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Surname, Initial(s). (2012) Title of the thesis or dissertation. PhD. (Chemistry)/ M.Sc. (Physics)/ M.A. (Philosophy)/M.Com. (Finance) etc. [Unpublished]: University of Johannesburg. Retrieved from: https://ujdigispace.uj.ac.za (Accessed: Date). {\Al0 Ol\\J

HAIL IN THETRANSVAAL

SOME GEOGRAPHICAL AND CLIMATOLOGICAL ASPECTS

by

lANA OLIVIER

THESIS

submitted in partial fulfilment of the requirements for

the degree

DOCTOR IN THE NATURAL SCIENCES

III

GEOGRAPHY

in the

FACULTY OF SCIENCE

at the

RAND AFRIKAANS UNIVERSITY

PROMOTOR: PROF. P.AJ. VAN RENSBURG

MAY 1990 111111111111 3 00539 71.72 RAU BIB DECLARATION

I declare that this thesis is my own, unaided work, and that it has not been submitted previously asa dissertation or thesis for any degree at any other university.

;.~~~~~ .

May 1990 SUMMARY

Hailstorms ~e well-known phenomena in the summer rainfall region of southern Africa where they cause extensive damage - especially in the agricultural sector. This thesis examines the geography and climatology of hail in the Transvaal. It deals with three main issues, namely a) the spatial and temporal characteristics of hail days (HDs); b) rainfall and atmospheric conditions prevailing during hail events; and c) the geography of hail damage as it pertains to maize.

In the Transvaal, hail day frequency (HDF) increases with altitude and latitude in a non-linear (exponential) manner. Variations in altitude, as reflected in the diverse physiography of the area, account for most of the spatial and temporal variations in hail occurrence. Seven hail regions can be distinguished which differ from one another in terms of the onset times of hail, its seasonal occurrence and annual HDF patterns. In general, most hailstorms occur during November with the peak onset time varying between 16:00 and 20:00.

The most notable finding concerning rain - hail interrelationships, is that rainfall and HDF appear to be inversely related, years/months with high hail incidence being dry and viceversa. Daily and seasonal precipitation characteristics also differ between 'high hail years' (HHYs; dry) and 'low hail years' (LHYs; wet). For instance, during HHYs, the peak hail season is delayed while the rain season peaks earlier. Moreover, while the frequency of convective systems remains nearly the same during HHYs as in 'normal' years, the average precipitation area and the volumetric production decreases significantly. These anomalies appear to be the result of changes in the large-scale circulation patterns (as reflected by the transition from baroclinic to quasi-barotropic conditions) which influence the precipitation from mesoscale convective systems. It is likely that the Southern Oscillation plays a role in these changes, particularly during November and December. However, although these Southern Oscillation Index (SOl) - HDF associations are weak, they are appreciably stronger than those between the SOl and rainfall.

Rainfall characteristics on HDs differ from those of non-hail rain days in that, on HDs, more rain falls and the rain-bearing systems are more extensive. In general, atmospheric conditions are less stable, and the humidity level is higher, on HDs than on other days. Furthermore, HDs are characterized by warm north easterly winds near the surface but cold, dry south westerlies at the 600 hPa level. Above this the south westerlies become progressively stronger. ii

Hail damage patterns with regard to maize in the Transvaal exhibit temporal and spatial variations. The hail damage season is necessarily restricted to the period between crop emergence and harvest and hence differs ~om the true hail season..Most hail damage occurs during February and March when the maize plant reaches its flowering and seed-filling stages. Various indices were devised to reflect crop damage. These showed that most hail damage occurs, and hail risk to maize is highest, in the central, southern and south eastern Transvaal. iii

OPSOMMING

Haelvoorkoms is 'n welbekende verskynsel in die somerreenvalgebied van. Suidelike Afrika en is periodiek verantwoordelik vir omvangryke skade in veral die landbousektor. Hierdie proefskrif ondersoek die geografiese en klimatologiese aspekte verwant aan hael in Transvaal. Die drie hooftemas is: a) die tyd-ruimtelike eienskappe van haelvoorkomstes b) atmosferiese en reenvaltoestande wat ondervind word tydens haelvoorkomstes c) die geografie van haelskade soos van toepassing op mielieverbouing.

In Transvaal vermeerder die haeldagfrekwensie (HDF) eksponensieel met toename in hoogte en breedtegraadligging. Hoogtevariasies, soos gereflekteer in die diverse fisiografie van die gebied, is grootliks verantwoordelik vir die variasie in ruimtelike en temporele eienskappe van haelvoorkoms. Op grond van onderlinge verskille in die aanvangstyd van haelneerslag, die seisoenale voorkoms daarvan en die jaarlikse HDF-patrone, kan sewe haelstreke onderskei word. Breedweg gesproke kom die meeste haelstorms gedurende November, tussen 16:00en 20:00voor.

Die belangrikste bevinding LO.V. reen-hael interaksies, is dat daar 'n negatiewe verwantskap tussen reenval en HDF bestaan, m.a.w. tydens droe periodes is die HDF laer as normaal, en andersom. Afwykings in die daaglikse en seisoenale eienskappe van neerslag is ook waargeneem tydens 'hoe haeljare' (HID; droe jare) en 'lae haeljare' (LHJ; nat jare). Dit kan heel moontlik toegeskryf word aan veranderinge in Iugsirkulasiepatrone, soos gereflekteer in die oorgang vanaf barokliniese na kwasi­ barotropiese toestande, wat die seisoenaliteit van mesoskaalse konvektiewe sisteme beinvloed, Byvoorbeeld, tydens HHJ kom die piek haelseisoen effens later as normaal voor terwyl die reenvalmaksimum vervroeg. Voorts blyk dit dat, alhoewel die voorkoms van konvektiewe sisteme bykans dieselfde is in HHJ en 'normale jare', die gemiddelde oppervlak wat neerslag ontvang asook die hoeveelheid reenval, merkbaar afneem. Die Suidelike-Ossilasie Indeks (501) speel waarskynlik 'n rol in die veranderinge veral tydens November en Desember. Alhoewel die SOI-HDF verwantskap relatief swak is,is dit heelwat sterker as die tussen die SOl en reenval.

Reenvalkenmerke van reendae, met en sonder hael, verskil deurdat dit op haeldae meer reen en die reenproduserende sisteme ook meer ekstensief is. Oor die algemeen is atmosferiese toestande meer onstabiel en die humiditeit hoer op haeldae. Voorts word hulle gekenrnerk deur warm noordoostelike oppervlakwinde en droe suidwestelike winde op die 600 hPa-vlak wat progressief in snelheid toeneem met hoogte. iv

Haelskade t.o.v. mie1ies in Transvaal varieer ruimtelik en oor tyd. Aangesien haelskade beperk is tot die tydperk tussen die opkoms van die saailing en oes, stem die haelskadeseisoen nie ooreen met die . . meteorologiese haelseisoen nie. Die meeste haelskade word gedurende Februarie en Maart aangerig tydens die blom en saadvullingstadia. Verskeie indekse is geformuleer en bereken ten einde die omvang van haelskade te weerspieel, Die het getoon dat sentraal-, suid- en suidoos-Transvaal die gebiede is waar die meeste haelskade voorkom en waar hae1-risiko m.b.t. mielieverbouing die hoogste is. v

ACKNOWLEDGEMENTS

I am deepl¥ indebted to many people without whom this study would not have been possible.

I gratefully acknowledge the South African Weather Bureau, Sentraoes Insurance Company and the CSIR for providing the data on which the analyses are based. In particular I would like to thank Mrs. G Swart of the SAWE for her friendly assistance in supplying me so promptly with the vast quantity of weather data needed for the study. I would also like to thank the University of Stellenbosch and the RandAfrikaans University for the financial assistance which they provided.

In particular, I thank Dr. G Held, Prof IH Van Heerden and Dr. AL DuPisani for their support, comments and constructive criticism; Dr. HL Zietsman for the extensive programming assistance and his advice regarding the statistical analyses and interpretation of results; Mr. AC Vlok for translating the summary and for the production of the computer-drawn maps; Dr. IH van der Merwe for aid in programming; and Mr. I Mouton for the newspaper articles included in the Appendix.

My heartfelt thanks also go to: Mrs. Alta Pool, not only for the skilled cartographic work, but for her invaluable contribution in the final preparation of the thesis; Mrs. Marianne Botha, who unstintingly spent many hours preparing of the final text and tables; and Mrs. Anna van Heerden for typing the thesis. Apart from their help in such technical matters, these ladies were consistently friendly and supportive. Thank you.

A very special thanks to Mr. MKR van Huyssteen for his valuable comments, advice, and language editing. Thanks also to all my other colleagues for their support, patience and sense of humour. To Professors CJ Swanevelder and WS Barnard, thank you for easing my way during the course of the research, especially during the final stages.

My sincere thanks to my promotor, Prof PAI van Rensburg, for his guidance, supervision, encouragement and interest. His sense of humour lightened many dark moments. vi

My heartfelt gratitude goes to my mother who helped and supported me in every step along the way - not only through her moral support, but also in the extraction and preparation of the data, proofreading etc. I thank her and the immediate members of my household for their understanding, unflagging loyalty and love. Without them I would not have been able to complete the work.

To my friends who understood and forgave my antisocial behaviour and to all the other people who helped me - either directly or through their support and comfort ­ thank you.

Illuminationi Domini Dei Nostri auxilioque mihiplacet tu muttas agam gratias. vii

HAIL IN THE TRANSVAAL: SOME GEOGRAPHICAL AND CLIMATOLOGICAL ASPECTS

CONTENTS Summary I

Opsomming III Acknowledgements v List ofFigures XlI List ofTables XVI

List ofAbbreviations XlX

CHAPTER 1 : BACKGROUND 1 1.1 Introduction : Weather hazards and hail 1 1.2 Hail research in South Africa 2 1.3 Hailstorms in South Africa - an overview 5 1.4 The Thesis: rationale, aims and layout 7 1.4.1 Rationale 7 1.4.2 J\inrrs 10 1.4.3 Layout 10

CHAPTER 2: DATAAND METHODOLOGY 12 2.1 Data and data manipulation 12 2.1.1 Hail data 12 2.1.1.1 Data sources 12 2.1.1.2 Data verification 19 2.1.2 Rainfall data 20 2.1.3 Hail damage data 22 2.1.4 Other 22 2.2 Methodology 23 2.2.1 General statistical techniques 23 2.2.2 Principal Components Analysis 24 2.2.3 Superposed epoch analysis 26 viii

ANALYSIS: Part 1 GEOGRAPHICAL ASPECTS OF HAIL IN THE TRANSVAAL 27 CHAPTER 3 : SPATIAL HAIL DAY PATTERNS 28 3.1 Hail day frequency (HDF) patterns in South Africa. 28 3.2 Mean annual HDF patterns in the Transvaal 29 3.2.1 Data 29 3.2.2 Discussion 29 3.3 Factors affecting spatial HDF patterns 32 3.3.1 Latitude 33 3.3.2 Altitude 35 3.3.3 Urban effects 41 3.4 Delimitation of homogeneous hail regions 44 3.4.1 Method 44 3.4.2 Discussion 46

CHAPTER 4 : TEMPORAL HAIL CHARACTERISTICS 49 4.1 Introduction 49 4.2 Data and Method 49 4.3 Annual HDF time series 51 4.4 Seasonality of hail days 53 4.4.1 General hailstorm patterns in the summer rainfall regions 53 4.4.2 Transvaal hailstorms 54 4.4.3 Spatial patterns of hail day seasonality 56 4.5 Diurnal hail day patterns 57 4.5.1 General 57 4.5.2 Diurnal incidence of Transvaal hailstorms 58 4.5.2.1 Spatial variation in onset times 59 4.5.2.2 Seasonal variation of diurnal hail incidence 60 4.6 Summary 62

Part II : CLIMATOLOGICAL ASPECTS OF HAIL IN THE TRANSVAAL 65 CHAPTER 5 : INTER AND INTRA-ANNUAL RELATIONSHIPS BE1WEEN RAINFALL AND HAIL DAYS 67 5.1 Introduction 67 ix

5.2 Inter-annual hail-rain relationships 67 5.3 Intra-seasonal hail-rain relationships 70 5.3.1 Data 70 5.3.1.1 Hail data 70 5.3.1.2 Rainfall data 71 5.3.2 HHY precipitation vs longterm mean conditions 71 5.3.2.1 Hail day frequencies: TVLHHHYvs TVLHmean 71 5.3.2.2 Rainfall seasonality: TVLRHHy vs TVLRmean 74 5.3.2.3 Monthly rain day distribution: TVLRDHHyVsTVLRDmean 75 5.3.2.4 Rainfall per rain day 76 5.3.2.5 Spatial changes in precipitation seasonality 76 5.3.3 HHY vs LHY conditions 78 5.4 Summary 80 5.5 Rainfall on hail days and non-hail rain days 82 5.5.1 Introduction "82 5.5.2 Data and Method 83 5.5.3 Temporal hail day and non-hail rain day rainfall characteristics in Pretoria and the Witwatersrand 83 5.5.3.1 Occurrence frequency 83 5.5.3.2 Rainfall amounts 87 5.5.3.3 Rainfall intensity and areal extent 88 5.5.4 Rainfall on HDs and RDs during wet and dry years 89 5.5.5 Summary 92

CHAPTER 6 : ATMOSPHERIC CONDITIONS ASSOCIATED WITH HAIL AND LARGE-SCALE PRECIPITATION CONTROLS 96 6.1 Introduction 96 6.2 Some thermodynamic characteristics of hail days, non- hail rain days and dry days at the Irene Weather Office 96 x

6.2.1 The incidence of mesoscale weather systemsin the Pretoria-Witwatersrand area and their contribution to HDs and NHRDs. 97 6.2.1.1 Background 97 6.2.1.2 Data and Method 98 6.2.1.3 Discussion 99 6.2.2 Atmospheric stability and humidity characteristics 102 6.2.2.1 Analysis of temperature and dew point temperature 102 6.2.2.2 Thermodynamic profiles 105 6.2.2.2.1 Background 105 6.2.2.2.2 Method 108 6.2.2.2.3 Discussion 110 6.3 Atmospheric kinematics and circulation associated with hail 113 6.3.1 Atmospheric kinematics on HDs, NHRDs and DDs 113 6.3.1.1 Introduction 113 6.3.1.2 Wind direction 115 6.3.1.3 Wind speed 118 6.3.1.4 Vertical wind shear 119 6.3.1.5 Summary 120 6.3.2 Thermalwinds and temperate-tropical systems 121 6.4 Some large-scale precipitation controls: the Southern Oscillation and hail in the Transvaal 124 6.4.1 Introduction 124 6.4.2 Data and Method 125 6.4.3 Results and Discussion 126 6.4.3.1 Inter-annual SOI-HDF associations 126 6.4.3.2 Intra-annual SOI-HDF relationships 127 6.4.3.3 Intra-annual lagged relationships 129 6.4.4 Summary and conclusion 130

Part III : THE GEOGRAPHY OF HAIL DAMAGE WITH REFERENCE TO MAIZE IN THE TRANSVAAL 132 CHAPTER 7: SPATIAL AND TEMPORAL HAIL DAMAGE PATTERNS 133 xi

7.1 Introduction 133 7.2 Data 134 7.3 Temporal hail and hail damage patterns 137 7.3.1 Hail seasonality 137 7.3.2 Hail damage seasonality 138 7.3.2.1 District hail-claimvalues

(Rclaims) 138 7.3.2.2 Hail claim frequency (Nclaims) 139 7.3.2.3 Intensity-Susceptibility index 141 7.3;2.4 Crop-loss and crop-loss intensity indices 142 7.3.3 Hail risk seasonality and planting date 144 7.3.4 Inter-annual hail damage patterns 147 7.4 Spatial hail damage patterns 150 7.4.1 Relative hall storm frequency and spatial extent 151 7.4.2 Intensity-Susceptibility index 151 7.4.3 Crop hail damage 154 7.5 Hail risk zones 160 7.6 Summary 162

CHAPTER 8 : SUMMARY AND CONCLUSION 165

APPENDICES 176 Appendix I 177 Appendix II 181 Appendix ill 199 Appendix IV 203 Appendix V 205 Appendix VI 207 Appendix VII 209

REFERENCES 216 xii

LISTOF FIGURES

Figure 1.1 . Agricultural production, total weather losses and weather 3 losses as a percentage of production in South Africa in 1969(After Carte, 1977,327; Gillooly, 1978,436). Figure 1.2 The impact of adverse weather on the South African 3 agricultural sector (After Carte, 1977, 328). Figure 1.3 Crop losses caused by hail (based on South African 9 agricultural production in 1969) (After Carte, 1977,328). Figure 2.1 Location of 60 hail recording stations (WB 40). 13 Figure 2.2 Location of 66 Transvaal hail recording stations. Open 17 circles indicate stations for which rain data are also available. The position of the CSIR hail recording network is shown by shading. Figure 2.3 1960-1980 annual time series of (a) area-averaged point 20 hail day frequency (HDF) for SAWB stations, and (b) HDF for the Pretoria-Witwatersrand area (CSIR hail network area). Figure 3.1 Mean annual point hail day frequency (HDF) 1950-1964 28 (after Schulze, 1965). Figure 3.2 Longterm mean annual HDF pattern. 30 Figure 3.3 Mean annual point HDF (1960-1986). 30 Figure 3.4 Annual variation of thunderstorm frequency at three 33 locations with different latitudes (After Neuberger & Cahir, 1969. In: Linacre & Hobbs, 1977,87). Figure 3.5 Data matrix showing the number of weather stations 34 falling into specific latitude - HDF categories (a) using 10 latitude categories (b) 2°latitude categories. Figure 3.6 Scatter diagram showingHDF - Latitude relationship. 34 Figure 3.7 Isopleth map showing average hail day frequency and 37 location of stations used in the study. The insert shows the relief (in metres) above mean sea level. Figure 3.8 Scatter diagram showing HDF - altitude relationship. 37 Figure 3.9 Residuals from regression equation. All values except for 39 those indicated lie between + and - l. Figure 3.10 Data matrix showing the number of stations falling into 41 specific latitudinal and altitude categories (the lower xiii

values give the percentage of the number of stations which fall into each category). Figure 3.11, Homogeneous hail regions (using PCA). 45 Figure 3.12 Relief map of the Transvaal showing some major rivers. 48 (Source: Philips Altas, 1964). Figure 4.1 Map of Transvaal showing regions to which stations have 50 been allocated. Figure 4.2 Area-averaged time series of annual (July-June) hail day 50 frequency (1960-1986). . Figure 4.3 Annual HDF time series for (a) Region 1 (SW TVL); (b) 52 Region 2 (SE TVL); (c) Region 3 (W TVL); (d) Region 4 (Lowveld); (e) Region -1 (Northern TVL); (f) Region 7 (W-Central TVL) and (g) 7E (far Eastern TVL). Figure 4.4 Monthly hail day frequencies (a) areally-averaged for the 55 Transvaal (1960-1986); (b) during heavy hail years (HHY) (expressed as percentage of annual total HDF). Figure 4.5 Spatial variation of hail day seasonality over the 56 Transvaal. The y-axis represents mean annual HDF/station (x 100). Figure 4.6 Diurnal incidence of Transvaal hailstorms (expressed as 59 percentage of total daily hail frequency). Figure 4.7 Diurnal and seasonal incidence of Transvaal hailstorms 61 showing peak periods (percentage of daily total hail/hail day). Figure 4.8 Diurnal and seasonal incidence of hailstorms for different 63 parts of the Transvaal (percentage of total annual HDF). Figure 5.1 Area averaged time series of annual (July - June) (a) hail 69 day frequency (1960-1986) (b) rainfall (mm) (1960-1985). Figure 5.2 Transvaal hail day frequency seasonality - longterm and 73 during HHYs. Figure 5.3 Transvaal rainfall seasonality - longterm and during 73 HHYs..

Figure 5.4 Longterm mean and HHY rainfall intensity. 77 Figure 5.5 The number of stations with above, similar and below 77 normal rainfall during HHYs. Figure 5.6 Monthly distribution of HDFs during wet (low hail) and 79 dry (high hail) conditions. xiv

Figure 5.7 Monthly distribution of rainfall duringwet (low hail) and 79 dry (high hail) conditions.

Figure 5.8 (a) Hail day (HD) and non-hail day (NHD) frequencies 86 (b) Rainfall on HD and NHD . (c) No. of stations with rain on HDs and NHDs at Pretoria (1977/78 - 1978/79). (All values expressed as % of annual total).

Figure 5.9 Daily precipitation characteristics on HDs and NHDs 94 during wet and dry periods.

Figure 6.1 Likelihood of hail day spells of different duration. 100

Figure 6.2 Contribution of general, scattered and isolated rain to 100 rain day, hail day and non-hail rain day precipitation in the Pretoria-Witwatersrand area.

Figure 6.3 Plot~ of T.850 -T ,500~ against (T-Td)S5.D. + (T-Tdhoo at 103 Helliniko, Greece (Arter Prezerakos, lY~9, 33).

Figure 6.4 Atmospheric stability and humidity characteristics at 104 Irene (1981/82) on HDs, RDs and dry days (DDs).

Figure 6.5 Normalized ee profiles computed from the mean 107 sounding for the mesoscale storm category days (After Steyn, 1988b).

Figure 6.6 Normalized 8e profiles for (a) HDs, RDs and DDs (b) 109 different category hail days at Irene.

Figure 6.7 Percentage of HDs, RDs and DDs with (a) northerly 116 (poleward) and (b) easterly wind components.

Figure 6.8 Seasonal trend in the magnitude of the thermal wind at 123 Irene during wet and dry conditions (a) 1979/80 and 1978/79 (b) 1980/81 and 1981/82.

Figure 6.9 (a) Point HDF (Transvaal) and (b) Southern Oscillation 128 Index values for 1960-1984; illustrating the inverse nature of the relationship.

Figure 6.10 Composite map showing significant (95%) SOl-rainfall 130 correlation fields in the Transvaal for the period 1935­ 1986 (Van Heerden, et al., 1988, modified). Isolines represent r = 0,25;0.-0,05.

Figure 7.1 Magisterial districts in the study area. 135

Figure 7.2 Representativeness of insurance data. Expressed as 136 percentage area insured at Sentraoes (hat/area of maize land (ha). xv

Figure 7.3 Hail and hail damage seasonality. Mean monthly (a) 140 RclAIMS per district (in thousands); (b) poi~t hail day frequency; (c) NCLAIMS per districts; (d) RClAIMS/NClAIMS per dtstnct. Figure 7.4 Crop-Loss - Intensity Index for selected districts. 143 Figure 7.5 Susceptibility of maize to hail damage (in terms of crop 146 lost per age of plant). Figure 7.6 Inter-annual hail damage patterns. Annual time series 148 1981/82 - 1986/87 for (a) NpOLICJES; (b) crop-loss; (c) NCIAIMS; (d) RCIAIMS/NCIAIMS; te) RClAIMS/ha. - Figure 7.7 Spatial characteristics of I-S Indices 152 (NCIAIMS/NPOLICIES)· Figure 7.8 Spatial characteristics of I-S Indices 153 (RCIAIMS/NCLAIMS)· Figure 7.9 Spatial patterns of Crop-Loss Indices (as percentage of 155 RClAIMS/RpOLICIES)·

Figure 7.10 Spatial crop damage patterns (RCIAIMS/haINSURED). 157 Figure 7.11 Relationship between Crop-Loss and Intensity­ 158 Susceptibility indices for all Transvaal districts (Insert shows sub-regions used for analyses). Figure 7.12 Hail risk zones for maize production in the Transvaal. 161

Figure 7.13 Spatial distribution of agricultural (all crops) hail risk 162 zones. Figure 1.1 Newspaper clippings reporting recent hailstorms in South 178 Africa.

Figure IT.l Allocation of station numbers to rainfall stations 184 (adapted from SAWB, 1981). Figure IT.2 Geometrical representation of variables and Principal 191 Components (after Johnston, 1978).

Figure IT.3 Rummel's data cube or data box (after Rummel,1970). 194 Figure VIT.1 South African maize production (tons) and maize 213 production area (ha) 1955/56 - 1983/84 (Source: 1988 Abstract of Agricultural Statistics). Figure VIT.2 Maize Price Index time series 1955-1985 (Source: 1988 214 Abstract ofAgricultural Statistics). .

Figure VIT.3 Chi-squared test for significance of relationship between 215 I-S and F-E indices. xvi

LISTOFTABLES

Table 1.1 Losses due to adverse weather in the United States (after 2 Thompson, 1977). . Table 3.1 Relationships between altitude, latitude and HDF: 42 summary of statistical analyses and results. Table 4.1 Diurnal variation of hail onset times in different parts of 59 the Transvaal. Table 4.2 Summary of diurnal and seasonal characteristics of 63 hailstorms in various parts of the Transvaal (given In descending order of occurrence frequency). Table 5.1 Summary of data sets used in section 5.3. 72 Table 5.2 Mean monthly rain day frequency for HHYs and normal 76 (N) conditions for the Transvaal stations. (underlined values indicate the higher value)

Table 5.3 Peak precipitation season (usingweighted means). 81 Table 5.4 Total regional and mean annual precipitation 84 characteristics of hail days and non-hail rain days for the Pretoria-Witwatersrand area (Sept - April).

Table 5.5 Monthly hail day and non-hail ram day rainfall 85 characteristics for the Pretoria-Witwatersrand region (1977/78 - 1979/80); Pretoria (1977/78 - 1978/79); the Witwatersrand (1978/79 - 1979/80).

Table 5.6 Precipitation characteristics of hail days (HDs) and non­ 91 hail rain days (NHDs) for Pretoria and the Witwatersrand during wet+ and dry+ years.

Table 6.1 Seasonal incidence of widespread, scattered and isolated 101 showers on hail days and non-hail rain days in the Pretoria­ Witwatersrand area, 1978 & 1979 (given as % of monthly precipitation days).

Table 6.2 Mean equivalent temperature (Oe) at various pressure 110 levels for hail days, non-hail rain days and dry days at the Irene weather station (1981/82).

Table 6.3 Differences in the 13:00 Oe values between surface and 112 mid-levels for hail days, non-hail rain days and dry days at the Irene weather station (1981/82).

Table 6.4 Meridional and zonal wind components on hail days, non­ 117 hail rain days and dry days at the Irene weather station (13:00, February, 1982). . xvii

Table 6.5 Mean wind speed (m/s) on hail days, non-hail rain days 117 and dry days at the Irene weather station (13:00, February, 1982). Table 6.6 Mean thermal wind strength (V ) between various pressure 120 levels and the surface on hail Jays, non-hail rain days and dry days (13:00, Irene, February, 1982).

Table 6.7 Ranked annual point HDFs (Jul - Jun) for the Transvaal 127 (1960/61 - 1984/'85) and the associated Cold and Warm Events in the Pacific.

Table 6.8 Correlation coefficients for monthly SOl and HDF values 128 (~970 - 1983; Pretoria-Witwatersrand area).

Table 6.9 Pearson's (r) and Spearman's Rank (r~) correlation 129 coefficients for three-monthly mean 501 values and monthly HDFs for the Pretoria-Witwatersrand area (1979 ­ 1983).

Table 7.1 Onset of.spring planting rains in the south eastern 145 Transvaal (1981/82 - 1984/85).

Table 7.2 Weekly crop-loss during the growing season (expressed as 146 percentage of the estimated yield destroyed and of the total crop lost).

Table 7.3 .Hail day frequency (HDF) and cumulative HDFs in the 147 Transvaal (1981/82 - 1986/87).

Table 704 Estimated cost of hail damage to maize (1987). 156

Table 1.1 Gross value of individual agricultural crops (excluding 180 horticultural and animal products) 1975/76 - 1986/87. (Source: Abstract of Agricultural Statistics, 1988,83)

Table IT.l Some locational and HDF characteristics of weather 182 stations. (Source: WB40, 1986)

Table IT.2 Codes for thunderstorms used by the SAWB since 1982. 185 (Source: SAWB, 1981)

Table IT.3 Time codes for hail (SAST). . 186

Table ITA Annual point HDF (1960 - 1986) at selected Transvaal 187 stations. (Year given as Julian year e.g. 1960 = July 1960­ June 1961)

Table IT.5 Matrix elements. 192

TableIT.6 Mode of analysis resulting from differentslices of a data 195 cube. xviii

Table ill.1 Test for difference in area with < 2HD/annum: 1950 ­ 200 1964 and 1960- 1986.

