that it shows simply the

average area of each topographic class from the total area of the region. At the same time,

this estimation gives the rainfall amounts (in percentage) for each individual corresponded

topographic classes.

Table 7.8 The areal distribution of thunderstorm rainfall by topographic classes in per cent Thunderstorm Rainfall Classes in m m

Topographic 35-60 61-90 91-120 121-150 151-180 181-200 Total Area Classes in m K m sq Per cent

1802.7 19.7

1313.2 14.3

942.0 10.3

588.6 6.4

1060 11.6

959.5 10.5

715.9 7.8

590.5 6.4

871.7 9.5

251.5 2.7

74.1 0.8

9170.4 100 19.3 4.1 0.4

* The area of each topographic class in per cent ** The areal distribution of thunderstorm rainfall for each topographic interval in per cent.

Because the initial analysis of thunderstorm rainfall distribution over the study area

indicated that local spatial variations could be influenced by small-scale topographic

features, an attempt was made to look at the thunderstorm rainfall - elevation relationships

using statistical techniques. T o do this, the Sydney region has been divided into four sub-

regions according to the main topographic units of the region: namely, the Blue

Mountains, Hornsby Plateau, Southern Tablelands and the Illawarra Plateau (see Figure

0-50

51 -100

101 - 150

151 - 200

200 - 300

301-400

401 - 500

501 - 600

601 - 800

801 -1000

above 1000

Total km^ %

3.1* 7.7** 0.8 1.4 4.3 5.7 7.6 6.3 10.0 14.9 7.0 9.6 5.9 6.0 8.9 7.4 32.0 39.2 5.6 2.0 0.0 0.0 711.8 7.8

36.3 18.2 60.8 22.2 44.8 11.8 31.6 5.2 36.8 10.9 28.9 7.7 37.2 7.4 51.3 8.5 33.2 8.0 0.3 0.02 0.0 0.0 3588.0 39.1

27.2 18.2 19.6 9.5 26.6 9.3 36.9 8.0 34.0 13.4 25.9 9.2 32.8 8.7 29.8 6.5 22.3 7.2 83.6 7.8 77.5 2.1 2697.8 29.3

24.5 25.3 15.6 11.6 22.5 12.0 23.3 7.8 15.8 9.5 26.4 14.3 20.2 8.2 9.6 3.2 11.7 5.8 10.1 1.4 22.0 0.9 1766.1 19.3

7.4 35.3 2.8 9.6 1.9 4.7 0.6 0.9 3.4 9.7 11.7 29.7 3.9 7.3 0.4 0.6 0.8 1.9 0.4 0.2 0.4 0.07 376.2 4.1

1.3 76.2 0.6 23. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 30.7 0.4


7.6). The relationships between thunderstorm rainfall amounts and the elevation in each of

these four topographic units were analysed using simple linear regression (Table 7.9).

Table 7.9 A linear regression analysis between thunderstorm rainfall amount and elevation of rainfall stations located in the four topographic units of the Sydney region.

Topographic Units n F value

Blue Mountains = A

Hornsby Plateau = B

Southern Tablelands = C

Illawarra Plateau = D

26

74

21

31

.82

0.002

-.42

.67

.67

.004

.17

.45

49.6

0.33

4.06

30.2

.0001

.0.56*

.05

.0001

* Not significant at 0.05 level

The liner regression analysis indicates that there is a close relationship between average

thunderstorm rainfall and elevation. Over the Blue Mountains and Illawarra Plateau, the

relationship between elevation and rainfall amounts for the average of the largest

thunderstorm rainfall are generally high. However, over the Southern Tablelands,

correlation coefficients between rainfall and elevation are negative. Also, over the Hornsby

Plateau, the effect of elevation upon rainfall amounts is not at the significant level of 0.05.

These results imply that the relationship may be masked by other controlling parameters.

7.4.3 Association between Aspect Classes and Rainfall

The gradient of thunderstorm rainfall with elevation depends not only on the height of

station, but also its aspect (Reid, 1973). In fact, slope, in relation to prevailing wind

direction, provides a basis for the identification of zones of potential relative thunderstorm

rainfall in the region (Sims, 1981; McCutchan and Fox, 1986).

To find an association between the aspect map - as an important topographic feature - and

the distribution of the thunderstorm rainfall map (Figure 7.1) firstly the aspect map of the

region was derived from the D E M model (Figure 7.3). There are 5 main aspect classes: 1)

aspects facing the sky (flat), 2) north, 3) east, 4) south and 5) west directions (see Table

7.4). This procedure helped to analyse and identify the spatial correlation between the

thunderstorm rainfall map and the exposure of each rainfall station to one of the main

aspect classes.

An area cross tabulation technique, was therefore, introduced between above-mentioned

maps. The results are shown in Table 7.10. The association between maps is significant at

0.05 level.

SEVEN Thunderstorm Rainfall and Physiographic Parameters 177

Table 7.10 Area cross tabulation results between the aspect map of the region and thunderstorm rainfall map.

Thunderstorm

Rainfall Map

Aspect

Map

Contingency

Coefficient

0.52*

Tschuprow's T

value

0.37

Cramer's V

value

0.41*

*significant at 0.05 level.

Accordingly, the position of each rainfall station with respect to these aspect classes was

found using the query function of the S P A N S GIS. The average distribution of

thunderstorm rainfall amounts for each individual aspect class was then found using all

rainfall stations data located in the region. A s it is clear from Figure 7.7, the distribution of

thunderstorm rainfall amount, based upon aspect classes, is not the same. There is a

considerable difference between the different aspect classes in obtaining rainfall from

thunderstorms. It seems that stations which are exposed to the west and east receive the

most rainfall from thunderstorms.

West Flat North South East

Aspect Classes

Figure 7.7 The distribution of thunderstorm rainfall in the Sydney region based upon aspect classes.

In order to prove statistically the association which was found by GIS technique, and to

calculate the different distribution pattern which is graphically shown in Figure 7.7, a

multiple regression method was introduced between all aspect classes (as nominal

independent variables) and thunderstorm rainfall amount as a dependent variable (Table

7.11). In this statistical procedure the aspect of west was entered into the model as a

constant variable.

E E

e '3

IB U <u •a B S


Table 7.11 A multiple regression analysis between aspect classes and thunderstorm rainfall amount. n= 152

Aspect Items

East-West

South-West

Flat-West

North-West

Beta value

(Std. Coef.)

0.08

-0.25

-0.37

-0.22

t-value

0.66

2.45

3.34

2.74

P

0.51*

0.01

0.0009

0.007

* not significant at 0.05 level, r2 = 0.22

In Chapter 2, it was generally argued that the study area can be affected by two main

weather systems associated with thunderstorms. First, air masses thunderstorms which are

created mainly over the elevated terrain of the Sydney region and, then, move from west to

east (Matthews 1993). Second, some weather systems, for instance, Tasman Sea lows and

associated weather features, occasionally, move from the sea over the Sydney region and

causing wide-spread thunderstorm activity. As Figure 7.7 shows, stations facing the west

and east were more exposed to rain-bearing thunderstorm systems. So, using a multiple

regression method (with some nominal variables), just one of these geographic directions

could be keept as a constant parameter, in the analyse of thunderstorm rainfalls.

Clearly, the relationship between west and east-facing aspects is not significant. Both

aspects are subject to the same amount of thunderstorm rain. The Beta value (Std. Coef. in

Table 7.11) and the associated t-value confirm the above-hypothesis. Also, this statistical

technique indicates that there are significant differences between stations which face the

west and stations which are exposed to other aspects, such as the south and north. Flat

topography is of minor importance in controlling thunderstorm rainfall. In total, aspect

classes can explain about 22 per cent of variance in the distribution of thunderstorm

rainfall in the region.

7.5 Proximity to the Sea and Thunderstorm Rainfall Distribution

As a physiographic parameter, proximity to the sea is also known to be a very important

factor in controlling thunderstorm rainfall amounts, especially near the coast (Merva et al.

1976; Berndtsson, 1989). For the Sydney region, it was suggested by several researchers,

for example James (1992), that proximity to coastal areas can increase the amount of

thunderstorm rainfall considerably. In this study, two methods were introduced to find the

possible relationships between proximity to the sea and thunderstorm rainfall distribution.

First, a GIS technique was produced to show the spatial correlation between thunderstorm

rainfall and proximity maps. Then a simple regression method was applied to assess the

statistical significance of the relationship.


As a first step, using a GIS buffering technique, a proximity map was constructed. Then,

an area cross tabulation was performed between the proximity and thunderstorm rainfall

maps. Results are shown in Table 7.12 and statistically indicate that, in the Sydney region,

there are high positive associations between the spatial distribution of rainfalls from

thunderstorms and the proximity to the coast.

Table 7.12 Area cross tabulation results between the proximity to sea map of the region and thunderstorm rainfall map.

Thunderstorm Rainfall Contingency Tschuprow's T Cramer's V

Maps Coefficient value value

Distance M a p 0.723* 0.394 0.468*

* significant at 0.05 level

Table 7.13 The areal distribution of thunderstorm rainfall by proximity classes in per cent.

Proximity Classes i Km 1-10

11- 20

21- 30

31- 40

41-50

51- 60

61- 70

71- 80

81- 90

91- 100

101- 110

Total km^ %

35-60

0.0* 0.0**

0.0 0.0

0.0 0.0

1.4 2.4

13.5 23.5

36.4 62.6

9.4 11.5

0.0 0.0

0.0 0.0

0.0 0.0

0.0 0.0

711.6 7.8

Thunderstorm Rainfall Classes

61-90

0.0 0.0

2.1 0.7

26.5 9.1

81.3 28.0

78.2 27.0

58.7 20.0

60.9 14.8

2.2 0.4

0.0 0.0

0.0 0.0

0.0 0.0

359.0 39.1

91-120

14.4 5.5

34.6 15.3

49.0 22.4

16.7 7.6

8.3 3.8

4.9 2.2

29.7 9.6

80.7 17.5

72.2 10.2

87.0 5.8

100.0 0.2

2699.3 29.4

121-150

59.1 34.5

53.1 35.8

24.3 17.0

0.6 0.4

0.0 0.0

0.0 0.0

0.0 0.0

17.0 5.6

25.4 5.5

12.4 1.3

0.0 0.0

1766.5 19.3

in m m

151-180

23.5 64.4

10.1 31.9

0.3 0.8

0.0 0.0

0.0 0.0

0.0 0.0

0.0 0.0

0.0 0.0

2.5 2.5

0.5 0.3

0.0 0.0

376.2 4.1

181-200

3.0 100.0

0.0 0.0

0.0 0.0

0.0 0.0

0.0 0.0

0.0 0.0

0.0 0.0

0.0 0.0

0.0 0.0

0.0 0.0

0.0 0.0

30.7 0.3

Total Area K m sq Per cent

1030.7

1188.5

1235.7

1234.1

1241.3

1223.4

871.1

584.2

380.0

179.8

3.3

9172.4

11.2

13.0

13.5

13.4

13.6

13.3

9.5

6.4

4.1

2.0

0.04

100

* The area of each proximity class in per cent ** The areal distribution of thunderstorm rainfall for each proximity interval in per cent.

In addition, areal distribution of thunderstorm rainfall amounts, which were calculated

based on the classes shown on the proximity map, are summarised in Table 7.13.

According to these tables, all rainfall in the highest class (180-200 m m ) was distributed

within 10 k m of the coast (zones 1). Also, more than 95 per cent of rainfall (in class 151-

CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 1M

180) fell on zones within 20 k m of the sea. Except for parts of the Blue Mountains, the

percentage of high rainfall decreased sharply westwards from the coast.

To verify these results statistically, a simple regression model was applied to the data to

find out the possible relationships. A computer program was first written to calculate the

shortest distance of each station to the average coastline (see the computer program 5,

Appendix A ) . The results of this phase of the analysis are summarised in Table 7.14.

Table 7.14 Correlation coefficients between the proximity to the sea (in Km) and thunderstorm rainfall (average of the biggest thunderstorm rainfall events).

Distance

(proximity to sea)

n

152

r

-0.61

r2

0.37

F-test

87.6

Probability

0.0001

As it is evident from Table 7.14, the proximity to the sea, as a physiographic parameter, is

negatively correlated with thunderstorm rainfall amounts (r2 = 0.37) at 0.0001 significant

level. There is a high possibility that the addition of other independent variables such as,

distance from the mountain ranges, might substantially improve the results of this kind of

analysis. The GIS map also shows small variations in thunderstorm rainfall along the coast

implying the influence of other factors.

7.6 Landuse Patterns and Thunderstorm Rainfall

The purpose of this section is to find and describe possible relationships between urban-

rural landuse patterns and maximum thunderstorm rainfall distribution in the Sydney

region. Recently, a great deal has been written about the influence of urban areas on

climatic factors (Auer, 1978; Henry et al., 1985; Bradshaw and Weaver, 1993). All

suggested that many climatic factors, for example temperature and rainfall patterns, can be

affected by city environments. Other investigations in urban climatology such as those by

Changnon (1973), Changnon and Huff.(1973), Landsberg (1981) and Houghton (1985),

have indicated that surface conditions such as the heat island effect or physical features of

a city, which influence most weather elements, also affect the subsequent rainfall,

especially in the case of meso-scale convective precipitation (see literature chapter).

In the Sydney region, where about 3.5 million people live (Department of Planning, 1995),

the ground surface is covered by houses, paved roads, factories, warehouses and tall office

and apartment blocks. These structures contrast with the ground cover of the surrounding

rural areas, such as forests and open rural areas, and they may produce local differences in

Sydney's climatic environment. In such an environment, land cover may affect, to some

degree, the distribution of thunderstorm rainfall patterns throughout the region. T o


correlate the distribution of thunderstorm rainfall over the region with landuse patterns,

both G I S methods and statistical techniques were again used.

Firstly, an area cross-tabulation was used to find the correlation between the landuse and

thunderstorm rainfall maps. It was found that chi-square coefficients are significant at 0.05

level.

Table 7.15 Area cross tabulation results between the landuse map of the region and thunderstorm rainfall map.

Thunderstorm Rainfall Contingency Tschuprow's T Cramer's V

M a p Coefficient value value

Landuse M a p 0.58* 0.30 0.32*


Secondly, a GIS technique was used to calculate the details of the areal distribution of

thunderstorm rainfall based on different landuse classes. For more details in the areal

distribution of thunderstorm rainfall based on landuse classes see Table 7.16.

Table 7.16 The areal distribution of thunderstorm rainfall by landuse classes in per cent. Thunderstorm Rainfall Classes in nun

Landuse 35-60 61-90 91-120 121-150 151-180 181-200 Total Area Classes K m sq Per cent

7.9 0.09

120.17 1.36

807.8 9.1

784.5 8.9

885.6 10.0

1292.9 14.6

4947.7 55.9

8846.4 100 19.1 4.0 0.3

* The area of each landuse class in per cent.

** The areal distribution of thunderstorm rainfall for each landuse interval in per cent.

The comparison of the rainfall amounts in the different landuse classes indicates that these

stations which are located in built-up areas have more rainfall from thunderstorms, on

average. The highest thunderstorm rainfall amounts are measured in the centre of the

Sydney (CBD).

CBD

IND

URB

URT

RUS

RUO

TNP

Total km^

%

0.0* 0.0**

0.0 0.0 0.0 0.0 1.5 1.8 9.5 12.7 10.5 20.3 8.8 65.2 665.4 7.5

0.0 0.0

39.5 1.4 44.0 10.2 15.5 3.5 88.3 22.3 63.4 23.4 27.9 39.4

3507.6 39.7

0.0 0.0

18.0 0.8 21.9 6.8 25.4 7.7 2.1 0.7 21.4 10.6 38.5 73.3

2597.5 29.4

0.0 0.0

35.6 2.5 20.0 9.6 49.7 23.0 0.0 0.0 4.6 3.5 21.0 61.4 1691.3 19.1

13.1 0.3

6.9 2.3 12.2 27.6 7.5 16.5 0.0 0.0 0.2 0.7 3.8 52.5 356.0 4.0

86.9 24.0

0.0 0.0 1.8 50.7 0.5 12.9 0.0 0.0 0.0 0.0 0.1 12.44 28.5 0.3


E E

OS

E t-

o • * *

(A L. Ol •O

e 3

200

180

1 60

140

1 20

1 00

80

60

40

20

0 •ttffll i l a a a • Ivffifl • E::::::l • Eaal |

CBD IND URB URT RUO RUS TNP

Landuse Classes

Figure 7.8 Distribution of thunderstorm rainfall in the Sydney region

based upon landuse classes.

Also, the average rainfalls in residential (barren and treed areas) were higher than other

landuse classes. The uneven distribution pattern of thunderstorm rainfall is clearly shown

by Figure 7.8.

Table 7.17 The result of a t-test for rainfall distribution in different landuse classes. Landuse C B D Classes

IND URB URT RUS RUO TNP

CBD

IND 6.7*

URB

URT

RUS

RUO

TNP

4.3*

4.65*

25.7*

9.29*

4.24*

-1.18

-2.68*

4.26*

2.65*

-0.61

-1.67*

4.61*

4.68*

0.73

7.25*

7.34*

2.4*

-1.25

-3.65* -3.60*

Significant Difference at 0.05 level

Statistically, to examine any possible difference among the means of the rainfall stations,

located in different landuse classes, a t-test technique was applied (Shaw, Wheeler, 1985).

The null hypothesis is that there is no difference in the mean thunderstorm rainfall

population between each paired sets of landuse classes, H0:X = Y. The alternative

hypothesis is that mean rainfall differs by a degree that is too great to be attributed to

random sampling variations from a c o m m o n thunderstorm rainfall population. Thus, the


mean for the stations located, for example in the CBD, should be higher than for those

stations located, for example in the industrial areas, HX:X>Y Thunderstorm rainfall

data were considered for the average of the biggest rainfall events.

The results are shown in Table 7.17. A significant level of 0.05 was set as the criteria for

accepting or rejecting the null hypothesis. The results of the t-test which was introduced

among all landuse classes adds support for the presence of significant differences between

different landuse classes concerning thunderstorm rainfall distribution in the Sydney

region.

Table 7.18 A multiple regression analysis between landuse classes and thunderstorm rainfall, r2 = 0.39

Landuse

Classes

IND-CBD

URB-CBD

URT-CBD

RUO-CBD

RUS-CBD

TNP-CBD

Beta value

(Std. Coef.)

-0.56

-0.84

-0.72

-0.1.03

-0.82

-0.91

t-value

4.92

4.70

3.97

6.97

7.01

5.09

P

0.0001

0.0001

0.0001

0.0001

0.0001

0.0001

Clearly, stations located in the C B D and urban-residential areas receive much more

thunderstorm rainfall amounts. To test this idea and to see the total effect of landuse

classes upon the distribution of thunderstorm rainfall, a multiple regression technique was

again introduced between all landuse classes (as nominal independent variables) and

thunderstorm rainfall amount as a dependent variable (see Table 7.18).

Statistically, it was found that there is a significant difference between the centre of Sydney

(CBD) and other landuse classes in obtaining rainfall from thunderstorms. Also, the r2

value indicates that landuse pattern of the region is a very important parameter in

explaining about 39 per cent of variance (in total) in terms of the biggest monthly

thunderstorm rainfall events from 1975 to 1993.