Table mz x2 test for independence between HDF and latitude. 201 Table m.s PCA loadings: Based on annual HDF characteristics of 202 various hail recording stations in the Transvaal. (PCA: correlation matrix: unrotated solution)

Table IV.l Year with the highest hail frequency (HHY) for each 204 Transvaal station. Table V.l Summary of rain and hail data for the Pretoria and 206 Witwatersrand areas for 1977/78 - 1978/79 and 1978/79 ­ 1979/80, respectively.

Table VI.l Dates of hail days, rain daxs and dry days in the Pretoria­ 208 Witwatersrand area, 1981/82. Widespread, scattered and isolated precipitation categories have been distinguished.

Table VII.l Hail damage indices: summary and meaning. 210

Table VII.2 Maize hail damage data given as the total for the six 211 seasons: 1981/82 - 1986/87.

Table VII.3 First maize production estimates for the 1986/87 season. 212 (Source: Maize Estimates: 1987 Area and Crop: RSA Development Regions, Dept. of Agricultural Economics and Marketing, Pretoria) xix

LISTOF ABBREVIATIONS

BEWMEX Bethlehem Weather Modification Experiment C-LIndex Crop-Loss Index CSIR Council for Scientific and Industrial Research DD dry days F-E Index Frequency-Extent Index HD hail days HDF hail day frequency HHY high hail year I-S Index Intensity-Susceptibility Index LHY low hail year NHDorNHRD non-hail rain day i.e, a rain day on which no hail fell

N CLAIM or NpOLICIES The number of hail claims submitted to Sentraoes by Transvaal maize farmers p pressure (hPa) Pta Pretoria r correlation coefficient R rainfall R2 Coefficient of Determination

R CLAIMS or RpOLICIES The value of hail insurance claims or policies taken out at Sentraoes by maize farmers RD rain days RDF rain day frequency SAWT South African Weather Time SAWBorWB South African Weather Bureau SO and SOl Souther Oscillation and Southern Oscillation Index T temperature in °c

Td dew point temperature TVL Transvaal u zonal wind component v meridional wind component thermal wind strength Water Research Commission w wind speed (rn/s) WWR Witwatersrand xx

level of statistical significance Chi-square statistic potential temperature equivalent potential temperature wind direction measured clockwise from north

(J standard deviation 1

CHAPTERl BACKGROUND

1.1 INTRODUCTION: WEATHER HAZARDS AND HAIL

Hailstorms have been known and feared by man since the earliest times. This is quite understandable as in the mid-latitudes, these atmospheric events are the most dangerous amongst all weather manifestations (Rakovec, 1989). One of the earliest records of hail damage is to be found in the Bible and describes the havoc wreaked by a hailstorm during the reign of Ramses II during the 12th century B.C.

"...It was the worst storm that Egypt had ever known in all its history. Allover Egypt the hail struck down everything in the open, including all the people and the animals. It beat down all the plants in the field and broke all the trees The flax and the barley were ruined because the barley was ripe, and the flax was budding. But none of the wheat was ruined, because it ripens later". (Exodus 9 verses 23-25; 31-32, Good News Bible, 1979).

In addition to the above account of the seventh plague, the Bible contains much direct and indirect information concerning climatic hazards of the past. Camuffo (1990), gives an interesting and informative resume of particularly momentous hail events recorded in the Bible and in early Roman chronicles.

Turning to the present, it is clear that hailstorms are equally destructive now as in earlier times (Hewitt & Burton, 1971; White & Haas, 1975; Hobbs, 1980; Newark, 1989). According to Todd & Howell (1985), losses from hail in the United States run into several hundred million dollars annually. The most severe loss from a single storm (prior to 1985) caused damage worth $200 million and resulted from a hailstorm which struck Dallas, Texas in May, 1981. Numerous weather and climate impact studies have shown that the agricultural sector suffers most from adverse weather (McQuigg, 1964; Thompson, 1977; Maunder, 1986). (See Table 1.1.) Moreover, it has been established that 80% of all hail losses in the United States result from damage to crops (Maunder, 1970). 2

Table 1.1 Losses due to adverse weather in the United States (after Thompson, 1977).

Overall losses Activity US $ in % of annual millions gross revenue

Agriculture 8 240 15,5 Construction 998 1,0 Manufacturing 597 0,2 Transportation (rail, highway and water) 96 0,3 - Aviation (commercial) 92 1 , 1 Communications 77 0,3 Electric Power 45 0,2 Energy (e.g. fossil fuels) 5 0,1 Other (general public, government) 2 531 2,0

Total 12 684 20,7

Source: Maunder, 1986,93

It is estimated that the South African agricultural output is decreased by about 23% as a direct result of adverse weather - with 41% of these losses caused by drought alone (Theron et al., 1973). Hail constitutes the next most important meteorological hazard accounting for 9% of these weather induced losses (Carte, 1977; Gillooly, 1978). Figures 1.1 and 1.2 show the impact of various weather factors on agricultural production in South Africa.

1.2 HAIL RESEARCH IN SOUTH AFRICA

As a result of the economic importance of the, drought hazard in South Africa, the primary focus of much meteorological and climatic research has been to assess, evaluate and ultimately forecast rainfall. This has stimulated a vast 'flood' of research over the years concerned with South African rainfall in general and specifically with marked defects in the rainfall (e.g. Zucchini & Adamson, 1984). In recent years, there have been repeated attempts to link rainfall changes to atmospheric circulation characteristics (Rubin, 1956; Triegaardt and Kits, 1963; Hofmeyr & Gouws, 1964; Tyson, 1981; Miron & Lindesay, 1983; Miron & Tyson, 3

ui w o '" >- z l- o UJ >- e >- UJ >- S W a: Z '"< "UJ UJ S a: '" a: N e, o S '" a: :::l UI a: W < ,: a: -' :::l Z o (J ... ~ ...J (J u, m ... ~ o '"~ (J :> ~ >­ a: ... a: ~ ;: :::l Z >- (J -' ri ... a: '" ~ ci :::l :::l I­ < ;( -' -c UJ UJ '" (J < ,: m o UJ :::l o a: o s m 200 o :::l :J: o UJ UJ a: o o '";;: lL U s e e, :J: ;: '" '" a > >- '" 100

'"w "..c: '"o '0- ...J 100b a: ..c: ~ ~ 50 : =c ·e ~ -= o :.: :- ;: ::L.._nJ:;'-.l-..I:O;J,--

PRODUCT GROUP

Figure 1.1 Agricultural production, total weather losses and weather losses as a percentage of production in South Africa in 1969 (After Carte, 1977, 327; Gillooly, 1978, 436).

c en 40 z en < 0 100 a: ...J u, a: 30 o w en :I: z !;( o W ::::i ;: 20 ...J 50 ~ ...J ;: ~ 0 10 en I- en U. 0 s 0 .. "* DROUGHT HIGH HAIL RAIN LOW WIND SUN- FROST HUMIDITY TEMPS. TEMPS SHINE

Figure 1.2 The impact ofadverse weather on the South African agricultural sector (Mer Carte, 1977, 328). 4

1984; Harrison, 1986; Lindesay, 1988a), and to various partial causes, inter alia, the Southern Oscillation Index (Walker & Bliss, 1930; Stoeckenius, 1981; Harrison, 1983a, 198~; Schulze, 1983; Pittock, 1984; Lindesay, 1988a&b; Van Heerden et aI., 1988).

In contrast, relatively little attention has been devoted to hail occurrence in South Africa or to its impact on the economy. However, the immense destructive ability of hail has led to large commitments by both private and government agencies to pursue research into the processes involved in cumulus cloud development, the formation and growth of hail and hail modification (Simpson, 1976; Dennis, 1980; Kelbe, 1984). There are three main South African hail research programmes which deserve mention. Firstly, there is the government sponsored research programme at Bethlehem (OFS), previously known as the South African Weather Modification Experiment (BEWMEX) (Harrison, 1974). When it was established in the mid­ 1970s it was jointly financed by the ' South African Weather Bureau (SAWB), Sentraoes Insurance Co. and the Water Research Commission (WRC). Although it initially investigated hail suppression, the emphasis has now shifted to rainfall stimulation (Held, 1985). Secondly, the hail research programme at the Council for Scientific and Industrial Research (CSIR) in Pretoria; which was started in 1962 and is partly government subsidized. However, in the late 1980s this institution changed its policy to one of market orientated research. It is not known what impact this will have on hail research in future. Atmospheric scientists at the Division of Earth, Marine and Atmospheric Sciences and Technology (formerly known as the National Physical Research Institute) of the CSIR obtain hail information from, inter alia, a large network of voluntary observers in the Pretoria-Witwatersrand area. The research is extremely diverse and includes aspects ranging from hailstorm dynamics, the growth of hailstones and the study of lightning. An extensive list of publications reflects the work done there and as such these scientists have made an extremely important contribution to understanding and predicting hail on the Transvaal Highveld. Thirdly: Between 1971 and 1980, a privately sponsored hail suppression programme was operational in the Nelspruit area of the Eastern Transvaal Lowveld (Mather, 1977). Subsequently, this programme has been converted into a research project aimed at assessing the potential of rain augmentation in the Lowveld. It now functions under the auspices of the WRC (Kelbe, 1984; Held, 1985). 5

Numerous publications have resulted from these programmes. For example, Free State rainfall processes and characteristics have been described by, inter alia, Harrison (1984b), Court (1979a&b), Greenacre & Pearce (1979) and Steyn (1988a&b). Papers on hailstorms in the Pretoria-Witwatersrand area were authored by Carte (1965, 1966a&b, 1979, 1980, 1981); Carte & Basson (1970); Carte & Held (1972, 1978); Held & Carte (1973, 1979); Held (1973, 1974, 1977a&b, 1978, 1985); Carte & Mader (1977); Held & Van den Berg (1977) and Mader (1979), while Garstang, Emmitt & Kelbe (1981); Kelbe & Garstang (1983); Kelbe, Garstang & Brier (1983) and Garstang et al., (1987) examined Eastern Transvaal storms. Since the early 1970s hailstorms have been studied by means of weather radar (Mader, 1979) and the results of many case studies have been published, for example, Held and Carte (1973), Carte and Mader (1977), Held (1977, 1982), Held and Van den Berg (1977), .Carte (1979, 1981), Mader et al. (1986).

1.3 HAILSTORMS IN SOUTH AFRICA: AN OVERVIEW

Perusal of the titles of the above-mentioned articles gives the impression that the majority of Highveld hailstorms occur as line storms along clearly defined squall­ lines. This is in fact not the case. Scattered and isolated hailstorms occur more often here (54% and 39% of the time, respectively) (Carte & Held, 1978) but line storms are more severe, causing greater damage and flooding. For this reason they are frequently the subject of investigation.

According to Held & Van den Berg (1977), squall-lines on the Highveld are rarely associated with the passage of cold fronts on the surface. In this respect they differ from hailstorms in the USA where + 75% are associated with cold fronts. In the Lowveld, the occurrence of severe hailstorms are closely allied to the passage of westerly waves (and, as such, are influenced by the latitudinal position of the Indian Ocean Anticyclone). During winter the westerly waves and their associated cold fronts are usually confined to the Lowveld and Escarpment by the steep topographic barrier along the south east coast and the prevailing upper air inversion. However, during spring and early summer, changes in the diurnal heating cycle cause concomitant changes in atmospheric stability and together with the "assistanceof the topographical features, initiate deep convection over the Highveld and Escarpment" (Kelbe et al., 1983, 250). Hailstorms associated with cold fronts are well documented (Held & Van den Berg, 1977; Held, 1977b, 1982) but according to 6

Tyson (1986, 131), "the passage of summer cold fronts across South Africa has long been a matter of debate (Triegaardt & Kraus, 1957; Taljaard et al., 1961; Taljaard, 1972). However, it is increasingly being accepted that such fronts may be associated with severe weather conditions (Tucker, 1971; Held and Van den Berg, 1977; Estle, 1978; Held & Carte, 1979; Carte, 1981; Garstang et al.; 1985)". Summer cold fronts over the interior are associated with marked mesoscale wind shears and low level convergence while changes in pressure and temperature are small (Garstang et al., 1985; Harrison, 1986).

Most severe thunderstorms tend to occur during the early part of the season. During this period (October-December) circulation controls over the interior are a combination of tropical and temperate, but during January, February and March they are largely tropical (Harrison, 1986). This implies that during mid-to-late summer, hailstorms are likely to be associated with easterly waves. These waves usually develop in the vicinity of an easterly jet and exhibit barotropic instability. They thus "take the form of barotropic waves or cool-cored closed lows that are evident at 850 and 700 hPa" (Tyson, 1986, 127). Moreover, "the surface trough of an easterly wave is usually associated with a well-defined boundary separating moist air from the north east from drier air to the south west. Line thunderstorms tend to develop within a convergence zone extending about 200-300 km eastward of the moisture discontinuity (Taljaard, 1958)" (Tyson, 1986, 128). In addition, easterly waves were found to be the main source of general rains over the interior (Jackson, 1951).

In general, line storms are orientated in directions varying from north west-south east through east-west to north east-south west. However, they usually travel from south west to north east, steered by the winds at mainly the 500 hPa level (Carte & Held, 1972; Held, 1973, 1977b, 1985; Held & Carte, 1973; Carte & Mader, 1977; Held & Van den Berg, 1977; Carte, 1981; and Mader et al., 1986). Tyson (1986, 132) describes the mechanisms responsible for hail generation in line storms as follows: "warm, moist northerly surface air fuels the convection; dry upper air with a southerly component steers the storms and allows cooling through evaporation of precipitation in the main convective towers and anvils of the cumulonimbus clouds; the vertical shear in the horizontal wind is instrumental in maintaining the necessary patterns of convergence and divergence and propagationof the squall-line, and 7

invigoration of cells occurs with the onset of precipitation and the initiation of down draughts."

In addition'to the synoptic forcing by easterly or westerly waves, thunderstorms may develop in association with a ridging anticyclone. This advects moisture from the Indian Ocean via easterly, south easterly or north easterly surface flows, creating potentially unstable near-surface air. With divergence in the upper air circulation, hailstorms may result (Held & Carte, 1979; Garstang et al.; 1981).

Scattered and isolat.ed thunderstorms are less the result of synoptic forcing and more the consequence of other factors such as the diurnal heating cycle and mesoscale forcing. The impact of the former is clearly illustrated by the occurrence of hail storm peaks during the late afternoon. It is thus apparent that most thunderstorms, especially those on the Plateau, are triggered by free convection. Although a high frequency of hailstorms occur on the Highveld, long-lived ones are uncommon. The lack of sustained steady-state storms, as well as the reduction in the severity of late summer storms, have been ascribed to the relatively weak upper winds that prevail over southern Africa - especially in summer (Carte & Held, 1978; Mader, 1979; Carte, 1980; Kelbe, 1984).

1.4 THE THESIS: RATIONALE, AIMS AND LAYOUT

1.4.1 Rationale

The majority of the above-mentioned studies describe the meteorological conditions associated with hail events, the properties of hail-producing cumulus clouds or the microphysics of hail production. Moreover, research concerning hail damage emphasize hailstone size, travel trajectory and hail intensity (Roos & Carte, 1973; Roos, 1978, 1979, 1980, 1981). Most of these papers mention - to a greater or lesser extent - annual or seasonal hail day' frequencies or diurnal occurrence patterns but few have been aimed at a comprehensive study of the geography of hail in South Africa. The one exception is the work by Schulze (1965) in which he describes spatial and temporal hail patterns and presents the former in terms of a hail distribution map. This work serves as the main source of general information concerning South African hail occurrence patterns. As far as can be ascertained, no attempt has been made during the last 25 years to update this study. This is 8

surprising considering the economic importance of hail, the advances made in understanding precipitation mechanisms and controls and the improved quality (and quantity) of hail data available. Moreover, the recent widespread hail damage which was reported in various parts of the Republic has highlighted the importance of hail as a meteorological hazard. During the 1989/90 season alone hail has damaged buildings and vehicles, it has destroyed maize, wheat and deciduous fruit crops; it has killed livestock and people - and has all but annihilated flocks of migrating birds (see Appendix I). The large number of reports which have appeared in print over the last few years has created an impression of increasing hailstorm frequency and severity and/or changes in hail seasonality. Whether this is in fact the case has as yet not been ascertained, but it is abundantly clear that a comprehensive geographic analysis of hail and hail damage is overdue. Such an analysis is the primaryobjective ofthis thesis.

A geographic study constitutes not only a description of spatial and temporal patterns but includes the study of man-environment interactions. The extensive hail damage sustained by the agricultural sector makes it an obvious choice for analysis. However, a meaningful assessment of the impact of any meteorological hazard on agriculture requires information on the climatic probability of the hazardous event occurring as well as the effect of that event upon the yield. Studies have shown that the severity of crop losses varies spatially and with the type of agricultural activity practiced in different parts of the country (Theron et al., 1973). Figure 1.3, which depicts the impact of hail on different commercially grown crops in South Africa, clearly illustrates the susceptibility of maize to hail damage. Moreover, maize is in fmancial terms, the most important crop in the Republic (see Table 11, Appendix I). The total 1986/87 maize production was estimated to have been 7 371 000 tons with a gross value in excess of R1 735 million (Republic of South Africa, 1988a). For these reasons, the studyfocuses on the impact ofhail on maize. 9

Figure 1.3 Crop losses caused by hail (based on South African agricultural production in 1969) (After Carte, 1977, 328).

Since hail is a weather hazard, this study necessarily includes an analysis of weather and climatic conditions associated with its occurrence. Furthermore, hail is inextricably associated with other forms of precipitation such as rain. An investigation of rain-hail interrelationships might be of some importance to climatologists.

The study area was confined to the Transvaal for a number of reasons. Firstly, there is considerable variation in hailstorm incidence over the area. In the southern and south eastern parts, hailstorms occur quite frequently while in the far northern and north eastern Transvaal, they are rare (Schulze, 1965). Secondly, according to Theron et al. (1973), some of the heaviest weather-induced crop losses in South Africa occur in the Transvaal. Thirdly, good quality data are available for this area. In addition, the area is large enough and topographically diverse enough to reveal 10

spatial hail and hail damage patterns, yet small enough to make such a multifarious study feasible.

1.4.2 Aims

The aim of this thesis is to describe the geography and climatologyof hail and hail damage in the Transvaal. In particular, the following aspects will be investigated:

1. spatial and temporal hail patterns

2. weather and climatic conditions associated with hail as well as . hail-rain relationships

3. spatial and temporal hail damage patterns with the aim of delimiting hail risk zones for maize production in the Transvaal.

1.4.3 Layout.

The thesis has been divided into eight chapters. Apart from the introduction (Chapter 1), the description of the data sets and techniques used (Chapter 2), and the summary and conclusion (Chapter 8), the remaining chapters have been devoted to examining various aspects of hail geography and climatology. They have been grouped into three sections - Parts I, II and ill, respectively - corresponding with the aims of the thesis as set out in paragraph 1.4.2 above. Moreover, each chapter (3 through 7) addresses a particular issue and as such can be considered as an independent study. Therefore each of these five chapters commences with an introduction which is followed by a comprehensive review of the relevant literature; a description of the data and methodology used; an analysis and discussion of the results obtained; and a summary of these.

Part I focuses on the geography of hail in the Transvaal and contains two chapters, namely Chapters 3 and 4. The spatial characteristics of hail in the Transvaal are presented in Chapter 3. The factors which influence spatial hail day patterns are identified and their importance assessed. Thereafter the study area is divided into 11

homogeneous hail regions. In Chapter 4, temporal hail day patterns are described on annual, seasonal and daily time scales. The second section, i.e. Part II, deals with various. climatic characteristics associated with hail occurrence. and also comprises two chapters. In Chapter 5, the relationship between hail and rain is examined in terms of annual time series and seasonal trends. Differences between rain and hail seasonality during wet and dry conditions are given some attention. This is followed by an analysis of the precipitation characteristics of hail and non­ hail rain days. Most of Chapter 6 is devoted to identifying (and differentiating between) the atmospheric conditions prevailing on hail days, non-hail rain days and dry days. In addition, tropical and temperate circulation patterns are described for wet and dry periods and links between the Southern Oscillation Index and Transvaal hail are explored. The last part of the analysis, Part III, is concerned with the geography of hail damage and comprises only one chapter, namely, Chapter 7. In it both spatial and temporal hail damage patterns are described. The section culminates in the delimitation of hail risk zones for maize production in the Transvaal.

Parts of the work presented in this thesis have been published during the course of the research. Part of Chapter 3 has been published in the South African Journal of Science while most of Chapter 4 appeared in the South African Geographer. Unpublished presentations of aspects of the work have been made at various SASAS (South African Society for Atmospheric Scientists) and Geographical Societyconferences.. 12

CHAPTER 2 DATA AND METHODOLOGY

The data required to undertake this study can be divided into three main categories, namely, hail data, rainfall data and hail damage data. The sources of these (and other smaller data sets) and the manipulations applied in order to render them in a format suitable for analysis, are discussed in the first part of the chapter. This is followed by a short description of the most important techniques used for analysis.

2.1 DATAAND DATAMANIPULATION

2.1.1 Hail data

Hail is a very difficult phenomenon to monitor accurately as it is extremely variable and patchy. Often only small areas are affected at a time and even within an area the severity of the hail can change quickly. This is mainly due to movement of hail cells of irregular shape, their repeated expansion and contraction and the passage of more than one cell past a given point (Carte & Held, 1972). The intermittent nature of hail falls thus makes it impossible to determine the number of individual storms emanating from a cloud and for this reason the number of hail days (HDs) has been used as an indicator ofhailstorm frequency.

2.1.1.1 Data: sources and extraction

The only readily available source of long-term hail data for South Africa is the 1986 Weather Bureau publication, commonly referred to as WB40. In it, information on long-term mean monthly and annual temperatures, humidities and precipitation data are given for a large number of weather stations. Consequently, WB40 served as the data source for all long-term mean rainfall, rain day frequency (RDF) and hail day frequency (HDF) values used in this study.

Ideally all Transvaal weather stations listed in WB40 should have been included in the data set. Unfortunately, HDF data were not available for 24 of these. The data set was further reduced by taking the spatial distribution of the remaining stations into account - hence eliminating possible bias caused by an over-representation of stations ina particular area. Thus, where a number of stations were clustered 30' 32' 22' 25' 26' 27" 20' 29' 31 /.-'.':.7:?f Macuville

23'

24'

25'

26'

Frankfort •

100 20' o

Figure 2.1 Location of 60 hail recording stations (WB 40). -> w 14

together, only the one with the longest record or the highest HDF was included in the data set. The location of the 60 stations so selected are shown in Figure 2.I. Additional.information concerning their location and characteristics is supplied in Table TI.1 (Appendix TI).

It is evident that the attempt to achieve an even spread of data points throughout the study area was not particularly successful and it is possible that the paucity of rainfall stations in the western, northern and eastern Transvaal could cause some spatial bias. This should be taken into account when interpreting results. The fact that rainfall in the-drier north western Transvaal is not normally distributed (Dyer, 1974; Onesta and Verhoef, 1976) and could influence the applicability of parametric statistical tests, is not particularly relevant since individual station data are rarely used in the analyses. Instead, most rainfall and HDF data were areally-averaged. This required a certain amount of data processing since it is not statistically valid to obtain means of means. Therefore, the relevant monthly or annual station mean (from WB40) was first multiplied by that station's record length (in years); whereafter these values were summed for all stations; and the total divided by the sum of the individual record lengths of all the stations. This procedure is summarized as follows:

m L: (Xi . n . ) ~ 1 X = m L: n.a, 1 where X = areally averaged long-term HDF

Xi = long-term mean HDF for station i (from WB40) m = number of stations n = record length (in years)

An additional data source, comprising a series of annual weather summaries, was published by the South African Weather Bureau (SAWB). However, these were discontinued in 1977 and were replaced by monthly weather summaries. These publications had only limited value as a source of hail data - mainly due to scanty hail information and, more importantly, the use of different suites of weather 15

stations in each publication. This prohibited the compilation of annual HDF time series for all but a few core stations. Moreover, the large number of monthly weather summaries that had to be consulted before a HDF record of an acceptable length could be obtained, discouraged their use. An added disadvantage was the fact that the dates on which hail occurred could not be obtained from these publications. Since hail dates were required for a number of analyses in Chapters 5 and 6, the above-mentioned source could not be used for hail data. However, monthly rainfall data for the 36 stations shown on Figure 2.2 were extracted from these publications.

The HDF data set which was used throughout this thesis was compiled from daily 'past weather' records for the 1960-1986 period. These were supplied on magnetic tapes and as printouts by the SAWE, Pretoria. Because of difficulties encountered in reading the tapes, data were extracted by hand from printouts. A programme which could successfully read and extract hail data was eventually obtained and the resulting information was used to verify the HDF data set. Since this data set formed the basis of most of the analyses, a detailed description of the data and the compilation of the HDF data set is given below.

Data: A past weather record consists of a station ID code; the year, month and time to which the data apply; followed by a long sequence of numerical codes which indicate the daily weather conditions which prevailed there.

Weather Bureau station ID code: The SAWE uses a system of grids to divide the Republic into 30/ latitude by 30/longitude blocks. These are numbered from west to east and from south to north. The first four digits of the ten-digit station ill code refers to the block in which the station lies. Each block is divided into I' x I' intervals, resulting in a total of 900 intersections. These are numbered from the top left hand comer, from top to bottom and in progressive longitudinal order. The following three digits (i.e. digits 5 to 7) in the ID code, refer to these numbers and as such indicate the position of the station within the block (see Fig. 11.1). The weather station ID code thus indicates its 16

exact geographical position within a 1,6 km x 1,6 km area. The final numbers and letters in the station ill code are simply test symbols and have no bearing on its position (SAWB 1986).

Data were extracted from daily past weather records for the following blocks: 396-399; 405-410; 433-446; 470-484; 507-520; 544-558; 584­ 596; 628-639; 672-682; 717-726; 762-768; 807-812.

The preliminary selection of stations was based on the duration and quality of the records and the location of the station. Stations which were operational for at least 15 years were considered for inclusion. Where clustering occurred, that station with the best quality record (and the highest HDF) was selected. However, in areas where no station met these requirements, ones with shorter records were used in order to obtain a better spatial distribution (Fig. 2.2). Even so large parts of the western, northern and eastern Transvaal are under­ represented and this limitation must be kept in mind when interpreting the results of the analyses.

Hail data

The occurrence of hail on a specific day is also indicated by means of numerical codes on the past weather records. Unfortunately, these codes were changed in 1978 and again in 1982. Prior to 1978, hail data were supplied in a semi-summarized form - with the codes 1, 2 and 3 indicating the relative intensity of the hail event. However, the time of the hailstorm could not be deduced from these records. Conversely, after 1977, this information was given - but intensity data were not supplied. Hail ill codes included all numbers ranging from 82 to 98 for 1978 to 1981; while for the post-1981 period, the following codes were used: 87, 88, 89, 90, 93, 94, 96 and 99 (SAWB 1982). A summary giving the meaning of each of these is presented as Table II.2 (Appendix II). Since 1978, past weather information has been given for four or eight-hourly periods and hence the approximate time of each hail event could be determined. The time codes used by the Weather Bureau and their meanings are listed in Table II3. 17

o

230 Marnitz Mara Levub\3 0 ° ° Giyani °Sandpan

0T~aneen Pietersburg •

24° Groendraai0 0Potgietersrus Hoecspruit Thabazimbi Tswelopele Nylsvley . &aalhoek Skuku~a WarmbadG 0 0Rooibokkop 0 Sa~je 0 Pilanesberq 0 Oudestad ° Lydenburg Pretoriuskop Undleypoort 0Loskopdam . G Brit~ ° Roodeplaat 0Nelspruit Marico R"".",,"~ I~tm G.m,bo'ili,"~:·"" 0 Barberton 26" }~{¢!lm Smuts o Carolina Piggs peal

o 100 200km

Figure 2.2 Location of66 Transvaal hail recording stations. Open circles indicate stations for which rain data are also available. The position of the CSIR hail recording network is shown by shading. 18

From the above discussion it is obvious that extraction of hail data was an exceptionally tedious and time-consuming activity - prone to error as a result of the sheer bulk of the raw data and the relative paucity of hail events. This was further exacerbated by the use of different hail ID codes during various periods. Data extraction by computer was as hazardous since the format of hail information differed from one tape to the next; field lengths varied; and records were incomplete, incorrect or unreliable. Incompatibility between the computers used for loading and reading the data added to the difficulties - all of which resulted in extensive delays. .

Nevertheless, 1960-1986 hail data were extracted from daily past weather records for the 66 Transvaal stations shown in Figure 2.2. These were used to obtain monthly and annual HDFs for each station (see Table II.4, Appendix II). However, only few stations had complete, uninterrupted records for the entire 26-year period. Periods with missing data obviously affect both HDF totals and means. Therefore, all periods for which data were missing or unreliable were carefully noted and the necessary adjustments made in the calculation of mean values.

The annual HDF time series for the Transvaal was obtained by areally-averaging annual HDF for all 66 stations as follows:

m z> x . 1 1 = m n Lni 1 where Xyear = areally averaged HDF for a particular year Xi = total HDF for that year for station i n = no. of months for which data were available m = total no. of stations operational during that year

(It should be noted that all annual values refer to July-June totals - unless specifically stated otherwise.)

The data extracted from the 'past weather records' apply only to particular weather stations and as such comprise a point HDF data set. However, the complexity of the 19

surface hail pattern - even within short distances - as well as the variability of hailfall over time has been well-documented (eg. Carte and Held, 1972). This, coupled with the sparse distribution of hail recording stations raises serious doubts concerning the validity of using a point HDF data set. Fortunately a detailed hail observer network does exist (albeit only for a small part of the study area) against which the accuracy and hence representativeness of the 1960-1986point data set could be assessed.

This observer network was established by the CSIR in the early 1960s and consisted of between 700 and 1 000 voluntary observers who reported on hail incidence. After 1970, the number of observers was increased to approximately 4000 (Carte & Held, 1972). The CSIR network covers an area of about 2 800 km2 in the Pretoria and the Witwatersrand area and extends from approximately 27°5f E to 28021'£ and from 26OZ1'S to 25°45'S. Summaries of these hail reports (from 1970 through 1986) were kindly supplied by the CSIR. They included the dates on which hail was reported from anywhere within the network area and the number of reports received (for that day). In addition, information was supplied for the Pretoria and the Witwatersrand sub-regions separately. Monthly HDF summaries were also included. Hail data were extracted from Carte and Basson (1970) for the 1962-1969 period and from Carte and Held (1972) for 1970-71.

The high density of observers obviously decreased the likelihood of an event not being recorded. Therefore, it was assumed that this data set accurately reflected hail occurrence for the Pretoria-Witwatersrand (Pta-WWR) area for the 1962-1986 period.

2.1.1.2 Data verification

Figure 2.3 depicts the 1962-1986 annual time series of both the areally-averaged point HDFs (for the Transvaal) and the area HDFs for Pretoria and the Witwatersrand. The general coincidence between the two graphs is immediately apparent - especially for the pre-1971 period (this portion represents HDF reported by the smaller observer network). Correlations of 0,84 and 0,64 were obtained for the two HDF data sets (i.e. for pre- and post-1971 periods), respectively. The former correlation is significant at a better than 1% level. Although the correlation between data sets is smaller during the post-1971 period, the similarity in inter­ annual HD pattern is evident. Moreover, perfect agreement cannot be expected to 20

occur between the two data sets since the CSIR data reflect conditions which occur in only a small part of the study area. Nevertheless, the correspondence between HDF peaks and troughs is reassuring and it was assumed that, although the point HDF data set could be verified for only 21 out of the 26 years, the remaining period would exhibit an equally good fit. These results thus indicate that the point HDF data set adequately reflected HDF trends in the Transvaal for the 1960-1986 period.

o>- C lal ::l 3 '"CT ~'" os>- "1:J 'iij 2 s: E '0 c. «i ::l C osC c os :::E'" 0 , I 1962 64 66 68 70 74 76 78 80 Years

os Q) 90 (bl (ij cr: ~ Os c:: 70 ,!: LI- 0 J: «i 50 • -0 I- 1962 64 66 68 70 74 76 78 80 Years Figure 2.3 1960-1980annual time series of (a) area-averaged point hail day frequency (HDF) for SAWB stations, and (b) HDF for the Pretoria- Witwatersrand area (CSIR hail network area).

2.1.2 Rainfall data

Long-term mean monthly and annual (Julian year) rainfall and RDF data were obtained from WB40 (SAWB, 1986). These were used to calculate areally-averaged annual and monthly rainfall and RDFs for the Transvaal (see section 2.1.1). Data concerning the number of rain days or the rainfall amount which was recorded at a 21

particular station during a specific period (month or year), were extracted from the relevant annual or monthlyweather summaries.

A third rainfall data set was kindly supplied by Prof. J.R. van Heerden, Dept. of Meteorology, Faculty of Engineering, University of Pretoria. This comprised Zunckle's (1985) monthly rainfall values for homogeneous climatic regions (i.e. rainfall districts) for the whole of South Africa for the period 1921 through 1987. A total of twenty five of these districts are located within the Transvaal and include districts number 32-35; 46-50; 62-65; 74-77; 84-87 and 90-93. Data from these were used to derive ar.eally-averaged, annual rainfall time series for the Transvaal. Rainfall values for a particular month or year were also obtained from this data set. District 74 values were used to indicate rainfall characteristics of the CSIR hail network area.

As before, more detailed information concerning daily rainfall patterns could only be obtained from SAWB daily rainfall records. Such information was required for the CSIR hail network area and for the area around the Irene weather station (Chapters 5 and 6). Unfortunately, the boundaries of the Weather Bureau 'blocks' did not coincide exactly with those of the CSIR network area and thus daily rainfall data had to be extracted for all rainfall stations located within the eastern half of block no. 475 as well as in blocks 476 and 513. The 'rainfall area' was deliberately chosen to exceed that of the hail observer network area in order to take that portion of the rainfall which. originates from systems active within the network area, but which falls outside it (the network area) into account. Thus, daily rainfall amounts were extracted for 66 stations for 1 095 days (i.e, July 1977 - June 1979; and July 1981 - June 1982) and averaged to obtain daily rainfall values for the Pretoria­ Witwatersrand (i,e, the hail observer network) area. Although data from the rainfall stations in block 513 only were used for analyses pertaining to either the Irene weather station or to the Pretoria hail observer network sub-region, the size of the data set acted as a disincentive for extending the study period beyond the minimum necessary to obtain meaningful results. Furthermore, despite the care taken to insure accuracy, it is likely that certain days were incorrectly classified ­ particularly ifhailfalls occurred outside the Pretoria-Witwatersrand area. 22

2.1.3 Hail damage data

Information concerning crop hail damage was obtained from Sentraoes Insurance Co., Ficksburg. This is the largest hail insurer in the southern hemisphere and itwas assumed that their data would adequately reflect hail damage patterns in the Transvaal. The data comprised cumulative weekly totals of: the number of hail insurance policies taken out; the value of these (in Rand); the number of hail claims paid out by Sentraoes; and their value (Rand) as well as the number of hectares insured. These data applied solely to maize hail insurance, and were given for each magisterial district.for the period commencing on 1 April 1981 and extending to 31 March 1987. Although data were originally supplied on magnetic tape, these could not be deciphered. Sentraoes Insurance Company kindly supplied a set of printouts from which data were extracted and analysed.

The Sentraoes data represents only a portion (albeit the largest) of the total hail insurance cover taken out by farmers and of the crop damage sustained in each magisterial district. Since this portion varied from one district to the next, as did the size of the maize-farming area, the district data were not directly comparable. This necessitated standardizing the data. Hence a number of indices such as a crop-loss index; a hail risk index etc. were derived. A detailed discussion on their determination and meaning can be found in the relevant sections in Chapter 7. Furthermore, it was assumed that business practices and special insurance 'packages' applied equally to maize farmers in all districts and would thus not influence the results of the analyses or the conclusions reached in the study.

2.1.4 Other

In addition to the above-mentioned hail day, rainfall and rain day data sets, which required some degree of processing prior to analysis, a few smaller data sets were acquired which could be used without modification. One such comprised the 1960­ 1983 Southern Oscillation Index values which were extracted from Lindesay (1988a). Another, consisting of data from the 13:00 SAST radiosonde ascents at the Irene weather station was obtained from the SAWB and was used to study atmospheric thermodynamics and kinematics on hail days, non-hail rain days and dry days (Chapter 6). It contained information on, inter alia, temperature, dew point temperature, pressure, wind direction and speed, given for the surface to the 300 23

hPa level in 100 hPa intervals and above this, to tropopause height, in 50 hPa intervals. Unfortunately data for the significant levels were not included.

The wind data were processed into zonal (u) and meridional (v) components using the conversions:

u = w Sin <\> and v = w Cos (\) where w = wind speed (m/s) and Q = wind direction (in degrees, measured clockwise from north).

Note that the wind direction has been defined trigonometrically and not in the conventional manner. Thus, the zonal winds are defined such that easterlies are positive and meridional components are defined with northerlies (poleward flow) positive.

The formulae which were used to calculate parameters such as equivalent temperature, meridional and zonal wind components and other stability and humidity indices are discussed in detail in Chapter 6.

2.2 METHODOLOGY

2.2.1 General statistical techniques

The diverse nature of the data used III the thesis and the variety of aspects investigated has resulted in different techniques being used for each of the sections. These are discussed in detail in the relevant chapters. However, a few statistical techniques were used throughout the study to identify relationships between variables and to establish their strengths. These include, inter alia, Pearson's correlation coefficient and multiple regression analysis. Student's t-test (2-tailed) was used to indicate the level of statistical significance (a) of the correlations while 2 the Coefficient of Determination (R ) reflects the proportion of the total variance which may be explained by the independent variables. In addition, various non­ linear regression analyses were performed on the data in Chapter 3. These included exponential, power and logarithmic functions. Keeping in mind that simple 24

logarithmic transformations change non-linear equations into linear ones, their format and the transformed equations are as follows:

Exponential function: Y = abx ; log Y = log a + logbX Power function: Y = axb ; log Y = log a + b log X Logarithmic function: Y = a + b(log X)

These analyses were achieved using a PC driven STATGRAPHICS package.

A number of non-parametric tests were used where the data were not normally distributed or where only an indication of the existence of a relationship between variables was sought. The latter involved either dichotomizing the data or grouping values into arbitrary chosen categories. Spearman's Rank Correlation Coefficient, the Chi-square test (X~ and phi-coefficients were used to identify the nature of these relationships while relevant statistical tables (Stoker, 1983) were consulted to determine their level of significance.

The Z-test was used to test for the difference between two means in non-normal populations with equal but unknown variance and in normal or non-normal populations with equal and known variance. The T-test was used where populations were normal with equal but unknown variance (Unisa, 1978).

The above-mentioned statistical techniques are fundamental and are discussed in any book on basic statistics. Therefore details concerning their formulae, limitations etc. will not be discussed here.

2.2.2 Principal Components Analysis

Principal Components Analysis (PCA) is a mathematical technique which is frequently used to divide data sets into orthogonal subsets. Moreover, it is a multivariate technique in which the number of dimensions of a data set is reduced without major loss of information. "Since no model is assumed, the extracted components are purely mathematical functions with no guaranteed real physical properties. However, experience has indicated that the components normally reflect physical properties inherent in the data set" (Harrison, 1986, 7). 25

A critical discussion on PCA and its applications is given in Appendix II. However, a few concepts_are pertinent to the technique used to delimit homogeneous hail regions (Chapter 3). These are mentioned below.

Components are linear combinations of the original variables in a data matrix defined such that:

(a) the first component extracts the maximum variance possible from. that of the set;

(b) the second component extracts the maximum possible of the . remaining variance (subject to the restriction of orthogonality with the first component) and so forth.

When components are extracted from the correlation matrix (as opposed to the covariance matrix), the correlation between the component and the original variables is given by the loading. The loadings thus indicate the specific variables which are associated with a component as well as the strength of the relationship.

Thus for regionalization purposes, all the stations' loadings are examined and that component on which each station loads highest, selected and mapped. All stations which load highest on the same component are thus grouped into a region. A number of refinements have been devised to determine the position of boundaries or to deal with the problem of non-contiguity. Often use is made of second or third highest loadings and the associated components. Interpolation is then used to position regional boundaries. This technique was first used by Dyer (1975) to delimit homogeneous rainfall regions in South Africa This was followed by a number of studies employing the same technique, for example, Willmott (1977, 1978), Olivier & Van Rensburg (1987).

In this study, PCA was performed using the SAS (1985) Factor Analysis programme. The computer facility used was the Stellenbosch University VAX 785 mainframe. The raw data matrix consisted of 1960-1984 annual HDF values for 49 stations. Since the component interpretation relies on the spatial distribution rather than on 26

the magnitude of the loadings, no rotation was applied. Loadings were calculated using a correlation matrix and all significant ones (CL = 0,05), were used for mapping purposes.

2.2.3 Superposed epoch analysis

Superposed epoch analysis has been used extensively to study differences in conditions prevailing between, for example, wet and dry periods. Parameters such as rainfall are used to stratify data into wet and dry period groups. Examples of studies employing .this technique are those by inter alia Rubin (1956); Triegaardt and Kits (1963); Hofmeyr and Gouws (1964); Lamb (1978); Nicholson (1981); Tyson (1981); Chu (1983); Harrison (1986) and between high and low Southern Oscillation phases: Van Loon and Madden (1981); Arkin (1982); Lindesay (1988a).

This technique was used extensively throughout Chapters 5 and 6 to highlight differences in precipitation characteristics, thermal wind strengths, etc. between years with high HDFs (denoted as high hail years, HHY) and low hail years. The atmospheric characteristics prevailing on hail days, non-hail rain days and dry days were distinguished in a like manner. 27

ANALYSIS: PART I GEOGRAPIDC ASPECTS OF HAIL IN THE TRANSVAAL 28

CHAYI'ER3 SPATIAL HAIL DAY PATIERNS

3.1 HAIL DAYFREQUENCY (HDF) PATTERNS IN SOUTH AFRICA

It was noted earlier (Chapter 1) that little research has been concerned with the geography, i.e. the spatial and temporal patterns, of hail in South Africa. The one exception is the paper by Schulze (1965) in which he analysed the 1950 - 1964 hail incidence at 150 hail recording stations located at sites throughout the country. From this he compiled the well-known hail distribution map presented here as Figure 3.1. He commented on the positive relationship between HDF on the one hand and altitude and thunderstorm days on the other.

f . J

/

2SI---~ 1'. \

301----+---"\

3S1----+------+-----t------+------t--i 1S 20 30 3S

Figure 3.1 Mean annual point hail day frequency (HDF) 1950-1964. (after Schulze, 1965).

The most striking feature depicted on this map is the concentric (albeit asymmetric) pattern of HDF isolines around the Lesotho Highlands. The steep gradient in the direction of the Indian Ocean and the more gradual decline in other directions, accentuates the influence of topography on HDF. According to this figure more than nine hailstorms can be expected at any particular point on the Highlands, while 29

the highest expected point HDF in the Transvaal lies between seven and eight per annum. The HDF decreases towards the northern and north eastern Transvaal with the 2 HD/ annum isopleth separating these parts from the higher HDF region to the south.

The patterns shown on Figure 3.1 are, however, of a general nature. The enormous hail damage which has occurred in recent years necessitates a more detailed analysis of spatial hail distribution patterns. This is the aim of chapter 3. Spatial HDF patterns of the Transvaal and the factors controlling them are discussed in the following sections..

3.2 MEAN ANNUAL HDF PATfERNS IN THE TRANSVAAL

3.2.1 Data

Long-term mean monthly and annual HDF data have been published for selected weather stations by the South African Weather Bureau (SAWB, 1986). This publication constitutes the source of the long-term mean HDF data for the 60 stations depicted in Figure 2.1. A second data set comprising 1960-1986 daily hail incidence was compiled from daily past weather records (See Chapter 2). These were used to determine mean annual HDF (for this 26 year period) for 66 sites in the Transvaal.

Thus, together with the information which can be extracted from Schulze (1965) there are a total of three partially overlapping data sets which reflect HDF characteristics in the Transvaal since 1950. Comparison of these should indicate spatial HDF patterns and may also reveal temporal changes in them over the last 40 years.

3.2.2 Discussion

Figures 3.2 and 3.3 show the long-term and the 1960-1986 mean annual HDF, respectively, for the sites indicated on the maps.

Although Figures 3.1, 3.2 and 3.3 are alike in a number of respects, some differences are apparent. The isoline patterns on Figures 3.1 and 3.2 are remarkably similar 30

.--.-', /.-.../ . ',-.---_.--- / ~ ~" I / \ I • \ 23" I • \ Jr' • I \ , \ • \ \ \ I I I 25"

26'

a

J(

Figure 3.2 Longterm mean annual HDF pattern.

25'

27"

zti

Figure 3.3 Mean annual point HDF (1960-1986) 31

with the highest HDF in the Transvaal occurring to the south and south east on all three figures. Moreover, the highest lIDF has consistently been recorded at Carolina. Yet, these similarities are superficial since the position of the individual isolines on these maps vary considerably. During the 1950-1964 period (Fig. 3.1), the position of the 2 lID/annum isoline is such that it (approximately) bisects the Transvaal, while on both Figures 3.2 and 3.3 it has apparently shifted southwards resulting in an expansion of the < 2 lID/annum area. The occurrence of such changes seems to imply temporal changes in lIDF patterns and therefore more detailed analysis of the spatial extent of areas with specific HDF is indicated.

This was done by determining the size of the areas with less than and greater than 2

HD/annum on Figures 3.1, 3.2 and 3.3. A V20 latitude by V20 longitude grid was superimposed over the study area on the maps and the number of grid elements falling into each category, estimated. Although this technique gives a very rough measure of the size of the relevant areas, it does give a data base on whichstatistical analyses can be performed. The Z-test was used to determine whether the size of the areas differed significantly from one another during the 1950-1964 and the 1960­ 1986 periods (see Appendix III).

The results of the analysis indicates that during the 1950-1964 period, about 40% of the Transvaal (in terms of surface area) received less than 2 HD/annum. The long­ term mean lIDF map showsthis value to be in the region of 60% while for the post­ 1960 period, < 2 HD/annum were recorded in 70% of the area. The Z-test for differences between ratios shows that there is a statistically significant difference in the size of the < 2 HD/annum area during the 1950-1964 and the 1960-1986 periods. (The four year overlap was not taken into consideration). A decrease in hail frequency during the late 1960's and early 1970'swas also noted by Held (1974), who found that it was "most likely due to a general decrease in thunderstorm activity" (Held, 1974, 771). Apparently the number of thunderstorm daysper year as recorded by the Weather Bureau for Pretoria, showed a corresponding decrease during this period.

The difference in mean annual HDF at Carolina (highest HDF in the Transvaal) during these two periods also indicates a changing temporal HDF pattern. During the 1950-1964 period, approximately 7 HD/annum were recorded at Carolina while during the 1960 - 1986 period, this value was only 5,6. The fact that the long-term 32

mean HDF for Carolina (8,5 HD/yr) exceeds both the 1950-1964 as well as the 1960-1986 means, seems to imply that the HDF was even higher prior to 1950.

That there was a period with higher hail activity followed by one with lower HDF has thus been established. A number of possible explanations may account for these differences, for example, local atmospheric conditions might have changed or meso­ synoptic and synoptic scale differences might have occurred. Changes in local atmospheric conditions would influence the incidence of isolated and scattered thundershowers - or simply the melting rate of hailstones; while changes in the occurrence of meso-synoptic or synoptic scale events would be reflected in anomalies in meso-synoptic convective- and line-storm frequency. It is also possible that a latitudinal shift occurred in the position of the pressure systems, so that the northern part of the Transvaal came increasingly under the influence of quasi­ barotropic, non-hail producing atmospheric conditions. This would have resulted in lower annual HDFs. Analysis of these scenarios in order to determine which one - if any - is the most applicable, falls outside the scope of this thesis. However, the question whether the high and low hail periods occurred contemporaneously over the whole area and whether there was a cyclical pattern to these high and low hail periods, will be addressed in chapter 4.

In the foregoing sections it was shown that the HDF isopleths on all three HDF maps (Figs. 3.1 to 3.3) exhibit a clear north-south gradient. Therefore it is likely that latitudinal position of a site affects the HDF there. Moreover, numerous authors (see later) have commented on the link between topography and mean annual HDF. It thus seems appropriate to investigate the factors which may influence the spatial variation of HDF.

3.3 FACfORS AFFECI1NG SPATIALHDF PATIERNS

Table II.l (Appendix II), lists those Transvaal hail recording stations for which data are available in WB40 (1986), together with their mean annual HDFs, latitude and altitude. These data were used for all subsequent analyses in this section. 33

3.3.1 Latitude

It seems likely that latitude may influence the mean annual HDF in a number of ways. On the one hand, it has been shown that thunderstorms occur more often (and are presumably more vigorous) in the subtropics and tropics than in the mid­ latitudes (Schulze, 1965; Neuberger & Cahir, 1969) (Fig. 3.4) - while conversely, the higher ambient temperatures in the latter zones may promote melting of hailstones which would, in tum, result in a decrease in hail frequency.

EQUATORIAL AFRICA ~

(J) 14 \ ~ '. ~ 12 : ~ i \ 2 .I \:: ~ ~\ 10 / J ! Figure 3.4 ~ /i~\ IND~·NESIA ~ 8 Annual variation of thunderstorm f- : '< -\ frequency at three locations with different ~ 6 II J-- ., :) latitudes (After Neuberger & Cahir, 1969. a: 4 w In: Linacre & Hobbs, 1977,87) co ~ ;:) 2 z oL-J--,-~==~..::::r:::::...L--l-...i.-J JFMAMJJASOND

Moreover, the position of the subtropical anticyclones, their intra-seasonal latitudinal and longitudinal migration as well as the influence of intra-hemispheric teleconnections might produce rainfall and rain day frequency anomalies in certain areas. These would obviously affect the hail day frequency as well. But whatever the cause for the steady northward decrease in HDF which is evident in the Transvaal (Figs 2.1 & 2.2), it is clear that the nature of this HDF - latitudinal relationship needs to be examined in some detail.

A rough estimate of the nature of this relationship was obtained by determining the number of stations falling into various HDF and latitudinal categories. The limits of these classes were arbitrarily selected. The results of this initial grouping (Fig, 3.5a) showed some evidence of a direct relationship between latitude and HDF and hence ·34

(a)

22 24 26 28 TOTAL

<1 8 4 0 12 22 23 24 25 26 27 28

<1 2 6 3 1 0 0 u.. 1-2,5 4 13 3 20 u.. o 0 1 - 2,5 1 3 5 8 1 2 ::c ::c : >2,5 0 7 16 23 > 2,5 0 0 0 7 11 5

TOTAL 12 24 19 55

Figure 3.5 Data matrix showing the number of weather stations falling into speeiflc latitude - HDF categories (a) using 1 latitude categories (b) 2 latitude categories

• 8

7

6

5 • ... • • Q •• :r • • 4 • •• • • • 3 • • •• • ••• • 2 • •• •• • • • •• • • • •• • • • ••• • • • • • • • • • • 22° 23° •24° 25° 26" 27° LATITUDE aS

Figure 3.6 Scatter diagram showing HDF - Latitude relationship 35

it was decided to apply the X2-test to the data in order to determine whether the relationship was significant or not. Because of the low frequencies occurring in many cells. the latitude class intervals were widened by merging adjacent classes. The resulting 3 by 3 matrix is shown in Fig 3.5b. The results of the test (see Appendix ill), confirm the direct nature of the relationship between latitude and HDF (significant at the 0,1% level).

Further analysis has revealed a statistically significant correlation (Pearson's Product Moment Correlation Coefficient (r) = 0,605; a = 0,001; n = 55) between these two parameters. (The HDF-latitude scatter diagram is presented as Figure 3.6). The following linear regression equation was found to apply: y = -15,52 + 0,702x where y = mean annual HDF and x = latitude (0) Although this linear relationship is statistically significant, the R2 value indicates that latitude explains only 36,6% of the variation in Transvaal HDF. The possible existence of a stronger, non-linear relationship between HDF and latitude was also investigated. Only simple non-linear relationships were sought and hence power, exponential and logarithmic functions were applied to the data using the relevant "STATGRAPHICSpackages.

It was found that the exponential function: log y = 4,472 + 0,185 x yielded the highest coefficient of determination (r = 0,67; R2 = 0,45; a= 0,001).

Although this function gives a slightly better fit than the linear relationship, the largest proportion of the variation in HDF has yet to be explained. Therefore the relationship between altitude and HDF was examined for the Transvaal.

3.3.2 Altitude

Correlations of hail incidence with major topographical features have been documented for various parts of the world (Omoto, 1973; Prodi, 1976; Summers, 1970; Salau, 1986). A positive relationship between hail frequency and topography has also been shown for South Africa (Schulze, 1965; Jackson, 1966) - since the point hail frequency reaches a maximum in the highlands of Lesotho and decreases towards the south east coast and the interior. In addition, topographic forcing 36

effects which are reflected on a more local scale have been shown to influence HDF (Changnon, 1977). Carte (1966a & b), Carte & Basson (1970), Held (1974) and Carte & Held (1978) found that the point hail day frequency is lower in Pretoria than on the nearby but higher lying Witwatersrand. This contrast is especially evident in the occurrence of hailstorms which produce hailstones of 10-30 mm diameter. Moreover, detailed spatial analysis of hail incidence has indicated that, in general, the highest hail frequency occurs to the north of high ridges (Held, 1974).

A direct relationship between hail frequency and altitude is to be expected, since the greater the elevation the less the distance between the height of the erc temperature layer and the ground and hence the less opportunity there is for melting. This relationship is, however, not necessarily linear. In an analysis of hailstorms in Nigeria (Frisby, 1967), it was found that stations below 1 200 m experience two hail days per year whereas those at higher elevations have three to eight hail days per annum.. Hail studies in China (Sun An-Jian, 1987) have also shown that at first the increase in hail day frequency with height is slow, but the rate accelerates until a maximum is reached at about 5000 m above mean sea level.

The long-term mean hail occurrence pattern in the Transvaal {expressed in terms of point HDFs) was depicted in Figure 3.2. This has been reproduced here as Figure 3.7. A simplified relief map of the area has been included (insert) in order to facilitate the comparison between HDF and altitude.

The spatial coincidence between the two maps (on Fig. 3.7) is immediately evident. The 1 hail day/annum isoline broadly follows the 1 000 m contour line while the estimated position of the 2,5 hail day/annum isoline coincides approximately with the 1 500 m contour line. However, Spearman's rank order correlation coefficient, which measures the degree of monotonic association between two sets of variables, was found to be only 0,66. Although this value is statistically significant at the 99% level, only about 44% of the variance of HDF is accounted for by a linear relationship with changes in altitude. This implies that a linear model is not necessarily the most applicable here. 37

'_~""-'_:::500-=-~- \ Figure 3.7 Isopleth map showing average hail day frequency and location ofstations used in the study. The insert shows 'the relief (in metres) above mean sea level r

.\ \ I I I \

27

28 o 100 25 26 27 28 29 30 31 32

6

• 5 •I >- • I c..> • •• I ....:z: • ~ I 0- 4 .... ./ ....e>: ••• • / >- ... • • / Q 3 • / • • -' • . • • ... '/ == ,/ ...-' 2 .-" ~ • -" :z: . • • ...:z: . ,...... " . • • .., • •••

• ..: ... J-- --- or • • • .. o 200 400 600 800 1000 1200 1400 1600 1800 2000 ALTITUDE IN METERS

Figure 3.8 Scatter diagram showing HDF - altitude relationship 38

The curvilinear nature of the relationship becomes clear once the HDF is plotted against altitude above mean sea level (Fig 3.8). Although the maximum elevation on the Transvaal Highveld and adjacent mountainous regions does not exceed 2 000m, the pattern which emerges appears to be remarkably similar to the one described by Sun An-Jian (1987) for China. The shape of the curve suggests that either an exponential or a power function would provide a satisfactory description of the relationship, in which case the relationship could be linearized by logarithmically transforming the data prior to performing a regression analysis. It was found that the following exponential function provided the best least squares fit: log y = -0,46 + 0,0006 x r = 0,69; R2 = 0,48 where y = annual HDF and x = altitude (m). However, after removal of the obvious outliers (Nelspruit, Barberton, Makatini & Belfast), the equation: log y = -0,69 + 0,00078 x gave the best fit. This regression equation gave a Pearson's correlation coefficient of 0,83 (significant at the 99% level) and hence explained more than 68% of the variance in log y.