7.7 Overlay Modelling / Multiple Relations

So far, by using the SPANS GIS software, which was employed for pre-processing of

two-map layers analysis, some initial associations among different variables with a spatial

nature, have been established. At the same time, some simple statistical techniques were

used to analyse and find correlations between data sets with a spatial context. In the final

stage, more procedures would be employed to consider the associations among

CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters ISA

physiographic parameters (as independent variables) and maximum thunderstorm rainfall

(as a dependent variable) synchronically. In fact, this is the main problem, and there are

two possible approaches to its solution.

The GIS overlaying techniques and the multiple stepwise regression method were applied

to find possible associations between all the variables. These techniques were also used to

consider three important questions (with a climatic content). These were:

1) where are the locations most likely in the region to be subject to the highest

rainfall from thunderstorms?

2) what are the physical characteristics of these locations?

3) and finally what are the statistically significant relationships between the

physiographic parameters and rainfall amounts?

7.7.1 GIS Overlay Modelling

At the first stage in the GIS environment, an overlay modelling technique was introduced

to visualise the area's different physiographic characteristics, for example, topography or

landuse with the highest thunderstorm rainfall. The main aim, was to derive a set of new

maps by imposing the areas of high rainfall (more than 120 m ) over all the independent

maps, in a process known as overlay modelling. For this work, from the rainfall map the

highest rainfall classes were first differentiated using GIS reclassification techniques. These

classes were then spatially imposed over all the physiographic maps of the study area by

writing some equations in the S P A N S GIS system (see Appendix E, Table 7.6). The

resulted Figure 7.5 (a to d) can easily be used to achieve the following information:

1) the amount of the highest thunderstorm rainfall at a specific distance from the

average coast-line (distance zones);

2) topographic classes in which the highest rainfall occurred;

3) variation of rainfall amount with aspect classes;

4) character of a landuse pattern in relation to the highest rainfall amounts;

5) and finally all classes from the physiographic maps exposed to the highest

thunderstorm rainfall amounts which can be visualised simultaneously on the

computer screen.

Results show that areas closer to the sea (zones 1 to 3), about 30 kilometres from the

average coastal line, had high rainfalls. Also, it is evident from Figure 7.5 (a) that zones 8,

9 and even 10, which were located in the west over part of the Blue Mountains, had been

subject to the highest thunderstorm rainfalls. Closer examinations of distant zones from the

coastal areas indicates that there is a considerable variation in rainfall distribution.


Clearly, high elevations near the coast, for example, the Illawarra Plateau located in the

south-east of the region, and parts of the Hornsby Plateau located in the north-east of the

study area, received the maximum amount of rainfall from thunderstorms. Also, parts of

the Blue Mountains located in the west of the Sydney region, were also susceptible to the

highest rainfall amounts from thunderstorms. However, Figure 7.5 (b) indicates that the

Southern Tablelands in the south-west of the region have not been susceptible to the

highest thunderstorm rainfalls.

A close examination of the aspect classes of the Sydney region shows that generally, in

those areas which were subject to the highest thunderstorm rainfall amounts, aspects

facing the east and west are dominant, except for areas with flat topography which are

located in the east near the coast.(Figure 7.5 (c) ). Exact view of the landuse map of the

region (Figure 7.3) indicates that these areas are used for urban purposes.

Finally, it is clear that built-up areas, for example residential areas, and in particular, areas

which are located in the centre of the City ( C B D ) and east of the Metropolitan area were

much more subject to the highest thunderstorm rainfall amounts. It can be also seen from

Figure 7.5 (d) that some areas in the National parks had higher thunderstorm rainfalls.

7.7.2 Multiple Relations Among Variables

The initial results of GIS and also the statistical techniques have indicated that there are

statistically significant associations among variables explaining the thunderstorm rainfall of

the region. Certainly, a stepwise multi-correlation regression technique and then a Z score

method are needed to verify the results obtained by GIS and statistical techniques to

determine the relative effect of each individual physiographic parameter upon the spatial

distribution of maximum thunderstorm rainfall in the model.

7.7.2.1 Stepwise Multi-Correlation Regression Technique

Before introducing a stepwise multi-correlation regression technique, a correlation matrix

between items of each scale of independent variables and dependent variable was applied.

A correlation matrix is therefore found to be a statistical technique to see the

interrelationships among all items.

The results show that the relationships between many physiographic items and

thunderstorm rainfall are significant at 0.05 level (shown by asterisk). In general, the

interrelations between all items presented in Table 7.19 confirm the reliability of data used.

Accordingly, it can be concluded that most physiographic items which have been used in

SEVEN Thunderstorm Rainfall and Physiographic Parameters 1 M

this study are relatively homogeneous items and therefore they have a c o m m o n

relationship to thunderstorm rainfall.

Table 7.19 Interrelations matrix among physiographic parameters and thunderstorm rainfall (n=152).

1

-.32*

-.14

-.14

-.21*

-.31*

.20*

1

-.14

-.16*

-.23*

-.32*

.27*

1

-.07

-.1

-.14

-.06

1

-.10

-.15

-.31*

1

-.22*

-.36*

1

-.03 1

Variables *1 X2 X3 X4 X; X{ X7 X8 X9 X10 X n X12 X13 X1 4 X15

X\ = Proximity to Sea \

X2 = Elevation 53* \

X3 = Aspect Flat 07 .13 j

X4= East .24* ..05 -.49* 1

X5= South ,12 ..05 -.29* -.35* 1

Xg= North 04 .08 -.13 -.16* -.09 1

X7 = West .06 .25* -.24* -.26* -.18* -.08 1

X8 = Landuse CBD . 15* ..JQ ..10 .04 .14 -.03 -.06 1

Xo= URB ..19* ..35* .32* ..04 -.08 -.11 -.21* .09

Xl0= URT ..23* -.08 -.26* .21* .06 -.04 .0 -.09

Xtl= IND ..os ..17* .21* -.14 .03 -.05 -.1 -.04

Xi2= RUS .23* ..14 .19* ..21* .09 -.05 -.03 -.04

Xi3= RUO .25* .21* .02 -.06 -.13 .22* .08 -.06

X M = TNP 16* 48* ..25* .05 -.01 .05 .24* -.09 -.31*

x15 = Rainfall -.61* .16* -.31* .38* -.14 -.17* .17* .35


Independent variables which have been entered into a stepwise regression equation were -

proximity to the sea (in K m ) and the spot elevation (in m ) as interval variables. Other

variables (aspect and landuse classes) were entered on a nominal scale. These two last

variables were transformed into d u m m y variables, and then applied with thunderstorm

rainfall amounts as the dependent variables. Because there were five levels of aspect

classes as nominal scale variables, four dummies were required in the regression model. In

terms of landuse classes, there were seven levels. In general, L-l dummies were required,

where L is the number of levels of the variable to be represented by them (Zar, 1984).

Each independent variable was entered into the regression equation in order to determine

its unique contribution in relation to the other variables. The order in which the

independent variables were entered into the equation has no impact on the outcome

because each variable is treated as though it were the last variable to be entered. The

stepwise regression procedure selects the strongest independent variable in the first stage

and at each new stage, the next most significant variable is added to the equation. The

results of the stepwise regression are presented in Table 7.20.


Table 7.20 Presents the result of stepwise multiple regression analysis for the average of the biggest thunderstorm rainfall amounts (n=152 ).

Step Multiple R Variance Number of

Numbe Predictor Variable R Square Added in F Value Variable in the

%

1

2

3

4

Total

Proximity to Sea

Landuse

Aspect

Elevation

0.607

0.735

0.810

0.835

0.369

0.54

0.656

0.697

37

17

12

4

70

87.62

42.3

24.5

22.3

1

2

3

4

* All F values are significant at 0.0001 level

The stepwise regression procedure automatically selected the strongest independent

variable (proximity to sea). At the first stage, and at each subsequent stage a new variable

was added to the equation in the order in which they increased variance (up to 70 per cent)

in thunderstorm rainfall amounts. Using a F test, statistically significant variables were

determined at less than 0.001 level of significance.

The rank ordering of variables in terms of their predictive strength for thunderstorm

rainfall is: distance to the sea; landuse pattern of the region; aspect classes; and the

elevation of rainfall stations. Generally, the following results were found in this analysis:

1) Despite the presence of variations in maximum thunderstorm rainfall amounts in

coastal areas (see Figure 7.5 (a)), the correlation coefficient between thunderstorm rainfall

and the distance from the sea is quite high (r2 = 0.37), indicating that proximity to the sea

is the main predictor of thunderstorm rainfall distribution in the region.

2) Although the urban area does not appear to be the best predictor of thunderstorm

rainfall amounts, it increases the variance added in the model significantly, (17 per cent).

This suggests that there is a strong difference between urban ( C B D , U R B and U R T ) and

non-urban ( R U O , R U S and T N P ) areas in the amount of rainfall during major

thunderstorms.

3) Exposure to rain-bearing winds (particularly the east and west aspects), appears

to be the most important topographic factor in explaining statistically some of the

distribution of thunderstorm rainfall in the region. It increases about 12 per cent of

variance in the model.

4) The elevation of the study area is the final important physiographic parameter in

explaining the spatial distribution of thunderstorm rainfall amount (about 4 per cent). It


can therefore be concluded that the spot elevation does not appear to be the only

topographic feature affecting thunderstorm rainfall distribution over all of the region and

more likely the amount of thunderstorm rainfall does not increase with elevation, as it was

shown in section 7.3.

In brief, these findings support the effects of different independent variables upon the

spatial distribution of thunderstorm rainfall (as a dependent variable) and together explain

about 70 per cent of the variance in thunderstorm rain. The remaining 30 per cent

unexplained variance may be attributed to the other parameters.

7.7.2.2 The Spatial Distribution of 2 Scores Over Sydney

The preceding stepwise analysis provides only summary statements for the entire study

area. Statistically, it summaries the relationships between thunderstorm rainfall and the

four above-mentioned independent variables. Therefore, it is impossible to distinguish

where, in the Sydney region, these relationships hold best.

However, Z scores, derived from the stepwise regression model, provide a means by which

the appropriateness of the model can be assessed spatially. (Berry and Marble, 1968; Shaw

and Wheeler, 1985). Z scores were calculated using the following formula:

x - x z =

SD

where x = observed thunderstorm rainfall values

x = mean of variable x

S D = standard deviation of variable x

z = obtained Z scores

A map was then prepared using GIS interpolation techniques to visualise the Z scores

spatially. The more positive the Z score the better the model fits for that site. For

generalisation purposes, five class intervals were used ranging from > -2 Z to < +2 (see

Figure 7.9).

The spatial distribution of Z scores suggests there are a considerable number of areas

above the mean (Z > 0). As Figure 7.9 shows, over the elevated areas, such as the Blue

Mountains, Illawarra Plateau and over a small part of the Southern Tablelands, Z values

are highly positive. Likewise, Z scores over the Metropolitan area, north of the Parramatta

River and over small areas in the centre of the Sydney region (one located between

Camden and Picton and another located in the northwest of Hornsby Plateau), show

positive spatial variations. These patterns of Z values may indicate that distribution of


thunderstorm rainfall is, in general, highly correlated positively to many of the parameters

in the stepwise regression.

The remainder of the study area, particularly the Hawkesbury - Nepean Valley and some

small pockets located in coastal areas, have negative Z values (Z < 0). These areas do not

fit the model well for one reason or another. Consequently, it is possible to examine only

those variations in the spatial pattern of potential thunderstorm rainfalls which have a close

relationship with physiographic parameters considered in this study. M o r e importantly, the

produced m a p may also provide valuable insights as follows:

1) establishing and modifying regional boundaries of thunderstorm rainfall

distribution in the Sydney region,

2) selecting unit areas in which to conduct field work,

3) and identifying additional independent variables to be included in future

investigations.

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7.8 Discussion

To date, most studies in the Greater Sydney region (for example: Colquhoun and

Shepherd, 1985; Speer and Geerts, 1994) have concentrated on thunderstorm activity over

a few days, examining the synoptic weather patterns which caused the thunderstorm

rainfalls. Although none have concentrated exclusively on the effect of the local

physiographic parameters upon the distribution of thunderstorm rainfall, they have already

acknowledged the importance of the Sydney 's climatic-environmental factors.

The present chapter has addressed this approach by examining the detailed spatial analyses

of thunderstorm rainfall (for the 6 biggest short-term events) with respect to important

physiographic parameters (elevation, aspect, proximity to the sea) and landuse patterns of

the region. These parameters were chosen according to the results of the spatial analyses in

Chapter 6. They were also selected because studies elsewhere indicated they might be

important in controlling the spatial distribution of thunderstorm rainfalls (see Chapter 2).

T w o different methods; GIS techniques, and statistical procedures have been used, one

following the other, to analyse the spatial data across the Sydney region. The information

provided using these techniques confirm and extent the results which have been found by

other researchers.

7.8.1 The Role of Coastal Area

It is evident from the results of this chapter that the coastal areas in the east of the Sydney

region receive much higher thunderstorm rainfall amounts than those located inland in

nearby high relief areas. Over the coastal areas, the following mechanisms are supposed to

be more importance in the controlling of the spatial distribution of thunderstorm rainfall.

It is possible that, meso-scale circulations in the lower troposphere over the coastal areas

develop in response to differential surface heating. This mechanism, in particular, between

the land and the adjacent sea, can cause convectional activity in response to differential

solar radiation during the day, depending on the geographic characteristics of each place

and on weather conditions. Gentry and Moore (1954) and L'hermitte (1974) stressed

mechanisms by which thunderstorm rainfall can increase along Florida's coastal areas. For

the N S W coasts, intense locally thunderstorm activity reflecting coastal influences has been

emphasised by several researchers.

Hobbs (1971), using harmonic analysis, investigated some spatial characteristics of the

rainfall regimes in north eastern N S W . The results indicated that there are four major

terrestrial determinants of the spatial variations in rainfall over the study area. The four

factors concerned were: distance from the coast; distance from the scarp; relief (the scarp)


and latitude. It appeared that from the four factors mentioned above, the most influential

was distance from the coast. Hobbs (1971) also mentioned that in summer - when

thunderstorms are the most important source of rainfall - although thunderstorms are

experienced over the Tablelands, they are more likely to be accentuated in coastal areas,

particularly when orographic influences operate on air streams with strong easterly

components. Also, Sumner (1983a) highlighted the importance of the coastal plains with

reference to both local convection and the role of sea-breeze fronts in the generation and

enhancement of meso-scale systems such as thunderstorms.

An alternative mechanism is the possibility that occasionally, some weather systems, for

example lows, are accompanied by convection activity enhanced by nearby seas or

evaporative heating processes over by advection at the coastline. In such circumstances,

onshore winds can supply the moisture to the thunderstorm systems evaporated from the

Tasman Sea (Speer and Geerts, 1994). It was also found by Reeder and Smith (1992) that

the coastal areas acts as a stationary convergence zone causing longer duration

thunderstorms. These thunderstorm systems can extend over the coastal areas with intense

rainfalls (James, 1992). However, it was found in the current research that, even when

rainfall from thunderstorms is general over the coastal areas there are still isolated centres

of high rainfall with sharp isohyet gradients.

In addition, thunderstorms which most often develop over the relatively high topography

west of the Sydney region (over the Blue Mountains) can move towards the east of the

region over the coastal areas (Matthews, 1993). Speer and Geerts (1994) supported the

findings of Matthews (see Chapter 2, section 2.6.2). They also found that, in a close

relation with synoptic systems (for example quasi-stationary or eastward moving troughs),

convective systems typically start around midday to the west of Sydney and reach the east

of Sydney in the afternoon to evening. It is therefore more likely that these systems follow

the sources of moisture, available mostly over the coast and nearby Tasman Sea which aids

the convective systems to produce higher rainfall totals over the coastal zones.

7.8.2 Impact of Topographic Factors

The results in this chapter also indicated that both aspect and elevation influence the

amounts of thunderstorm rainfall in the study area, particularly in high relief areas over the

Blue Mountains and the Illawarra Plateau. This occurs mainly because both factors have a

strong effect on the initiation of thunderstorms. The effect of Sydney's high lands on the

distribution of thunderstorm rainfall amount is most clearly seen on maps (figures 6.7, 6.8

and 7.1) showing the relationship between rainfall patterns and terrain height. There are

three possibilities to explain how the region's terrain is able to influence thunderstorm

rainfall so considerably.


Firstly, one of the most regular and predictable types of thunderstorm activity can occur in

warm seasons over the Blue Mountains (Gentilli, 1971). The daily heating of the hillsides

generates warm up-slope winds which continue rising after reaching the top of the ridges

and trigger deep vertical convection (Maine, 1962). The thunderstorm rainfall patterns

over the Blue Mountains throughout the warm months may be dominated by this

mechanism. More recently, using data recorded by the Lightning Position Tracking System

(see Chapter 2) Laudet et al., (1994) found that the spatial distribution of lightning

(associated with thunderstorms) is closely related to the topography of the region. Their

results support the concept that elevation is a very important physiographic parameter in

controlling thunderstorm occurrence. The relatively high rainfall over parts of the Blue

Mountains from thunderstorms which was clearly shown in the results in Chapter 6 and the

current chapter, may be explained by the above-mentioned mechanism.

Secondly, convection systems can occasionally be developed over the Tasman Sea (Bureau

of Meteorology, 1989) during unstable conditions. These systems may move toward the

west of the region, and as a result, they may be cut off by the elevated terrains due to an

air-mass modification effect. Low-level air may be scavenged of its water by drops falling

from a seeder cloud above. It is likely that as the air descends beyond the high elevated

areas it is dry and cannot restore its water vapour by evaporation from the Tasman Sea,

therefore, without the low-level moisture, convective precipitation would be suppressed.

In fact, this mechanism may explain some of the thunderstorms with high rainfalls over

parts of the Blue Mountains (for example, Katoomba), which is another sign of orographic

control upon the distribution of thunderstorm rainfalls over the region.

Finally, topographic units which are located near the coast have an extra influence upon

thunderstorm rainfall amounts. For example, places along the Illawarra Plateau or Hornsby

Plateau experience very high rainfalls from thunderstorms, illustrating the effect of

elevation and exposure on wind directions. It is more likely that the height and exposures

of these topographic units to the Tasman Sea acts as a barrier to thunderstorms moving

from the east, and isolates coastal areas from those to the west of the plateau. For

example, Shepherd and Colquhoun (1985) studied the meteorological aspects of an

extraordinary flash flood event (17-19 February 1984) near Dapto just south of

Wollongong. They found that a trough had moved slowly from east to west over the

Illawarra area and formed several convective cells. These systems produced maximised

rainfalls during the event. In such a situation, the orographic lifting mechanism clearly

contributed to the short-duration heavy rainfalls along the escarpment. While an

extraordinary amount of rain (in excess of 200 m m ) fell over the region in a band from

Stanwell Tops to Jervis Bay, the heaviest point rainfall recorded was 803 m m at

Wongawilli located along the escarpment facing the east. This topographic effect causes

CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters L9A

different rainfall distribution patterns in the region, as is clearly evident from the results of rthe current chapter. The Illawarra Plateau provides an ideal example of thunderstorms

producing more rain parallel to the inland and coast-lines.