Some interesting patterns, as depicted in Figure 3.9, emerge when the standardised residuals from the regression equation are mapped. Stations with negative residual values are mostly confined to areas of high and broken relief such as the Witwatersrand, the western edge of the escarpment, and the Pietersburg Plateau as well as to the low-lying northern and western border regions of the Transvaal. In these zones, the model consistently over-estimates the HDF. Unexpectedly large negative deviations from the expected HDF occur in the vicinity of Letaba, Potgietersrus and Belfast. The lower than expected value at Letaba may be linked to the very low rainfall and rain day frequency experienced there. This may be due to the location of those cloud bands which connect the tropical cloud masses over central Africa to temperate cyclones to the south of the continent, and which supply approximately 50% of the summer rainfall over Africa (Harrison, 1984c). During wet months the cloud bands lie preferentially to the west of the Limpopo Valley, while during dry months they lie to the east of it. According to Harrison (1984c), the infrequent positioning of the cloud bands across north eastern South Africa may be the fundamental cause of the dry zone along the Limpopo Valley. This effect may extend to the northern parts of the lowveld. 39

22

23 RESIDUALS -x-;.... +

~.#•••• -

24

25

26

o 100

25 2e 27 2e 29 30 31 J2

Figure 3.9 Residuals from regression equation. All values except for those indicated lie between + and - 1

When the mean annual minimum temperature and the average summer (October­ March) temperatures are corrected for altitude, Potgietersrus and its immediate surroundings (Uitloop and Zebediela) form a 'warm island' compared with conditions at Marken, Pietersburg, Nylstroom, Warmbad and Dusseldorp (Fig. 3.9). These high temperatures, which could possibly be due to the effect of the local topography, may accelerate melting of the hailstones, resulting in the negative anomaly there.

The high altitude, relatively cool summer temperatures and high rain day frequency at Belfast suggests a higher HDF than that which occurs. Although no explanation for this anomalous situation is evident, it has been noted that Belfast is situated in a zone with exceptional high air pollution concentrations (G Held, personal 40

communication). Whether this affects the hail frequency pattern has yet to be established.

Although the largest part of the central Transvaal exhibits positive residual values, the higher values and the positive outliers (Nelspruit, Barberton and Makatini) are confined to an area to the east of the escarpment. This spatial coherence suggests that some convective rain-bearing system - possibly originating over the Indian Ocean (Dyer, 1979) and being enhanced by the orographic effect of the nearby escarpment (Kelbe, Garstang & Brier, 1983) - is responsible for increasing the HDF there.

It thus transpires that both latitude and altitude exhibit linear and strong non-linear relationships with HDF. The influence of both these factors was determined by using multivariate statistical techniques - specifically multiple regression analysis. The linear multiple regression equation: y = -10,70 + 0,0014 Xl + 0,4545 ~ (where y = mean annual HDF; Xl = altitude (m); and ~ = latitude (degrees», indicates that altitude and latitude together explain only 43,9% of HDF variance. Upon substituting log y values for y, the following equation provides the best 'fit': log y = -2,97 + 0,0004 ~ + 0,108 ~ with r = 0,76; R2 = 0,57.

However, even cursory examination of a topographic map of the Transvaal shows a concomitant decrease in altitude with decreasing latitude (Fig. 3.10). A correlation coefficient of r = 0,61 between these two parameters confirms this covariation.

In order to find the strength of the relationship of each of these variables separately (the effect of the other being eliminated), partial correlation coefficients were calculated. It was found that both rYXl/x2 and ryx2/xl gave identical values, namely, 0.38. Thus, in the linear model, both latitude and altitude are equally important. The partial correlations r)ogyxl/x2 and r)ogyx2/xl gave values of 0,707 and 0,344, respectively, highlighting the relative importance of altitude in the variability of HDF. Hence, altitude alone accounts for almost 50% of the variation in HDF. 41

LATITUDE eS)

22-24 24 - 26 26 - 28

<1000 11 7 1 (92) (28) (5)

UJ C ::J 1000- 1 15 8 t- -t­ 1500 (8) (60) (42) ~ <

~1500 0 3 10 (0) (12) (53)

100 100 100

Figure 3.10 Data matrix showing the number of stations falling into specific latitudinal and altitude categories (the lower values give the percentage of the number of stations which fall into each category)

The results of all correlation and regression analyses concerning HDF, latitude and altitude, have been summarized in Table 3.1.

3.3.3 Urban effects

"Convective precipitation requires a supply of water vapour and nuclei to form droplets, and uplift to carry these materials to sufficient heights so that cooling and condensation can occur. The urban 'plume' would seem to be characterized by these properties to a greater or less extent than the surrounding rural area" (Oke, 1978,302).

It thus seems obvious that convective showers are more likely to form over urban and industrial areas. Furthermore, it is thought to increase the frequency and intensity of thunder and hailstorms (Changnon et al., 1981).

The occurrence of a 'heat island' over cities and the consequent enhancement of precipitation has been found in many independent investigations, inter alia, Landsberg (1956), Changnon (1968), Huff and Changnon (1973), Tyson (1970) and Braham (1976). Some studies have provided evidence for urban-enhanced Table 3.1 Relationships between altitude, latitude and HDF: summary of statistical analyses and results. ------r'-~------~ Vn ri u b l o n Type of to n r Res III t s ~ ~ ______1--____ s g n f 1. can c e (CI.) ~I 1------1------,.r------ji 2 l.atitude x 33,87 0,001 I & t: (Pearson's) 0,61 0,001 2 I lllJF Linear Regressioll HDF = -15.52 + 0,702 LAT 0,001 R = 0,37 ! 2 Expollential !lOF + 0,185 !~5 I LOG = 11,472 LAT 0,001 R = 0, I Power r = OJ10 O,OOl ~ ._~ ~_ Logarithmic r = 0,61 0,001 ______+ 1i I ---.,i i ,\ It i t u tI c r . r 0,61 ; 0,66 1 & Li;le

2 Latitude, 0F = -10,7 + 0,001 ALT + 0,45 LAT R 0,44 Z = A I. t it: u d o , LOG IlDF = -2,97 + 0,0004 ALT R = 0,57 1I1H' I + 0,108 LAT

Altitude I­ r 0,61 0,001 l.atitude Partial correlations (both) 0,3788 0,01 Pnrtial correlations: log 110£0' ALT/LAT 0,707 0,001 log IIDF LAT/ALT °, 3l~4 0,05 n = 55

~ '" 43

precipitation totals and/or frequency of rain days (Sanderson & Gorski, 1978; Yonetani, 1982; Nkemdirim, 1988). Other studies have found no evidence of urban effects of precipitations for certain cities (Huff & Changnon, 1973; Jones & Jiusto, 1980; Nkemdirim, 1981; Balling & Brazel, 1987). In cases where urban effects have been detected, there is disagreement as to whether the city initiated rainfall or simply enhanced an existing situation. Some studies offer evidence that urban areas initiate as well as intensify existing regional precipitation events (Changnon et al., 1981) while others maintain that urban areas enhance rather than initiate the precipitation process (Changnon, 1984; Changnon & Huff, 1986; Rosenberger & Suckling, 1989). -

Held (1974) found that the urban areas of Johannesburg experience a higher hail episode frequency than the adjacent rural areas. According to Carte & Held (1978, 367) "the increased precipitation in these (Pretoria and Johannesburg) areas may be due to the combined influence of urban and topographic effects, ...(but) the rather small differences in topography suggest that urban effects would be more important."

Tyson (1970,8), however, introduces a cautionary note - "Despite the high degree of statistical significance associated with urban/rural precipitation differences, it is often difficult to separate the purely urban from the purely mesoclimatic and topographical effects acting to produce local variations in precipitation". Similar misgivings concerning.the separation of urban and topographic effects were voiced by Englehart & Douglas (1985).

Lacking the necessary data with a fine-enough resolution which could be used to substantiate Held's findings, the role of the urban factor on HDF and its importance could not be assessed.

The preceding sections have shown that there are distinctly different hail day frequency zones in the Transvaal - their existence indubitably influenced by their proximity to the equator and the topography of the area - and probably also by the 'heat island' effect. Before attempting any meaningful analysis to elucidate the temporal characteristics of HDF in the Transvaal, it would be advisable to subdivide it into homogeneous hail regions using some objective classificatory technique. This is attempted.in the following section. 44

3.4 DELIMITATION OF HOMOGENEOUS HAIL REGIONS

There are numerous techniques that can be used to subdivide a region into smaller homogeneous parts. Over the last 15 years, Principal Components Analysis (PCA) has been used extensively for this purpose. Although Kendall (1939) and Hagood, Danilevsky & Beum (1941) pioneered the use of PCA for geographical regionalization, Dyer (1975b) spearheaded its use in climatology. Since 1975, a number of climatologists, inter alia, Paterson et al. (1977), Willmott (1977), Tabony (1981) and Harrison (1984) have used component loadings or scores as mapping units. In this study, the former have been used to delineate homogeneous hail regions in the Transvaal.

3.4.1 Method

The annual HDF records for the Transvaal stations were used as the criterion for subdivision. Unfortunately not all stations were operative for the entire 26 year period (1960-1986) and thus a shorter record length had to be chosen during which as many stations as possible were operational. This was found to be the 15 year period extending from 1960 to 1974, when complete records exist for 49 out of the 66 stations. A 49 x 15 data matrix of annual HDF was thus compiled and submitted to S-mode PCA using a SAS programme (see Chapter 2). Contrary to the traditional format of the data matrix for S-mode PCA, (which requires the entities (stations) to form the columns and occasions (years) to form the rows) the data needed to be transposed for the SAS procedures. Nevertheless, a station x station correlation matrix was calculated from which principal components were identified. The Scree test (Cattell, 1966) was used to select the significant components. No rotation was applied.

In this analysis, eight significant components were selected. These explained a total of 80,15% of the variance. A list of the stations' loadings on each of these principal components is presented as Table III.3 in Appendix III. All significant loadings (ex = 0,05) have been indicated.

For each station, all the components for which the loadings (correlations) were statistically significant, were identified. These were indicated in descending order of 25' 26' 27' 28' 32"

23'

24' V';il~ "·"""~·III@y -. 25' Sabia I\-:: ~~u.~~zan '11I11I~lr1:~iOP NelsprUiI .,:.....: -S-a-rb-erton ::: 1 .::..:::::::0:::< 26' Carlelonville ~-g.. ~~-~~~~=~~r Doomlaagta Vereeniging Potchefstroom

Figure 3.11 Homogeneous hail regions (using peA)

>l::> U1 46

importance on a map of the region. All stations which loaded highest on the same component were grouped into the same region. Where anomalies occurred and at boundaries between groups, the second or third most 'important' component was taken into account. Regions were numbered according to the component used for delimitation purposes. The resulting map, depicting homogeneous hail regions, is given as Figure 3.11.

3.4.2 Discussion

In general, those stations which loaded highest on components 1, 2, 3, 4 and 7 are (mostly) spatially contiguous and hence form clearly defined regions.

Region 1 comprises the southern and south western Transvaal and includes the following stations: Bothaville, Doornlaagte, Carletonville, Vereeniging, Zwartkoppies, Jan Smuts, Krugersdorp and Rustenburg. Although Potchefstroom, which lies in the middle of the region, has a higher loading on component 2 (r = 0,51), its second highest loading (r = 0,45) is on component 1 and it has therefore been allocated to this region. Conversely, Frankfort, with significant loadings on components 5, 7 and 3 has not been included. There is little difference in the magnitude of the loadings on components 1 and 3 for Mafikeng (r = 0,42 and 0,39 resp.). Thus, despite being part of region 1, its close connection with region 3 has been kept in mind and the boundary between these two regions drawn just to the north of Mafikeng.

Regions 2 and 3 are also relatively easily defined. Standerton and Bethal (region 2) exhibit some degree of association with their neighbouring regions (regions 1 and 3 resp.) and hence the relevant boundaries have been drawn close to these towns. Similarly with Rustenburg (regions 1 and 7) and Pigg's Peak (regions 4 and 7).

Subdivision of the northern Transvaal is not as clear-cut. Groendraai, Potgietersrus and Pietersburg loaded highest on component 1 and thus form a separate region (designated as region -1 to differentiate it from its counterpart in the south western Transvaal). However, such a delineation would result in Marnitz, Rooibokkop and Levubu each forming a one-station region; while Tzaneen and Mara would form a horse-shoe shaped region, partially enclosing Levubu. However, if the components associated with the second highest loadings are taken into account, a completely 47

different picture emerges. Now, Rooibokkop (significant loadings on components 5 and 6) links to both Groendraai and Marnitz (through component 5) and to Tzaneen (component 6). However, Tzaneen belongs to the region comprising Pietersburg and Levubu (component 1). Even Phalaborwa in region 4 has ties with Marnitz (via component 8)

The above discussion clearly illustrates some of the difficulties encountered in subdividing the northern Transvaal into homogeneous regions. These difficulties are partly due to the sparseness of the weather stations in the region as well as their unsatisfactory weather records. Nevertheless, tentative regions have been defined ­ their boundaries drawn by means of dotted lines to accentuate the uncertainty regarding their position.

Notwithstanding the problems outlined above, a number of homogeneous hail regions have been delineated. These exhibit some interesting spatial patterns. Firstly, a distinct east - west trend is evident in the western Transvaal regions. Region 1 lies furthest south, followed by regions 7, 3, and -1 & 6. This zonal arrangement accentuates the influence of latitude on HDF - and thus on the definition of the regions.

Secondly, to the east of 29~, the regional boundaries exhibit a strong meridional pattern - demonstrating the topographic control, since these boundaries (between regions 2, 4 and 7E) coincide with the position of the Transvaal Drakensberg.

Thirdly, examination of the position of all regional boundaries reveals a strong coincidence with other topographic features. (A topographic map of the Transvaal has been included (Fig. 3.12) to facilitate comparisons). For instance, the boundaries between the western Transvaal regions 1, 7, 3, -1 and 6 correspond with the Witwatersrand, Magaliesberg, Waterberg and Soutpansberg, respectively. Furthermore, low-lying areas such as river valleys also form hail region boundaries. For example, the Letaba River coincides with the region 4 - northern Transvaal boundary; the Mogalakwena River separates the Mara subregion from that containing Marnitz; and the Palala River corresponds spatially with the Marnitz­ Region 1 boundary. The tributary of the Vaal River (which seems to separate regions 1 and 2) which flows through Standerton, highlights - and partially explains - 48

the duality of this town regarding the magnitude of its loadings on components 1 and 2.

2. 2. 30 32 22

24

2.

2. '0 0 '" 2. -=--==--===

2. 2. 30 32

Figure 3.ll Relief map of the Transvaal showing some major rivers. (Source: Philips Altas, 1964)

It is evident that the technique which was used to delimit homogeneous hail regions has some merit since the factors which are known to affect hail incidence (i.e. altitude and latitude), also appear to influence the position of the regional boundaries. Furthermore, since this classification was based on annual HDFs of the weather stations, it seems likely that the regions will differ from each other in terms of their temporal hail characteristics. Therefore, in the next chapter, the diurnal, monthly seasonal and inter-annual hail occurrence patterns are investigated for each of these regions and for the Transvaal as a whole. 49

CHAPTER 4 TEMPORAL HAIL CHARACTERISTICS

4.1 INTRODUCTION

Temporal climate information can be extremely useful to decision-makers in a variety of disciplines and could provide potential savings in areas such as insurance and food production. This is especially true as regards hazardous climatic phenomena such as hail. For instance, hail crop damage is not solely a function of the occurrence frequency of storms or their intensity, but is largely determined by their seasonal incidence. Furthermore, knowledge of temporal variation is essential for policy decisions regarding climate and long-range forecasting.

The aim of this chapter is to investigate temporal hail characteristics III the Transvaal. Diurnal, seasonal and annual hail patterns are examined for the province as a whole as well as for the homogeneous hail regions defined in Chapter 3.

4.2 DATAAND METHOD

As for Chapter 3, all data were obtained from the South African Weather Bureau (SAWB) for the period 1960 - 1986 -either from published reports, or in the form of daily weather records. The former were used to obtain mean monthly hail day frequencies (HDFs) while the latter served as the data source for the analyses of annual and diurnal patterns. For convenience, a simplified version of Figure 3.11, showing the locations of the 66 hail recording stations used in this study as well as the geographical regions to which they were assigned, has been included as Figure 4.1.

Data from only those stations for which no ambiguity existed concerning their membership to a particular homogeneous region, were used for the regional analyses. In most cases, this excluded those stations situated on or close to the regional boundaries - with the result that some regions had too few stations to give a meaningful, unbiased picture of seasonal and diurnal conditions prevailing there. 50

4 South western Transvaal 23" a South eastern Transvaal

• Northern TransvaaJ

+ Eastern Lowveld

• Rooibokkop a Oudestad • Lydenburg Pretoriuskop \ a Loskopdam +Nelspruit • Roodeplaat a Belfast • Pta Forum \ Gemst£'kfontein + Barberton 4frene • 1 4Jan Smuts a. Carolina Piggs Peak 2 Mbabane 4 Zwartkopjes Bema! • Carletonviile 4 a a 4 Vereeniging Noo~gedacht Standerton a Piet Retief, • VoIksrust Wakkerstroom ~ Paulpletersburg.--­ o 100 200km

Figure 4.1 Map ofTransvaal showing regions to which stations have been allocated.

50 (a)

-30 f:~:~:~:~:f~ 5 Term binomial filter

(Mitchell, 1966) -50 '----r--r--r---r~-r--..,.....__r_-r-~__.___r-r--.,--...,...._r__r___r__.___.-,__...... _r_"""T""~ 1960/61 65/66 70/71 75/76 80/81 85/86

Years

Figure 4.2 Area-averaged time series ofannual (July-June) hail day frequency (1960-1986) 51

Accordingly, these temporal characteristics were investigated only in regions 1, 2, -1, and 4; which will henceforth be referred to as the western, south eastern, northern Transvaal and the Lowveld, respectively. On the other hand, annual HDF means are not affected to the same extent by one or two missing values as are monthly means and therefore annual HDF time series were calculated for all seven regions.

4.3 ANNUAL HDF TIME SERIES

Numerous authors have speculated on the cyclical nature of rainfall over southern Africa. According to Tyson (1986), evidence for an 18-year rainfall oscillation was reported for Natal as early as 1908 (Nevill, 1908). Recent research indicates that the 18 - 20 year rainfall oscillation appears to be ubiquitous over the whole country. The use of spectral analysis has further indicated oscillations with wave-lengths of! 16, 10 to 12 and 3,5 years, as well as a quasi-biennial oscillation (Keen, 1971;Tyson, 1971, 1980; Dyer, 1975; Tyson, Dyer & Mametse, 1975; Van Heerden, 1988 pers. comm). The question arises whether hail occurrence in the Transvaal accords with any such periodicity.

The areally-averaged time series for the 1960 - 1986 HDF for the Transvaal was calculated (see Chapter 2 and Table IIA, Appendix II) and is presented as Figure 4.2. Although inter-annual HDFs show considerable variation, periods with above ( +) and below (-) normal HDFs are readily discernable. Smoothing the raw data (using a 5-term binomial filter as suggested by Mitchell, 1966), reveals a much clearer picture with the following + and - spells for the Transvaal: before 1962/63 (-); 1963/64 to 1971/72 (+); 1972/73 to 1980/81 (-); from 1981/82 onwards, (+). The shortness of the data set precluded the use of spectral analysis to accurately determine the periodicities inherent in the data. Yet, for the period under review, it is apparent that the duration of the alternating high and low hail episodes were approximately eight to nine years.

Separate graphs have been drawn for the annual HDF time series for each of the homogeneous hail regions (Fig. 4.3). At first glance, the disparity between them is striking. However, this impression is to some extent specious. Similarities do exist in the time series of regions 1, 2 and 7 on the one hand and regions 4, -1 and 7E, on the other. The data records of the stations in region 3 are too short for comparison purposes. 52

(a) 1 x= 2,8 x= 0,9 a = 1 1 2 a= 0,6 u, Q "'":::: :I: 4 :~: f r ° ~ ~~~ ~lh~ t ~~: I tm~l!!! 6%61 85As8 :I: :::1:0: •..••:.:. .... :::: ::: :"'1:0:0 :.'. ':: :: ::: :::: :~: ::: (e) 7

4 X= 1,0 (f = 0,5 lO. Q '111111111111i1i111 111111 :I: 80/ 2 °6%61 /81

x= 3 1 1 a 2 1 7 YEARS 6 i (f) 8 7E

u, ill ~.... : :::: X'" 2,8 ::: r (f Q 4 '.' 1 7 ::: 6 :I: ~~: '.' ~~~ ~l~~ j 1: ':.:. ::: : I r: .'. .... I ::: .'. I :: - ~~~~ ~~~ :~: :~~ ~I 2 ~~ ~l~~ .... ~~ -, ~ ~. 7 :ll: f ~~~ : }~. : ~~~~ ] ~.:l.:.j...... :. ".:.1.:.•:·:.•.:.•.:.•..•: ~llj ; T.:j...l.: j i'l: ~~~ ~'~I ~~j' ~j~~ 1~ ~~ ~~ ~ ~ ::::riij: ~ ~~ m I I' I I I I I 84l 70/I 75/ 85/ I I /71 /76 /85 /66 ~.::.;:}:.:.::.: ~.::.;:~.j.::: ~.::::.:.:.l.:~::.~.:n.:i.:j::.!: ~.::l.:~:: .;::.j::.~: ~:i::.j.::~::. :~~~ 2 .. ..l.:.· .. .. 1t ? YEARS (c) 3 t;;;;1;;J;:;:~~;J;;:JI:i:J:j~~~:£;;;;i~;;t;;;;I~~:":"'""""'""::;-:it ••• ': ;Hil ° nil 4/ 60/ 65/ 70/ 75/ 80/ 8 /61 /66 /71 /76 / 81 /85 YEARS (g) - 1,6

4 lO. i .1 Q ) rf. 1,2 .... :I: '.' lO. r .... Q 21------+'t-liffl--f8f-­ .1,2 X :I: rf= 0,4

65 70 75 YEARS YEARS

Figure 4.3 Annual HDF time series for (a) Region 1 (SW TVL); (b) Region 2 (SE TVL); (c) Region 3 (WTVL); (d) Region 4 (Lowveld); (e) Region -1. (Northern TVL); (I) Region 7 (W-Central TVL) and (g) 7E (far Eastern TVL). 53

The most noticeable features on the graphs of the eastern and northern regions (regions 4, -1 and 1£) are the low mean annual HDFs and inter-annual variabilities (x :: 1 HD/annum; O'~ 0,6). A weak upward trend can be discerned in region 7E for the 1963/64 to 1974/75 period, but there is no indication of a periodicity in the time series here. However, during the 1973/74 to 1981/82 period, stations in region 4 seem to have received less hail than usual.

Owing to the higher mean annual HDF and variance in regions 1, 2 and 7, inter­ regional similarities and discrepancies are more apparent. Region 2 has the highest mean annual HDf and standard deviation, followed by region 7. In all three regions there is a period during the 1960s when the annual HDFs exceeded the mean; and one in the 1970s with below average HDFs. In regions 1 and 2, the reversal took place contemporaneously in 1972 (from + to -) and in 1981 (from - to +). The below normal HDF period appears to have started slightly earlier and extended for longer in region 7. In fact, the annual HDFs seem to have decreased more-or-less consistently since 1965 in this region.

Notwithstanding the inter-regional similarities mentioned above, differences abound. These are accentuated by the non-concurrence of HDF peaks and troughs. Closer scrutiny of the time series reveals little correspondence between the annual HDF patterns in regions 1, 2 and 7. For example, the HDF trends in regions 1 and 7, are out of phase for 19 out of the 25 years. In other words, an increase in HDF in a particular year, relative to that of the previous year, in region 1 coincides with a decrease in region 7 and vice versa. This finding is of considerable importance and may have far-reaching implications for the study since the CSIR data set, which is used extensively in Chapters 5 and 6, comprises data from the Pretoria (region 7) and the Witwatersrand (region 1) areas. Combining these into a single data set, could obscure underlying patterns, which could, in turn, lead to erroneous conclusions.

4.4 SEASONALITY OF HAIL DAYS

4.4.1 General hailstorm patterns in the summer rainfall region

The incidence of hail varies markedly from one season to the next due to changes in atmospheric conditions. Hail is usually a feature of the spring and summer months 54

in most regions (Carte & Held, 1978). In the summer rainfall region of South Africa the monthly HDF increases rapidly from October onwards to reach a peak during November,. after which it gradually declines through December to March. The season is effectively over by the beginning of April (Schulze, 1965). Held (1973), in a study of hailstorms in the Pretoria-Witwatersrand area, found that about 75% of all storms occurred during October and November, while Schulze (1965) noted that the Nov-Dec peak over the eastern half of South Africa shifted to January to the west of 260£.

The two essential" ingredients for hailstorm development, namely instability and shear in upper air winds, both reach their peak in early summer. The strong wind shear is associated with the high level of baroclinicity of the atmosphere (Van Heerden et al.; 1988), while the lapse rate is steepest due to the very high temperatures reached at the earth's surface and the influx of maritime air at this time (Schulze, 1965; Carte, 1966a). These factors may account for the severity of hailstorms experienced during spring and early summer (Held, 1974). In the late summer, the wind fields over large parts of the Transvaal are more characteristic of the tropics with quasi-barotropic systems and relatively light wind speeds prevailing. In the eastern Transvaal, a low level easterly flow occurs during late summer, and, since hailstorms are usually associated with low level westerly flow there, the easterly winds would tend to inhibit storm development. (Kelbe & Garstang, 1983; Kelbe, 1984).

4.4.2 Transvaal hailstorms

The seasonality of Transvaal hailstorms as obtained from the analysis of mean monthly hail day frequencies (HDF) for the 1960-1986 period, is depicted graphically in Figure 4.4a. This pattern closely resembles that of the South African summer rainfall region as a whole. A clear November peak is evident while the hail day frequencies are approximately the same for October and December. Noticeably fewer hail days occur in January than during the preceding three months. Because the use of long-term mean values tend to mask extreme values and may even obscure underlying patterns, it was decided to analyse the monthly HDF for those years during which extreme hail events occurred. Consequently, the year with the highest hail frequency (HHY) was identified for each station (Table IV.1, Appendix IV); the monthly HDF extracted for that year (for each station); and these values 55

areally-averaged in order to obtain mean monthly HDFs for the Transvaal (Fig. 4.4b).

Ca) 25 20 (bl

20 P 15 7":"" F ,0 15 0'

5 = 5 I::.· • ••••• :.:. ~ ·1 .. :y :::1 J A S 0 N 0 J F M A M J J A S 0 N 0 J F M A M J

Months MonthS Figure 4.4 Monthly hail day frequencies (a) areally-averaged for the Transvaal (1960-1986); (b) during heavy hail years (HHY) (expressed as percentage of annual total HDF)

Comparison of these two Figures (4.4a & b) shows that the mid-summer HDF pattern for a HHY deviates significantly from normal conditions. The November peak is more pronounced while fewer hail days occur during August, March and April. The most striking difference occurs in December and January, however, where the HDF decreases during the former month and increases in the latter. The December-January shift in HDF coincides with the change from predominantly temperate to predominantly tropical rain-bearing systems over the Transvaal (Lindesay, 1988a; Van Heerden et al., 1988). This aspect requires more detailed analysis in the study area since it may have far-reaching implications. It is given some attention in Chapter 5.