How important the above-mentioned mechanisms are, however, can not be clearly

identified. This is because in mountainous areas, for example over the Blue Mountains,

thunderstorm rainfall distribution patterns are very complicated, showing strong

differences over short distances. In contrast with the Blue Mountains or the Illawarra

Plateau, over the Southern Tablelands (located on the south-west of the study area) the

results showed that there is a general decrease in the vigour of thunderstorm rainfalls at all

seasons. This occurs because, a large number of local factors may reduce the amounts of

rainfall received from thunderstorms, and most of these local factors vary greatly with

relief. They include elevation, steepness of slope, relief forms which cause convergence or

divergence of air streams, and aspects, that is, exposure to the rain-bringing winds. A s it is

evident from the aspect map (Figure 7.3), over the Southern Tablelands - where a

relatively flat aspect dominates - topography may not strongly affect the distribution of

thunderstorm rainfall in the region.

7.8.3 Effect of Landuse on Rainfall Distribution

Results of the various analyses may finally lead to the conclusion that different landuse

patterns, for example, residential or C B D areas, are able to affect the spatial distribution of

thunderstorm rainfalls. Evidence of a localised increase in total rainfall from thunderstorms

in spring and summer, over the C B D and generally over the Metropolitan area, can be seen

from figures 6.7 and 6.8 in Chapter 6. This increase was found stronger for the biggest

thunderstorm rainfall events (Figure 7.1). Statistical tests, consisting of t-tests and a

stepwise analysis - which were applied for the point data sets - confirmed the assumption

that in the Sydney region, the effect of 'built-up areas' upon the distribution of

thunderstorm rainfall is real.

More recently, Speer and Geerts (1994) gave examples of high rainfall from thunderstorms

in the study of flash-floods. Radar images, taken from the three storm events (namely for

the 9 March 1989; 10th February 1990; and 2nd April 1992 at the time of the flash-flood

rainfall), indicated that maximum rainfall amounts of more than 100 m m occurred mostly

over the Metropolitan area. This study found that such storms are most c o m m o n in the

summer months in the Sydney Metropolitan area, during the afternoon or evening hours.

O n the basis of these findings, some of the physical-environmental elements of the urban

area appear to be important factors in affecting the amount of thunderstorm rainfall.

SEVEN Thunderstorm Rainfall and Physiographic Parameters 121

It is more likely that urban areas can affect incoming solar radiation changing albedo rates

and heating processes. This happens because the materials used in the City environment,

such as paved surfaces and the multi-faceted nature of the rough urban surface, not only

increase the absorption of heat energy, but also increase heat storage. The results of

investigators such as McGrath (1971) and K e m p and Armstrong (1972) indicate that

generally there is a considerable difference in temperatures between the City and the

outlying rural areas in the Sydney region. The development of a 'heat island' may enhance

vertical motion of air over the City and, as a result, enhance the subsequent convectional

thunderstorm rainfalls. Although no measured data for Sydney's heat island exists,

particularly for over a long time-span, experimental studies by Fitzpatrick and Armstrong

(1973) and Kalma et al. (1973) confirmed that, in the Sydney region, there is a great

difference between urban and non-urban areas in producing artificial energy. The high

spatial variation in artificial heat generation during the day in summer may help the

production of a heat island over the City. This effect can be increased by the high density

of buildings within the City centre ( C B D ) and can create a heat island with greater

cloudiness and higher rainfall.

It is also possible that increased suspended particles in Sydney's atmosphere which cause

pollution, indirectly increase rainfall amounts during thunderstorms. Linacre and Edgar

(1972) give evidence on the atmospheric pollution of Sydney which can be caused by

urban development. There are different sources of pollutants emitted into Sydney's

atmosphere. Industrial and commercial activities, including motor vehicles are important

sources of particle emission (Carras and Johnson, 1982). It is likely that, under calm

weather conditions, urban aerosols such as chemical materials (for example, nitrogen

oxides and hydrocarbons) may act as nuclei or ice nuclei materials and therefore, help to

induce cloud condensation in the atmosphere of Sydney. Despite intensive efforts devoted

to the understanding of Sydney's atmospheric environment in the past (for example,

Taylor, 1992), the role of pollutants upon thunderstorm rainfall remains uncertain.

However, the present thesis shows that such a relationship may exist.

Moreover, the surface roughness of the CBD by way of tall buildings interspersed with

roadways may modify the thunderstorm rainfall distribution to some degree. These

structures contrast with the ground cover of surrounding rural areas, such as forests

(TNP) and open rural areas (RUO), and they can produce local differences in Sydney's

thunderstorm rainfall distribution patterns. Also, the aerodynamic roughness of Sydney's

Metropolitan structure may enhance the development of thunderstorm activity. Generally,

the peak in maximum thunderstorm rainfall over the central part of Sydney is evidence of

the marked impact of the urban area upon rainfall processes. This mechanism is responsible

for 17 per cent of variance in thunderstorm rainfall (Table 7.19).



A "climatologically oriented" GIS was utilised to store, manipulate and display topo-

climate data related to Sydney's physical environment. The GIS provided an effective way

of displaying and manipulating the spatial data. Research presented in this chapter also

investigated alternative ways in which the GIS can provide visual support to the analysis of

rainfall distribution in relation to the physiographic parameters of the Sydney region. The

rainfall data (averaged from the six biggest thunderstorm rainfall events) was, therefore,

the fundamental skeleton over which other information, such as physiographic data, has

been performed.

The GIS technique was then followed by a statistical procedure to verify correlations

found between thunderstorm rainfall distribution and all independent variables. The results

obtained can be closely linked to the major physiographic parameters of the Sydney region

which were considered in this study. Analysing the available data, the following

conclusions can be deduced.

1) Increases can be seen in thunderstorm rainfall when the distance from the coast

decreases. Clearly, rainfall increases with proximity to the sea, but there are considerable

variations in rainfall distribution along the coastal areas.

2) The urban area can affect or enhance the development of thunderstorms

particularly over the Metropolitan area. As a result, it can increase thunderstorm rainfall

amounts. The urban maximum results from increases in temperature, humidity, turbulence

and the number of condensation and ice-nuclei substances. Such a physical environment

can considerably influence the spatial distribution of thunderstorm rainfall amounts, hence,

indicating the reality of the urban effects. The strongest effect of urbanisation can be seen

over the C B D and the eastern part of the City near the coast.

3) Rainfall increases with elevation, but exposure to rain-bearing directions (east

and west aspects) also seems to be important factors. Despite the effects of aspect, high-

elevated areas such as the Blue Mountains, the Illawarra Plateau and less importantly the

Hornsby Plateau, are seen to be the more significant topographic units affecting the

distribution of rainfall patterns. These areas are thus found to be subject to the highest

thunderstorm rainfall amounts.

As the percentages of variance from the step-wise regression method has indicated, there

is still considerable unexplained variance (about 30 per cent) which suggests that new

independent variables need to be incorporated in the models predicting the distribution of

thunderstorm rainfall. In fact, the existence of this unexplained variance illustrates that

Thunderstorm Rainfall and Physiographic Parameters 197

factors, other than those under consideration, may contribute to the thunderstorm rainfall

distribution over the Sydney region.

To sum up, although synoptic weather patterns, such as those which were highlighted in

the literature review, can introduce the relatively big and widespread thunderstorm rainfall

events in the Sydney region, most often, as the results of the current chapter have

indicated, there are more than one or two physical-environmental factors which

simultaneously control the spatial distribution of the maximum thunderstorm rainfall

amounts quite significantly.

CHAPTER FIGHT Conclusions 128

CHAPTER 8

CONCLUSIONS

8.1 Introduction

This chapter summarises the findings and outcomes of this thesis. The results, given in

detail in chapters 3 to 7, are presented briefly in the next section. The limitations of the

data and techniques are outlined in section 3. In section 4 the relevant implications of the

research are presented. Suggestions for future work are outlined in section 5.

8.2 Major Conclusions of the Thesis

Historically, the organisation of data concerning thunderstorm rainfall in time and space

has been an important objective for many climatologists. During the last few decades, the

Sydney region has experienced some of the heaviest rainfalls on record from

thunderstorms. The consequent flooding has caused considerable property damage and the

loss of human life (Riley et al., 1985; Colquhoun and Shepherd, 1985; Colls, 1991). The

structure and causes of Sydney's thunderstorms has been well studied (Colquhoun, 1972

and 1994; Speer and Geerts, 1994). Severe thunderstorms can be generated by active

fronts, troughs or squall-lines (Morgan, 1979b), by local disturbances controlled by

climatic-environmental factors (Linacre and Hobbs, 1977), or by supercells (Dickins,

1994). Often thunderstorms originate in response to the daily heating of hill slopes

(Gentilli, 1971). The mountain ranges in the west of the Sydney area can set off

thunderstorms in potentially unstable airflows, and these thunderstorms can then drift over

the adjacent lowlands and coast (Foreman and Rigby, 1990). Local convection in the

Sydney region as a result of surface heating and within warm, humid and unstable air

masses can generate thunderstorms that produce light precipitation (Mitchell and Griffiths,

1993; Batt, 1994). These thunderstorms are common and comprise nearly 95 per cent of

all thunderstorms in the region. O n average they generally produce less than 11 m m of

rainfall. Although these kinds of thunderstorms are normally small in size, they can contain

vigorous parcels of rising and descending air occasionally accompanied by intense rainfalls

(Morgan, 1979a). In coastal areas the presence of the Tasman Sea has a great influence on

the occurrence of precipitation, because it can furnish and sustain a plentiful supply of

moisture. Sea-breeze circulation (Linacre, 1992) along with surface heating of land can

enhance convective activity over coastal areas (Clarke, 1955 and 1960; Drake, 1982; Abbs

CHAPTER EIGHT Conclusions 122

and Physick, 1992). Individual events linked to these factors and producing heavy rainfalls

have been described in the thesis.

Thunderstorm rainfalls are thus neither spatially nor temporally uniform. The prime

objective of this thesis was to examine this variability and relate it to the main climatic

characteristics and physiography of the Sydney region. Four sets of results were obtained.

First, the general behaviour of thunderstorm rainfall (frequency and amount) were

characterised over time (at yearly, seasonal, monthly and diurnal levels) using measures of

central tendency and dispersion. Results, presented in Chapter 3, indicate that

thunderstorms have marked diurnal and seasonal variations, and are most frequent in the

spring (October to November) and summer (January to March) during the late afternoon

and early evening. Thunderstorms are most frequent over the west of the region and least

frequent over the lowland interior. Stations which are located near the ocean receive more

thunderstorm rainfall than those located inland, even near the Blue Mountains. Second

more detailed associations exist between thunderstorm rainfall and climatic factors such as

air and sea temperatures, and air humidity. Results presented in Chapter 4 indicate that

there are casual relationships between these climatic variables and thunderstorms,

particularly for coastal stations. This association becomes weaker as one moves inland.

Specifically, the amount of thunderstorm rainfall is affected by sea-surface temperature,

the effects of unequal heating of land surfaces and the availability of moisture in the

atmosphere. Third, the patterns of spatial variation and distribution of thunderstorm

rainfall during the thundery months of the year (October to March) were examined.

Results are presented in Chapter 6 using data from 191 stations in the Sydney region, for

the 34-year period 1960 to 1993. The probability distribution of thunderstorm rainfall

amounts was shown to be described well using the g a m m a distribution. This technique

provided two measures (beta and alpha values) which described the patterns of

thunderstorm rainfall in the Sydney region. In addition, a GIS method was used to

characterise the spatial pattern of thunderstorm rainfall over the Sydney region. The

distribution of mean thunderstorm rainfall in the Sydney region, reflects topographic,

coastal and urban effects. Thunderstorm rainfall increases with proximity to the ocean, in

the vicinity of elevated topography over the Illawarra Plateau and Blue Mountains, and

over built-up metropolitan areas, especially the C B D and eastern suburbs of the city. In the

latter case, rainfall may be enhanced by urban heating, increased surface roughness and air

pollution. These urban areas are more subject to flash flooding. Finally, because the

physical environment affects the spatial distribution of thunderstorm rainfall, more detailed

analysis was undertaken using a "climatologically oriented" GIS in conjunction with a

stepwise regression technique. These results are presented in Chapter 7. While synoptic

conditions initiate thunderstorm weather systems, physiographic parameters considerably

influence the spatial distribution of the resulting rainfall amounts.


8.3 Limitations of the Study

8.3 1 Limitations of Data Used

During the research considerable time was spent in collecting data from different relevant

sources and then summarising these. The main limitations of these data can be summarised

as follows:

1) The distribution of the stations recording thunder reflects the distribution of

major population clusters, older suburbs, dams, post offices and railway stations. This

sampling network is spatially uneven and forms only a first approximation of the

distribution of any rainfall. In addition, some records were sporadic over time. To

overcome this limitation, only the best and longest records were initially chosen to study

the generalised distribution of thunderstorm rainfall over time.

2) The number of daily thunderstorm observations for each particular station was

not the same, because some stations reported every three hours and some only once or

twice a day. To overcome this problem, thunderstorm occurrence was studied on a daily

basis. If a station recorded at least one observation of thunder on a particular day, then all

of the rainfall for that day was considered as being thunderstorm derived. All rainfall

values used in this study should thus be considered maximum amounts.

3) Some rainfall stations did not have a complete record for the 34 years under

consideration. Therefore, the period of study may not be represented adequately for some

parts of the region. While no temporal constraints appear to be defined in the literature for

rainfall records (Alexander, 1945; Longley, 1952 and 1974), in this study, only records of

ten years or more were utilised.

4) Some problems were encountered with missing data. Generally, both thunder-

recording and rainfall stations with extensive missing records have been removed from the

data base. N o attempt was made to compensate for missing values in the remaining

records. There is, therefore, a possibility of error being introduced into the data set

because of missing data.

5) The sea-surface temperature data were recorded weekly. Therefore, it was not

possible to calculate and analyse the associations between thunderstorm data and sea

surface temperature on a daily basis.

6) Generally, the north-east and south-west areas of the Sydney region suffer from a

poor coverage of rain-gauges, leading to an incomplete picture of thunderstorm rainfall.


This uneven distribution of rainfall stations, particularly in mountainous areas, together

with the equally problematic issue of missing data, should be satisfactorily dealt with in

future research in order to understand accurately the distribution of thunderstorm rainfall.

One of the best ways to overcome this problem is to increase the density of the rain-gauge

network in the Greater Sydney Region.

8.3.2 Limitations of Techniques Applied

GIS was found to be a useful tool for data analysis and display of climatic variables. GIS

has many advantages. For example, it has the capability of combining large amounts of

spatial data in varied formats. Furthermore, GIS was very helpful in extracting the various

study area characteristics, derived from topographic and landuse maps. While the simple

functions of GIS provided the means for handling massive data files, GIS also allowed

multivariate analyses of the rainfall data and associated physiographic variables of both a

spatial and non-spatial nature. However, there are some disadvantages in using the S P A N S

GIS technique for climatic purposes:

1) The major limitation to GIS, in climatic modelling, is the current inability of the

S P A N S system to incorporate temporal change. The application of dynamic GIS in this

field requires specialised analytical tools. The author hopes to carry out further study on

this topic.

2) The raster-based GIS is limited by the fact that the minimum resolution of a

raster map has to be decided when the map is first created. This resolution has to be set

small enough in order not to lose spatial information; however this sometimes leads to very

large grids where interpolation of variables may occur without much underlying data

control. Currently the interpolation functions in S P A N S GIS are primitive. A broader

range of interpolation algorithms is required to overcome this problem. For example, a

very flexible TIN function is needed to create smoother isohyets.

3) Given that our future climatic data and our future modelling requirements will

become more complex, it is necessary that the S P A N S GIS can include new functions

which will permit improved transfer of data, more efficient storage, and more flexibility

and faster modelling capabilities. If these improvements are made, the S P A N S GIS can be

more widely used in research of climatic data.

8.4 Advantages and Implications of the Study

While researchers have examined many different aspects of thunderstorm activity in the

Sydney region over the last four decades, three points have not been thoroughly

considered. These are:


1) the general characteristics of the thunderstorm rainfall distribution over time;

2) the causes of variation in thunderstorm rainfalls across the region; and

3) the spatial variability and distribution of thunderstorm rainfall over time.

These aspects of thunderstorm rainfall climatology of the Sydney region were examined in

the present thesis.

8.4.1 Advantages of the Study

The results of the above mentioned aims are presented in chapters 3 to 7. Thunderstorm

activity can be viewed not only in terms of climatic variables, but also in terms of

physiographic parameters which control the variation and distribution of thunderstorm

rainfall. Three important techniques used in defining these relationships may have wider

applicability in climate studies:

1) First, and of great importance, the gamma method offers summarised

mathematical information about the variability of rainfall amounts from thunderstorms.

This provides a convenient means of estimating the probabilities of receiving rainfall based

on point observations, showing consistent spatial patterns. These patterns can be

realistically compared with the real rainfall data which can then be linked to the

mechanisms creating and controlling thunderstorms, such as synoptic systems and

physiographic parameters. The spatial distribution of alpha and beta values help in broad-

scale environmental planning and in establishing climatic regions where further detailed

analyses, such as time series, can be performed.

2) Second, GIS computer technology offers an excellent means of analysing

multivariate climatic-physiographic relationships. GIS can play a useful role in the analysis

of the spatial distribution of rainfalls from thunderstorms. GIS also offers modelling

capability of data from up to 19 map-layers. The application of GIS to the modelling of

thunderstorm rainfall potential based upon physiographic features is one of the important

outcomes of this thesis (see Chapter 5). M a n y climatologists (for example, Sajecki, 1991;

Brignall et al., 1991) believe that the S P A N S GIS system provides the logical framework

for complex analysis and mapping of such climatic data.

3) Finally, the stepwise multiple regression technique is a useful technique for

defining the relative importance of important climatic and physiographic factors influencing

thunderstorm rainfall. This procedure was used twice in the study. The most important

advantage of this technique is that it can be used to obtain the maximum degree of

explanation of a dependent variable from a composite of independent variables.


8.4.2 Implications of the Study

Information obtained about the distribution of thunderstorm rainfall in the Sydney region

in this thesis has some important management consequences as follows:

First, the results of this study explain, simply, the times when thunderstorm rainfall

should occur in different zones of the Sydney region. This was highlighted in Chapter 3

where the temporal distribution of thunderstorm rainfall was discussed. Such information

can be used by the State Emergency Service to narrow down the time of day when a

response to heavy precipitation during thunderstorms is most likely to be required.

Second, spatial fluctuations in thunderstorms impact differently on human activity

and development across the Sydney region. While isohyet maps contain very

comprehensive information about the distribution of rainfall over the region, the gamma

estimators (beta and alpha values) facilitate easy reading of the displayed probability values

of thunderstorm rainfall at any point in the study area. Urbanisation is progressively

increasing the amount of run-off during short rainfall events and leading to flash flooding.

Areas where this is most common can be targeted by planners and engineers using these

gamma maps and their associated Z-Score values.

Third, results from the study should make planners aware of the increased risk of

heavy rain from thunderstorms induced by heavy industrial, residential and commercial

development. The State Planning Authority of N S W (1970 and 1994) believes that within

25 years, more than 70 per cent of the people of N S W will live in the Greater Sydney

Region. This will only exacerbate the chance and impact of heavy thunderstorm rainfall. It

is imperative that detailed data on the urban heat island be acquired in order to understand

how intense urbanisation affects the rainfall process. Perhaps a project like M E T R O M E X

(Changnon et al., 1971) should be conducted in the Sydney region or the Metropolitan Air

Quality Study ( M A Q S ) extended in scope to cover the flooding risk from intense

thunderstorms.

Fourth, the spatial variations in urban rainfall are more important than those in rural

areas, simply because more people will be affected. Adequate information on thunderstorm

rainfall within Sydney can be of major importance in the planning and design of drainage

systems, outdoor social and recreational activities, intra-city transport systems, watershed

protection, and finally flood prevention. It is hoped that the recognition of climatological

landuse types defined in the thesis can be of assistance in this planning. Urban

climatologists should work closely with the landuse planners, zoning authorities,

architects, and hydrologists to ensure that heat islands are reduced as much as possible;

CHAPTER EIGHT Conclusions 20A

that vegetative areas are interspersed throughout the Metropolitan area; that air pollution

is reduced at its source, particularly over the C B D and nearby coast; and that hydrologic

problems are reduced by increasing absorptive areas, providing ample storage in streams

and lakes, and by constructing more channels to handle the rate of run-off. This planning is

needed particularly in those areas which have been shown by this study to have a high

potential for heavy amounts of thunderstorm rainfall and subsequent flash flooding.

8.5 Suggestions for Future Studies

This thesis is not inclusive. Important areas of further research should include the

following:

1) the regional development of synoptic weather maps for the Greater Sydney

Region that can be linked to thunderstorm development. This would allow rainfall

predictions to be made in advance of the progress of a thunderstorm;

2) the application of the gamma distribution to other types of synoptic patterns

generating heavy rainfall in the Sydney region. The present study did not include all

rainfalls produced from east coast lows which are a major factor in generating heavy

rainfall in the Sydney region (Bryant, 1991);

3) the techniques used in the study should be applied to a wider area of the New

South Wales coast where other heavy thunderstorm rainfall events are know to occur; and

4) sophisticated time series models should be applied to the data to better

characterise temporal change in thunderstorm rainfalls. The application of advanced

models, such as the Auto-Regressive Integrated Moving Averages ( A R T M A ) technique,

may help to construct better thunderstorm rainfall prediction models over time.

8.6 Concluding Remarks

Although the problems caused by thunderstorms are many and varied and the solutions do

not seem simple, this current thesis has been built upon solid research provided over the

last 25 years by meteorologists and climatologists (for example Hobbs, 1971, 1972, 1995;

Colquhoun et al., 1985; Bryant, 1991; Williams, 1991; Linacre, 1992; Griffiths et al.,

1993; Matthews, 1993; Colquhoun, 1994; Laudet et al., 1994; Speer and Geerts, 1994;

Batt et al., 1995; and Matthews and Geerts, 1995). It is hoped that information and models

contained within this thesis can also contribute to this understanding of thunderstorm

rainfall. In terms of the spatial variation and distribution of thunderstorm rainfalls, most

specifically from the analyses of the available data, the following conclusions can also be

deduced:


1) The distribution of thunderstorms over the Sydney region varies from year-to-

year and reflects the overall impacts of synoptic patterns and physiographic parameters. In

general, the probability of receiving heavy precipitation (gamma estimators) from

thunderstorms is greatest in the east of the Sydney region, particularly over the

Metropolitan area, in spring and summer. This rainfall decreases towards the western

suburbs and then increases again over the Blue Mountains. The decrease is most marked

over the Southern Tableland.

2) The proximity to the Tasman Sea is the most important physiographic parameters

controlling the spatial patterns in rainfall from thunderstorms. Generally, areas near the

coast play a major part in the 'turning-on' of thunderstorms producing heavy rainfalls.

3) Spatial rainfall variations are associated with thermodynamic and/or kinematic

characteristics of the landuse patterns. Intense urbanisation results in the heaviest rainfalls.

4) Thunderstorm rainfall intensity is also dependent on aspect and elevation.

Generally areas of higher relief receive more rainfall.

The information obtained here can be used in many areas such as urban planning, design of

rain-gauge network, flash flood control programs and emergency response management.

Hopefully the study will spur other research into the identification and explanation of

thunderstorm rainfall patterns along the east coast of Australia.

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A List of Computer Programs 210.

APPENDIX A

List of Computer Programs

A.l Computer Program Number 1

To find the common thunderstorm-days in the Sydney region the Computer Program

N u m b e r 1 was used (see Chapter 4). This program was written using a C1"4" programming

language.

#include <stdio.h> long lines (FILE *file); main()

long int thun[1000][12]; int nil, nl2, nl3, nl4, nl5, n!6, nl7, nl8; int nl9, nllO, nlll, nll2, il, i2, i3, i4, i5, i6, i7, i8, i9; int ilO, ill, il2, i, j, k, m, n; long int xl, x2, x3, x4, x5, x6, x7, x8, x9, xlO, xll, xl2; FILE *fpl, *fp2, *fp3, *fp4, *fp5, *fp6, *fp7, *fp8, *fp9; FILE*fplO, *fpll, *fpl2; fpl =fopen("a:thul.txt", V ) ; fp2 = fopen("a:thu2.txt", "r"); fp3 =fopen(,,a:thu3.txt", "r"); fp4 = fopen("a:thu4.txt", V ) ; fp5 = fopen("a:thu5.txt", "r"); fp6 = fopen("a:thu6.txt", "r"); fp7 = fopen("a:thu7.txt", "r"); Q)8 = fopen("a:thu8.txt", "r"); fp9 = fopen("a:thu9.txt", "r"); fplO= fopen("a:thul0.txt", "r"); fpll= fopen("a:thull.txt", "r"); fpl2= fopen("a:thul2.txt", "r"); nll = lines(Q)l); n!2 = lines(fi)2); nl3 = lines(Q)3); nl4 = lines(^)4); nl5 = lines(fp5); nl6 = lines(lp6); nl7 = lines(fp7); nl8 = lines(lp8); nl9 = lines(fp9); nllO= lines(fpl0); nlll=lines(fpll); nll2= lines(fpl2); rewind(fpl) rewind(jfp2) rewind(fp3) rewind(Q)4) rewind(jQ)5) rewind(fp6) rewind(fp7)

APPENDIX A List of Computer Programs 211

rewind(fp8); rewind(fp9); rewind(fplO); rewind(fpll); rewind(fpl2); k=0; for(il=0; iKnll; ++il)

{ fscanf(fpl,"%ld\n",&xl); thun[il][0]=xl;

} for (il=nll; iKIOOO; ++il)

wun[il][0]=0; for(i2=0; i2<nl2; ++i2)

{ fscanf(fp2,"%ld\n",&x2); thun[i2][l]=x2;

} for (i2=nl2; i2<1000; ++i2)

thun[i2][l]=0; for(i3=0; i3<nl3; ++i3)

{ fscanf(fp3,"%ld\n",&x3); thun[i3][2]=x3;

} for (i3=nl3; i3<1000; ++i3)

thun[i3][2]=0; for(i4=0; i4<nl4; ++i4)


} for (i4=nl4; i4<1000; ++i4)

thun[i4][3]=0; for(i5=0; i5<nl5; ++i5)


} for (i5=nl5; i5<1000; ++i5)

thun[i5][4]=0; for(i6=0; 16<nl6; ++i6)


} for (i6=nl6; i6<1000; ++i6)

thun[i6][5]=0; for(i7=0; i7<nl7; ++i7)

{ fscanf(fp7,"%ld\n",&x7); thun[i71[6]=x7;

} for(i7=nl7;i7<1000;++i7)

thun[i7][6]=0; for(i8=0; i8<nl8; ++i8)

I

APPENDIX A List of Computer Programs 212.

fscanf(fp8,',%ld\n",&x8); thun[i8][7]=x8;

} for (i8=nl8; i8<1000; ++i8)

thun[i8][7]=0; for(i9=0; i9<nl9; ++i9)


} for (i9=nl9; i9<1000; ++i9)

thun[i9][8]=0; for(ilO=0; il0<nll0; -H-ilO)

{ fscanf(fplO,"%ld\n",&xlO); thun[il0][9]=xl0;

} for (ilO=nllO; il0<1000; ++ilO)

thun[il0][9]=0; for(ill=0;ill<nlll;++ill)

{ fscanf(fpl l,"%ld\n",&xl 1); thun[ill][10]=xll;

> for (ill=nlll; ilKlOOO; ++ill)

thun[ill][10]=0; for(il2=0; il2<nll2; ++il2)

{ fscanf(fpl2,"%ld\n",&xl2); thun[il2][ll]=xl2;

} for (il2=nll2; il2<1000; ++il2)

thun[il2][ll]=0; for(i=0; i<12; ++i) for(n=l; n<12; ++n)

{ for(j=0;j<1000;++j) for(m=0; m<1000; ++m)

{ if(thun[j][i] != 0 & & thun[j][i]==thun[m][n]) k=k+l; if(thun[j] [i]<thun[m] [n]) m=1000;

} printf("k%ld,%ld=%d\n",thun[0] [i] ,thun[0] [n],k); k=0;

} return 0;

/* */

long lines (FILE *file) long nl, c; nl = 0; while ((c = getc(file)) != EOF) if(c = V )

++nl; return nl;

DATA EXTRACTION

A.2 Computer Program Number 2 (C1^)

Due to the large volume of thunderstorm rainfall data recorded on a daily basis, it was

very difficult and time consuming to extract the specified data sets. This was due to the data-set

files from more than 350 rainfall stations located in the Sydney region operated by the Bureau of

Meteorology during the last 34 years (1960 to 1993). There were also more than 100 stations

which have been covered by the Sydney Water. These data sets were in different formats, so it

was impossible to extract the data manually, and the possibility of errors were very high. To

overcome these difficulties three computer programs were developed, each for a specific format,

using three different computer programming languages as follows:

This computer program was written to extract the daily rainfall events from the Sydney

Water data sets. Each rainfall station data was in ASCI format.

#include <stdio.h> #include <stdlib.h> #include <math.h> int lines (FILE *file); main(int argc, char *argv[]) {

FILE *fpl, *fp2, *lp3; char c[500], cc; intij, k, kk, nil, nl2;

float x, y, z, ml, m2, m3, m4, m5; if((fpl = fopen(*++argv, "r"))==NULL)

{ printf("File \"%s\" does not exist.\n", *argv); printf("The usage is: Y'Alil inputfile outputfile\"\n"); return 1;

} else if

((fp3 = fopen(*++argv, "w")) == NULL) {

printf("File \"%s\" does not exist.\n", *argv); printffThe usage is: V'Alil inputfile outputfileVW); return 1;

} fp2 = fopen("match.txt", "r"); nil = lines (fpl); nl2 = lines (fp2); rewind (fpl); rewind (fp2); prinrf("nll=%d nl2=%d\n", nll,nl2); for (k=0; k<nl2; k++) {

fscanf (fp2, "%f % f %f\n", &x, &y, &z); /•printf ("%f % f %nn", x, y, z);*/ = = ^ = = = _

for (kk=0; kk<nll; kk++) " {

fscanf (fpl, "%f %f %f %f %f\n", &ml, &m2, &m3, &m4, &m5); /*printf ("%f %f %f %f %f\n", ml, m2, m3, m4, m5);*/ if (m3==z & & m 2 = y & & ml==x) { fprintf (fp3, "%2.0f%2.0f %2.0f %. lf\t %.0f\nM, ml, m2, m3, m4, m5); printf ("%2.0f %2.0f %2.0f %.lf\t%.0f\n", ml, m2, m3, m4, m5); kk = nll;

} > rewind (fpl);

} rewind (fpl);

} /* */

/* This function counts the number of lines in a file */ int lines (FILE *file)

{ int c, nl;

nl = 0; while ((c = getc(file)) != EOF) if(c = V )

++nl; return nl;

A.3 Computer Program Number 3 (Fortran 77)


Water data sets. Each rainfall station data was not in ASCI format. This program is written in a

standard Fortran which can be used on mainframes or PCs. The program has two input files as

follows:

a file named "MATCH"; and

a file named "RAINFALL".

Every record of the MATCH consists of a date on which the value of rainfall is required.

The R A I N F A L L contains dates and their corresponding values of rainfall for several years. First

the program reads a data from the M A T C H file and then searches that date in the RAINFALL.

After finding the date from the RAINFALL, the value of rainfall which corresponds to this date

is also read and both the date and value of rainfall will be written in the output file called

O U T P U T . This procedure is repeated for all records of the M A T C H file.

dimension iday (400), month (400), isal (400) character*5 A A A open (unit =5, file= 'match.txt' ,status= 'old') open (unit = 6, file = 'match.out', status = 'unknown') open (uint = 7 . file = '566020', status = old')


open (uint = 8 , file = 'final', status = unknown) do 1100 i = 1,383 read (5, *) iday (i), month (i), isal (i) isal (i) = isal (i) + 1900 1100 continue

do 12 i = 1, 383 c print *, 'i = ',i

do 120 m = 1, 10 c print *, 'm= *,m

if(m. eq. 1) then nn=3 do 10 i i = 1, nn read (7,*)

c print *, 'i i- ,i i, m 10 continue

end if read (7,11) year

11 format (127X, i4) print *, *year=', year do 151 k = 1,4 read (7,*)

151 continue if (isal (i). eq. year) then do 121 j= 1,31

124 read (7, 122) irooz 122 format (9x, i3)

if (irooz.eq.iday (i)) go to 123 go to 124

123 if (month (i) .eq. 1) then backspace (unit = 7) read (7, 13) A A A

13 format (18x, A5) write (8, *) iday (i), month (i), isal (i),' \ A AA go to 1020 end if if (month (i). eq. 2) then backspace (unit = 7) read (7, 14) A A A

14 format (27x, A5) write (8, *) iday (i), month (i), isal (i),' ', A A A go to 1020 end if if (month (i). eq. 3) then backspace (unit = 7) read (7, 15) A A A

15 format (36x, A5) write (8, *) iday (i) month (i), isal (i),' ', A A A go to 1020 end if if (month (i) .eq. 10) then backspace (unit = 7) read (7, 16) A A A

16 format (lOOx, A5) write (8, *) iday (i), month (i), isal (i),' ', A A A go to 1020 end if if(month(i).eq. 11) then

APPENDIX A List of Computer Programs 216.

backspace (unit = 7) read (7, 17) A A A

17 format (108x,A5) write (8, *) iday (i), month (i), isal (i),' ' A A A go to 1020 end if if (month (i) .eq. 12) then backspace (unit = 7) read (7, 18) A A A

18 format (117X,A5) write (8, *) iday (i), month (i), isal (i),' ';AAA go to 1020 end if

121 continue else dol25j = l, 58 read (7, *)

125 continue end if

120 continue 1020 rewind (unit = 7) 12 continue

stop end

A.4 Computer Program Number 4 (Quick Basic)


Bureau of Meteorology data sets which are in a long ASCI format, in each rainfall station.

CLS 100 REM 110 REM ** initialisation **** 120 DIM nm(12), P(12, 31), PM(12), PMAX(12), NOP(12), dy(500), mon(500), year(500) 130 Y$ = "#######.#": X$ = " ##": V$ = "####.#": W$ = " ##" 140 Z$ =" M ## #### ###.#" 150 DATA 31,28,31 FOR i = 1 TO 3 READ nm(i) NEXTi DATA 31,30,31 FOR i = 10 TO 12 READ nm(i) NEXTi INPUT "ENTER INPUT MATCH FILE NAME:"; 12$ OPEN 12$ FOR INPUT AS #3 WHILE NOT EOF(3) K = K+1 nmatch = nmatch + 1 INPUT #3, daym, monm, yearm dy(K) = daym mon(K) = monm year(K) = yearm + 1900


' PRINT dy(k), mon(k), year(k) WEND 190 INPUT "ENTER INPUT FILE NAME:"; i$ 200 OPEN i$ FOR INPUT AS #1 INPUT "ENTER OUTPUT FILE NAME:"; 0$ OPEN 0$ FOR OUTPUT AS #2 otyp= 1 330 WHILE NOT EOF(l) 340 INPUT #1,A$ 365 SW=1 370 year = 1000 + VAL(MID$(A$, 1, 6)) 380 IF year MOD 4 = 0 THEN nm(2) = 29 ELSE nm(2) = 28 390 mon = VAL(MID$(A$, 7, 6)) 440 MAXP = -1 450 FOR i = 1 TO nm(mon) '460 P$ = MID$(A$, 13 +1 * 8, 8) P$ = MID$(A$, 5 + i * 8, 8)

470 IFP$ = " " THEN P(mon, i) =-1 ELSE P(mon, i) = VAL(P$) •480 PCD$ = MLD$(A$, 24 +1 * 6, 1) PRINT

490 IF P(mon, i) > MAXP THEN MAXP = P(mon, i) 500 NEXTi 510 PMAX(mon) = MAXP 520 IF otyp = 1 THEN GOSUB 770 ' printing the results in a file 560 WEND 570 CLOSE 580 END 590 REM ** SETTING UNAVAILABLE DATA ** 600 FOR i = mon+1 TO 12 610 PM(i) = -l:NOP(i) = -l:PMAX(i) = -l 620 FORJ=lT0 31 630 P(i, J) = -1 640 NEXT J 650 NEXTi •670 RETURN 770 REM ** PRINTING OUTPUT FILE *** 780 FOR i = 1 TO nm(mon) FOR c = 1 TO nmatch

IF year(c) o year THEN GOTO 999 IF mon(c) o mon THEN GOTO 999 IF dy(c) = i THEN PRINT #2, USING Z$; i; mon; year; P(mon, i) 'c = nmatch PRINT" year = "; year ELSE ENDIF

999 NEXT c 666 PRINT 800 NEXTi PRINT " it is working "; c 810 RETURN

A.5 Computer Program Number 5

This computer program was written in the C1"4" computer programming language

environment. The program calculates the horizontal distance between a rainfall station and the

average coastline in the study area. To run the program, three files should be entered after the

name of the program. One file contains the data of the stations; a second file for coastline data,

and a third file for the output.