A shift in the seasonality of hail day occurrence has also been observed in Kenya where the peak hail season starts earlier during years with high hail day frequencies (Alusa, 1976). 56

4.4.3 Spatial patterns ofhail day seasonality

Two aspects of hail seasonality are discussed in this section, namely, changes in the commencement of the hail season; and changes in the incidence of peak hail day frequency. It is evident from Figure 4.5 that although the hail season starts slightly later in the north eastern and south western parts of the Transvaal, there is no sign of a westward progression in starting date as is the case with rainfall. The peak hail season, however, does exhibit such a pattern. In the northern Transvaal the peak HDF occurs during early November (Nov-Oct peak) whereas the south-central parts exhibit a slightly Iater hail maximum (Nov-Dec peak). Moreover, the relative contribution of the December HDF to the total annual HDF increases from east (Lowveld) to west. January hail is relatively more important in the south eastern and south western Transvaal. A westward shift in hail season was reported by Schulze in 1965, with a Nov-Dec peak to the east, and a January peak to the west of 26°E. However, since most stations in the study area lie to the east of 26 °E, it is not surprising that a January peak is not found here. I"'r_-"-j -~,._._._._, ./ H,TY\.. '. /"_..... \ /',' "~n \, / ."v< ~ , / ril ., I i I I II '-. J • , 0 III 0 J , .... iii J " I i I ./ ~llli~::

... ,O .. OJ' ...... , ! I ___-_ I -', , ~ I " \ I

Figure 4.5 Spatial variation ofhail day seasonality over the Transvaal. The y-axis represents mean annual HDF/station (x 100) Hail patterns in the Lowveld differ from those in the remainder of the Transvaal. Not only does hail occur less frequently here than in other parts of the Transvaal, but the hail days are scattered more or less evenly throughout a nine month period, commencing in August and extending until April. The relatively high August incidence suggests an early start to the season here. Moreover, in contrast to other 57

areas where a single peak hail month is experienced, bimodality is evident in the Lowveld, with peak hail day periods occurring during both October and January. The below normal values for November and December result in the hail season being split into spring and autumn maxima. Distinct minima occur during September, December and throughout the winter.

4.5 DIURNAL HAIL DAY PATIERNS

4.5.1 General

Numerous papers have described the nature of diurnal precipitation regimes, inter alia, those by Dexter (1944), Raman & Raghavan (1961), Hering & Borden (1962), Solovlev (1965), Frisby & Sansom (1967), Pedgley (1971), Wallace (1975), Gray & Jacobson (1977), Schwartz & Bosart (1979), and Easterling & Robinson (1985). According to Schulze (1965) and Visagie (1965), hailstorm activity in South Africa is confined to the period between 12:00 and 22:00, peaking around 17:00-18:00 SAST (hereafter all times in SAST). Detailed analyses of hail activity in the Pretoria­ Witwatersrand area indicate that 66% of the storms occur between 14:00-19:00 (Carte & Basson, 1970; Held, 1973). If only the earliest reported onset times are taken into account, the hail frequency peaks between 15:00 and 16:00, coinciding with the period of maximum instability (Carte & Basson, 1970). In the Lowveld less than 5% of the hailstorm activity takes place during the night or early morning (Kelbe, 1984), while. this value increases to about 10% in the Pretoria­ Witwatersrand area. These nocturnal hailstorms have been found to be the most severe (Held, 1973).

Night-time hailstorms have been reported in various parts of the world, for example, in Armenia (Sulakvelidze, 1967 : in Carte & Held, 1978), Canada (Summers & Paul, 1970), Kenya (Alusa, 1976) and also South Africa (Carte & Basson, 1970and Held, 1973). The mechanisms giving rise to these nocturnal hailstorms have been the subject of much debate and include: the presence of low level jets at night; semi­ diurnal atmospheric tidal oscillations; radiative cooling from cloud tops; low level warm air advection; the inhibitions of radiative cooling from the upper stratiform clouds in the cyclonic curvature of an easterly wave; the undercutting of warm air by katabatic airflow from mountainous regions; or simply as a spill-over from showers that develop during the late afternoon (Neumann, 1951; Blackadar, 1957; Pitchford 58

& London, 1962; Summers & Paul, 1970; and Salau, 1986). According to Held (1973), hailstorms on the South African plateau are either of frontal or air mass origin. Since the latter are associated with periods of maximum instability, they are more likely to occur during the late afternoon, while the more severe storms are often of frontal origin and, as such, are liable to occur at any time. It thus follows that nocturnal hailstorms are mainly associated with frontal activity.

Little is known about spatial variations in hail onset times over South Africa, A slight north(east)ward diurnal shift in onset time has, however, been recorded for various parts of the Highveld due to the general storm movement (Carte & Held, 1972).

Seasonal variations in hail onset times have been identified in certain countries. In Canada, for instance, hailstorms during the early part of the hail season (May to July) start late in the day, after which onset times become progressively earlier during August and September. During this latter period, hailstorms are shorter in duration but with a more pronounced diurnal peak (Summers & Paul, 1970). According to Carte & Basson (1970), no such seasonal variation was found to occur on the Witwatersrand and in the Pretoria area. Yet in this study, the onset time of hail was found to vary appreciably between seasons.

4.5.2 Diurnal incidence of Transvaal hailstorms

The onset times of hailfalls were extracted from SAWE 'past weather records' for those stations which were operational after 1977. The meaning of the time codes used by the SAWE and the method used for extracting these data were discussed in Chapter 2. Figure 4.6 displays the areally-averaged onset times of hailstorms for the Transvaal as a whole. From this it is evident that more than 70% of hailstorms commence between 12:00 and 20:00, with 40% starting between 16:00 and 20:00. Nocturnal hailstorm activity appears to be greater (16%) than in other parts of the country. It is also shown that hail-producing thunderstorms are least likely to occur between midnight and noon (14%). This pattern accords well with that occurring in the rest of the summer rainfall region. 59

Figure 4.6 Diurnal incidence of Transvaal hailstorms (expressed as percentage of total daily hail frequency)

4.5.2.1 Spatial variation in onset times

It seems likely that the relative heterogeneity of the Transvaal in terms of climate and physiography may affect some aspects of precipitation processes. The diurnal incidence of hailstorms for the various sub-regions was therefore investigated separately. The results of the analysis are presented in Table 4.1.

Table 4.1 Diurnal variation of hail onset times in different parts of the Transvaal.

SOUTHERN SOUTH- AVERAGE TIME & SOUTH- EASTERN NORTHERN LOWVELD FOR TRANS- WESTERN TRANSVAAL TRANSVAAL VAAL TRANSVAAL (66 stations)

00-08:00' 5,5 6.3 12,2 14,2 8,0

08-12:00 5,5 2,4 9.8 0 6.6

12-16:00 33,0 32,2 43,9 20,1 30,5

16-20:00 47,11 47,2 29,3 54,2 38,7

20-24:00 11,0 11,8 4.9 11,5 16,3

The diurnal distribution of hail onset times in the western and south eastern Transvaal is similar to the general pattern, as discussed above, However, this is not so for the northern Transvaal and the Lowveld. In the former, the likelihood of 60

hailstorm occurrence between noon and 16:00 is considerably higher (43,9%) than in the other regions, while relatively few hailstorms have been recorded during the 16:00-24:0Q period. In the Lowveld, the 16:00-20:00 peak hail onset period is more pronounced than elsewhere whilst about 25% of the hailstorms begin at night and in the early morning. No storms were recorded between 08:00 and 12:00for the period under review (1960 - 1986). The sparse distribution of hail recording stations as well as the low HDF may have caused some bias in the Lowveld data.

The very high morning ground temperatures in the northern Transvaal probably explain the earlier- onset of convective activity there. Although the Lowveld is also known for its high morning temperatures, it seems possible that the relative proximity of the ocean may influence precipitation processes in that region.

4.5.2.2 Seasonal variation ofdiurnal hail incidence

In contrast to earlier findings (Carte & Basson, 1970), this study has revealed the existence of seasonal variations in the onset time of hail in the Transvaal. In certain respects they are similar to the variations described by Summers & Paul (1970) for Canada.

The diurnal variation was analysed using four "hail seasons", namely a pre-season (July to September); spring (October, November - which comprises the peak HDF period); summer (December - January); and the late-season (February to June). The significance of various times of day, with respect to hailstorm occurrence during different seasons, can be gleaned from Figure 4.7. This is summarized as follows:

12:00-14:00 : Although few storms occur during this period, its relative importance increases throughout the year to reach a peak in the late-season. 14:00-16:00 : Pre- and late-season (i.e. dry season) hail-storms occur most frequently at this time; wet season hailstorms are rare. 16:00-18:00 : Summer hailstorm activity reaches its peak. Late season hailstorms also occur frequently during this period. 18:00-20:00 : The frequency of these storms increases progressively throughout the season to reach a peak during late summer and autumn. 61

20:00-24:00 : Many Transvaal hailstorms have been recorded at night ­ especially those occurring before October. The contribution of nocturnal storms to total hail incidence decreases steadily throughout the season to reach a minimum in autumn. However, during this latter period, the frequency of midnight ­ 08:00 thunderstorms increases. This tendency continues until October.

r-- 1 1 40 I r-i I r--l I I III 1---' I I 1 I I 1 1 I 1 1 r--' I I I I I 30 I I I I 1 I I I II I I I 1 I 1 1 I I 1----1 ,---I I r-l I I I 1 I 1 I oR. III I I I 1 1 I I I I I'I I I I II I 20 I II I I I 1 I 1 I I I I I I I I I I I I I I I 10 I I I

0 24 8

June-Sept. Oa.-NOY. II II Dec. -Jan. Feb. -June PRE-SEASON SPRING SUMMER LATE SEASON

~ PNk hall peood Cumulative tOlailot short periodI [IT] Sec:ondafy pOoa Cumulaliv. lCUIlot long'" pe

NOdumal &!()(ml (20: 0Q.ll: JO) Figure 4.7 Diurnal and seasonal incidence of Transvaal hailstorms showingpeak periods (percentage of daily total hail/hail day)

In general, it can be seen that morning and' early afternoon hailstorms seem to predominate during the pre- and late-seasons while hail activity peaks at later times during spring and summer. Furthermore, a more pronounced diurnal hail distribution peak occurs in the late-season with more than 80% of the storms occurring between 12:00 and 20:00. These variations in diurnal hail incidence during different seasons accord with those reported for Canada (Summers & Paul, 1970). 62

A distinct bimodal pattern is evident in both the pre-season and the summer due to the high afternoon and night-time hailstorm frequency on the one hand and the relative de'crease in 18:00-20:00 hail incidence, on the other. According to Salau (1986), Nigerian hailstorm activity also exhibits afternoon and evening multi-peak patterns.

The mechanisms responsible for this seasonal shift in times of hailstorm activity have yet to be satisfactorily explained. It was shown previously that there appears to be a higher proportion of night-time (20:00-08:00) hailstorms during July to September than during the rest of the year. This would be expected in storms associated with frontal or pre-frontal activity, since fronts - not tied to the daily rhythm of morning heating and night-time cooling - are more likely to traverse South Africa during the late winter months and are least likely to occur during February, March and April. The relatively higher frequency of mid-morning storms during October and November can probably be ascribed to the increased atmospheric instability and associated convective activity which prevails during this period.

Figure 4.8 and Table 4.2 serve to summarize some diurnal and seasonal aspects of Transvaal hailfaIl and their variation over space.

4.6 SUMMARY

In this chapter, the annual, seasonal and diurnal hailstorm patterns were investigated for the Transvaal. Comparison with the rest of the summer rainfall region indicates that, despite the general similarity in hailstorm patterns, a number of important spatial and temporal differences do occur.

Although hailstorms occur most frequently in the late afternoon and early evening, it has been shown that, in the northern Transvaal and Lowveld, their diurnal distribution differs from that occurring in the western and south eastern Transvaal. For instance, there are more morning (8:00-12:00) storms in the northern Transvaal but far fewer in the Lowveld; nocturnal Lowveld storms make a greater contribution to the daily total than elsewhere; and hail activity extends over a shorter period there than in the other parts of the Transvaal. In contrast to previous studies, 63

Western Transvaal South eastern Transvaal 20 20

~ 10 -- --- o

o 8 12 16 20 24 8 12 16 20 24 8 12 16 20 24 8 12 18 20 24 o 8 12 16 20 24 8 12 16 20 24 B 12 16 20 24 8 12 16 20 24 Time Time I Jul-Sept Od-Noll Dec-Jan Feb-Jun Jul-Sept Od-Nov Dec-Jan Feb-Jun

30 Northern Transvaal 30

Eastern Lowveld

10 10

o o o 8 12 16 20 24 8 12 16 20 4:4 8 12 16 20 24 8 12 18 20 24 o B 12 16 20 24 B 12 16 20 24 8 12 16 20 24 B 12 16 20 24 Time Tome , ! JuI-Sept Cd-Nov Dec-Jan Feb-Jun J~ Cd-NOI/ Dec-Jan Feb-Jun

Figure 4.8 Diurnal and seasonal incidence ofhailstorms for different parts of the Transvaal (percentage of total annual HDF)

Table 4.2 Summary of diurnal and seasonal characteristics of hailstorms in various parts of the Transvaal (given in descending order ofoccurrence frequency).

SOUTHERN SOUTII- I ~RDER OF & SOUTII- NORTHERN HSTERN LOWVELD I ~CCURRENCE WESTERN TRANSVAAL TRANSVAAL TRANSVAAL I I I (peak) tiee 12:00-16:00 12:00-16:00 16:00-20:00 16:00-20:00 I season su••er su ••er su ••er spring 2 tiae 16:00-20:00 16:00-20:00 16:00-20:00 12:00-16:00: 16:00-20:00 season su ••er spring spring Oct, Nov. Dec-June. I I feb-June 3 tiae 16:00-20:00 12:00-16:00 16:00-20:00 , season I spring I spring late-season I It. tilDe I 16:00-20:00 i 12:00-1~:00 12:00-1":00 season late-season late-season; 00:00-08:00 I Oct-Hov I ; ~ovest i tilDe I 00:00-08:00 00:00-08 00 occurrence I. su ••er; Feb-Sept I 08:00-12:00 08:00-12 00 08:00-12:00 08:00-12:00 I Oct-Jan; Feb-Sept Dec-June throURhout ,ear 20:00-24:00 20:00-24 00 20:00-24:00 20:00-24:00 I late-season Dec-June feb-June Oct-Nov oc cus-naI j highest I 8,5% 12,5% 7,1: 11,4% jctoras I occurrence I s ....er Oct-Nov Feb-June (20:00-08:00) I 64

seasonal differences in diurnal hail incidence have been found. These are most evident in the dry season hailstorm activity when almost 40% of the February - June hailstorms .occur earlier in the day i.e. between 12:00 and 16:00. The incidence of these and nocturnal storms are also particularly high during the July to September period. These differences are probably due to the seasonal variation in the incidence of rain-bearing systems.

Evidence of an east to west progression in peak hail season has also been found. In addition, there are indications of possible changes in the seasonality of hailstorms during years with- an exceptionally high hail day incidence. Further research is required to investigate this aspect.

Time series analysis has shown alternating periods of high and low hail day frequency, each lasting for eight to nine years. For the period between 1910 and 1984, the rainfall in the summer rainfall region also exhibited alternating episodes of nine wet (+) followed by ten dry (-) years (Tyson et al., 1975). It would be interesting and informative to compare the temporal patterns of hail and rainfall over this region. This is investigated in Chapter 5. 65

ANALYSIS: PART II CLIMATOLOGICAL ASPECTS OF HAIL IN THE TRANSVAAL 67

CHAPTERS INTER- AND INTRA-ANNUAL RELATIONSIDPS BElWEEN RAINFALL AND HAIL DAYS

5.1 INTRODUCl10N

Although hail is usually accompanied by rain, relatively little is known about the quantitative inter-relationship between them. The few studies touching upon this issue have yielded conflicting results. For instance, Carte (1966) and Carte and Held (1978) found a high positive correlation between hail day frequency (HDF) and rainfall for the South African Plateau, whereas analysis of precipitation at Kimberley showed that a low incidence of high intensity rain occurred during months with a high hail frequency (Du Preez, 1972). It has also been established that the wettest years do not necessarily have the greatest number of hail days (Carte & Basson, 1970). Furthermore, Prins & Loth (1988) found that hailstorms predominate in the dry season in Tanzania.

The contradictory findings cited above emphasize the need to know more about the nature of the relationship between hail day frequencies and rainfall for this part of the summer rainfall region. The aim of this chapter is therefore to describe the natureof the relationship between HDF and rainfall by comparing:

(a) annual hail day (HD) and rainfall time series (b) seasonal HD and rainfall patterns. This will be achieved by comparing monthly precipitation patterns which occur during years with a high frequency of hail days (i.e. high hail years, HHY) with those of low HDF periods (LHY) as well as with long-term mean values; and by analysing these differences. (c) precipitation patterns on HDs to those of non-hail rain days (NHDs).

5.2 INTER-ANNUAL HAIL-RAIN RELATIONSHIPS

It was noted in the previous chapter that the annual HDF time series for the Transvaal showed evidence of alternating nine year high and low HDF periods. The established existence of rainfall cycles in the summer rainfall region as a whole (Tyson et al., 1975; Tyson & Dyer, 1975, 1978) and in the north central Transvaal 68

(Kelbe, 1984) in particular; together with their ubiquitous 18-year periodicity, point to a number of interesting hail - rain analogies. These were investigated by comparing .the Transvaal hail day and rainfall time series.

The 1960-1985 annual HD time series for the Transvaal has been reproduced as Figure 5.1a. The compilation of the corresponding rainfall time series was described in Chapter 2. To summarize: The monthly district rainfall data set was used to obtain the July-June annual total rainfall for each of the 25 rainfall districts located in the Transvaal. These were used to calculate the mean annual rainfall for each year (1960 - 1986); This represents the areally-averaged rainfall time series for the Transvaal and is portrayed as deviation from the mean in Figure 5.1b. Smoothed values (using a 5-term binomial filter), are also indicated.

Figure 5.1a clearly shows a below normal hail period in the early 1960s; followed by a nine year high hail period; then a nine year low hail period; after which the HDF was again above normal. The 'switch over' from low-to-high hail phases occurred in 1963 and 1981 while the high-to-low reversal took place in 1972/73. Although the inter-annual rainfall was more variable (Fig. 5.1b), alternating wet and dry periods are evident. Conditions were generally dry during the 1960s; wet in the 70s; and again dry during the early 1980s. The respective wet (+) to dry (-), and - to + reversals took place in 1961, 1970 and 1981.

Comparison of the two figures reveals that, III general, the high hail period coincided with dry conditions over the Transvaal, while below normal HDFs occurred during wet years. This was the case for 18 out of the 25 year period under review. Although the first two rain and hail phase changes did not occur simultaneously (a two-year difference occurred, with hail lagging), the last low-hail­ wet to high-hail - dry reversal was contemporaneous.

At first glance, these results appear to reflect an anomalous situation since hail is usually accompanied by rain and thus it would be expected that heavy rain should be recorded during periods with increased HDF and vice versa. However, this is not necessarily true because hail is specific to convective thundershowers and is not usually associated with widespread general rains. 69

50 (a)

f:~:~;~;~;f3 5 Term binomial filter

Years

(bl 900

~ 800 ~ ~ ~ ":'7':'7r:':' .sE ~ c:: "ro -i7OQ ::::l c:: ~ c:: ra c:: ra %8 r:-1 i= ~irlll¢t£~ "~ f':-':-':~ .:.:;,;,;,. "~5oo ..:.:.::.:: ~ L:::.:.:.' c ~

~;;;;;;:;;;~;i~ 5 Term binomial rylter

Years

Figure 5.1 Area averaged time series ofannual (July - June) (a) hail day frequency (1960-1986) (b) rainfall (mm) (1960-1985) 70

Ifan inverse relationship does indeed exist between hail days and rainfall, it follows that significant intra-annual differences in precipitation patterns would occur during periods with high HDF as compared with those occurring during periods with low or normal hail incidence. This is examined in the following section.

5.3 INTRA-SEASONAL HAIL-RAINRELATIONSHIPS

The main objective of this section is to establish whether the seasonality of precipitation (i.e. rain and hail) exhibit different temporal patterns during HHYs than during LHYs (or mean conditions). Inevitably, such a comparison requires a number of data sets. Since the data sources vary, as do the techniques used to compile the data sets, a brief resume of these are given below.

5.3.1 Data

5.3.1.1 Hail data

1. Daily 'past weather' records for the 1960- 1986 period were used to compile a monthly HDF data set for 66 sites in the Transvaal (Fig. 2.2). Hail days were identified using the applicable hail identification codes described in Chapter 2 (and Appendix II) and the relevant hail data extracted for these hail recording stations. Monthly and annual (July-June) HDF totals were calculated for each station and areally-averaged to obtain regional totals and means. The latter will henceforth be referred to as TVLH, while the areally-averaged data set for any particular year will be denoted by 1VLH(year).

2. Table IV.l (Appendix IV) lists the year during which each of the above 66 stations recorded the highest HDF (for the 1960 - 1986 period). These are the HHYs. The monthly HDFs were extracted for that year (for each station) and areally-averaged to obtain mean monthly HDFs for the Transvaal. This data set is indicated by TVLHHHY.

3. It was assumed that long-term mean values are representative of 'normal' or 'expected' conditions. These mean monthly and annual HDFs were extracted from WB40 (1986), for the 60 stations (as depicted in Fig. 2.1), whereafter 71

regional means were calculated for the Transvaal as a whole. This data set is

referred to as TVLHmean•

4. The CSIR has been collecting hail data from an extensive observer network in the Pretoria-Witwatersrand area since the early 1960s (see Fig. 2.2). Monthly HDFs were obtained from the CSIR data set for the post-1970 period for this

entire area (CSIRNETW) as well as for the Pretoria and Witwatersrand areas separately (CSIRPta & CSIRWWR , respectively).

5.3.1.2 Rainfall data

1. Rainfall amounts (R) and rain day frequencies (RDFs) were extracted for HHYs from the relevant SAWB publications (see Chapter 2) and are ally­

averaged to obtain the TVLRHHY and TVLRDHHY, respectively. These could only be obtained for those 36 stations indicated on Fig. 2.2.

2. Mean monthly rainfall values and RDFs were extracted from WB40 (1986)

and the data sets TVLRmeall and TVLRDm eall compiled using the same

procedure as was used for TVLHm eall •

3 & 4. Monthly rainfall values which were supplied for each of the rainfall districts in the Republic, served as the source of these data sets. By areally-averaging the monthly rainfall values for all 25 'districts' which occur in the Transvaal, a rainfall seasonality data set was obtained for a ). specific year (TVLRyear District 74 (DIS174) data were used to represent rainfall in the Pretoria-Witwatersrand area.

A summary of the above-mentioned hail and rainfall data sets is presented as Table 5.1.

5.3.2. HHY precipitation vs long-term mean conditions

5.3.2.1 Hail day frequencies: TVLHHHY vs TVLHmeall •

Figure 5.2 depicts the monthly distribution of hail days during normal (i.e. mean) conditions and during HHYs. From this it can be seen that the hail season usually Table 5.1 Summary of data sets used in section 5.3. fype Abbre­ Meaning Origin of data viation

TVL Il Areally-averaged hail day frequencies (IlDF) - SAWB printout for 66 stations in the Transvaal (1960-1986) (past weather records)

II TVL Il(yr) Areally-averaged "DF for all TVL stations - SAWB printout operational during a particular year (past weather records)

A TVLII IIIIy Mean BDF for that year when each station recorded - SAWB printout the highest IlDF (past weather records)

I TVLB Areally-averaged IIDF for all TVL stations listed SAWB, 1986 (WB40) mean in Wll40; calculated from longtcrm mean values

L CSIRNETl-J; 1970-1986 "DF data for the CSIR hail observer net­ CSIR CSIR p t a; \~WR work in Pta-WWR area

TVL R Areally-averaged rainfall values for all 25 rainfall SAwa printouts districts comprising the Transvaal (district weather records) R Various SAWB publications TVL RIIlly ; Areally-averaged rainfall and rain day values - as A RD""y for TVL",,"y (annual and monthly reports) I TVLR Areally-averaged rainfall and rain days - as for SAWB 1986; WB40 RD mea n; mea n TVL"mean N DIST 74 Rainfall amounts for rainfall district no. 74 WB printouts (district rainfall values)

-....I ~,J 73

o> heavy hail yearS(HHV) z 20 rn W ::::l m normal 0 w c::W u. 15 > < C if:::::: -I< :I: 10 -I < ::::l Z Z < 5 u, 0 ~ ~I::}

J A S o N o J F M A M J MONTHS

Figure 5.2 Transvaal hail day frequency seasonality - longterm and during HHYs.

20

[ill heavy hail years (HHY...

15 [] normal

~

-I -I Lf z10 < c::

~ :I: l- S 5 ~ z ~ :.~~:·:.;.~..l:.....:.::.::.. ::::: ::::.- A S o N o J MF MA J MONTHS

Figure 5.3 Transvaal rainfall seasonality -longterm and during HHYs. 74

starts during September-October, reaches a peak during November, after which the HDF gradually decreases to reach a minimum in winter. Approximately 70% of the annual tot~l hail occurrences are recorded between October and February.

While the general seasonal distribution of hail days occurring during HHYs follows a pattern very similar to the one above, intra-seasonal variations are evident. Firstly, a greater concentration of hail activity (77%) occurs during the October to February period of HHYs, and secondly, HDFs are above normal during October, November, January and February and below normal during December, March and April. While a higher November peak is to be expected for periods with increased hail activity (i.e. the HHYs), the reason for the striking increase in the January HDF during such periods is less obvious. The lower HDFs during December and February of HHYs enhance the effect of the November and January peaks, while giving an impression of a shift away from December towards January. This effect is further illustrated by a comparison of the Dec: Jan HDF ratios, for which values of

1,26 and 0,84 were obtained for TVLHmeoll and TVLHHHY' respectively.

5.3.2.2 Rainfall seasonality: TVLRHHY vs TVLRmeall

Mean monthly rainfall values for HHYs and the corresponding long-term means are shown in Figure 5.3. As anticipated, it shows that the rainy season in the Transvaal is mainly confined to the summer months (October to March). The long-term mean values indicate that approximately 60% of the annual rainfall is recorded during November, December and January, with the peak occurring in January.

However, the summer rainfall distribution during HHYs deviates slightly from the above pattern in that a general advancement towards an earlier rainfall season seems to occur here. This tendency is illustrated by the following:

(a) The early summer (Oct, Nov, Dec) rainfall is above normal during HHYs while the post-December values show a corresponding decrease;

(b) the peak rainfall month shifts from January to December. 75

Confirmation of this shift to an earlier rainfall season is obtained from the Oct-Nov­ Dec: Jan-Feb-March rainfall ratios which yield values of 1,14 and 0,96 for HHY

(TVLRHHY) and normal (TVLRmeal1 ) conditions, respectively.

This deviation from the long-term mean rainfall pattern seems to be directly associated with intra-seasonal changes in hail day occurrence during December, January and February. For instance, during December, above normal rainfall but below normal hail activity occurs, while conversely, lower rainfall values in January and February are accompanied by above normal hail incidence.

5.3.2.3 Monthly rain day distribution: TVLRDHHY vs TVLRDmeal1

Studies of Orange Free State (Harrison, 1983b & 1984b) and south eastern Transvaal (Olivier, 1985) rainfall characteristics have shown that it is primarily the number of rain days with significant rainfall rather than the amount of rain per rain day which determines the total annual rainfall. However, because rain is also recorded during a hailstorm, a hail day is necessarily also a rain day (although the converse is not true). Thus an increase in the occurrence of either rain (without hail) or hail events should be reflected in the annual rain day frequency (RDF). Such changes may not be evident if the rainfall and HDFs are inversely related or if the amount of rain falling on hail days and non-hail rain days differ significantly.

The long-term mean and HHY RDFs are presented in Table 5.2. It can be seen that significantly fewer RDs occur during August of the HHYs. This is to be expected since both rainfall and HDF are below normal during this month (Figs 5.2 & 5.3). Similarly, during October, high HDF and rainfall are accompanied by higher RDFs. However, during December, above normal RDFs are associated with higher rainfall but lower HDFs; while in January the opposite occurs with high RDFs occurring in conjunction with more frequent hail events but lower rainfall. This, together with the small difference between HHY-RDF and the long-term mean (81 as opposed to 79), indicates the presence of the afore-mentioned confounding relationships. Departure from the mean monthly RDF patterns can be expected during HHYs if the mechanisms or systems causing precipitation during non-hail producing storms differ from those associated with hailstorms. Consequently, adequate information relating to the rainfall associated with hail days 76

and with non-hail rain days is a prerequisite for the understanding and explanation of these anomalies.