#include "include.h" #include "function, h" #include "func.c" main(int argc, char *argv[])

{ int nil, nl2, i, ii; char sn[50]; float x, y, xl, yl, x2, y2, x3, y3, x4, y4, gl, g2, dist; float dxl, dyl, dx2, dy2, m, ml, m2, h; FILE *fl, *f2, *f3; printf ("\n"); printf ("\n"); printf ("This program calculate the distance between the horizontal^"); printf ("line from a station and the coastal line.\n"); printf ("\n"); printf ("\n"); printf ("To run the program three files should be entered after the \n"); printf ("name of the program. File contains the data of the stations,\n"); printf ("file contains the coastline data, and output file.\n"); printf ("\n"); printf ("\n"); if ((fl = fopen (*++argv, "r")) = NULL)

{ printf ("Program can't open file %s as input.\n", *argv); exit (0);

} if ((f2 = fopen (*++argv, "r")) == NULL) {

printf ("Program can't open file %s as input.\n", *argv); exit (0);

} if ((f3 = fopen (*++argv, "w")) = NULL) {

printf ("Program can't open file %s as output.\n", *argv); exit (0);

} printf ("argc = %d\n", argc); printf ("\n"); printf ("\n"); nil = lines (fl); nl2 = lines (f2); rewind (fl); rewind (f2); for (i=0; i<nll; i++)

{

= fscanf (fl, "%s %f %f %f % f %f\n", sn, &xl, &yl, &gl, &g2, &h);


x2 = xl+100; y2 = yi; fscanf (12, "%f %f\n", &x3, &y3); for (ii=0; ii<nl2-l; ii++)

{ fscanf (f2, "%f %f\n", &x4, &y4); dxl =x2-xl;

1 dyl=y2-yl; dx2 = x4-x3; dy2 = y4-y3; m = dxl*dy2-dyl*dx2; if(m!=0)

{ ml m2 x = y =

= xl*dyl-yl*dxl; = x3*dy2-y3*dx2; (dxl*m2-dx2*ml)/m; (dyl*m2-dy2*ml)/m;

if ((x>=x3 & & x<=x4 & & y>=y3 & & y<=y4 ||

{

line is %.3fm\n", sn, dist);

coastal line is %.3fm\n", sn, dist);

} else {

} }

} rewind (f2); } fclose (fl); fclose (f2); fclose (0); return (0);

}

x<=x3 & & x>=x4 & & y>=y3 & & y<=y4 || x>=x3 & & x<=x4 & & y<=y3 & & y>=y4 || x<=x3 & & x>=x4 & & y<=y3 & & y>=y4) & & y<=yl)

dist = distance (xl, yl, x, y); printf ("The distance between Station %s and the coastal

fprintf (f3, "The distance between Station %s and the

ii = nl2-l; rewind (f2);

x3 =x4; y3=y4;

**********************************

rstorm

APPENDIX B

Thunderstorm Rainfall Data

B.l Common thunderstorm-days in the Sydney region between 12 thunder-recording

stations

* The numbers in the table are representative of the thunder-recording stations.

1 = Richmond

2 = Katoomba

3 = Parramatta

4 = Prospect D a m

5 = Sydney Regional Office

6 = Liverpool

7 = Bankstown

8 = Sydney Airport

9 = Lucas Heights

10 = Camden Airport

11 = Wollongong University

12 = Bowral

Date is represented as Year, Month and Day.

Thunderstorm Rainfall Data

Table 4.2 C o m m o n thunderstorm-days in Sydney region.

Row

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

Date

19600103

19600129

19600209

19600214

19600307

19600314

19600322

19600402

19600420

19600421

19600518

19600519

19600520

19600916

19600917

19600924

19601004

19601024

19601025

19601026

19601030

19601031

19601103

19601118

19601121

19601124

19601126

19601203

19601212

19601214

19601215

19601216

19610101

19610112

19610113

19610131

19610205

19610206

19610207

19610208

19610209

19610223

19610228

19610315

19610405

19610413

19610503

19610604

19610608

19610821

19610823

19610824

19610826

19610829

19610905

19611011

19611023

19611101

19611102

19611103

19611104

19611107

19611108

19611114

19611115

19611120

19611121

19611123

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

I

1

1

1

1

1

1

1

1

1

1

1

1

1

5*

5

5

5

5

5

5

5

5

5

5

5

5

5

5

8

8

8

5

5

8

5

5

5

5

5

8

8

5

5

5

5

5

5

8

5

5

5

8

8

8

8

8

8

8

5

5

5

8

5

5

5

5

5

8

8

8

8

8

8

8

8

8

8

8

8

8

8

8

8

8

8

8

8

8

8

8

! 1

!

1

i

i i

Row

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

548

549

550

551

552

553

554

555

556

557

558

559

560

561

562

563

564

565

566

567

568

569

570

571

572

573

574

575

576

577

578

579

580

581

582

583

584

585

586

587

588

589

590

591

592

593

594

595

596

597

598

Date

19731010

19731011

19731101

19731102

19731104

19731111

19731120

19731121

19731126

19731129

19731206

19731215

19731227

19731228

19740213

19740214

19740218

19740219

19740323

19740328

19740411

19740425

19740601

19740616

19740901

19740902

19740924

19740930

19741017

19741022

19741023

19741028

19741029

19741030

19741031

19741101

19741102

19741126

19741207

19741217

19750102

19750103

19750104

19750108

19750109

19750110

19750117

19750125

19750126

19750227

19750228

19750310

19750311

19750313

19750315

19750328

19750329

19750330

19750414

19750415

19750416

19750419

19750501

19750621

19750622

19750623

19750810

19750923

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

9

5

3

7

8

7

8

5

4

5

5

3

10

5

9

4

5

7

5

5

3

9

5

10

5

5

4

9

4

11

10

8

5

4

5

5

9

9

2

8

10

5

5

5

4

10

5

7

2

7

5

10

2

9

9

7

8

8

9

7

8

7

7

8

5

8

7

4

8

8

9

5

10

7

8

7

8

7

10

10

10

8

7

5

8

9

4

9

7

12

5

8

8

8

11

7

8

6

10

7

8

10

8

12

9

8

6

12

10

5

10

9

9

10

12

8

9

7

11

8

9

10

7

8

8

11

10

10

8

9

10

9

12

Row 1061

1062

1063

1064

1065

1066

1067

1068

1069

1070

1071

1072

1073

1074

1075

1076

1077

1078

1079

1080

1081

1082

1083

1084

1085

1086

1087

1088

1089

1090

1091

1092

1093

1094

1095

1096

1097

1098

1099

1100

1101

1102

1103

1104

1105

1106

1107

1108

1109

1110

1111

1112

1113

1114

1115

1116

1117

1118

1119

1120

1121

1122

1123

1124

1125

1126

1127

1128

Date

19850402

19850403

19850404

19850406

19850407

19850408

19850409

19850425

19850426

19850501

19850506

19850507

19850520

19850606

19850620

19850707

19850901

19850911

19850912

19850913

19850925

19851007

19851011

19851012

19851016

19851017

19851018

19851019

19851023

19851106

19851107

19851108

19851109

19851115

1985112S

19851126

19851127

19851128

19851130

19851201

19851208

19851209

19851210

19851211

19851214

19851216

19851217

19851223

19851224

19860104

19860105

19860108

19860115

19860116

19860117

19860122

19860130

19860131

19860204

19860205

19860206

19860209

19860222

19860223

19860225

19860309

19860318

19860326

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

12

3

4

12

8

5

7

4

4

5

7

5

5

5

5

10

2

4

5

4

12

8

3

5

12

12

5

8

7

5

7

8

5

5

7

12

12

8

3

5

7

5

7

5

7

5

7

5

7

8

7

5

7

2

5

7

8

5

7

2

5

2

5

5

10

7

8

11

7

8

8

8

11

3

11

7

11

12

7

7

7

8

7

12

8

7

10

10

6

8

8

8

7

8

10

8

8

11

8

11

5

7

8

8

8

6

12

8

4

8

12

8

8

10

8

12

8

7

10

10

8

10

10

10

11

8

8

7

11

8

12

10

11

10

10

8

10

12

12

10

10

8

10

12

12

10

12

11 12

APPENDIX R Thunderstorm Rainfall Data

Table 4.2 cont....

Row

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

Date

19611124

19611125

19611130

19611201

19611215

19611217

19611218

19611231

19620104

19620109

19620110

19620111

19620118

19620129

19620131

19620205

19620208

19620209

19620214

19620215

19620220

19620302

19620308

19620318

19620319

19620320

19620429

19620522

19620806

19620915

19620927

19621104

19621111

19621128

19621203

19621204

19621207

19621208

19621214

19621218

19621219

19630102

19630103

19630127

19630129

19630201

19630204

19630218

19630310

19630314

19630409

19630420

19630421

19630424

19630517

19630603

19630604

19630625

19630626

19630710

19630713

19630814

19630822

19630823

19630825

19630829

19630916

19630923

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

5

5

5

8

8

5

5

5

5

9

8

5

5

8

9

5

8

8

5

8

5

5

5

5

5

5

5

5

5

5

5

5

8

5

6

8

6

5

8

5

8

5

5

9

5

5

5

5

5

5

5

5

5

5

8

5

8

8

8

9

8

8

8

8

8

8

8

8

6

8

8

8

8

8

8

6

8

8

8

9

i

I ! ! ! I

i

1 i 1 !

i i

I

|

Row

599

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

616

617

618

619

620

621

622

623

624

625

626

627

628

629

630

631

632

633

634

635

636

637

638

639

640

641

642

643

644

645

646

647

648

649

650

651

652

653

654

655

656

657

658

659

660

661

662

663

664

665

666

Date

19750928

19750930

19751002

19751010

19751011

19751020

19751024

19751031

19751104

19751109

19751116

19751123

19751124

19760109

19760205

19760206

19760219

19760220

19760225

19760226

19760228

19760327

19760328

19760405

19760418

19760615

19760617

19760701

19760813

19760823

19760827

19760828

19760917

19760918

19760920

19760921

19760922

19761001

19761006

19761007

19761014

19761015

19761017

19761018

19761030

19761102

19761103

19761104

19761110

19761111

19761112

19761114

19761115

19761117

19761118

19761120

19761121

19761122

19761123

19761203

19761210

19761215

19761216

19761217

19770101

19770102

19770110

19770113

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

8

5

5

9

4

5

8

4

8

3

8

4

5

2

4

7

2

3

5

4

5

9

3

5

5

5

8

10

10

4

5

2

4

8

5

10

5

7

3

5

3

5

4

5

5

2

2

5

5

8

4

2

5

9

7

5

5

4

5

5

9

5

10

8

8

5

9

7

12

4

3

6

5

8

9

7

7

8

8

3

9

7

7

9

8

4

8

5

8

8

9

3

7

8

9

5

5

7

8

8

8

-7

6

8

12

10

7

10

8

7

4

7

8

9

8

4

10

8

8

5

9

9

11

5

8

6

7

8

12

9

7

8

9

10

5

9

5

9

7

7

11

7

8

9

11

8

10

7

10

8

10

9

8

8

9

9

8

10

10

9 12

10

9

11

11

10

Row

1129

1130

1131

1132

1133

1134

1135

1136

1137

1138

1139

1140

1141

1142

1143

1144

1145

1146

1147

1148

1149

1150

1151

1152

1153

1154

1155

1156

1157

1158

1159

1160

1161

1162

1163

1164

1165

1166

1167

1168

1169

1170

1171

1172

1173

1174

1175

1176

1177

1178

1179

1180

1181

1182

1183

1184

1185

1186

1187

1188

1189

1190

1191

1192

1193

1194

1195

1196

Date

19860327

19860412

19860413

19860417

19860418

19860430

19860804

19860805

19860925

19861001

19861003

19861004

19861019

19861028

19861111

19861117

19861118

19861119

19861126

19861127

19861205

19861215

19861216

19861231

19870101

19870103

19870104

19870202

19870210

19870211

19870215

19870221

19870319

19870320

19870326

19870327

19870328

19870329

19870409

19870514

19870515

19870722

19870723

19870728

19870810

19870920

19871016

19871017

19871019

19871023

19871024

19871025

19871108

19871109

19871110

19871111

19871115

19871116

19871117

19871118

19871120

19871201

19871205

19871210

19871211

19871216

19871220

19871221

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

4

5

4

10

4

8

4

4

5

5

3

8

10

5

5

7

5

5

12

7

12

10

10

5

2

7

10

5

2

5

2

8

5

7

8

7

5

7

5

10

5

2

2

5

5

5

2

2

2

6

12

2

6

2

5

8

12

5

8

2 2

11

7

10

7

7

7

7

8

7

8

7

8

8

5

11

7

7

11

7

8

7

7

10

8

6

7

8

6

7

5

7

7

8

10

12 7

1 l\

8

8

8

8

8

8

8

8

10

10

8

8

8

8

8

12

8

8

7

12

8

8

10

10

12

12

10

10

10

10

10

12

12

12

11

11

11

11

12

12

1

APPENDIX R Thunderstorm Rainfall Data 243

Table 4.2 cont....

Row

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

Date

19630930

19631012

19631025

19631029

19631030

19631031

19631111

19631120

19631122

19631129

19631211

19631213

19631215

19631216

19631217

19631223

19631227

19640124

19640126

19640207

19640209

19640215

19640216

19640223

19640301

19640609

19640610

19640702

19640824

19640827

19640828

19640829

19641008

19641023

19641029

19641103

19641109

19641119

19641209

19641223

19641226

19641229

19650111

19650124

19650130

19650216

19650217

19650218

19650219

19650222

19650410

19650622

19650623

19650718

19650802

19650909

19650913

19651024

19651025

19651027

19651103

19651124

19651202

19651203

19651214

19651230

19651231

19660116

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

5

5

5

5

5

9

6

8

5

5

8

5

5

8

8

5

5

8

8

5

5

5

5

5

5

5

5

5

5

5

6

5

8

5

5

5

5

5

5

5

6

5

5

5

5

5

5

5

8

5

5

5

5

9

9

5

5

8

5

6

6

6

6

6

6

8

6

8

8

8

8

8

8

8

8

8

8

6

8

8

8

8

8

8

8

8

8

8

6

8

8

8

8

8

8

8

8

8

8

8

8

9

8

9

9

8

9

9

9

9

9

9

|

! 1

I

! 1 1

1 1

!

1

1

! 1

|

Row

667

668

669

670

671

672

673

674

675

676

677

678

679

680

681

682

683

684

685

686

687

688

689

690

691

692

693

694

695

696

697

698

699

700

701

702

703

704

705

706

707

708

709

710

711

712

713

714

715

716

717

718

719

720

721

722

723

724

725

726

727

72S

729

730

731

732

733

734

Date

19770114

19770115

19770116

19770117

19770121

19770122

19770123

19770124

19770128

19770129

19770202

19770204

19770205

19770206

19770218

19770219

19770223

19770225

19770228

19770304

19770305

19770306

19770320

19770321

19770407

19770528

19770608

19770806

19770812

19770927

19770928

19771019

19771029

19771101

19771104

19771114

19771115

19771117

19771118

19771119

19771120

19771130

19771201

19771202

19771210

19771211

19771214

19771215

19771221

19771225

19771226

19780101

19780102

19780104

19780115

19780116

19780118

19780123

19780124

19780125

19780127

19780211

19780219

19780221

19780222

19780225

19780227

19780228

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

10

9

5

7

5

7

11

12

9

5

12

9

5

9

2

2

2

9

8

5

5

5

5

2

7

5

5

3

3

7

8

2

9

5

2

4

8

11

4

5

5

8

5

7

12

8

4

5

5

5

2

5

7

2

3

8

4

3

5

4

10

8

9

8

10

11

7

8

11

12

12

5

7

6

8

7

3

8

7

4

9

3

8

3

10

9

9

8

12

8

7

7

5

8

3

5

10

9

8

7

12

8

8

8

7

8

4

12

5

4

5

12

8

8

10

5

8

—r

9

9

8

9

5

8

5

8

9

9

11

8

11

12

11

10

6

10

7

10

11

11

12

11

7

11

8

11

12

8

11

9 10 11 12

Row

1197

1198

1199

1200

1201

1202

1203

1204

1205

1206

1207

1208

1209

1210

1211

1212

1213

1214

1215

1216

1217

1218

1219

1220

1221

1222

1223

1224

1225

1226

1227

1228

1229

1230

1231

1232

1233

1234

1235

1236

1237

1238

1239

1240

1241

1242

1243

1244

1245

1246

1247

1248

1249

1250

1251

1252

1253

1254

1255

1256

1257

1258

1259

1260

1261

1262

1263

1264

Date

19871228

19880101

19880102

19880108

19880109

19880120

19880121

19880122

19880123

19880124

19880131

19880207

19880213

19880214

19880228

19880229

19880301

19880304

19880324

19880325

19880429

19880430

19880521

19880527

19880528

19880615

19880827

19880828

19880917

19880919

19880920

19880927

19880928

19881103

19881110

19881117

19881120

19881121

19881122

19881123

19881124

19881125

19881126

19881127

19881203

19881209

19881210

19881211

19881216

19881220

19881222

19881223

19881226

19890102

19890104

19890105

19890106

19890110

19890117

19890118

19890119

19890207

19890219

19890220

19890226

19890305

19890309

19890310

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

10

2

2

2

2

2

2

2

2

10

2

2

7

6

10

10

2

2

2

10

5

2

8

10

5

5

5

10

5

5

6

5

5

5

2

2

5

5

3

5

7

2

2

2

2

2

2

2

2

6

10

2

2

5

2

8

2

3

2

2

2

6

2

5

3

5

7

2

7

3

7

5

11

10

6

5

8

6

7

8

7

6

6

5

3

6

7

8

8

3

5

3

5

7

6

3

7

5

5

7

8

6

8

10

5

10

5

10

6

7

7

7

8

7

7

8

5

7

8

10

5

8

5

6

10

5

7

7

8

6

7

10

8

8

10

8

8

6

8

6

6

7

7

8

7

8

10

11

10

7

7

7

8

8

8

10

8

8

8

10

10

10

11

10

11

10

11

11

11

11


Table 4.2 cont....

Row

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

Date

19660117

19660128

19660129

19660130

19660204

19660205

19660214

19660215

19660216

19660309

19660310

19660311

19660318

19660321

19660322

19660323

19660324

19660414

19660415

19660519

19660521

19660610

19660817

19660831

19660916

19660921

19661002

19661003

19661006

19661016

19661017

19661018

19661019

19661020

19661026

19661027

19661109

19661110

19661111

19661121

19661123

19661124

19661205

19661214

19661222

19661223

19661226

19661229

19661230

19661231

19670102

19670111

19670116

19670129

19670208

19670213

19670225

19670226

19670306

19670504

19670505

19670806

19670817

19670905

19671011

19671013

19671018

19671028

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

4

5

8

4

4

4

4

5

5

4

5

5

8

4

5

4

12

9

4

5

5

5

4

12

4

5

5

9

8

9

8

9

5

9

8

5

5

5

8

5

9

9

5

5

5

9

12

12

5

5

5

5

5

5

8

5

8

8

8

5

8

8

9

12

9

8

5

5

8

8

5

5

8

6

9

8

8

6

9

12

6

6

8

8

8

8

8

8

9

8

8

12

8

9

8

8

8

8

8

8

9

9

9

1

j 1

1 i i i

1

12l

i

i i

;'

i

'! | : ' 1 !

8 9

| ! ' i

9

!

;

1

—r Row

735

736

737

738

739

740

741

742

743

744

745

746

747

748

749

750

751

752

753

754

755

756

757

758

759

760

761

762

763

764

765

766

767

768

769

770

771

772

773

774

775

776

777

778

779

780

781

782

783

784

785

786

787

788

789

790

791

792

793

794

795

796

797

798

799

800

801

802

Date

19780303

19780308

19780313

19780321

19780322

19780323

19780327

19780401

19780410

19780518

19780602

19780831

19780912

19780918

19780919

19780922

19781004

19781006

19781018

19781031

19781101

19781107

19781111

19781113

19781117

19781126

19781128

19781129

19781130

19781203

19781212

19781213

19781214

19781215

19781217

19781218

19781222

19781225

19790102

19790103

19790107

19790108

19790211

19790226

19790302

19790303

19790314

19790315

19790321

19790324

19790402

19790403

19790415

19790416

19790418

19790510

19790719

19790720

19790726

19790819

19790911

19790919

19790920

19791004

19791006

19791011

19791016

19791022

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

5

12

2

11

10

4

9

12

11

5

8

5

5

2

5

8

5

9

7

2

8

2

8

8

9

5

2

4

5

7

5

4

12

5

4

8

5

5

4

5

5

5

12

11

5

12

9

5

5

4

8

8

5

5

11

10

5

5

5

7

17

5

7

8

5

8

8

9

7

12

8

5

9

9

8

9

8

7

8

5

7

8

8

7

8

8

9

10

7

8

2]

8

8

8

8

10

8

8

10

10

10

9

7

8

8

10

8

9

11

11

10

9

10

11

9

9

11

12

12

10 11

Row

1265

1266

1267

1268

1269

1270

1271

1272

1273

1274

1275

1276

1277

1278

1279

1280

1281

1282

1283

1284

1285

1286

1287

1288

1289

1290

1291

1292

1293

1294

1295

1296

1297

1298

1299

1300

1301

1302

1303

1304

1305

1306

1307

1308

1309

1310

1311

1312

1313

1314

1315

1316

1317

1318

1319

1320

1321

1322

1323

1324

1325

1326

1327

1328

1329

1330

1331

1332

Date

19890311

19890312

19890313

19890331

19890406

19890412

19890413

19890421

19890422

19890423

19890426

19890504

19890505

19890506

19890507

19890623

19890624

19890816

19890817

19890820

19890823

19890826

19890926

19891004

19891023

19891025

19891105

19891106

19891107

19891112

19891116

19891117

19891118

19891202

19891203

19891205

19891209

19891210

19891211

19891212

19891214

19891219

19891220

19891221

19900101

19900102

19900106

19900107

19900108

19900112

19900113

19900114

19900119

19900120

19900121

19900205

19900206

19900207

19900208

19900209

19900210

19900211

19900217

19900218

19900222

19900223

19900224

19900225

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2

2

7

7

2

5

6

5

5

7

11

11

4

8

5

5

2

5

3

2

2

2

6

2

3

2

3

5

3

2

2

6

2

2

3

3

2

3

6

5

6

2

2

2

2

2

3

2

3

2

2

2

2

2

3

2

3

2

6

3

11

7

8

8

7

8

6

7

7

5

11

5

5

8

6

3

7

5

5

6

5

5

8

7

3

3

3

5

6

5

6

3

3

3

3

6

5

6

7

5

8

8

8

7

7

7

5

11

8

7

8

11

5

5

8

8

7

5

7

5

5

7

8

7

7

11

8

8

7

6

6

7

6

6

11

11

8

7

7

8

7

7

11

8

8

8

8

11

—

APPENDIX R Thunderstorm Rainfall Dat>

Table 4.2 cont....

Row

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

Date

19671029

19671104

19671105

19671108

19671119

19671120

19671130

19671214

19671215

19671229

19680104

19680117

19680122

19680206

19680207

19680304

19680312

19680316

19680318

19680319

19680320

19680323

19680324

19680325

19680412

19680415

19680416

19680612

19680722

19680815

19680820

19680913

19680916

19681019

19681104

19681110

19681111

19681120

19681209

19681210

19681225

19681226

19681227

19681228

19690102

19690103

19690114

19690122

19690205

19690206

19690207

19690223

19690224

19690225

19690314

19690319

19690327

19690328

19690329

19690330

19690331

19690401

19690415

19690501

19690515

19690609

19690610

19690715

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

9

5

9

5

8

9

8

9

5

3

5

3

5

5

5

8

5

5

5

8

8

8

5

8

5

5

4

3

3

5

3

5

3

4

5

6

4

8

4

5

9

5

8

9

12

5

9

5

5

5

8

8

8

6

8

8

8

8

8

8

8

8

8

9

8

4

4

8

8

5

6

8

8

8

8

8

8

9

9

9

5

5

8

7

9

9

12

I 8

6 8

i 9l

— Row

803

804

805

806

807

808

809

810

811

812

813

814

815

816

817

818

819

820

821

822

823

824

825

826

827

828

829

830

831

832

833

834

835

836

837

838

839

840

841

842

843

844

845

846

847

848

849

850

851

852

853

854

855

856

857

858

859

860

861

862

863

864

865

866

867

868

869

870

Date

19791023

19791024

19791028

19791104

19791105

19791106

19791111

19791112

19791115

19791116

19791119

19791120

19791123

19791124

19791126

19791204

19791205

19791206

19791208

19791221

19791231

19800106

19800110

19800111

19800112

19800113

19800131

19800201

19800202

19800205

19800206

19800301

19800416

19800417

19800430

19800501

19800528

19800529

19800609

19800610

19800824

19800826

19800918

19801002

19801012

19801013

19801018

19801019

19801020

19801021

19801028

19801107

19801109

19801110

19801203

19801204

19801216

19801229

19801230

19810103

19810106

19810107

19810112

19810121

19810122

19810125

19810126

19810128

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

5

9

8

2

7

5

3

5

4

9

5

5

4

5

8

5

7

5

8

2

3

9

5

5

2

4

12

7

2

2

5

8

5

8

5

7

10

8

2

3

4

2

2

2

7

7

7

7

12

5

3

10

2

2

7

7

2

4

2

2

12

8

10

10

5

8

4

8

7

7

8

5

6

8

8

9

4

7

8

5 7

8

8

7

5

3

8

8

5

12

4

7

8

9

12

11

8

10

7

9

9

6

7

10

9

5

8

6

9

9

8

8

4

6

5

10

10

10

10

11

8

10

10

7

8

10

6

8

11

11

9

9

5

7

8

11

12

11

8

9

8

10

12

8

8

10

9

10

9

11

9

10

12

10

11

10

12

10

11

12

Row

1333

1334

1335

1336

1337

1338

1339

1340

1341

1342

1343

1344

1345

1346

1347

1348

1349

1350

1351

1352

1353

1354

1355

1356

1357

1358

1359

1360

1361

1362

1363

1364

1365

1366

1367

1368

1369

1370

1371

1372

1373

1374

1375

1376

1377

1378

1379

1380

1381

1382

1383

1384

1385

1386

1387

1388

1389

1390

1391

1392

1393

1394

1395

1396

1397

1398

1399

1400

Date

19900305

19900306

19900307

19900316

19900318

19900319

19900403

19900413

19900416

19900701

19900719

19900724

19900725

19900801

19900802

19900815

19900901

19900913

19900914

19900915

19901002

19901011

19901012

19901015

19901019

19901021

19901101

19901103

19901104

19901109

19901110

1990U15

19901116

19901129

19901130

19901201

19901203

19901204

19901208

19901209

19901210

19901211

19901220

19901221

19901227

19901231

19910101

19910109

19910110

19910U1

19910112

19910113

19910115

19910U6

19910118

19910119

19910120

19910121

19910122

19910125

19910126

19910127

19910205

19910206

19910207

19910215

19910216

19910222

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2

3

5

3

2

6

8

11

8

11

11

2

11

2

6

5

3

4

7

2

7

2

5

5

4

2

4

2

2

2

2

2

5

2

3

5

4

7

12

2

2

2

2

2

2

3

2

3

2

7

2

2

3

12

2

3

2

2

3

2

2

2

5

5

6

6

5

7

3

8

8

5

5

8

7

5

7

5

3

4

3

4

8

3

7

12

10

12

5

3

4

8

10

3

3

4

3

4

8

3

4

5

5

12

8

6

7

7

8

4

7

12

8

12

11

8

4

5

7

5

5

8

4

11

10

5

4

6

4

12

4

6

8

7

11

7

8

5

8

11

12

8

8

8

10

7

12

12

7

5

7

5

8

8

12

8

8

11

7

12

12

12

12

10

8

7

10

7

11

11

11

8

12

11

10

8

8

12

11

12

11

10

11

12

11

12

12


Table 4.2 cont...

Row

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

Date

19690721

19690901

19690908

19690918

19690922

19690929

19690930

19691004

19691015

19691021

19691022

19691026

19691029

19691030

19691101

19691106

19691108

19691110

19691114

19691118

19691119

19691128

19691211

19691212

19691222

19691230

19700101

19700102

19700104

19700110

19700111

19700118

19700119

19700121

19700125

19700126

19700210

19700212

19700215

19700216

19700226

19700227

19700228

19700306

19700307

19700316

19700318

19700319

19700320

19700425

19700528

19700603

19700621

19700802

19700831

19700901

19700902

19700909

19700923

19700928

19701019

19701024

19701106

19701108

19701111

19701112

19701115

19701123

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

5

8

7

3

5

3

3

5

5

5

4

5

5

4

6

5

5

4

5

3

8

5

9

5

5

5

3

5

4

5

4

7

5

5

5

5

12

12

5

8

5

5

5

8

7

5

7

5

5

5

4

5

9

3

5

7

4

7

8

9

8

7

5

8

5

7

8

7

4

5

5

8

8

8

8

8

8

8

8

8

5

8

8

8

8

8

8

6

12

7

8

8

5

6

8

9

8

9

7

8

9

7

7

12

1

8

12

8

8

12

12

12

i 1

j

i !

i

I

i 1 1

Row

871

I 872 873

874

875

876

877

878

879

880

881

882

883

884

885

886

887

888

889

890

891

892

893

894

895

896

897

898

899

900

901

902

903

904

905

906

907

908

909

910

911

912

913

914

915

916

917

918

919

920

921

922

923

924

925

926

927

928

929

930

931

932

933

934

935

936

937

938

Date

19810205

19810206

19810210

19810211

19810212

19810219

19810302

19810303

19810310

19810405

19810406

19810407

19810424

19810504

19810827

19811011

19811012

19811015

19811021

19811029

19811030

19811104

19811105

19811113

19811114

19811115

19811116

19811121

19811128

19811205

19811212

19811213

19811219

19811220

19811223

19811224

19811225

19811226

19811229

19811230

19820101

19820102

19820107

19820113

19820117

19820124

19820130

19820131

19820226

19820227

19820321

19820324

19820417

19820425

19820426

19820612

19820709

19820816

19820927

19820930

19821008

19821016

19821203

19821207

19821214

19821215

19821216

19821230

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2

2

2

4

8

11

5

4

11

4

2

4

8

7

5

2

9

5

5

2

8

7

10

4

5

4

2

5

5

4

4

7

2

5

7

5

8

2

2

8

2

2

10

3

7

12

11

2

8

7

2

5

5

8

3

5

2

5

4

10

5

4

8

8

7

4

9

11

8

12

8

8

9

12

10

8

9

8

8

5

7

9

7

3

12

7

8

5

8

8

11

5

10

8

8

7

10

12

9

5

10

8

8

4

8

6

10

10

10

10

6

8

5

8

11

12

11

7

9

8

9

12

8

12

12

10

10

Row

1401

1402

1403

1404

1405

1406

1407

1408

1409

1410

1411

1412

1413

1414

1415

1416

1417

1418

1419

1420

1421

1422

1423

1424

1425

1426

1427

1428

1429

1430

1431

1432

1433

1434

1435

1436

1437

1438

1439

1440

1441

1442

1443

1444

1445

1446

1447

1448

1449

1450

1451

1452

1453

1454

1455

1456

1457

1458

1459

1460

1461

1462

1463

1464

1465

1466

1467

1468

Date

19910223

19910310

19910311

19910312

19910320

19910411

19910412

19910426

19910509

19910515

19910522

19910530

19910609

19910612

19910703

19910823

19910926

19910927

19911005

19911007

19911024

19911025

19911026

19911031

19911116

19911117

19911126

19911127

19911130

19911203

19911204

19911210

19911211

19911215

19911221

19911222

19911224

19911227

19911228

19920101

19920103

19920104

19920105

19920106

19920109

19920110

19920121

19920122

19920123

19920124

19920131

19920201

19920202

19920203

19920204

19920205

19920206

19920211

19920212

19920213

19920222

19920223

19920224

19920303

19920304

19920305

19920322

19920328

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2

2

2

7

12

4

2

5

8

2

5

2

11

8

12

2

3

2

3

2

5

3

11

2

7

5

2

3

8

2

12

3

2

5

2

5

3

12

2

3

5

7

2

2

12

2

2

3

2

2

2

5

2

3

12

3

2

3

2

2

2

11

11

10

5

10

5

3

8

8

3

4

4

5

10

12

5

8

12

12

3

5

7

8

5

5

5

7

5

12

7

5

4

12

8

3

4

4

3

4

3

12

12

7

7

5

4

7

8

8

12

4

7

8

12

7

8

7

8

12

12

8

7

4

8

5

6

4

8

8

7

5

11

5

8

12

8

12

5

12

8

7

11

10

8

7

12

7

8

11

11

11

8

8

10

12

12

10

12

12

—

Thunderstorm Rainfall Data 247

Table 4.2 cont....

Row

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

Date

19701124

19701201

19701205

19701211

19701212

19701214

19701215

19701218

19701219

19701222

19701223

19701228

19701229

19710103

19710104

19710113

19710115

19710116

19710117

19710118

19710126

19710128

19710210

19710315

19710322

19710323

19710413

19710520

19710521

19710821

19710916

19711031

19711107

19711109

19711113

19711114

19711128

19711129

19711202

19711206

19711214

19711225

19711226

19711227

19720105

19720118

19720122

19720123

19720126

19720127

19720203

19720214

19720217

19720218

19720219

19720220

19720221

19720303

19720306

19720307

19720422

19720605

19720621

19720623

19720826

19720829

19721004

19721016

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

3

7

12

9

8

9

5

9

8

3

5

4

8

3

5

4

3

3

5

3

5

5

8

3

3

4

3

5

12

3

3

4

4

5

10

5

5

3

5

3

4

5

5

5

5

5

5

5

8

3

3

8

5

4

8

8

5

9

7

4

8

8

4

8

6

4

5

4

8

5

4

5

5

8

8

8

5

8

8

8

8

8

7

7

4

11

5

7

7

7

7

5

8

9

5

6

9

7

9

8

8

11

7

8

12

8

7

9

8

8

10

8

10

9

11

8

9

8

9

9

10

9

9

12

12

Row

939

940

941

942

943

944

945

946

947

948

949

950

951

952

953

954

955

956

957

958

959

960

961

962

963

964

965

966

967

968

969

970

971

972

973

974

975

976

977

978

979

980

981

982

983

984

985

986

987

988

989

990

991

992

993

994

995

996

997

998

999

1000

1001

1002

1003

1004

1005

1006

Date

19821231

19830102

19830103

19830109

19830121

19830122

19830126

19830201

19830202

19830203

19830204

19830207

19830209

19830210

19830211

19830212

19830213

19830222

19830223

19830225

19830226

19830305

19830306

19830314

19830315

19830730

19830903

19830904

19830906

19830908

19830909

19830915

19830929

19830930

19831003

19831004

19831015

19831019

19831020

19831024

19831127

19831130

19831201

19831207

19831208

19831210

19831211

19831212

19831213

19831214

19840108

19840109

19840110

19840113

19840121

19840204

19840205

19840206

19840207

19840208

19840214

19840215

19840216

19840217

19840218

19840221

19840319

19840325

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

10

8

7

3

5

2

2

10

12

7

8

5

7

10

2

2

7

4

2

10

5

4

7

5

7

5

7

2

7

7

2

7

5

8

5

4

5

4

2

4

4

5

4

5

7

7

4

5

4

4

2

4

2

5

4

2

7

10

7

5

7

7

5

8

7

10

7

8

5

8

5

10

3

10

7

10

8

5

8

7

12

8

7

7

8

10

5

8

5

7

5

7

5

8

12

12

10

8

8

10

8

10

12

8

7

8

5

8

7

12

8

11

7

7

11

8

7

12

11

10

12

12

5

12

10

8

12

10

,

7

12

8 10

Row

1469

1470

1471

1472

1473

1474

1475

1476

1477

1478

1479

1480

1481

1482

1483

1484

1485

1486

1487

1488

1489

1490

1491

1492

1493

1494

1495

1496

1497

1498

1499

1500

1501

1502

1503

1504

1505

1506

1507

1508

1509

1510

1511

1512

1513

1514

1515

1516

1517

1518

1519

1520

1521

1522

1523

1524

1525

1526

1527

1528

1529

1530

1531

1532

1533

1534

1535

1536

Date

19920426

19920427

19920428

19920429

19920510

19920518

19920626

19920628

19920804

19920824

19920828

19920919

19921015

19921016

19921018

19921020

19921031

19921101

19921102

19921104

19921105

19921106

19921109

19921110

19921111

19921116

19921117

19921119

19921120

19921121

19921122

19921124

19921125

19921129

19921130

19921204

19921205

19921206

19921213

19921214

19921221

19921222

19921223

19921224

19921225

19921226

19930102

19930104

19930106

19930107

19930111

19930115

19930116

19930118

19930119

19930120

19930121

19930122

19930124

19930125

19930202

19930203

19930204

19930205

19930209

19930210

19930212

19930216

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2

8

4

10

4

2

5

8

5

5

5

2

5

3

3

8

2

2

2

5

4

3

5

4

8

12

10

2

2

10

2

4

4

12

3

2

2

2

2

2

2

2

2

4

2

2

5

3

2

12

3

4

12

8

4

2

3

2

4

8

4

2

2

5

6

8

8

6

4

4

5

4

8

10

8

5

4

5

3

5

3

8

4

3

4

4

3

8

4

12

12

5

4

12

12

11

12

12

7

7

8

5

8

8

11

12

8

5

6

5

8

12

8

4

8

8

4

11

11

8

12

8

8

6

12

12

6

8

5

12

10

8

11

11

5

12

12

10

8

8

12

8

11

12

12

12

8

12

11

12

12

12

10

12

11 12


Table 4.2 cont....

Row

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

522

523

524

525

526

527

528

529

530

Date

19721020

19721024

19721025

19721026

19721029

19721101

19721104

19721106

19721109

19721110

19721111

19721116

19721125

19721207

19721208

19721215

19721216

19721221

19721222

19730109

19730113

19730125

19730126

19730127

19730129

19730131

19730201

19730202

19730203

19730204

19730205

19730217

19730221

19730222

19730223

19730226

19730227

19730228

19730301

19730304

19730312

19730406

19730407

19730408

19730409

19730430

19730501

19730502

19730710

19730811

19730825

19730912

19730913

19731004

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

11

12

10

4

5

5

5

5

11

3

5

9

5

5

5

10

5

10

12

10

10

5

4

5

10

7

8

7

12

10

9

12

5

10

5

5

11

7

8

4

5

5

5

7

8

8

8

7

7

5

8

7

8

8

5

7

8

8

8

7

8

8

10

10

9

6

8

10

12

11

8

51

8!