Table 5.2 Mean monthly rain day frequency for HHYs and normal (N) conditions for the Transvaal stations. (underlined values indicate the higher value)

Jul Aug* Se>Jt Oct Nov Dec Jan Feb March Apr May Jun Annual Tot. HR HR RHH HHY 1 ,2 1,0 2,7 g 11,2 12,2 13.6 9,4 8,7 6,6 3,2 1,5 80,5 HR H R R HR N 1 ,6 ~ 3,3 7,8 11 ,5 11,6 12,0 9,5 8,5 6·,4 3,2 1,9 79,2

*significant difference at a = 0,05 using Z-statistic H = higher hail day frequency R = higher rainfall

5.3.2.4 Rainfall per rain day

The monthly distribution of rainfall intensities, as shown in Figure 5.4, accords with the anomalies in rainfall patterns noted earlier. Rainfall intensity usually peaks around 10 mm/rain day in January, i.e. the month with the highest rainfall. During HHYs, however, the highest rainfall intensity is associated with November rainfall. This change coincides with a general seasonal advancement of rainfall during these years. The Nov: Jan rainfall intensity ratio of 0,92 and 1,15 for normal and HHYs, respectively, confirms this intra-annual change in occurrence of hail and rain.

5.3.2.5 Spatial changes in precipitation seasonality

An arithmetic mean is influenced by the presence of extreme values within the data set. This is especially true of annual or monthly rainfall means because rainfall amounts vary considerably over time and space and therefore erroneous and misleading results may be obtained from analyses employing mean rainfall values as a variable. A comparison of the number of stations which experience above normal rainfall with those experiencing below normal rainfall during a particular period would give a better indication of relative dry or wet conditions, than rainfall depth, perse. 77

heavy hail yearsQiHV) ~ tj:.:.: 10 normal : : ~ : 7: : ~ ~

: -, " : : : :: 8 : :-:::::' : : IT ~ f : i >- : ~~~~~ III I:::'::':' I "0 : <, E J E 6 : > : ~ " : 7:::- (ij I I:~:~: : : ~ :0:' :.:.: I:~:~: z ~~~~~ w : I E ~~~~~ :.:.: ~ 4 ~~~~~ ~ ~~~~~ ~l~l~ ~~~~~ :::: -, " " -, :.:.: " "" " "" m : : ...J ~~~~jj 11111 [iii! ...J iiiii ·Ii

MONTHS

Figure 5.4 Longterm mean and HHY rainfall intensity

No. of stations with: ~:: =::0::::: ::RH RN < 100 ±- ~ ~ ~ ~ '7'"-' r.;.;.;.- ~ : : 77 '77 : :: : : : :: : [2 [l 0, : : : : : : : : ::: : : : : ~ :::::: : :: ~ 75 ~ : : ~ : :: : :: ~ : ;::;;:;;; : : en :'0 : 'W':': : tt : : : z ~ o : ;2i ~50 - : : ill ;:;:;:;:;:: ­en W 0:::::::: 25 II :~1~~1~11 I:~:~:~:~ ~;~~~~~~~ II :.:.:.:. ::::::::: ~:~:~:~:~ I I ...... j~jlilil ...... I :~1~j~~ij :~:~:~:~: ~r~m~ llll!l!l~ :~:~:~:~ ~!jl~lil :.:.:.:.: ::::;::: I .:.:.:.: I J AS o N o J F M A M J

MONTHS

Figure 5.5 The number of stations with above, similar and belownormal rainfall during HHYs 78

Figure 5.5 indicates the relative proportions of the number of stations recording below normal, similar, and above normal rainfall during the HHYs. It is obvious from this figure that the vast majority of stations receive less rain during HHYs. This occurs throughout most of the year with the exception of November and April. However, the wet conditions which exist at most stations during November seem to substantiate previous fmdings concerning a HHY advancement of the rainy season towards an earlier peak.

It thus appears as though conditions are considerably drier than normal over most of the Transvaal during HHYs. This inverse hail - rain relationship as well as the seasonal precipitation anomalies noted above, should be more marked if rainfall and HDFs are compared for HHYs and LHYs. This is the focus of the following section.

5.3.3 HHY vs LHY conditions

It was previously shown that the annual HDF data sets obtained from both the CSIR and the SAWB exhibit similar time series patterns (Chapter 2; Olivier, 1989). Due to the general superiority of areal data compared with point data, it seemed appropriate to use the more detailed CSIR data set in order to verify the findings discussed in the previous section. Therefore the seasonal occurrence of hail days and rainfall were analysed using the following data sets:

(a) TVLH (1981/82) and TVLH (1980/81) b) CSIR..NEXW(l978/79) and CS1RN?wJ1979/80) c) TVLR (lYtJ1/82) and TVLR (19lJUjl51) 1d) DIST 74 (1978/79) and DIST 74 (1979/80) In all cases (a to d) the first mentioned data set represents HHY-, and the second, LHY, conditions. Also, below average rainfall was recorded during 1978/79 and 1981/82 in the areas concerned, while 1979/80 and 1980/81 were wet there.

Comparison of the two Transvaal HDF distributions (illustrated in Figure 5.6) indicates that the peak hail season occurred during December in the LHY, while in the HHY it peaked in January. Unfortunately, the CSIRNETW HDF data sets do not give such a clear indication of seasonality changes during periods with above and below average HDF, nevertheless, a slight shift towards a later hail season was evident during the HHY (dry year). 79

n 1981/82 {2,5 HD/STAT.} U HHY DRY 40 r.:::I 1980/81 (0,9 HD!STAT.) t:;;;;) LHY WET

.30

ll. o 20 J:

10

m En J A 'S 0 N D J FMAM J MONTHS

Figure 5.6 Monthly distribution of HDFs during wet (low hail) and dry (high hail) conditions .

n 1981/82 U HHY DRY (497mm)

"7.' f0l 1980/81 !1J LHY WET (768 mm lilt <:7 :~~~: 20 ~~~j

.....,~ ~. ..J ..J

Figure 5.7 Monthly distribution of rainfall during wet (low hail) and dry (high hail) conditions 80

Hail day seasonality trends found in all three HDF data sets (viz. TVLHHHY &

TVLHmean ; TVLH(l981/82) & (1980/81); CSIRNETW(1978/89) & (1979/80) were weighted and combined in order to obtain a general indication of changes in the temporal nature of the peak hail season. Weighting consisted of allocating a 'score' of 3 points to that month in which maximum HDF occurred; 2 points to the month with the second highest HDF; and 1 to the third highest HDF month (see Table 5.3). When averaged it was found that the weighted hail peak changed from late November in HHYs (wet years) to early November in normal and LHYs.

Analysis of the rainfall characteristics of data sets (c) and (d) above, i.e, TVLR1981/82 & 1980/81 and DIS1741978/79 & 1979/80' show similar trends to those found previously, in that, lower rainfall was recorded in the Transvaal during January of the HHY and higher rainfall during that December, than in the corresponding months of the LHYs (Fig. 5.7). Calculation of weighted rainfall peaks reveals that the rainfall peak occurred in mid-December during the LHYs (wet) in contrast to an early December peak in the HHYs (Table 5.3).

These results confirm previous findings in which it was shown that the hail season occurs later while the rain season peaks earlier during dry (HHY) conditions and vice versa during LHY (wet). Therefore it seems apparent that differences do exist in the seasonality of rain and hail during high and low hail years.

5.4 SUMMARY

A number of points have emerged from the above investigation.

Firstly, it appears that an inverse inter-annual relationship occurred between rainfall and hail days in the Transvaal during the 1960to 1986period.

Secondly, the existence of such a relationship was confirmed - not only on an annual scale but also for shorter seasonal and intra-seasonal periods - by comparing the number of stations receiving below normal rainfall with those registering above normal amounts during HHYs. It was found that the majority of stations receive significantly less rainfall during HHYs. Moreover, those months during which the 81

Table 5.3 Peak precipitation season (using weighted means).

HAIL DATA SETS: TVLH HHy TVL1I1981/82 CSIRNETI/ 1978/79 weight weighted weighted weighted

~ Peak HD month 3 Nov (5) 15 Jan (7) 21 Nov (5) 15 Second highest HDF 2 Jan (7) 14 Oct (4) 8 Oct (4) 8 Third highest HDF 1 Oct i 4) 4 Aoril (10) 10 Jan hi 7 TOTAL 33 39 30 • 102 102 Mean 110 peak • 18 • 5.7 • LATE NOV. - DATA SETS: TVLII mean TVLJl1980/81 CSIRNETI/ 1979/80 weight weighted weighted weighted ill Peak 110 month 3 Nov (5 ) 15 Jan (6) 18 Nov (5) 15 Second highest HDF 2 Oct (4) 8 Nov (5) 10 Dec (6); Jan (7); Third highest IIDF 1 Dec (6) 6 Sept (3) ; March (9) 14.6 Oct (4) ; March (9) ; . Aoril (10) 6 5 TOTAL 29 30.5 29,6 • 89.1 !!2...! Hean liD peak • 17 . 5.2 • EARLY NOV.

RAINFALL DATA SETS: TVLR lllly TVLR1981/82 DIST74 1978/79 weight weighted weighted weighted .!!!!!. Peak R month 3 Dec (6) 18 Jan (7) 21 Oce (4) 12 S~cond highest R 2 Jan (7) 14 Nov (5) 10 Jan (7) 14 Third highest R 1 ~ov (5 ) 4 Dec i6 \ 6 Mar (9\ 9 TOTAL 37 37 35 . 109 109 Mean R peak 18 • 6,06 ·• EARl.Y DEC.

DATA SETS: TVLR mean TVLR1980/81 DIST74 1979/80 ",eight weighted weighted wei~hted ill Peak R month 3 Jan (7) 21 Jan (7) 21 Jan (7) 21 Second highest R 2 Dec (6) 12 Nov (5) 10 Nov (5) 10 Third highest R 1 Nov (5) 5 Feb i8i 8 Feb (8) 8 TOTAL 38 39 39 . 116 . ill Mean R peak 18 • 6.44 ·• IUD DEC.

Months are numbered from July to June e.g. July = 1; August = 2 etc. Values in brackets denote month number. A score of 3 is allocated'to the peak precipitation month; 2 to the month with the second highest precipitation and 1 to the month with the third highest value. 82

HDF was above normal coincided with the periods when significantly more stations received below average rainfall, and viceversa.

Thirdly, analyses of a number of data sets showed that the seasonality of hail and rain differ during periods with high and low HDF i.e. during dry and wet conditions. Deviation from long-term mean rainfall and hail day patterns was also apparent in the distribution of precipitation during heavy hail years. This once again indicates an inverse relationship between rainfall and HDF since a simultaneous shift to a later hail season and an advancement of the rainfall season occurs during HHYs.

These results seem to imply that precipitation regimes associated with convective hail generating mechanisms possess different seasonal characteristics to those producing rainfall alone. Analyses of rainfall associated with each of the different precipitation generating mechanisms may help to explain apparent spatial and temporal anomalies in South African rainfall distribution; clarify the nature of precipitation controls; identify rainfall periodicities; and facilitate a more accurate prediction of the occurrence of these events. It is obvious that further research is required to elucidate, inter alia, the atmospheric conditions associated with these regimes together with the spatial and temporal patterns of their occurrence and the variability thereof and also the nature of the resulting precipitation on a daily scale. The latter aspect receives attention in the following section while Chapter 6 deals with the atmospheric conditions prevailing on HDs and non-hail RDs.

5.5 RAINFALL ON HAIL DAYSAND NON-HAIL RAIN DAYS

5.5.1 Introduction

The majority of publications dealing with the climatological aspects of South African precipitation patterns either make use of or analyse monthly, seasonal or annual rainfall data. Notable exceptions are the papers by Court (1979a&b), Taljaard (1982) and Harrison (1983b) in which daily rainfall patterns are analysed. Monthly and longer-than-monthly time scales are often too long to be of value in elucidating short-term rainfall events which are of especial importance to agriculturalists and hydrologists. Moreover, questions relating to precipitation associated with specific rain-bearing systems cannot be answered adequately when using meso-timescale data. 83

The aim of this section is to describe the nature of hail day (HD) - and non-hail rain day (NHD) rainfall in general terms as well as during a wet and a dry year.

5.5.2 Data and method

The study area, which comprises only the Pretoria-Witwatersrand area, was dictated by the availability of good quality data. The study period (1977/78-1979/80) was selected to include one wet and one dry year in each of the sub-regions (i.e. Pretoria and the Witwatersrand).

Daily rainfall data were extracted for all the South African Weather Bureau (SAWE) rainfall stations located within or in the immediate vicinity of the CSIR hail network area (i.e, from the SAWB blocks 513, 476 and the eastern half of 475). The dates on which hail were recorded within this area during the 1977/78 ­ 1979/80 period were obtained from the CSIR printouts. Daily rainfall totals were calculated for each RD, as well as for the NHDs. Thereafter, monthly and annual rainfall values were computed for HDs and for NHDs. All the statistics shown in Tables 5.4 to 5.6 were obtained in this manner. Since conditions were not wet (or dry) contemporaneously in the Pretoria and the Witwatersrand areas, 1977/78 and 1978/79 data were used for the Witwatersrand but 1977/78 and 1978/79 data, for Pretoria.

5.5.3 Temporal hail day and non-hail rain day rainfall characteristics in Pretoria and the Witwatersrand

Table V.I in Appendix V summarizes all relevant data concerning the occurrence frequency of precipitation (rain & hail), together with the associated rainfall amounts and the number ofstations receiving rain, for one wet and one dry year, for the Pretoria and Witwatersrand areas separately. Tables 5.4 to 5.6 were compiled using these data.

5.5.3.1 Occurrence frequency.

Table 5.4 shows that there were substantially fewer HDs per annum than there were NHDs. This is to be expected since the atmospheric conditions necessary for hail 84

formation are more exacting than are necessary for the formation of ram. Moreover, many hailstones melt while falling to earth, further decreasing the probability.of hail. Table 5.4 Total regional and mean annual precipitation characteristics of hail days and non-hail rain days for the Pretoria-Witwatersrand area (Sept - April).

JlAIL DAYS NON-JIAIL DAYS

Total· Sub-regions Total· Sub-regions Region Pta WWR Region Pta WWR

A Precipitation day frequency 164 32,5 65,5 362 111,5 114, ~ B Rainfall ( .... ) (for all stations for all ppt daysL 30478 2232,8 13006,2 31762,9 5415,5 10466,(

e No. records 2730 219 1146 3452 581 1140 (No. of recording stations for all ppt days)

B/A Rainfa11/ppt day 185,84 68,7 198,7 87,7 48,6 91,4

D/e Rainfall/station/day 11,2 10,2 11,4 9,2 9.3 9.2

e/A No. stations with ppt/day 16,7 6,7 17,5 9.5 5.2 10,0

• Sept-April for 1977-1980 period. A number of interesting differences can be observed between HDF and NHDF -~ patterns for the Pretoria and Witwatersrand sub-regions. In Chapter 3 it was mentioned that the rainfall on the Witwatersrand is higher than that of the Pretoria area. This was ascribed to the effect of topography, However, according to Table 5.4, there is very little difference between the Pretoria and the Witwatersrand NHDFs - the main disparity lying in the frequency of HDs. This clearly supports the results of the HDF - altitude analysis in Chapter 3 and indicates that it is specifically the incidence of convective hail-producing thundershowers which is affected by topography.

Analysis of the monthly precipitation characteristics (Table 5.5) reveals that the highest HDF occurred in November in the region as a whole, with the average monthly value of 7,5 and 12,5 recorded for Pretoria and the Witwatersrand, respectively. The NHDF peaked during January in Pretoria (18,5 days/annum) and during February on the Witwatersrand (20 days/annum), The difference in the seasonal incidence of HD and NHD is especially clear in the Pretoria data set (Fig. 5.8a) in which the October, November and December HDF accounted for more than half of the annual total; the highest NHDF occurring during January, February and March. 85

Table 5.5 Monthly hail day and non-hail rain day rainfall characteristics for the Pretoria-Witwatersrand region (1977/78 - 1979/80); Pretoria (1977/78 - 1978/79); the Witwatersrand (1978/79 - 1979/80). a) Pta-WWR (Jut 1977 - Jun 1980) "All-st. *No. re- "All-sto No. re- "nDF rainfall cording R/HD R/HD/ST ST/HD "lIHDF rainfall cording R/lIHD Il/NHD/ST ST/NHD I stations stations

Sept I 7 992.4 76 141,8 13.1 10.9 32 2150.1 264 67.2 8.1 8.3 ' - I Oct I 22 4787.5 485 217.6 9.9 2~.1 44 2750,8 293 62.5 9,4 6,7 Nov 32 4809.9 533 150.3 9,0 16,7 43 6306.1 426 146.7 14,8 9,9

Dec 28 4079.0 372 145.7 11.0 13.3 46 3072.0 483 66.8 6,4 10.5

Jan 23 7403.9 419 321.9 17,7 18.2 58 8377 • 4 681 144.4 12.3 11,7

Feb 19 3420.9 315 180.1 10.9 16,6 60 5141,0 632 85.7 8,1 10.5 March 19 3085.9 298 162.4 10,4 15.7 I 43 2769.8 494 64.4 5.6 11,5 April 14 1898.5 232 135.6 8.2 16.6 36 1195.7 179 33,2 6.7 5,0 I . I I 164 30478.0 2730 362 31762.9 3452

b) Pretoria (Jul 1977 • Jun 1979)

HAlL DAYS NON-HAIL DAYS

*All-s t , *No. re­ "All-se • No. re­ "HDF rainfall cording IlIHD RlHD/ST STlHD II "NHDF rainfall cording Ii/NHD Ii/SHD/ST ST/SHD (%) stations (%) stations , ,Sept 2 201.1 16 100 6 12 6 8 0 I 18 6-5 4 63 37 5 10 7 3 5 \, (4.5) (6,2) 10 705.6 71 70.6 9.9 7.1 29 1191.4 -127 41,1 9,4 4,4 I•• (15.8) II (l1.0) I Mev 15 973.8 98 64.9 9,9 6.5 I 27 70g.0 llO 26,2 6,4 4.1 (21.8) (6,5)

Dec 11 438.7 74 39,9 5.9 6.7 24 789.5 108 32.9 7.3 4,5 (9.8) (7.3)

Jan 8 1431.0 62 178,9 23.1 7.8 37 4197.8 254 113,5 16.5 6.9 (32.1) (38,8)

Feb 7 190,6 43 27.3 4.4 6,1 34 1483.1 195 43.6 7,6 5.7 (4.2) (l3,7)

~arch 5 204,2 28 .40.8 7,3 5.6 34 1092.7 251 32.1 4,4 7,4 (4.6) (l0.1)

April 7 320.6 46 45.8 7.0 6.6 20 693.0 64 34,7 10.8 3,2 (7.2) (6.4)

TOTAL 65 4465.6 438 223 10830.9 ll72 c) Witwatersrand (JuJ 1978 - Jun 1980)

HAIL DAYS NON-HAIL DAYS

"All-st. ·So. re- "All-st. So. re- I"HDF rainfall cording R/HD R/HD/ST ST/HD I"lIHDF rainfall cording ll/lIHD ll/lIHD/ST STlSHD stations stations

I Sept 5 791.3 60 158.3 13.2 12.0 21 1474.7 201 70.2 7.3 9,6 I I I Oct 22 4081.9 414 185.5 9.9 18.8 24 1559.4 166 65.0 9.4 6.9 I ~ov 25 3836.1 435 153.4 8.8 17.4 31 5598.1 316 180.6 17.7 10.2 Dec 20 3640.3 - 298 182,0 12.2 1 4.9 33 2282.5 375 69.2 6,1 11.4 \ !Jan 18 5972.9 357 331,8 16.7 19.8 35 4179.6 427 119,4 9.8 12,2 I Feb 14 3230.3 272 230,7 11.9 19,4 40 3657,9 437 91.5 8.4 10,9 !larch , 16 2881.7 270 180,1 10.7 16.9 24 1677.1 243 .69,9 6.9 10.1 April I 11 1577.9 186 143.5 8.5 16.9 21 502.7 115 23,9 4.4 5.5

TOTAL 131 26012.4 2292 229 20932.0 2180

*Tota1 for two year period 86

The afore-mentioned clearly illustrates the difference between HD and RD seasonality, and supports previous findings by Schulze (1965) and Olivier (1989). These showed that the hail season usually reaches a peak during November while highest rainfall is usually confined to later in the season, viz. the December to February period, in most parts of the summer rainfall season.

(a) (b) 50 [].... HO r NHD , 40 m 1 .' , .' . l , . , " o , ~ -, , : 30 .' "," I .. o ;; J .: ~ ~ % ro .:!... ." ; d1 "::' ~~~r.~ .. '11d : :' :: " ~S~~O:.&...l.:~H:.l-J;,;;o~..J:.:.l.:.~~~...J:.:lw.. o :~: I' J F MA 20 ~i~ , (c) 30 10

I, f:\: \ -, . ". 20 ... " . f [: i, , . ! ::: " :~ S o N o J FM A % .". " 0 .; :..' t 10 ~~~ ., : .. :: ~~, ... ., 0 .. ::: - 0 0 :: ... " ::;~ " 'I ~ ;' '. ::: ~ I , .:. , 0 0 , :: , ~ :::~ , I ~ \\\ :::: . o IBm " M 5 o iN ID J FM A

Figure 5.8 (a) Hail day (HD) and non-bail day (NHD) frequencies (b) Rainfall on HD and NHD (c) No. of stations with rain on HDs and NHDs at Pretoria (1977'78 ­ 1978/79). (All values expressed as % of annual total)

A number of authors maintain that the early hail season coincides with periods with greatest instability and wind shear (Kelbe, 1984; Salau, 1986; Schulze, 1974; Jackson & Tyson, 1971). ~s seems to imply that there are no major large-scale differences in those rain-bearing systems causing rain and those associated with hail - the greater convective activity simply resulting from the prevailing kinematic and thermodynamic conditions. On the other hand, Harrison (1986), Lindesay (1988a) and Van Heerden et aI. (1988), found that large-scale temperate rain-bearing 87

systems are associated with early summer rainfall while late summer rain is under the control of tropical systems. This points to large-scale systematic changes in atmospheric dynamics forcing the rainfall variations throughout the year, as well as during wet and dry periods.

5.5.3.2 Rainfall amounts -...... It is interesting to note that the total annual all-station rainfall measured on HDs did not differ markedly from that of NHDs (30478 mm vs 31 762,9 mm). This is contrary to expectation because there were many more NHD than there were HDs (362 and 164, respectively). In addition, comparison of Pretoria and Witwatersrand values reveals significant HD and NHD rainfall differencesl Whereas the Pretoria area received less rain on the hail days - no doubt due to the lower HD:NHD ratio ­ the opposite applied to the Witwatersrand. The latter is clearly illustrated by the high rain intensity which occurred on HDs on the Witwatersrand (Table 5.4).

~e monthly rainfall pattern differed from that described above in that the HD and NHD rainfall peaks coincided. Both had highest rainfall during January and second highest during November (Table 5.5a). This tendency towards rainfall 'bimodality' is clearly reflected in the November, December and January rainfall for the study area, where the following HD and NHD rainfall amounts were recorded: Nov = 11 116 mm (i.e. 4 809,9 + 6 306,1); Dec = 7 151 mm; January = 15 781 mm. The Pretoria HD rainfall (Fig. 5.5b) as well as the NHD rainfall on the Witwatersrand, also showed signs of a November-January bimodality.z

The decrease in December rainfall, i.e. the "mid-summer drought" or the "little dry period" (Ireland, 1962), is a well-known phenomenon in many summer rainfall regions (Olaniran, 1988; Balogun, 1981), and is a feature of the South African rainfall pattern (Harrison, 1986). It has important agricultural implications because certain crops - such as maize - often reach the most drought-sensitive phenological stage during the December-January period. Drought during this period results in an excessive decrease in crop yield. This rainfall anomaly should be intensively researched since it would be of interest to determine whether this 'little dry period' is due to changes in the HD- or the NHD-rainfall. However, this aspect is not pursued further at this stage. 88

~omparisonof the monthly HD : NHD total rainfall ratios for Pretoria (Table 5.5b), reveals that the biggest discrepancy in HD and NHD rainfall occurred during February, when 88,6% of the monthly rain was recorded on NHDs (i.e. 1 483,1 mm) and only 11,4% (190,6 mm) on HDs. It is notable that the total monthly NHD­ rainfall exceeded that of HDs except during November when HD-rainfall predominated. In contrast to the Pretoria rainfall patterns, HD-rainfall was higher for most of the year on the Witwatersrand (Table 5.5c) - with the NHD-rainfall exceeding the HD-rainfall only during September, November and February. The greatest HD - NHD-rainfall disparity occurred during October when 72,4% (4 081,9

vs 155914) of the rain fell on HDs.

Comparison of monthly rainfall and RDF values indicates that although rain day frequency (HDF + NHDF) influences the rainfall total to some extent, it is not the only contributing factor - nor is it the most important. I

5.5.3.3 Rainfall intensity and areal extent

The 'number of records', as shown on Tables 5.4 and 5.5, gives the total number of stations at which precipitation was recorded on rain days - and thus is partially dependent upon the RDF (compare Figs. 5.8 a & c). Therefore the total rainfall divided by this number, yields the rainfall intensity at a station per HD or NHD, while the number of records divided by the RDF reflects the spatial extent of the precipitation. n

,{{ainfall intensity is obviously influenced by both rainfall amount and the number of precipitation days. For instance, the high rainfall intensity on HDs in January (Table 5.5a) was clearly due to both the high rainfall as well as the relatively low HDF; while the peaks in January NHD rainfall intensity can be ascribed to the monthly rainfall values alone. Furthermore.. the fact that NHD rainfall intensity values were lower than their HD counterparts for most months (Tables 5.4 & 5.5) indicates that NHD rainfall is of a more general, less intense type. Rainfall intensity anomalies occurred during November on the Witwatersrand and December, February and April in Pretoria, when the NHD rain intensity was higher.

The spatial extent of precipitation, given by the ST/day values on Tables 5.4 and 5.5, show that during the study period more stations received rain per HD than per 89

NHD. This seems to imply that the systems which produce hail-associated precipitation are spatially more extensive than those producing rain alone. Moreover, .the spatial extent of rain events was generally greatest in January (Table 5.5) - coinciding with the period when the rainfall is highest.

The large number of Pretoria stations (Table 5.5b) which received rain on NHDs during March, together with the extremely low rainfall intensity value of 4,4 mrn/NHD/st, emphasizes the non-convective, widespread nature of late season rainfall there.!t

5.5.4 Rainfall on HDs and RDs during wet and dry years ...... ,

As previously stated, the period used for analysis was selected so as to include one wet and one dry year. However, annual rainfall totals are not readily available for the Pretoria and Witwatersrand sub-regions, and hence the terms 'wet' and 'dry' refer only to relative rainfall amounts. Since the total all-station rainfall for 1977/78 in the Pretoria region (SAWB block 513) was almost 250% of that in 1978/79

(18 623J6 mrn vs 7 562,8 mm), it was assumed that 1977/78 could be considered as being a wet year and 1978/79 as being dry. Likewise, in the Witwatersrand area, the 1979/80 rainfall was found to be 186% above that of 1978/79. Hence it was assumed that 1978/79 and 1979/80 would reflect dry and wet conditions on the Witwatersrand, respectively. Precipitation characteristics of these wet and dry years are shown on Table 5.6.1 I

Contrary to expectation, and to previous findings (Harrison, 1983b; Olivier, 1985) where annual rainfall was found to vary directly with the frequency of days with significant rainfall amounts, no significant differences occurred between the RDF of the wet and dry years in the study area. In fact, there were slightly fewer RDs in the wet year on the Witwatersrand. These differences are, however, small and the time period too short to reach any firm conclusions vis a vis general rainfall - RD relationships.