101

RI

9

11

11

12

10

7

9

11

8

10

9

1

10

i

i I 1 | i

9 10 11

Row

1007

1008

1009

1010

1011

1012

1013

1014

1015

1016

1017

1018

1019

1020

1021

1022

1023

1024

1025

1026

1027

1028

1029

1030

1031

1032

1033

1034

1035

1036

1037

1038

1039

1040

1041

1042

1043

1044

1045

1046

1047

1048

1049

1050

1051

1052

1053

1054

1055

1056

1057

1058

1059

1060

Date

19840530

19840620

19840806

19840810

19840901

19840902

19840904

19840905

19840908

19840909

19840914

19840915

19840921

19840928

19841013

19841014

19841025

19841026

19841028

19841029

19841103

19841104

19841105

19841106

19841107

19841108

19841109

19841111

19841112

19841113

19841115

19841211

19841212

19841215

19841221

19841225

19841226

19841230

19850102

19850103

19850109

19850116

19850117

19850122

19850123

19850128

19850129

19850207

19850208

19850222

19850319

19850320

19850324

19850325

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

I

1

1

1

5

5

4

5

5

4

5

4

2

7

10

4

11

11

8

4

11

2

4

2

7

5

11

8

3

5

5

7

5

11

2

4

7

11

5

4

7

5

7

11

5

5

8

11

3

10

7

3

2

2

5

7

8

7

7

7

8

7

8

5

5

10

7

4

7

5

8

5

7

7

8

7

5

7

8

7

8

8

8

7

5

12

8

11

8

8

7

8

5

8

6

8

8

11

8

8

11

10

6

12

8

8

12

7

11

10

11

7

8

12

8

10 11

Row 1537

1538

1539

1540

1541

1542

1543

1544

1545

1546

1547

1548

1549

1550

1551

1552

1553

1554

1555

1556

1557

1558

1559

1560

1561

1562

1563

1564

1565

1566

1567

1568

1569

1570

1571

1572

1573

1574

1575

1576

1577

1578

1579

1580

1581

1582

1583

1584

Date

19930217

19930218

19930220

19930221

19930226

19930307

19930308

19930309

19930320

19930321

19930324

19930325

19930326

19930327

19930328

19930329

19930405

19930406

19930428

19930429

19930510

19930523

19930804

19930810

19930825

19930826

19930913

19930914

19930919

19930920

19931004

19931018

19931023

19931024

19931025

19931102

19931113

19931114

19931117

19931118

19931119

19931120

19931124

19931204

19931212

19931213

19931214

19931226

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

3

12

2

2

3

2

5

3

2

2

2

3

12

3

3

5

5

2

3

3

2

3

2

8

2

12

12

12

ll

5

2

8

2

2

2

2

3

12

2

12

2

8

2

4

5

4

3

8

4

12

12

4

4

8

8

8

4

5

5

3

8

5

3

5

8

3

8

5

6

5

5

11

8

5

8

8

5

8

6

8

12

6

8

8

8

11

8

11

6

12

10

11

12

11

12

ll|

8

12

8

11

12

11

8

12

12

APPENDIX B Thunderstorm Rainfall Data 249

B.2 Monthly Thunderstorm Rainfall Data at Richmond

Table 5.1 Monthly thunderstorm rainfall frequency at Richmond station.

Year

1960 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90

Total Average

Jan

1 2 5 2 1 0 3 1 2 3 7 2 5 2 0 5 1 5 8 0 4 5 6 1 3 1 3 2 5 2 6 93 2.74

Feb

6 4 2 0 3 1 1 7 73 2.15

Mar

3 1 0 1 0 0 5 0 4 4 2 2 2 2 1 4 1 2 4 3 0 1 1 2 2 0 0 5 1 0 3 56 1.65

Apr

1 1 0 1 0 0 0 0 1 1 0 0 1 1 1 3 0 1 1 1 0 1 1 0 0 5 0 0 1 1 0 23 0.68

May

0 0 1 1 0 0 0 0 0 1 1 2 0 1 0 1 0 0 0 0 2 0 0 0 0 2 0 0 0 0 0 12

0.35

Jun

0 0 0 0 1 1 0 0 0 1 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 9

0.26

Jul

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 2 5

0.15

Aug

0 0 1 2 1 0 2 0 0 0 0 1 2 0 0 1 0 2 0 0 0 0 0 0 0 0 1 1 0 2 2 18

0.53

Sep

0 0 0 3 0 2 0 0 1 3 2 0 0 1 2 3 1 1 2 1 0 0 0 2 4 2 1 0 3 1 3 38 1.12

Oct

1 2 0 3 0 2 3 2 0 6 1 0 4 2 3 2 0 0 2 2 1 4 1 3 3 4 1 4 0 1 0 57 1.68

Nov

1 7 0 1 2 1 1 1 0 6 4 4 6 4 2 1 7 6 3 5 2 2 0 2 7 6 1 3 6 1 4 96 2.82

Dec

3 0 2 2 2 2 5 0 3 3 7 3 4 1 0 0 2 2 4 3 1 3 4 2 2 5 0 2 4 5 5 81 2.38

Total

11 16 12 18 8 10 20 5 13 31 29 15 29 22 10 22 13 25 26 16 12 17 14 18 25 28 7 21 21 15 32 561 16.50

Table 5.2 Monthly thunderstorm rainfall (in m m ) at Richmond station.

Year

1960 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90

Total Average

Jan

2.5 9.5 21 33.3

17.3

0.5 12.7

0.3 5.7 7.5 92.6

11.5

60.2

7.8 0

22.2

8.6 44.2

43.6

0 3.7 34.2

58 3.8 9.8 3.6 28.6

4.6 46.8

14.2

40.2

648.5

19.1

Feb

24.6

8.6 47.3

19 2.5 0.5 0.8 17.3

5 5.1 18.3

12.7

19.6

46 1.2 25 18.2

61.2

0.8 0

22.2

7.4 11.6

51.4

36.2

24.5

0 16 7.4 12

79.8

602.2

17.7

Mar

25 5 0 0.3 0 0

12.3

0 52.2

126 19 13 10 5 8

34.2

25.8

8.2 26.8

20.2

0 23.8

5 4.4 3 0 0

26.8

1.2 0

48.2

503.4

14.8

Apr

2.5 9 0 6.4 0 0 0 0 1.8 15.2

0 0 1.5 26.7

27.4

20.4

0 4 0.2 0.8 0 5.8 17 0 0

33.8

0 0 19 6.4 0

197.9

5.8

May

0 0 4.8 22 0 0 0 0 0 1 3

29.7

0 9 0 8.8 0 0 0 0

14.4

0 0 0 0 3 0 0 0 0 0

957 2.8

Jun

0 0 0 0

257 7.6 0 0 0 8.6 3.6 0 0 0 0 11 0 5.8 27 0 0 0 0 0 0 47 0 0 0 0.8 0

137.1

4.0

Jul

0 0 0 0 0

93.2

0 0 0

. 0

0 0 0 0 0 0 0 0 0 27 0 0 0 0 0 0 0 3.2 0 0 18

141.4

4.2

Aug

0 0 0.5 9 4.8 0 1.8 0 0 0 0

20.3

8 0 0 7.4 0 6 0 0 0 0 0 0 0 0 2 12 0 7.6 75 154.4

4.5

Sep

0 0 0

15.6

0 37 0 0 11 12.5

16.5

0 0

10.2

2.6 23 1.6 9.2 6.7 4.2 0 0 0

24.6

7.8 12.6

0.4 0

20.6

8 64.5

288.6

8.5

Oct

0.8 8.2 0 10.9

0 26.2

7.4 22.3

0 73.5

2.5 0 32 8.1 32.6

22.8

0 0 12 3.2 11.6

20.8

2 15.8

4.6 19.4

7 59.3

0 16.8

0 419.8

12.3

Nov

44.1

59.7

0 0.5 59.2

1.6 23.4

18.5

0 57 70 20.4

21 21.4

3.8 32.2

32.6

28.8

38 19 13.8

3.2 0 10 52.4

81.2

25.2

3.6 26.3

6 31

803.9

23.6

Dec

18.5

0 12.5

8 23.5

1.1 31.2

0 21 4.7 38.3

21.5

9 38.1

0 0 18

25.2

30.8

13 4.3 39.8

17.8

5.4 12.2

28.4

0 12

64.5

63.7

13 575.5

16.9

Total

118 100 86.1 125 133 167.7 89.6 58.4 96.7 311.1 263.8 129.1 161.3 172.3 75.6 207 104.8 192.6 185.9 87.4 70 135 111.4 115.4 126 253.5 63.2 137.5 185.8 135.5 369.7 4568 147.3

APPENDIX B Thunderstorm Rainfall Data

B.3 Monthly Thunderstorm Rainfall Data at Sydney Regional Office

Table 5.3 Monthly thunderstorm frequency at Sydney Regional Office station.

Year

1960

61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90

Total

Average

Jan

2 2 3 1 0 3 3 1 0 2 6 2 3 1 0 2 0 4 5 1 3 1 1 3 1 2 3 1 4 2 4 66 1.9

Feb

2 3 2 1 1 3 2 3 1 2 2 0 4 3 1 2 2 2 1 1 0 1 0 1 5 1 3 2 0 2 5 58 1.7

Mar

0 0 3 0 1 0 4 0 6 1 4 1 3 0 1 4 1 1 2 4 0 1 0 0 0 1 1 2 0 1 3 45 1.3

Apr

2 1 1 2 0 1 0 0 0 0 0 1 1 2 1 1 1 1 0 1 0 1 0 0 0 2 1 0 2 2 0 24 0.7

May

3 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 2 0 0 0 0 2 0 0 1 0 0 14

Jun

0 1 0 3 2 2 1 0 0 2 2 0 0 0 2 1 0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 21

0.4 ! 0.6

Jul

0 0 0 2 1 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 9 0.3

Aug

0 0 0 3 3 1 1 2 1 0 1 1 0 1 0 0 2 1 1 0 0 1 0 0 1 0 0 0 1 2 1 24 0.7

Sep

2 0 1 2 0 0 2 1 0 2 1 0 0 2 1 0 2 1 1 1 0 0 2 3 4 0 1 0 4 0 1 34 1.0

Oct

3 1 0 2 3 2 3 1 0 3 2 0 1 1 3 4 4 2 2 4 2 1 1 3 1 2 1 4 0 0 2 58 1.7

Nov

2 9 0 1 2 1 1 2 0 3 4 3 4 3 0 1 9 4 0 7 0 2 0 1 6 3 4 2 6 2 5 87 2.6

Dec

2 2 6 4 2 2 0 1 1 3 2 4 2 1 0 0 2 2 4 2 1 3 2 2 2 3 0 1 3 4 2 65 1.9

Total

18 20 16 22 15 16 18 11 10 20 24 12 18 15 9 15 24 20 16 21 8 11 6 13 21 18 14 13 22 16 23 505 14.9

Table 5.4 Monthly thunderstorm rainfall (in m m ) at Sydney Regional Office station.

Year

1960

61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90

Total

Average

Jan

8.7 5.6 8.4 18.3

0 21.4

10.4

0.3 0

29.2

69.2

8.2 23.7

7.4 0 3.6 0

28.2

25.7

7.8 21.5

9.2 5.6 20.7

30.7

5 33 30.6

43.3

50 28.4

554.1

16.3

Feb

18 12.1

38.4

4.8 12.2

3.2 15.8

53 10.4

17.5

29.5

0 47 143 16.4

12 65.5

10.2

2.2 0.4 0

23.6

0 8.6 54 15.3

23 9.3 0 7

85.4

737.8 21.7

Mar

0 0

33.2

0 14 0

52.1

0 45 33 88 25 31.8

0 14.2

240 6

18.4

17.2

16.8

0 25.8

0 0 0 9.4 5 11 0 36 48.4

770.3 22.7

Apr

7.2 2 0.5 5.8 0 2.3 0 0 0 0 0 1 4.3 43 7.5 7 1.8 5.4 0 7.4 0 14.2

0 0 0 12.4

0.6 0

75.5

32.2

0 230.1

6.8

May

38 2.5 0 5.8 0 0

20.1

0 0 1.8 0 0 0

29.5

0 0 0 9.2 0 0 64 0 0 0 0 10 0 0 4 0 0

184.9 5.4

Jun

0 8.1 0

53.8

71 41.6

4.6 0 0 82 14.7

0 0 0

26.8

17.6

0 0.6 0 0 0 0 0 0

21.7

16.4

0 0 2 4 0

364.9

10.7

Jul

0 0 0 16.8

4.6 15 0 0 8.4 3 0 0 0 0 0 0 17.6

0 0 0 0 0 0 0 0 8 0 4.6 0 0 0 78 2.3

Aug

0 0 0

53.8

6 0.8 1.6 37.6

2.3 0 5.8 112.7

0 6.4 0 0 3.4 6.8 18.2

0 0 1.4 0 0 1.8 0 0 0 3.4 35 4 301 8.9

Sep

7.3 0 2.5 7.9 0 0 4

41.4

0 18.3

2 0 0 7.4 5.4 0

25.4

12.1

5.1 6 0 0

27.8

15.7

21 0 0.2 0

26.4

0 34

269.9

7.9

Oct

12 10.7

0 13 10 19 21.2

2 0 10 2.3 0

28.7

3.6 17 41.6

40 1.5 6

27.3

3.6 8.8 2.2 53.5

2.6 13.6

6.6 128.8

0 0 1

486.6

14.3

Nov

21.8

133.8

0 3.6 61.5

2.3 3.3 89.4

0 11.5

68.6

18 8.7 24.3

0 0.7 90.4

15 0

49.1

0 3.2 0 3.6 334 10.3

8.6 8.6 51 12 45 1078

31.7

Dec

22.3

41 29 47.5

16.5

3.8 0 5.8 4.6 20.5

29.7

20 14.8

6.6 0 0 23 12.6

38.6

0.4 23.6

23.6

10.4

22.2

21 16 0 3 69 40.3

4.4 570.2

16.8

Total

135.3

215.8

112 231.1

195.8

109.4

133.1

229.5

70.7

226.8

309.8

184.9

159 271.2

87.3

322.5

273.1

120 113 115.2

112.7

109.8

46 124.3

486.8

116.4

77 195.9

274.6

216.5

250.6

5626

181.5


B.4 Monthly Thunderstorm Rainfall Data at Sydney Airport

Table 5.5 Monthly thunderstorm rainfall frequency at Sydney Airport station.

Year

1960

61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90

Total

Average

Jan

2 2 1 2 0 2 3 0 1 1 5 0 2 1 0 4 0 3 5 1 3 3 2 3 2 3 5 1 4 2 5 68

2.00

Feb

1 2 2 1 4 2 3 3 2 2 1 2 5 3 1 1 2 2 3 1

[___!_ 3 0 1 4 1 4 1 0 1 4 63 1.85

Mar

0 0 4 1 1 0 4 0 5 1 3 1 3 0 1 4 1 2 2 2 0 1 0 1 0 1 2 3 0 2 4 49 1.44

Apr

0 1 0 1 0 1 0 0 0 0 0 1 1 2 0 0 0 1 0 1 2 2 0 0 0 1 2 0 1 3 2 22

0.65

May

1 1 0 0 2 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 3 0 0 0 0 2 0 1 1 1 0 16

0.47

Jun

0 1 0 1 1 2 1 0 1 2 2 0 1 0 2 0 1 1 0 0 0 0 0 0 1 1 0 0 0 1 0 19

0.56

Jul

0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 1 0 1 0 0 0 8

0.24

Aug

0 5 0 1 0 1 1 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 0 2 1 1 1 2 29

0.85

Sep

1 1 0 2 0 1 1 1 0 2 1 1 0 1 2 1 2 2 3 0 0 0 2 2 3 1 1 0 4 0 1 36

1.06

Oct

5 1 0 2 2 2 4 3 0 1 1 0 0 1 2 4 4 1 3 3 2 2 1 3 1 3 2 4 0 0 1 58 1.71

Nov

0 4 0 2 2 1 2 3 1 4 5 3 3 6 1 2 9 4 1 7 1 3 0 2 8 5 4 2 5 2 4 96

2.82

Dec

4 2 4 5 1 3 1 0 1 3 4 2 1 1 1 0 4 3 3 1 2 3 2 2 2 4 1 3 3 2 1 69

2.03

Total

14 20 11 19 13 15 20 11 13 17 23 11 17 17 11 16 25 21 21 20 15 18 8 14 22 23 23 17 19 15 24 533 15.68

Table 5.6 Monthly thunderstorm rainfall (in m m ) at Sydney Airport station.

Year

1960

61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90

Total

Average

Jan j Feb

30 5 1.3 26 0 15 12.5

0 2 16.5

24 0 16.5

6.6 0 6.2 0 19.5

47.2

5.4 18.2

13.5

18.5

9.2 15.3

5.2 44 11.5

36.5

12 19

436.6

12.8

9 2

46.5

1.8 5.7 1

15.5

92.5

19 12.5

26 44 56.5

30.5

13.2

42 23 14 0.8 10 40 33.2

0 1.2 27 3.4 30.4

18 0 0

69.3

688 20.2

Mar

0 0 4.1 2.5 8.5 0 102 0 26 22 85 10 51 0 17 202 4.6 14.2

35 10 0 1 0 9.6 0 6.6 2.5 41 0

26.6

32.2

713.4

21.0

Apr

0 15.5

0 2.5 0 2 0 0 0 0 0 49 10.5

54 0 0 0 10 0 3.6 5.5 24 0 0 0 18.2

10 0 70 74 17

365.8

10.8

May

2 0.5 0 0 0 0 0 0 0 0 0 0 0 16 19 0 0 3.6 0 0.2 18.5

0 0 0 0 20 0 1 3 10 0

93.8

2.8

Jun

0 0.3 0

10.2

59.2

33 1.3 0 6.5 26 7.5 0 1.3 0 13 0

29.2

0.2 0 0 0 0 0 0 15 17 0 0 0 7.2 0

226.9

6.7

Jul

0 0 0 2.5 2.8 0 0 0 10 0.5 0 0 0 0 0 0 35 0 0 12 0 0 0 0 0 0.4 0 9 0 0 0

72.2

2.1

Aug

0 13.2

0 33.5

0 0.8 7 24 7 0 8

48.5

6.6 16 0 0 3.6 4.6 24.5

1 0.4 0.2 15.5

0 4 0 111 2 6

11.1

10 358.5

10.5

Sep

3.6 2.8 0 17 0 0.5 1 35 0 4 28 15.5

0 4

20.5

16 13 18 4 0 0 0

21.6

19 15.5

0.4 0.4 0 19 0 41

299.8

8.8

Oct

15.7

11.5

0 13 7 15 7.8 4 0 0.3 1.3 0 0 6.6 13 22.6

67 0.8 56 25.6

17 3.6 1.8 54.2

2.8 13.6

23.5

109 0 0 11

503.7

14.8

Nov

0 67.5

0 1.5 17.3

1.5 5 54 0.3 14.2

95.5

21 12.2

23 0.4 14.5

98 15.4

1.4 51.5

33 20 0

21.5

265.5

62 24 9.5 65.5

11 11 1017

29.9

Dec

55.5

10 30 91.5

8.4 23 9.5 0 6 29 38.6

28.5

10 2.3 5 0 20 4.6 31.5

1.2 14 59.2

6 28.5

46.5

17.6

8 24.5

112.5

33 3

757.4

22.3

Total

115.8

128.3

81.9

202 108.9

91.8

161.6

209.5

76.8

125 313.9

216.5

164.6

159 101.1

303.3

293.4

104.9

200.4

120.5

146.6

154.7

63.4

143.2

391.6

164.4

253.8

225.5

312.5

184.9

213.5

5533

173.4


B.5 List of Rainfall Stations

All stations were sorted by latitudes and longitudes (in decimal) from northwest to south-east of the Sydney region. * The Sydney Water stations are identified by a prefix of the number 5.