On the other hand, wet and dry year HDF and NHDF do exhibit clearer patterns. In the Pretoria area there were significantly (>80%) more HD in the dry year (42 vs 23) - a pattern which occurs in the Transvaal as a whole. Conversely, 20% fewer NHD occur in the dry year in this area. The opposite relationship between HDF (or 90

NHDF) and rainfall on the Witwatersrand emphasizes the basic difference in precipitation patterns between these two sub-regionsf Such differences can be ascribed to the fact that the Witwatersrand and Pretoria fall into entirely different homogeneous hail regions, as was shown in Chapter 3.

From the above it appears as if the precipitation patterns occurring in Pretoria are more representative of those of the entire province.

~alysis of wet and dry year rainfall in Pretoria reveals that rainfall on both HDs and on NHDs was greater during the wet year than during the dry one, despite the lower HDF during the former period. A similar situation prevailed on the Witwatersrand. The anomalous rainfall seasonality pattern which occurred during HHYs (dry years) and LHYs (wet years) were discussed in section 5.3.2.2. Similar patterns occurred in the Pretoria rainfall data set (see Table V.1 Appendix V). Figure 5.3, which shows the distribution of monthly rainfall (expressed as a percentage of the annual total), clearly indicates that a relatively greater proportion of the annual total rain occurred during the January and February of the LHY/wet year. The opposite tendency occurred during December when more rain was recorded during the dry year. This 'mid-summer rainfall anomaly' is especially noticeable in the Pretoria HD data set, when there was significantly more rain on HDs during the December of 1978/79 (the dry year) than in December 1977/78 (254 mm and 184 mm resp.) (Table V.1, Appendix V). Such an anomalous increase in dry year December rainfall occurred on NHDs on the Witwatersrand (but not on HDs)./

Although more stations in the Pretoria area received rain during dry years, the opposite occurred on the Witwatersrand. Hence no definite conclusions can be reached concerning the relative size of the wet area during wet and dry years. This is, however, of lesser importance because it is not the number of stations receiving rain during an entire season which is important, but rather the amount of rain occurring per station or the size of the area receiving rain on a particular rain day, which is relevant.,

~alysis of rainfall per rain event (Table 5.6) shows that the rainfall intensity was higher during wet years than during dry ones. Although this result is to be expected, the magnitude of the differences are surprisingly large' In both the Pretoria and the l Table 5.6 Precipitation characteristics of hail days (H~s) and non-hail rain days (NHDs) for Pretoria and the Witwatersrand during wet and dry+ years.

Total Precipitation·· Hail days. NOli hail days·

Rain all R/ No. ST/ Rain on R/ No. R/ ST/ Rain on R/ R/ ST/ . RDF ROs RD ST RD IIDF UDs 110 5T ST/ liD NIIOF NUDs NIID/ ST S1'/ NIlD liD 5T NUD

PRETORIAI

1911/78 Wet 151 10599,2 61,S 847 5,4 23 2481,4 107,9 171 14,5 7,4 125 8024,4 64,2 633 12,7 5, I

1978/79 Dry ISS 5118,2 33,0 883 5,1 42 1984,2 47,2 267 7,4 6,4 88 2444,6 21,8 514 4,8 5,8

~:

1979/80 Wet 209 33052,4 158,2 2827 13,5 72 16871,1 234,3 1356 12,4 18,8 137 16181,8 118,1 1471 11.0 10,7 1918/19 Dry 222 18167,4 81,8 2302 10,4 68 9890,7 145,5 1080 9,2 15,9 141 8200,0 58,2 1245 6,5 8,9

for hail seaSon -_. July-June "total

....\.0 92

Witwatersrand areas, the wet year rainfall intensity was almost double that of the dry one/Pta: 67,5 vs 33,0 mm/RD/st; WWR: 158,2vs 81,8 mm/Rfr/st).

It has previously been shown that the HD rainfall intensity exceeded that of the NHDs (Tables 5.4 & 5.5a). Perusal of Table 5.6 shows that the rainfall intensity on a HD was much higher in wet years than in dry years (107,9 vs 47,2 mm/HD and

234J3 vs 145J5 mm/Hl) for Pretoria and the Witwatersrand respectively). Wet year NHD rainfall intensities were also considerably greater (more than double) than that of the dry.

A similar pattern to the above is shown for the mean station rainfall (mm/ST/RD). In the Pretoria area, for instance, an average of 14,5 mm rain was recorded at each station per HD during the 1977/78 (wet year) season while the corresponding value for 1978/79 was 7.4 mm. The point rainfall recorded on NHDs were consistently lower than on HDs.

Despite the relatively small difference between the number of stations which received rain per rain event during the wet and dry year (in both Pretoria and on the Witwatersrand), the spatial extent of a wet year HD rain event (in terms of the number of stations with rain per rain event), was mostly larger than its dry year counterpart. Furthermore, the size of the dry year HD rainfall zone exceeded that of the NHD, during both wet and dry years.

5.5.5 Summary

Analysis of rainfall on hail days and non-hail rain days revealed that the following precipitation patterns occurred in the Pretoria-witwatersrand area during the period under review:

1. There were more NHDs than HDs per annum;

2. Each rainfall station received more rain on a HD than on a NHD and therefore the HD rainfall was more intense than the NHD rainfall;

3. More stations received rain on a HD than on a NHD - thereby implying that the rain-bearing systems were more extensive on HDs than on NHDs. 93

When contrasting Pretoria's wet and dry year precipitation patterns (Fig. 5.9), it was found that:

1. There were fewer HDs during wet years and more during dry years. These results confirmed previous findings relating to HDF patterns in the Transvaal. Furthermore, since the total number of rain days during wet years did not differ significantly from that of dry years, a higher NHDF necessarily occurred during wet years. It thus follows that there were more general non-convective rain episodes during wet years - thereby supporting finding by Court (1979a, 10) for the Orange Free State rainfall:

"it seems that in the wet season a much larger contribution is made by general rainfall than in a dry season" and "It seems therefore that a wet year is largely the result of a large amount of general rain"

In a similar vein, comments by Preston-Whyte & Tyson (1988, 224) :

"It is now appreciated that it is largely due to these rains (general rains over the Plateau which originate from a northerly direction) which distinguish abnormally wet years from dry years".

2. Notwithstanding the lower wet year HDF, the HD rainfall intensity (as was the case with NHD rainfall intensity) was much greater during the wet year than during the dry. Therefore the highest station rainfall (RjST/event) occurred on HD of wet years, followed by wet year NHD rainfall, then by dry year HD rainfall and finally by dry year NHD rainfall.

3. The spatial extent of wet areas (given by st/HD values) was greatest on HDs of wet years. Moreover, the size of the area in which rain was recorded on dry year HDs exceeded that recorded for both wet and dry year NHD rain events.

This study has thus shown that rainfall characteristics of hail days differ from those of non-hail rain days and that these are influenced by general wet and dry conditions. These results seem to imply that there are fundamental mesoscale 94

WET (1977178) DRY (1978179)

A. HAll DAYS & NON-HAll DAYS

Rain-day frequency = 157 Rain-day frequency =155

B. TOTAL RAINFALL

5118 mm in region 67.5 • RAINFALL/RD (mml ' 33.0 10599 mm in region 107,9 - RAINFALL/HD (mm). 47,2

C. SPATIAL EXTENT (No. of stations!

12.5 • RAINFALL/STATION (mrn) ,. 5.8 7,4 - No. STATIONS WITH PPT/HD • 6.4 847 883

Figure 5.9 Daily precipitation characteristics on HDs and NHDs during wet and dry periods 95

differences in hail and non-hail producing mechanisms over and above the short term kinematic and thermodynamic conditions which prevail in the study area. The interaction of these meso- and micro-timescale conditions influence the occurrence, size, relative frequency as well as the efficiency of precipitation generating mechanisms. Comments by Fritsch et al., (1986, 1332), on Nigerian rainfall patterns, seem to apply equally well to the Transvaal:

"changes in the large-scale circulation patterns affected the season precipitation from mesoscale convective weather systems by altering the precipitation characteristics of individual systems. ...the frequency of convective systems remained nearly the same as in 'normal' years, however, the average precipitation area and the average volumetric production significantly decreased (in drought years)".

These results show that explanations for differences in wet and dry or hail or non­ hail conditions should not be sought in the meteorological conditions prevailing on a daily basis alone, but also in large scale, longer term weather patterns. Numerous recent studies have shown that teleconnections exist between rainfall in South Africa and sea surface temperatures in the eastern Pacific. If such teleconnections also apply to the incidence of hail producing thundershowers, it is conceivable that more accurate and timeous hail prediction might become a reality. These and the local conditions which characterize rain and hail occurrence, are described in Chapter 6. 96

CHAPTER 6 ATMOSPHERIC CONDITIONS ASSOCIATED WITH HAIL AND LARGE-SCALE PRECIPITATION CONTROLS

6.1 INTRODUCfION

An overview of current literature concerning precipitation in South Africa highlights two seemingly opposing research perspectives. On the one hand, papers by meteorologists concentrate almost exclusively on the role of local, meso-synoptic and synoptic-scale.controls. On the other hand, climatologists view rainfall controls from a much larger space-in-time perspective. Few studies have bridged this gap. Of these, Harrison's (1986) is surely one of the most comprehensive. He emphasizes the importance of elucidating interactions between local and global mechanisms in an attempt to understand the dynamics of rainfall and to interpret rainfall variations. This implies a thorough understanding of both local conditions prevailing during a precipitation event as well as possible macro-scale controls. Although not representing the main thrust of the thesis, it is nevertheless important to give some attention to both these aspects. This chapter examines, in a purely cursory manner, the thermodynamic and kinematic characteristics of hail days (HDs), non-hail rain days (NHRDs) and dry days (DDs). It also touches on seasonal variations in certain circulation parameters during years with high hail occurrence (HHYs). Lastly, it examines some links between global circulation controls and hail in the Transvaal.

6.2 SOME THERMODYNAMIC CHARACTERISTICS OF HAIL DAYS, NON­ HAIL RAIN DAYS AND DRY DAYS AS OBSERVED AT THE IRENE WEATHER OFFICE

It is generally accepted that the environment in which convective precipitation occurs is characterised by certain atmospheric conditions. The factors thought to control cumulonimbus behaviour include buoyancy, wind shear, the interaction of the effects of precipitation with the atmospheric dynamics, the influence of downdraughts and gust fronts, the amount of lifting required to the Level of Free Convection, and the nature of mesoscale forcing (Foote, 1985). Of these, buoyancy (instability) and vertical wind shear are believed to be particularly important (Weisman & Klemp, 1982, 1984). For example, the difference between the 97

occurrence of thunderstorms with or without hail has been ascribed to stronger wind shear or stronger upper air winds on hail days; or simply to the height of the O°C isotherm (Held, 1978).

Most studies relating to the thermodynamics of convective storms distinguish between single cell, multicell and supercell storms. Therefore, any investigation of the atmospheric characteristics associated with thunderstorms and hail will necessarily have to be preceded by the identification of the type of weather system prevailing during particular events. This section of the chapter thus addresses: a) the identification of different types of mesoscale weather systems and their contribution to HDs and NHRDs in the Pretoria-Witwatersrand area, and b) an analysis of the thermodynamic characteristics of HDs, NHRDs and DDs (as well as those associated with isolated, scattered and meso-synoptic systems) at the Irene weather office (henceforth referred to as Irene).

6.2.1 The incidence of mesoscale weather systems in the Pretoria­ Witwatersrand area and their contribution to HDs and NHRDs

6.2.1.1 Background

It is important to establish the type of weather systems which are associated with precipitation on a particular day because this will provide an insight into the nature of the atmospheric controls active on these occasions. This would be of value in understanding spatial and temporal precipitation patterns and could aid in forecasting these events.

According to Bodin (Steyn, 1988b) mesoscale weather phenomena become important when considering short-term atmospheric conditions. "... for time scales of up to six hours the synoptic scale develops much slower than the mesoscale phenomena The synoptic scale in some 'cases can be seen as a stationary background in which the shorter-lived mesoscale systems evolve" (Steyn, 1988b, 1).

Matthews (1983) designated four types of mesoscale organizations which produce convective rainfall, namely, isolated convective systems; clusters or cloud complexes; meso-synoptic convective systems and line storms. Isolated storms are controlled by local conditions alone; clustered and meso-synoptic systems are under the 98

combined influence of local, sub-synoptic and synoptic controls; while line storms are controlled by prevailing synoptic conditions (Steyn, 1988b). The rainfall which results from cloud clusters will usually produce a scattered rainfall pattern on the ground. On the other hand, meso-synoptic systems will give rise to scattered and widespread rainfall. Thus in small areas the rainfall pattern resulting from meso­ synoptic and line storms cannot be distinguished (and therefore will be denoted as 'general' or 'widespread' in this section).

A number of papers have described the relative contribution of mesoscale weather systems to precipitation days in various parts of South Africa. For instance, Court (1979a) analysed the rainfall types which occur in the Bethlehem area while Garstang, Emmitt & Kelbe (1981), Held (1973) and Carte & Held (1978) summarized research results pertaining to the weather types associated with hail. Garstang's article deals with conditions in the Nelspruit area while the latter two papers describe conditions which occurred during the 1962 - 1972 period on the Highveld. It would be interesting to compare these with more recent (post-1970) trends.

6.2.1.2 Data and Method

In order to determine the type of mesoscale system associated with precipitation, the specific dates of hail and non-hail rain events are required as well as information concerning the spatial extent of the rainfall on each day. The previously compiled daily HD and RD rainfall data set (see section 5.5), was used for this purpose. It comprised daily rainfall amounts recorded at all the rainfall stations in the vicinity of Pretoria and the Witwatersrand on each precipitation day during the 1978 and 1979,September to April, period. It also contains a record of the number of stations which received rain on each of the precipitation days as well as the number of stations which were operational. Precipitation days were subdivided into hail days (HOs) and non-hail rain days (NHRDs) using the hail date information contained in the CSIR data set Days on which no precipitation occurred were designated as dry days (DDs).

Days on which widespread, scattered and isolated thundershowers occurred were differentiated in terms of the proportion of the total number of stations in the area which received rain. However, ''without direct reports from the time of occurrence 99

it is impossible to say categorically which days experience (for example) general rain" (Court, 1979a,2). Nevertheless, Court (1979a) defined a general (widespread) rain day as one on which > 2/3 of the stations receive at least 5 mm rainfall while isolated rain days are those where < 15% of the stations receive measurable rain. Therefore rain days which do not belong to either of these two categories can be classified as scattered rain days.

It is obvious that incorrect classification of precipitation days could quite easily occur. The problem of differentiating between meso-synoptic and line storms in small-area studieshas already been mentioned. Furthermore, days classified as 'isolated' or 'scattered' may in fact have had wider rainfall - a situation which would occur if the bulk of the rain fell outside the study area.

6.2.1.3 Discussion

Although Court (1979a) initially included the persistence of rain events, from one day to the next, as a criterion for 'general rain days', she found this to be too limiting. However, analysis of the 1970-1986 CSIR hail day data set for the Pretoria-Witwatersrand area, shows that hail day spells occurred quite frequently. Out of the total of 1 082 HDs recorded during the 16 year period, 308 were isolated events - the rest forming part of a hail day 'spell' or 'sequence' (Fig. 6.1). The longest hail day spell was recorded during 1972/73 when ten consecutive days with hail occurred. The frequency of hail day spells of different lengths, shown in Figure 6.1, agree with that found by Held (1973) for the 1962-1972 period. Moreover, since synoptic systems generally remain in any given area for a period of one to two days (Van Heerden, pers. comm.), the results depicted here accord well with the behaviour of synoptically driven (as well as mesoscale) systems.

Figure 6.2 and Table 6.1 indicate the relative contribution of widespread, scattered and isolated rain to precipitation days in the Pretoria-Witwatersrand area (as obtained by applying Court's criteria). According to this figure, scattered precipitation days occur most often (46,5% of the time), closelyfollowed by isolated rain episodes. Widespread rain only occurs on 13,5% of the precipitation days. These results agree in principle with the values obtained by Court (1979a) for Bethlehem (59,9; 31,2 and 8,9%, respectively). However, separate analyses of HD and NHRD weather systems reveal a completely different picture. On HDs, 100

40

>"' ~ 30

... ;t 20 u. o

10

2 3 4 5 6 8 9 10 LENGTH OF SPELL (CAYS)

Figure 6.1 Likelihood of hail day spells ofdifferent duration

80 §ill ALL PPT DAYS HD 70 m [J... NHRD 60

> 50 o z w ::J 40 0 w a:: u, 30 ~ 20

10

WIDESPREAD SCATTERED ISOLATED

Figure 6.2 Contribution ofgeneral, scattered and isolated rain to rain day, hail day and non-hail rain day precipitation in the Pretoria­ Witwatersrand area 101

scattered thundershowers predominate; the incidence of widespread storm days exceeding that of isolated ones. Conversely, isolated rain occurs more often on NHRDs, followed by scattered and then widespread rains.

Table 6.1 Seasonal incidence of widespread, scattered and isolated showers on hail days and non-hail rain days in the Pretoria-Witwatersrand area, 1978 & 1979 (given as % ofmonthly precipitation days).

NON-HAIL RAIN DAYS (RD) HAIL DAYS (HD)

. W SI W SI January 30,8 28,2 41,0 15,8 68,4 15,8 February 12,5 37,5 50,0 13,3 80,0 6,7

March 5,1 51,3 43,6 30,8 69,2 0

April 9,7 19,4 71 ,0 8,3 75,0 16,7

September 0 41,7 58,0 50,0 33,3 16,7

October 3,7 33,3 63,0 27,3 68,2 4,5

November 12,1 36,4 51,5 20,0 64,0 16,0

December 6,3 34,8 59,4 15,4 61,5 23,1

Mean Annual 10,9 35,5 53,6 18,5 68,2 13.3

W = Widespread i.e. ~ 66,7% of all stations reported rain

S = Scattered i.e. 15 - 66,7% of all stations reported rain

I = Isolated i.e. s 15% of all stations reported rain

Not only do these results differ from these obtained by Carte and Held (1978) for the same area, but they contradict the commonly held belief that hail is not usually accompanied by general, widespread rains. The former may be explained by the use of different techniques for defining rain" types (Carte and Held used radar reflectivity data), while the latter reflects a more serious aberration - and as such highlights the danger of drawing generalized conclusions from case studies - either in time or space. It also raises doubts concerning the validity of the findings by Court (1979) - a study which was confined to Bethlehem and its immediate surroundings. 102

Unfortunately the detailed data required to obtain more accurate results are not available and the results obtained here will have to suffice for all further analyses performed In this chapter. However, the shortcomings noted above must be kept in mind.

The monthly incidence of the various rainfall categories (Table 6.1) conform in general to the patterns described above for HDs and NHRDs. However, on HDs in February and April, scattered thundershowers occurred more frequently than anticipated; while in September, widespread rainfall predominated. The contribution of widespread, scattered and isolated rainfall events to total NHRDs deviated from the expected pattern during March when scattered showers occurred on 51% of all NHRDs in the area.

6.2.2 Atmospheric stability and humidity characteristics

6.2.2.1 Analysis of temperature and dew point temperature

There are a number of indices which can be used to indicate the level of atmospheric stability/instability and humidity prevailing on certain occasions. One of the simplest involves the use of temperature (T) and dew point temperature (To) information from radiosonde ascents.

In a recently published paper, Prezerakos (1989) analysed the stability and humidity characteristics of the atmosphere during air mass thunderstorms at Athens. He used the temperature difference between the 850 and 500 hPa levels (Tsso-Tsoo) as a measure of atmospheric instability and (T-TD)sso + (T-Tohoo as an index of the humidity in the lower layers. Since a high dew point temperature, relative to T, is synonymous with high humidity levels, the magnitude of the difference between the ambient temperature and the dew point at a specific pressure level is inversely related to the humidity of the air. By comparing atmospheric instability and low level humidities, Prezerakos was able to distinguish between conditions prevailing during severe air mass thunderstorms of long duration; thunderstorms of shorter duration and those where no precipitation was recorded (Fig. 6.3). . 103

32 ------,--... A B '0 w 3 0 THUNDER­ 8 STORMS It) OF .... 28 SHORT I o DURATION ::; 26 .... 24

6 20 24

( T- T) (T - T) d 850 + d 700 (Oe)

Figure 6.3 Plots of Tsso - T.soo against (T-Td)SS.D + (T-Td),oo at Helliniko, Greece tArter Prezerakos, 1~~9, 33).

Upper air data for Irene were only available for the early 1980s, hence the 1981/82 data were selected for analysis. The dates of HDs, NHRDs and DDs, as well as the different category wet days were determined following the same procedure as was outlined in section 6.2.1.2. Instability and humidity indices were computed for each day for October, November, December 1981 and January and February, 1982. Because the surface pressure at Irene is often below 850 hPa, surface T and TD values were used in lieu of the 850 hPa values. The instability (Tsurface - T soo) and humidity «T - TD)surface + (T - TD)700) indices were plotted for Irene, for HDs, NHRDs and DDs (Fig. 6.4).

Some interesting points emerge from this figure.

Firstly - HDs and NHRDs occupy discrete areas on the graph - with relatively little overlap occurring. However, the DD category is not as clearly defined. This may be due to the incorrect classification of DDs as rain may have fallen in areas contiguous to the study area. Moreover, the amount of scatter is indicative of the wide diversity in conditions prevailing during non rain days.

Secondly - Conditions were more humid and unstable on HDs than on NHRDs. In turn, atmospheric humidity was higher on NHRDs than on DDs. 104

CG - CENTRE OF GRAVITY / I • HAIL I I • RAIN / / I o o DRY 40 / / / / / / / / / 311 / / / / I o / / o / I 36 /. / A I / I / / . I.. / • • @ SCATTERED / / A ... o @HD C/ o / :A: I •• / o ...'" @ DO CG J 32 / • • • I / ... • 0 I • 0 (J / ~WIDESPREAD / c( ... ,II,eJ'0 • a: / :::l •• IO@ / / @'ISOLATEOGENERAL NHRD CG ...'" 30 o /" / / / 0 A / /0 2. // / " / 26 // / 0 / / ,,/ /

24 " / . / / / • 12 2t 32

Figure 6.4 Atmospheric stability and humidity characteristics at Irene (1981/82) on HDs, RDs and dry days (DDs) 105

Thirdly, - Of the two indices, the humidity index is the better indicator of HDs, NHRDs and DDs.

These findings accord with those of Prezerakos. In fact, comparison of Figures 6.3 and 6.4 show that the stability and humidity characteristics of the atmosphere at Irene closely resemble those of Greece for the different weather conditions. This is. quite surprising if the latitudinal and altitudinal differences between these places are taken into account.

The mean humidity and stability characteristics of HDs with widespread, scattered and isolated rain have also been indicated on Figure 6.4. Apparently the humidity is higher and the atmosphere is more stable on HDs with widespread and isolated rain than when scattered rainfall occurs. Furthermore, the level of instability seems to be the more important in differentiating between widespread and isolated HDs.

The use of temperature and dew point temperatures at various pressure levels has thus successfully differentiated between atmospheric conditions prevailing on HDs, NHRDs and DDs. However, in order to understand the reason for these differences, it is necessary to analyse the entire vertical profile of the atmosphere. According to Steyn (1988a,1) "mesoscale weather systems develop in environmental conditions where the atmospheric temperature and humidity profile in a particular air mass plays a significant role with respect to the degree of convection." It therefore follows that vertical temperature - humidity profiles of HDs, NHRDs and DDs should be analysed for the study area.

6.2.2.2 Thermodynamic profiles

6.2221 Background

Both humidity and thermal properties of the atmosphere are inextricably combined to define conditions associated with specific weather patterns, and thus separate vertical profiles of these parameters will not adequately reflect such differences. For this reason, atmospheric temperature and humidity are often combined into one variable such as wet bulk temperature, equivalent potential temperature (ee), or static energy etc. It has been shown that these variables are approximately proportional (Madder and Robitaille, 1970; Betts, 1974). Rossby (1932), suggested 106

that potential (8) and equivalent potential (8e) temperature be used to analyse the structure of air masses. According to Gomes & Held (1988, 6), Riehl (1954) made "extensive .use of (8e,p) plots to study the vertical structure of the tropical atmosphere ... (and) Paluch (1979) introduced plots of wet equivalent potential temperature (8es) and total water to study vertical mixing in non-precipitating clouds". Equivalent potential temperature profiles which were associated with severe weather have been analysed by, inter alia, Morgan and Beebe (1971), Steyn (1988 a&b), Gomes and Held (1988), Anderson et al., (1989) and Gomes, O'Breime and Held (1989). Spatial and temporal links between mean monthly Oe and rainfall patterns have also been studied in West Africa by Oduro-Afriyie (1989).

The equivalent potential temperature is defined as the potential temperature that a parcel ofair would have if all the moisture were condensed out and the resultant latent heat used to warm the parcel (Huschke, 1959). It has been found (Newton, 1950; Browning and Ludlam, 1962; Darkow, 1967; Zipser, 1969; Morgan and Beebe, 1971), that the environment in which severe storms develop is characterised by a Oes (and 8e) profile which exhibits a minimum at middle levels. This mid-level Oe minimum can bring about very intensive convection in two ways, namely: a) "When air with low Oe is lifted or stretched, it will become colder than the undisturbed environment, hence steepening the lapse rate." b) "If rained into, it will become colder in situ than the surrounding undisturbed environment, and a downdraught will result which can serve to provide lift to low level, high-Be air" (Morgan & Beebe, 1971,54).

Throughout the summer months there is usually an elevated Be minimum. According to Morgan and Beebe (1971, 54), the contrast in low- and mid-level Be values are sometimes so great as "to denote the presence of two distinct air masses. In some cases this is due to actual overrunning of low level warm air by cold middle level air, and in others the contrast is due to heating of the lower layers of a cold air mass ...", The higher ground level Be's "are diffused upwards by turbulent and convective mixingwhich tend to sharpen the minimum in the profile".

High 8e values in the lower layers, combined with cooling aloft, will result in the development of potential instability. Such instability may be released by any 107

dynamic mechanism which causes air to rise (Steyn, 1988b). Convection is suppressed or shallow when the 8e minimum occurs at lower levels. In an analysis of a devas~ating hailstorm which hit Pretoria on 1 November 1985, Terblanche (1985) highlighted the importance of an elevated inversion topped by cold dry air between 600 hPa and 500 hPa. In this instance, the cold mid-level air must have originated over the Atlantic Ocean since its path could be traced as it moved over southern Namibia and the north western Cape/Botswana until it became established over Pretoria.

The presence of cold air aloft and the concomitant potentially unstable conditions which result, cannot always be deduced from surface synoptic charts. This enhances the usefulness of analysis of 8e profiles.

300

400 ,...... ~ 500 "--' I­ :z: Cl ~ 600

700

800

-2 o 1 2 NORMALIZED ge

Figure 6.5 Normalized ee profiles computed from the mean sounding for the mesoscale storm category days (After Steyn, 1988b)

Steyn (1988 a,b) analysed the characteristics of ge and Ses profiles for mesoscale weather systems at Bethlehem (OFS). Using radar data, he subdivided convective rain days into those on which isolated, scattered, mesosynoptic and line storms occurred. He then calculated normalized 8e profiles from the mean soundings for the different categories. (Normalization removes the seasonal differences in Be 108

values but retains its vertical stratification characteristics.) When plotted (Fig. 6.5), these categories exhibited a number of distinct differences, for example, low 8e values ext~nded to greater heights on line storm days; cluster and mesosynoptic days had high Be values near the surface and relatively high values extending throughout the whole column; while on isolated convection days the surface and mid-level Bewere considerably lower than for the other categories.

6.2.2.2.2 Method

The above technique was used to analyse the ee characteristics of HDs, NHRDs and DDs and to determine whether there were significant differences between the various category HDs at Irene.