Table 6.2 List of stations and the periods from which data were used.

Row Station Number Name of Stations Latitude0 Longitude0;

Elevation in m Period

No. of Events

63057 Mount Wilson 33.51 150.37

63184 [

63246

Blaxland Ridge

Mount Wilson

33.51

33.52

150.90

150.37

1027

177

!1960-78 100

1005

1962-79

1969-86

109

133

63118

63013

Bilpin (Fern Grov.)

Berambing

33.52

33.53

150.50

150.43

610

792

1960-93

1960-93

282

104

63043 Kurrajong Heights 33.53 150.63 495 1960-93 227 63042

-AAJW U U U A J J U U U U U

Kurrajong P.Of. 33.55 150.67 152 1960-91 184 . ,A**JÛUIAJJUUUUUUU«JJUUMU' HII

67090 Arcadia 33.58 151.07 205 1964-93 202 66143 Kuring-Gai Chase 33.58 151.30 170 1969-91 140 63056 Mount Victoria 33.60 150.27 1064 1960-90 159

11 63248 Grose Wold 33.60 150.68 61 1969-93 233 12 67033 Richmond AMO/MO 33.60 150.78 19 1960-93 271

1960-93

66128 Palm Beach (G.C) 151.32 1965-89

17 67021 Richmond (H.A.C.) 33.62 150.75 20 1960-93 268 18 67073 Maralya Boudary RD. 33.62 150.90 49 1963-93 263 19 63009 Blackheathp P.Of. 33.63 150.28 1065 1960-93 198

* 567100; Riverstone ivwvvvwevvvn rr * V W W V W W W * V W V I r t Y**1

33.65 150.83 25 1984-93 120 21 66119 Mount Kuring-Gai 33.65 151.13 215 1964-93 242 22 67002 Castlereagh 33.67 150.68 15 1965-93 216

«*wu M***WWiV^^WI

23 66183 Ingleside Walter Ave. 33.67 151.27 160 1984-93 130 24 66045 Newport B.C. 33.67 151.32 1960-93 213 25 563059 Katoomba 33.68 150.30 950 1984-93 122 26 67086

"h Dural (Old Northern RD) 33.68 151.03 216 1973-93 181

27 566051 Wamewood 33.68 151.30 15 1982-93 135 28 63227 Wentworth Falls 33.70 150.37 900 1967-93 176

150.48 29 563070 Linden W.F.Dam 33.70 520 i1984-93 123 30 63077 Springwood P.Of. 33.70

• rrrr>rr*rrT+rr**

150.57 ;366 1960-93 240 31 567076 Castle Hill 33.70 150.98 65 1984-93 123 32 66028 Hornsby Police STN 33.70 151.10 181 1960-93 110 33 63044 Lawson P.Of. 33.71 150.43 1715 1960-93 193 34 63039 Katoomba Composite 33.72 150.30 1030 1960-93 190

51 36

563067

63045

Wentworth Falls

Leura P.Of™

33.72

33.72

150.38 823 150.43 1975

1982-93

1960-93

101

186

37 66063 WWAMMMArtMNMrVVV -

Wahroonga Reservoir *?_

33.72 151.12

ST. Marys S.T.P. 33.73 205 1960-93

f V A W A V A W A W V A W ^M^^M^'J^^^^V^W^WVt ^VJWIWVMMIÂVWVriJl 188

^^.NWÂVÂ^WMVA^VW

38 567087 150.77 20 1984-93 129 67076 Quarkers Hill 33.73 150.88 33 1966-92 184 66158 Turramurra (kiss. PT.RD) 33.73 151.13 160 1960-93 301 66157 Pymble(Canisius College) 33.73 1151.15 1165 1960-93 281 66044 Cromer 33.73 151.27 10 1960-93 172 66182 Frenchs Forest

...•JJUUIIIU'WT' rwowvwwwv»»l»TW> irvvwwvwwwwvww 33.74 151.23 155 1960-93 277

63230 Blaxland western Highway 33.75 150.60 234 1968-80 100


Table 6.2 cont....

45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97

67018

67089

67098

66118

63185

67067

67024

67059

67080

566040

66020

66156

66120

67026

66032

66056

66145

66153

566025

66089

67084

66124

66087

66081

566017

66010

66167

66002

567092

67019

67032

66130

66042

66138

67070

66085

66134

66131

66075

66166

566027

66082

567079

66013

66163

67029

67068

567077

67008 > „..«....—

566022

66017

66062

66006

Penrrith

West Pnnant Hills

Pennant Hills West

Frenchs Forest (F. Av)

Glenbrook B.C.

E m u Plains Gough ST

ST Marys B.C

Blacktown Kidare R D

Winston Hills

West Epping

Epping (Chester St)

Marsfield (Macq.Uni.)

Gordon B.C.

Seven Hills Exp.Farm

West Lindfield

Roseville B.C.

Seaforth (Castle Circuit)

Manly Vale(ManDam)

Manly Dam

North Manly B.C.

Orchard Hill

Parramatta North

Eastwood B.C.

North Ryde Stround St.

Chastswood

Chatswood Council

Northbridge B.C.

Balgowlah

South Prospect

Prospect Dam

Westmead Austral Av.

Northbridge

Mosman (Bapaume R D )

Manly Army North Head

Merrylands Wellsford ST

Auburn/ Granville Composite

Granville Shell Refinery

Riverview Observatory

Waverton B.C.

Cremorne Grasmere R D

Mosman

Concord West Plaster Mills

Guildford

Concord G.C. r Watsons Bay

Wallacia

Badgerys Creek RES STN

Fairfield

Guildford

Home Bush

Fivedock Council DEP

Sydney Regional Office

Sydney Botanic Gardens

33.75

33.75

33.75

33.76

33.77

33.77

33.77

33.77

33.77

33.77

33.77

33.77

33.77

33.78

33.78

33.78

33.78

33.78

33.78

33.78

33.80

33.80

33.80

33.80

33.80

33.80

33.80

33.80

33.82

33.82

33.82

33.82

33.82

33.82

33.83

33.83

33.83

33.83

33.83

33.83

33.83

33.84

33.85

33.85

33.85

33.87

33.87

33.87

33.87

33.87

33.87

33.87

33.87

150.68

151.04

151.05

151.23

150.62

150.65

150.77

150.88

151.00

151.05

151.08

151.12

151.15

150.93

151.15

151.18

151.23

151.25

151.24

151.27

150.72

151.02

151.08

151.13

151.18

151.20

151.22

151.25

150.90

150.92

150.98

151.22

151.24

151.30

150.98

151.02

151.03

151.17

151.20

151.22

151.23

151.08

150.97

151.10

151.28

150.63

150.73

150.95

150.98

151.08

151.12

151.20

151.22

25 120 168 150 183 31 35 58 75 100 92 55 96 55 60 116 85 20 21 5 93 60 78 70 92 96 35 70 65 61 26 80 70 85 45 8 3 23 21 61 85 5 50 15 25 50 65 5 31 10 6 42 15

1960-93

1960-93

1960-93

1964-82

1963-93

1960-93

1960-84

1963-93

1968-93

1980-93

1960-93

1970-92

1960-93

1960-90

1960-92

1960-79

1968-92

1968-93

1963-93

1961-87

1970-92

1965-93

1960-93

1960-79

1963-93

1960-93

1980-92

1960-89

1975-93

1960-93

1960-92

1960-80

1960-93

1968-88

1968-93

1960-93

1960-93

1960-78

1960-93

1963-89

1960-93

1961-82

1972-93

'1960-93

1968-93

1960-93

1960-93

1981-93

1960-77

1969-93

1960-93

,1960-93

1960-93

r*'""v*'"v'~wwww,iMWV

256 187 233 129 229 274 127 256 215 153 273 210 274 224 248 106 208 |

147 201 113 188 273 |

150 101 243 214 128 217 153 283 268 127 280 111 254 242 197 103 258 185 164 121 190 245 138 267 230 138 105 152 224 287 257

B Thunderstorm Rainfall Data

Table 6.2 cont....

98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150

66098

566038

563046

568045

567093

566050

66164

66070

66000

566032

66005

66050

566020

66160

66073

67035

66025

566049

66003

566026

66052

67036

66137

566033

66076

66037

66171

66054

66004

67015

67009

66148

566047

567078

66181

66069

66051

66058

66072

68007

566018

66086

68192

66078

566056

66040

66014

563037

568138

568130

66090

66001

68081

Rose Bay R/S G. C.

Vaucluse B.C.

Mcmahons Loo

Wairagamba

St.Johns Park

Villawood

Rookwood

Strathfield G.C.

Ashfield B.C.

Paddington

Bondi B.C.

Potts Hill (pumping st.)

Enfield

Centennial Park

Randwick Racecourse

Liverpool Council

Warwick Farm

Liverpool

Bankstown (Condell Park)

Marrickville

Randwick B.C.

Austral Eighth Ave

Bankstown A M O

Padstow

Wiley Park

Sydney Airport

MoorebankN.B.

Revesby

Bexley G.C.

Bringelly (Maryland)

Glenfield Composite Macquarie

Peakhust Golf Course

Mortdale B.C.

Glenfield

Oatley (Woronora Parade)

Hurstville Grove

Little Bay (Coast G.C.)

Sans Souci

Kurnell(A.6.R)

Camden (Brownlow Hill)

Cronulla S.T.P.

Cronulla W.P.C.P.

Camden j irport

Lucas Hts (A.A.E.)

Yarrawarrah

Miranda Blackwood ST

Cronulla South B.C.

Barragorang

Oakdale

West Camden

Engadine

Audley National Park Bottom

Campbelltown S.C. MWWWMJWM1MIMI r„..nn^nflJiJuvll

33.87

33.88

33.88

33.88

33.88

33.88

33.88

33.88 ]

33.88

33.88

33.88

33.90

33.90

33.90

33.91 |

33.92

33.92 ]

33.91 !

33.92 ;

33.92

33.92

33.93

33.93 1

33.93

33.93 ^

33.93

33.95

33.95

33.95

33.97

33.97

33.97

33.97

33.98

33.98

33.98

33.98

34.00

34.02

34.03

34.03

34.03

34.05

34.05

34.05

34.05

34.05

34.07

34.07

34.07

34.07

34.07

34.08

151.27

151.27

150.38

150.58

150.88 .

150.98

151.05

151.07

151.13

151.22

151.27

151.03

151.08

151.23

151.23

150.92

150.93

150.93

151.02

151.15

151.24

150.82

150.98

151.02

151.07

151.17

150.95

151.00

151.10

150.72

150.90

151.05 ,

151.07

150.90

151.08

151.10

151.25

151.13

151.22

150.65

151.15

151.17

150.68

150.98

151.02

151.10

151.15

150.40

150.43

150.67

151.02

151.05

150.52

6 75 655 180 35 30 41 21 25 45 15 55 10 38 25 21 5 5 10

5 75 60 9 20 45 6 22 15 10 122 23 39 40 15 42 5 22 9 3 61 10 |10_

70 140 50 40 30 180 410

1979-93

1980-93

1982-93

1982-93

1980-93

1980-93

1974-93

1960-93

1960-93

1961-87

1960-82

1960-93

1983-93

1960-93

1960-93

1962-93

1960-90

1960-93

1960-79

1980-93

1960-93

1964-89

1968-92

1981-93

1960-87

1960-93

1968-80

1960-93

1960-93

1960-91

1960-83

1969-87

1978-93

1984-93

1982-93

1960-81

1962-93

1960-93

11960-93

1960-93

1972-93

;1960-93

!1960-93

1960-93

1983-92

1960-93

1960-93

!1983-93

1984-93

75 j 1983-93

170 j 1962-93

23 f1960-79

75 1960-92

136 159 119 133 144 143 142 191 259 100 120 267 128 225 196 272 189 258 127 |

145 248 158 223 109 146 288 100 164 208 196 132 114 140 125 149 140 216 290 284 269 200 234

i 171

183 112 274 266 132 129 110 128 100 171


Table 6.2 cont..

151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191

66176

66116

566052

563036

68013

68052

568072

568069

568051

68001

68028

568038

68024

568139

568048

68016

568065

568004

568050

568060

568047

568049

568067

568046

568097

68030

568061

568068

68188

568099

568118

68044

568136

68033

568058

568071

68023

68102

568054

68053

68022

Audley Royal Notional Park

Bundeena Composite

Woronora Dam

Yerranderie

Menangle (JMAI)

Picton Composite

Cobbong

Reverces

Oakdale

Appin (Bulli Road)

'Helensburgh P.Of.

Wollondilly River

Darker Forest (Kintyre)

Buxton P.O.

Cataract Dam(w.B.S)

Cataract Dam

Letle Box Tower

Cordeaux Air.St.

Hill Top

Ironbark

Nepean Dam

Cordeaux Quart

Beth Salem

Avon Dam

Mount Keira

Mittagong

Browns Roard

Upper Cordeaux

Wollongong Uni.

Leicester Park

Wollongong STP

Mittagong Pool

Wollongong

Mittagong (K.O)

Hambridge

Upper Avon

Dapto West

Bowral (P.D)

Mittagong Ma. Cr.

Port Kembla S.ST.

Dapto B.C.

34.08

34.09

34.12

34.13

34.13

34.18

34.18

34.18

34.20

34.20

34.20

34.23

34.23

34.25

34.27

34.27

34.27

34.28

34.30

34.30

34.33

34.33

34.33

34.35

34.37

34.39

34.40

34.40

34.40

34.43

34.43

34.45

34.45

34.47

34.47

34.47

34.47

34.48

34.48

34.48

34.49

151.05

151.15

150.93

150.30

150.73

150.62

150.85

150.92

150.50

150.78

150.98

150.32

150.92

150.53

150.80

150.81

150.87

150.72

150.42

150.67 '

150.60

150.75

150.85 }

150.63 j

150.82

150.30 j

150.70

150.77 >s

150.88 •

150.38

150.90

150.47

150.90

150.50

150.63

150.73

150.77

150.40

150.52

150.90

150.78

120 45 180 298 80 171 280 305 410 230 150 200 370 390 340 340 449 380 580 300 390 335 366 390 430 735 442 330 30 670 5 625 15 625 491 330 42 690 570 11 10

1979-93

1964-78

1983-93

1982-93

1960-92

1960-92

.1984-93

1983-92

1982-93

1960-93

1960-93

1982-93

1960-93

1966-93

1982-93

1960-93

1982-93

1984-93

1981-93

1983-92

1982-93

1984-93

1982-93

1982-93

1982-93

1960-93

1984-93

1982-93

1960-92

1960-93

1980-93

1960-93

1975-93

1960-93

1982-93

1982-93

1960-87

1960-93

1981-93

1960-92

1960-93

156 101 126 139

; 229

215 128 108 148

1 135 182 133 j

276 |

245 133 279 146 122 137 103 118 100 142 135 I

138 245 133 141 224 220 155 187 |

148 244 141 154 143 237 126 138 235

APPENDIX C Synoptic Weather Charts 256

APPENDIX C

Synoptic Weather Charts

This appendix contains the 6 sets of different synoptic charts. During the domination of

these weather systems the biggest thunderstorm rainfall events (for each thundery mounts,

October to March 1975 to 1993) occurred in the Sydney region (see Chapter 6 for more

details). Synoptic charts were taken from the Monthly Weather Review of the N e w South

Wales (Bureau of Meteorology). All attempts have been made to maintain the clarity and

detail of information on the synoptic charts.

Synoptic charts 6.1 from 23th to 25th October 1987.


Synoptic charts 6.2 from 5th to 11th November 1984.

-~"jl,"__**H006* ^»: i

lo)" ~ ^—' / "JOB 'oo*


Synoptic charts 6.3 from 9th to 11th December 1988.


Synoptic charts 6.4 from 19th to 22th January 1991.

,,0T—~+"~~ X ^

\ A T

\ / *?°jj—=»-r~-'

wi2 \ V r \ J 1

\ V \iv Jk s \ \

+ \

18) -10 W-\- Jr

-A F"^^

1 ^^^-W20

^ X + X i

/ '

'• + \

r0W\ J>

T?K ' -U^H -

+ o 1012

jXT~ ' L

+

—'—^?—=—f-ita*

— r ^ +

I 1012

ki 1020 + 1oW \

\ • r " -w^xVi /

V +i t ,AT K

\ / , J E ™ ^ ' J r—Ksr\ x.

J012 \ \ .\ + V T \

M ^w\ y -ioiii40----r"" CX yx. JX-^"

+ \\ A (v \JL

Q

'iooo\ V

1004*"

^—-+^'°^\ \ \ ni

t^ion V. + 10VT ihi T "

' +\ 10 \

+/

+ f

T* * ' m

f +

V"^ <L 1012 M /

f Y+VLi-1 wo / /

/ / I J ° — / — _

*J N-J012

b-1020^_ /


Synoptic charts 6.5 from 7th to 11th February 1990.

APPENDIX C Synoptic. Weather Charts 261

Synoptic charts 6.6 from 10th to 11th March 1975.

APPENDIX D Data Used for GIS and Statistical Models 262

APPENDIX D

Data Used for GIS and Statistical Models

Geographical Location of Rainfall Stations and their Attributes

Landuse classes

CBD = Central Business District URT = Urban-Residential (treed) IND = Industrial areas R U S = Rural / Semi-Urban U R B = Urban-Residential (barren) R U O = Rural / Open areas T N P = Treed (National / Urban parks)

* Indicates the location of a rainfall station located in one of the four sub-topographic regions A, B, C and D. For stations numbers and their names see Appendix B,Table 6.2.

Table 7.2 Geographical location of rainfall stations and their attributes and average rainfall of the 6 biggest monthly thunderstorm rainfall events (October to March - 1975 to 1993).


Table 7.2 cont....


Table 7.2 cont.

Equations Used in SPANS GIS

APPENDIX E

Equations

Table 7.6 Equations which were written in SPANS GIS environment.

E Reclass Reclassification of Rainfall Map

: This equation reclassifies the thunderstorm rainfall map having more than 120 mm rainfall

A = { 1 if class (biggrain)>3, 0};

: where 'biggrain' is the name of the rainfall map (average of the biggest thunderstorm rainfall events

: where 1 and 3 are the number of classes on thunderstorm rainfall map

Result (A)

:The resulted map is the study area's map showing the areas having >120 rainfall amount

E Overlay Overlaying of the Reclassified Map

: This equation overlays (imposes) the reclassified rainfall map upon the physiographic maps

A = {class (input map) if class ('reclass map') = 1, 0 or o 0, 0};

: where input map is the name of each physiographic map in the equation. This equation was applied

for all of the physiographic maps such as the proximity, elevation, aspect and landuse maps of the

Sydney region.

Result (A)

:The result of the 'GIS overlaying modelling technique' is shown in Figure 7.5 (a-d).

1996 temporal and spatial study of thunderstorm rainfall

Documents