Upper air data for the October 1981 to March 1982 period, as obtained from 13:00 SAST radiosonde ascents at Irene, were used for this analysis. Equivalent potential temperatures were calculated for surface (~850 hPa), 700 hPa, 600 hPa, 500 hPa and 400 hPa levels for each day, using Bolton's (1980) technique. This consisted of an iterative procedure using the following formulae:

1) es = 6,112 exp (17,67T/(T+243,5» 2) e = es U/lOO 3) r = 0,622 e/(p-e)

4) TL = 1/«T+273,15 - 55) -In(U/lOO)/2840) + 55 5) e = (T + 273,15) «1000/p)O,286) 6) 8e=8 exp«3376/TL-O,00254) r (1+0,81r/1000» where e(s) = (saturated) vapour pressure res) = (saturated) mixing ratio U =relative humidity T = temperature (oC) L = lifting condensation level p =pressure (hPa) 9 = potential temperature 8e =equivalent potential temperature

Mean ee values were then computed for each of the categories shown in Table VI.l (Appendix VI), whereafter they were normalized using the formula: 108

values but retains its vertical stratification characteristics.) When plotted (Fig. 6.5), these categories exhibited a number of distinct differences, for example, low Be values ext~nded to greater heights on line storm days; cluster and mesosynoptic days had high ee values near the surface and relatively high values extending throughout the whole column; while on isolated convection days the surface and mid-level ee were considerably lower than for the other categories.

6.2.2.2.2 Method

The above technique was used to analyse the 8e characteristics of HDs, NHRDs and DDs and to determine whether there were significant differences between the various category HDs at Irene.

Upper air data for the October 1981 to March 1982 period, as obtained from 13:00 SAST radiosonde ascents at Irene, were used for this analysis. Equivalent potential temperatures were calculated for surface (~850 hPa), 700 hPa, 600 hPa, 500 hPa and 400 hPa levels for each day, using Bolton's (1980) technique. This consisted of an iterative procedure using the following formulae:

1) es = 6,112 exp (17,67 T/(T+243,5) 2) e = es U/lOO 3) r = 0,622 e/(p-e) 4) TL = 1/«T+273,15 - 55) - In(U/100) /2840) + 55 5) e = (T + 273,15) «1000/p)O,286) 6) Be = B exp «3 376/TL - 0,00254) r (1+0,81 r/IOOO» where e(s) = (saturated) vapour pressure res) = (saturated) mixing ratio U = relative humidity T = temperature (oC) L = lifting condensation level p = pressure (hPa) e =potential temperature Be = equivalent potential temperature

Mean ee values were then computed for each of the categories shown in Table VI.I (Appendix VI), whereafter they were normalized using the formula: 109

HAIL (WIDESPREAD) NRD 400 j;"/GENERAL RD ~ /' - . /' \ .4' // 500 V" I ,,' \

cn 60 0 en w a: Q.

700

800

850L-----....L.------'------''------'''==-...... :;;-=--t -1 o + 1 NORMAlI seD· 8e

WIDESPREAD R & H 400~ H R & H

500~

600

700

800

850'-----.-1------'------'------:::...... ;;:==-...... :;;::=...--I _1 +1

Figure 6.6 Normalized Se profiles for (a) HDs, RDs and DDs (b) different category hail days at Irene. 110

Zi =(8e i - 8e)/rr whe~e Zi = normalized value of 8e at pressure i 8ei = original value of 8e at pressure i ee = mean of ee values measured over all the pressure levels

(J =standard deviation of 8e

The ee profiles for HDs and NHRDs (both with widespread rain) as well as for DDs, are presented in Figure 6.6a, while those for the different category HDs are shown in Figure 6.6b.

6.2.2.2.3 Discussion

Although the observed ee profiles do not show great variety, differences between HDs, NHRDs and DDs are, nevertheless, discernable. As expected, all three profiles (on Fig. 6.6a) have a mid-level minimum but the ee minimum on HDs and general NHRDs occur at a higher level (:!:500 hPa) than on DDs (:!:600 hPa) ­ indicating greater potential buoyancy on wet days. Furthermore, the 500 hPa air was relatively colder on HDs, enhancing the prevailing potential instability.

Table 6.2 Mean equivalent temperature (Se) at various pressure levels for hail days, non-hail rain days and dry days at the Irene weather station (1981/82).

Pressure Precipitation level Surface Standard category (hPa) * 850 800 700 600 500 400 Hean deviation

Dry days 341,3 334,4 327,5 324,1 325,8 332,6 330,95 5,88

General rain days 350,9 341.4 333.1 331,0 330,5 335,3 337,05 7,19

Hail days with : ~idespread rain 352.1 344,1 337,8 330,9 328,6 334,0 337,93 8,08

Scattered rain 344,4 337,9 330,7 324,8 325,6 329,4 332,14 6,94

Isolated rain 353.2 345.9 330,9 325,0 332.8 335,9 337,28 9,51

Perusal of the actual 8e values (Table 6.2) sheds more light on temperature ­ humidity differences between HDs, NHRDs and DDs. Although the mean 8e values of HDs and NHRDs are almost identical, the air on HDs was clearly warmer and more moist between the surface and the 700 hPa level, and colder at the 500 111

hPa level. Comparison of the Be standard deviations highlights differences in conditions prevailing between the pressure levels. Not only was the Be variance smallest on DDs, but the air was cooler and/or drier throughout.

Minor discrepancies in the normalized Be profiles of widespread, scattered and isolated HDs can also be distinguished. These are not easily explained because they reveal a number of apparent anomalies. For instance, on scattered and isolated HDs, Be minimum values occur at approximately 600 hPa, i.e. at even lower levels than on NHRDs. In fact, the scattered HD profile is remarkably similar to that of dry days. Secondly, the normalized Be values on isolated HDs decline sharply below 600 hPa - above which an equally sudden increase occurs - possibly limiting the extent of the vertical ascent of the air. This would effectively have prohibited hail formation.

However, scrutiny of actual ee values once again helps to explain these apparent anomalies. Comparison of ee means as well as surface Be values, indicate that higher temperatures and/or humidities prevailed on all category rain days than on dry days. This implies that, in reality, the potential instability on both scattered and isolated HDs exceeded that of DDs. The fact that the highest surface Be values and the greatest inter-level Be variability occurred on isolated HDs, accentuates the dominance of local controls. According to Matthews (1983), isolated convective showers will occur "in an environment with little or no low-level mesoscale convergence and lifting; cloud development is driven primarily by solar heating, with local variations in stability and moisture controlling the location and intensity of cloud growth and precipitation" (Steyn, 1988b, 13). Furthermore, "if other stability and dynamic factors contribute in the proper sense this type of profile (the strong slope in Be values between ground level and the Be minimum at 600 hPa) would be conducive to severe activity" (Morgan and Beebe, 1971, 56).

Notwithstanding the above explanation, it is still evident that the scattered HD Be profile does not accord with expected patterns, in that, the surface Be values as well as the overall variance should be higher than on NHRDs. However, the lack of data for the intermediate pressure levels might account for these apparent anomalies; a truer picture should emerge with finer resolution data. Another possible explanation for the discrepancy is the time lag between data recording and the 112

occurrence of the storm since atmospheric conditions can change considerably between 13:00 and the late afternoon.

Darkow (1968), suggested that an index based on the difference between Be values at 850 hPa and 500 hPa could be valuable in determining the degree of instability; high values for this index being indicative of unstable conditions (Steyn, 1988a, 2). These values were calculated for Irene. However, some ee profiles reach a minimum at 600hPa, and hence eesurface-Se6OQ values were also calculated.

Table 6.3 Differences in the 13:00Se values between surface and mid-levels for hail days, non-hail rain days and dry days at the Irene weather station (1981/82).

ge 6e ge surface - SOO surface - ge600

DD 15,47 17,23 RD 20,43 19,93 HD with widespread rain 23,50 21,27 scattered rain 18,81 19,53

isolated rain 20,48 28,23

Inspection ofTable 6.3 shows that 8esurface-SeSOO values decrease consistently from a high value on widespread HDs, through isolated HDs, widespread NHRDs and scattered HDs to reach a minimum on DDs. The large Sesurface-Se600 value for isolated HDs is striking, highlighting the potential instability which occurred on these days. However, 8esurface - Sesoo as well as the Sesurface - Se60Q values are unexpectedly low on scattered HOs. Although clusters of convective clouds which result in scattered precipitation patterns are not affected by local factors alone, they still dominate and a greater surface to mid-level ee difference is expected. Once again it seems likely that either the pressure level increments used for this analysis were too large to accurately differentiate between the categories or that the tiine interval between data recording and the subsequent storm was too long - hence limiting the representativeness of the data. Moreover, the possible inaccuracies which may arise from the relatively small size of the study area, should be borne in mind. 113

In this section the factors controlling buoyancy on HDs, NHRDs and DDs have been analysed and compared. However, it is necessary to investigate other weather parameters in order to obtain a more comprehensive view of conditions prevailing on the different category days and to differentiate between them. The importance of wind shear has previously been mentioned in this regard. Therefore, the kinematics of HDs, NHRDs and DDs receive attention in the following section.

6.3 ATMOSPHERIC KINEMATICS AND CIRCULATION ASSOCIATED WITH HAIL

6.3.1 Atmospheric kinematics on HDs, NHRDs and DDs

6.3.1.1 Introduction

Wind component variations with height are more complex than those of temperatures. Nevertheless, consistent results have been obtained from studies of circulation differences between wet and dry periods on scales ranging from days (Triegaardt & Kits, 1963; Hofmeyr & Gouws, 1964), a few months (Rubin, 1956), to years (Tyson, 1981). These studies revealed that high rainfall over the central interior is accompanied by decreased temperature, pressure and increased moisture and intensified poleward air flows. Thus wet period surface and near surface circulation was found to be anomalously northerly and easterly (Hofmeyr & Gouws, 1964; Miron & Lindesay, 1983; Miron & Tyson, 1984). Harrison (1986, 167) too, found that the "easterly components increase with rainfall in most months as do northerly components, results supportive of the concept of an intensified anti­ cyclonic circulation". However, he found that "poleward flows are weaker in the wetter months of February, March, July and October, changes not directly indicative of an intensified anticyclonic circulation" (Harrison 1986, 167). It is possible that north easterly winds over the interior could also be associated with a low pressure cell situated over the central or western parts of the sub-continent. Triegaardt & Kits (1963), Hofmeyr & Gouws (1964), Miron & Lindesay (1983) and Miron and Tyson (1984) found that the poleward wind components also increased in the upper troposphere during wet spells. Harrison's (1986) studies confirmed this - but only for the 'high' season (January, February and March); the flow being anomalously equatorward during the transition (October, November and December) months. He thus came to the conclusion that there are systematic intra-seasonal changes to the 114

circulation with quasi-barotropic circulation systems from the tropics associated with high rainfall during summer and temperate baroclinic disturbances predominating during spring and early summer (Tyson, 1986). "Neither anomalous circulation results simply from frequency changes of rain and no rain days but also from appropriate intensifications of either circulation type on both rain and no rain days" (Harrison, 1986, 174).

In contrast to the surface and upper air north easterlies which predominate during wet spells, investigation of individual storm days have shown that, at least at the 500 hPa level, winds are usually from the west or south west. Regardless of the surface flow, most storms on the Highveld move towards the north east, steered by these 500 hPa level winds (Carte & Held, 1972; Held, 1973, 1977, 1985; Held & Carte, 1973; Carte & Mader, 1977; Held & Van den Berg, 1977; Carte, 1981; Mader et al.; 1986). This was also the case for Lowveld hail days (Garstang et al.; 1981; Kelbe et al., 1983; Kelbe, 1984). In addition, storm areas, lifetimes and velocities can be related to mean 500 hPa winds (as well as to thermal instability and the directional wind shear) (Mader, 1979). When these winds are strong ( > 35 km/h), storm tracts are long and parallel, normally curving to the left of the wind direction. This directional deviation was found to increase with increasing storm lifetimes and areas. However, on days when winds are weak, storm tracts are short and erratic (Mader, 1979; Mader et al., 1986).

Another important factor which influences storm development is the amount of vertical wind shear present in the troposphere. Wind shear controls the severity and persistence of storms - albeit not in a simple manner (Marwitz, 1972; Fankhauser & Mohr, 1977). In fact, Carte (1980), Kelbe et al. (1983) and Kelbe (1984), ascribe the lack of sustained storms in the Transvaal to the relatively weak upper winds and the lowwind shear which are characteristic of South African conditions.

It thus seems as if there is consensus of opinion concerning the importance of the predominantly westerly/south westerly 500 hPa winds as the major steering mechanism of hailstorms in the Transvaal and that vertical wind shear is a necessary prerequisite to their development. These two tenets receive attention in the following sections - the principal aim being to determine whether it is possible to distinguish between HDs, NHRDs and DDs in terms of prevailing wind speed, direction and shear. 115

Since the main purpose of this thesis does not encompass a detailed analysis of atmospheric dynamics, the analyses are of a purely exploratory nature. For this purpose, only' a small, randomly selected sample of the total data set has been examined. The month so chosen was February, 1982. Accordingly, wind speed and direction data were obtained for daily 13:30SAST radiosonde ascents at Irene; and stratified accordingly to HDs, NHRDs and DDs.

6.3.1.2 Wind direction

Reference to Figure 6.7a shows that winds with a northerly component blew at the surface and at the 800 hPa level on most days in February 1982. Furthermore, a directional change occurred with height so that the 600 hPa level winds were predominantly equatorward. .Above this, the frequency of events with poleward wind components increased once again.

The HD, NHRD and DD values are remarkably alike - except for the 700 hPa level. Here relatively fewer DDs experienced poleward winds. The relative proportion of HDs and NHRDs with northerly component winds were essentially the same for all pressure levels.

An indication of the mean strength of the meridional wind component (Cos S; with \l =wind direction) is given in Table 6.4. Perusal of the values reveals that:

a) Surface winds on HDs and NHRDs had a relatively stronger poleward (northerly) component than on DDs. b) The opposite situation applied to the 800 hPa level. c) The wind direction changed at :!: the 700 hPa level to become weak equatorwards. d) At 700 hPa, winds on DDs were more southerly than on either HDs or NHRDs; while e) at the 600 hPa level, HD-winds had an appreciably greater southerly (equatorward) component than on other days. Above this level, the meridional components decreased in strength. 116

(a) e,oo z W u..z DAYS 00 a. W :E 80 u O ZU W a:> a:-I ::Ja: U W 80 U:I: 01- a: ~O o Z

40 :I: I- DAYS

~ 20 Z ~

O~---"""" ...L- ""' -'- .....L ---JL-_ 600 400 300 SURFACE 800 700 PRESSURE LEVEL (hP~)

(b) (/)100 I- Z W / ...... Z o / ...... a. /1 ...... , :E BO o " , OU " ',DRY, DAYS -~J/ ''I. ~ , w~ '- ua: . .... z WBO " ',-RAIN DAYS \ UJI­ " , I \ a:(/) ,,~ , a:< " , ::Jw , \ , g:I: " \ 40 "" \ o !:: \ ~~ \ HAIL \ \ (/) a .------. ~20 3:

oL----:-:=----=----=~--~=__--~=====i~­ 800 700 600 500 400 300 SURFACE PRESSURE LEVEL (h pa)

Figure 6.7 Percentage ofHDs, RDs and DDs with (a) northerly (poleward) and (b) easterly wind components 117

Table 6.4 Meridional and zonal wind components on hail days, non-hail rain days and dry days at the Irene weather station (13:00, February, 1982).

PRESSURE LEVEL (hPa) Sur- 800 700 600 500 400 300 face

Mean HD 0,56 0,32 0,50 -0,84 -0,20 -0,24 -0,04 Meridional RD 0,55 0,31 -0,23 -0,43 -0,31 0,48 0,31 Component DD 0,39 0,75 -0,49 -0,58 -0,24 -0,26 -0,05 (Cos 0) - Mean HD 0,02 -0,15 0,29 -0,23 -0,33 -0,90 -0,92 Zonal RD 0,09 0,07 -0,08 -0,63 -0,65 -0,58 -0,86 Component ·DD -0,10 0,41 0,63 0,49 0,39 -0,12 -0,45 (Sin 0)

Meridional wind component: + = N (polewards); - = S (equator- ward) Zonal wind component: + = E; - = W

Table 6.5 Mean wind speed (m/s) on hail days, non-hail rain days and dry days at tbe Irene weather station (13:00, February, 1982).

PRESSURE LEVEL (hPa) Surface 800 700 600 500 400 300

HD 3,42 3,58 2,78 5,35 6,00 9,98 15,95 RD 3,40 4,30 3,63 5,43 5,46 6,20 13,07 DD 2,70 1,80 5,21 5,14 3,91 4,25 7,95 118

The latter two points indicate that a strong directional shear occurred between the 700 and 600 hPa levels on HDs (+0,50 to -0,84) while on DDs, a wind shear of comparable magnitude was also present - but at much lower levels (800 and 700 hPa). Apart from these anomalies, there was little to differentiate HDs, NHRDs and DDs.

Analysis of the zonal wind components reveal clearer differences. For instance, despite the easterly wind component that was present at or near the surface on most wet days, the air current was shallow with westerlies prevailing above the 700 hPa level (Fig. 6.Th). In fact, upper air (above the 500 hPa level) westerlies occurred on all HDs, while easterlies were recorded at these levels on a number of DDs. It is also evident that this air stream was relatively deep on the majority of DDs - 82% of DDs having winds with an easterly component at the 600 hPa level.

The reversal in the mean zonal component with height is also reflected in Table 6.4. In general, westerlies were already established at the 700 hPa level on NHRDs and at the 600 hPa level on HDs. However, the deep easterlies persisted up to the 400 hPa level on DDs. Noticeable too, is the greater westerly component which prevailed in the upper levels on HDs. Slight differences in the zonal components of surface winds also seem to have occurred. On DDs, surface air flow had an easterly component while on wet days, winds with a weak westerly component prevailed.

6.3.1.3 Wind speed

Table 6.5 shows that the mean wind speed increased with height on all category days in February 1982. As expected, wind speeds were generally higher on HDs and NHRDs than on DDs, the differences becoming more apparent at levels above 500 hPa. However, at the 700 hPa level, winds were strongest on DDs. This was probably due to exceptionally strong winds at this level on the 1st, 2nd and 4th of February (when wind speeds of 10,4; 8,2 and 10,0 mls respectively were recorded). At and above the 500 hPa level, mean HD-wind speeds exceeded those of both NHRDs and DDs. Most noticeable are the extremely low mean DD wind speeds recorded in the boundary layer and the anomalously strong upper troposphere winds on the HDs ofFebruary, 1982. 119

6.3.1.4 Vertical wind shear

Wind shear refers to a change in wind velocity (i.e, direction and/or wind speed) with height. Since velocity is a vector quantity, wind shear is, in effect, the vectorial difference between the geostrophic winds, V0 and V, at two pressure levels Po and p. ) This corresponds to the definition of a thermal wind (Vt and hence the latter can be used to indicate the magnitude of vertical wind shear.

Thermal winds reflect horizontal mean column temperature gradients and hence Vt values will be large in baroclinic systems. However, when conditions are barotropic, the column temperature gradient is zero and hence thermal winds cannot occur. The concept of equivalent barotropicity is used to denote that situation where the wind speed changes with height but no change in direction occurs (Preston-Whyte & Tyson, 1986; Van Heerden, 1990, pers. comm.).

The strength of thermal winds were calculated by applying the following formulae:

Vt =j(u? + v?) with ~2 ~I) ut = (w2 Sin - WI Sin and ~l vt = (w2 COS Q2 - WI COS = = - where ut zonal component of the thermal wind u2 ul = = - vt meridional component v2 vl w = wind speed ~ =wind direction in degrees, measured clockwise from North The subscripts 1 and 2 refer to lower and upper levels, respectively.

Table 6.6 shows the mean HD, NHRD and DD thermal wind strengths between upper pressure levels and the surface. Perusal of these reveals no obvious discrepancies. However, greater shear is evident in the 400 hPa-surface Vt on HDs than on NHRDs or DDs. This is consistent with what is expected. The previously noted change in wind direction from north east to south west above the 600 hPa level is, however, not reflected in the thermal wind strength. This is surprising and clearly indicates the need for more research on this topic. 120

Table 6.6 Mean thermal wind strength (Vt) between various pressure levels and the surface on hail days, non-hail rain days and dry days (13:00, Irene, February, 1982).

Pressure levels (hPa) 800 700 600 500 400 300

HD 2,6 2,5 6,7 5,6 10,1 15,5 RD 2,2 5,5 5,7 4,1 4,1 8,8 DD 2,6 4,2 7,3 6,9 6,5 13,7

6.3.1.5 Summary

This cursory analysis of vertical wind characteristics on HDs, NHRDs and DDs has shown that, in general, the mean surface wind directions observed during February 1982 at Irene accord with results obtained for the Orange Free State and the summer rainfall region as a whole. However, the expected upper tropospheric north easterly winds were not present on wet days.

A few differences between mean HD, NHRD and DD wind characteristics did emerge. These are also consistent with what is expected from previous studies. In particular the following kinematic pattern have been distinguished:

1) HDs were characterized by relatively weak (!.3,3 m/s) north easterlies below the 600 hPa level. Above this, winds reversed rather suddenly to become south westerly with a concomitant increase in speed.

2) On NHRDs, the change from north easterly surface winds to a south westerly circulation took place at considerably lower levels (:t700 hPa). Although the wind speed increased with height, the increment was substantially less than onHDs.

3) The vertical wind pattern on DDs was extremely variable. In general, weak surface north westerlies prevailed - veering to very weak north easterlies at 800 hPa. Above this, south easterly winds occurred up to the 500 hPa level. 121

The mean speed of these winds was:!: 4,8 tn]s. Only the winds above 400 hPa were south westerlies - although much weaker than on wet days.

Vertical shear in both direction and speed occurred - especially between the + 400 hPa and surface layers on HDs.

It should be kept in mind that the differences observed in the vertical wind patterns on HDs, NHRDs and DDs, reflect mean conditions only - the day-to-day variations being too large to be of value for predictive purposes. However, the occurrence of south westerly winds at and above the 600 hPa level on wet days may, to some extent, explain the equivalent potential temperature minima observed there, because it indicates that this dry cool air probably originates from the Atlantic. This, together with the north easterly surface flow which advects warm, moist air from the Indian Ocean, creates conditions which are conducive to potential instability.

Although the above gives some explanation for the observed thermodynamic conditions occurring on HDs and NHRDs, the results seem to conflict with the expected 'high' season airflow patterns. According to Harrison (1986), precipitation during a wet February should originate from barotropic systems with weak surface and stronger upper level north easterlies advecting warm moist air from the Indian Oceanor equatorial regions. The fact that this was not the case during February 1982 can probably be ascribed to the extremely dry conditions which prevailed during this month, when the rainfall over the Transvaal was only 46,5% of the long­ term mean.

6.3.2 Thermal winds and temperate - tropical systems

It has been established (Chapters 4 & 5), that the temporal incidence of the peak hail and rain seasons differ between HHYs (dry conditions) and LHYs (wet conditions). Therefore, it seems likely that corresponding changes would occur in the intra-annual distribution of the associated rain-bearing systems (i.e. convective thunderstorms and non-hail producing, general rain-bearing systems) during wet and dry years. Moreover, a number of authors (Harrison, 1986; Tyson, 1984; Lindesay et al., 1986; Lindesay, 1988a and Van Heerden et aI., 1988) have shown that the seasonality of rainfall in the summer rainfall region is influenced by temperate - 122

tropical interactions. On the Highveld, tropical barotropic circulations are associated with widespread, heavy rain in summer while temperate baroclinic disturbances are linked to high rainfall during the transition seasons and exert a more local effect (Tyson, 1986). Since thermal winds not only reflect the magnitude of vertical wind shear but can also be used to indicate the level of barotropicity or baroclinicity in an area, a comparative analysis of the relative strength of thermal winds during wet (LHY) and dry (HHY) years should establish the validity of the above assumption.

Consequently, mean monthly 800-500 hPa thermal wind strengths were determined for Irene for the 1978 to 1982 period (Figs 6.8 a & b). Both graphs display the ) expected pattern of high baroclinicity (high Vt during winter and more barotropic ) conditions (low Vt during summer. However, comparison of wet (LHYs; 1979/80 & 1980/81) and dry (HHYs; 1978/79 & 1981/82) conditions reveal clear discrepancies. During each of the HHYs, baroclinicity peaked during August, after which conditions became progressively more barotropic. According to Figure 6.8a, quasi-barotropicity was established by the December of 1978/79 while in 1981/82, this already occurred during November. Lowest mean monthly Vt values (and hence quasi-barotropicity) prevailed during February in both dry years, which supports the findings of Harrison (1986). In the wet years, high Vt occurred during September and November (Figs 6.8 a & b, respectively) - thus considerably later than in the dry years. Moreover, the thermal wind strength decreased more gradually during spring in LHYs and only reached their lowest values in late summer i.e. February, March and April. It is thus apparent that quasi-barotropicity became established earlier in the season during HHYs - corresponding with changes in the peak rainfall season.

It can thus be concluded that, not only do these results support current theories linking summer rainfall with tropical controls, they also point to fundamental differences in the seasonal incidence of rain-bearing systems in wet (LHYs) and dry

(HHY) years. Moreover, the difference in annual Vt patterns accords with changes in the onset times of the peak rainfall season during HHYsand LHYs.

The above analyses have shown that atmospheric conditions differ markedly between dry/high hail and wet/low hail periods (days, months and years). This is so on various scales ranging from local to meso-synoptic and synoptic - suggesting the 123 (a) .20 mHHY dry 1978/79 :::::: LHY wet 1979/80 E'" [J ...... lQ a. ~ s: o o T III : J ] : -, o ~ : o : .e- : ~ .:: IT i -~10- : I ~ : ~ 7:0 ~ TI u; : : ~ '0: : .1...... 7 : : : ...... : -. I -. -, ...... : ...... : '7 ...... :::::: '0' l)i ...... : :::::: t : "»:- I II ~~~~~~ : : : .:.;.: : !))))) jI : I : f : :::::: ~~~~~ : : I :1 : JJJlli ;1 ~m~ ~m~~ ~~~~~~ : I~~ ~~~j~- 0 I II 11 J A S o I.N o J F M IA M J MONTHS

1m HHY 1981/82 0 'rr? LHY 1980/81 E []

7s: o o It' I o B o ~ ..,...

~ b ~ : 10 ~ I '7'. II: !lllli T N .- :~~~~~ 111~11 -, 1!!!1~ ..... :( .::::: ~ :::

Figure 6.8 Seasonal trend in the magnitude ofthe thermal wind at Irene during wet and dry conditions (a) 1979/80 and 1978/79 (b) 1980/81 and 1981/82 124

presence of some mechanism which affects both rainfall and hail occurrence. The influence of the EI Nino - Southern Oscillation phenomenon on southern African rainfall is a subject which has begun to attract much attention. The following section examines the role of the Southern Oscillation on hail occurrence by analysing Southern Oscillation Index - hail inter-relationships.

6.4 SOME LARGE-SCALE PRECIPITATION CONTROLS: THE SOUTHERN OSCILLATION AND HAIL IN THE TRANSVAAL

6.4.1 Introduction-

The EI Nino-Southern Oscillation (ENSO) phenomenon affects the atmosphere and ocean over most of the globe (Nicholls, 1988). According to Wright (1985):... "it accounts for a greater proportion of variance of climatic and oceanic fields on time scales from a season to ten years than any other single phenomenon, excepting only the annual cycle" (Nicholls, 1988, 173). Thus, in addition to the Southern Oscillation (SO) - rainfall association which is discernable over the tropical Pacific, (Musk, 1976; Dennett et al., 1978; Donguy and Henin, 1980; Van Loon et al., 1981, 1985, 1987; Van Loon, 1984; Hackert & Hastenrath, 1986; Morliere & Rebert, 1986), numerous studies have established SO teleconnections with rainfall elsewhere. A comprehensive review of the literature is given in Lindesay (1988a).

In southern Africa, research by inter alia Walker and Bliss (1930), Stoeckenius (1981), Harrison (1983a, 1986), Schulze (1983), Pittock (1984), Lindesay et al. (1986), Ismail (1987), Lindesay (1988a,b) and Van Heerden et al. (1988) have confirmed a positive relationship between the Southern Oscillation Index (501)

(PTahiti - POarwin) and rainfall in the summer rainfall region. Moreover, circulation mechanisms which successfully explain these relationships have been proposed (Harrison, 1983a, 1986; Lindesay, 1988 a&b). The SOl-rainfall relationship is particularly strong in the late summer i.e. January to March. Significant lagged relationships have also been identified by Harrison (1986) and Van Heerden et al. (1988) - the latter authors evincing significant correlations between winter and spring SOl and December and March rainfall. The existence of such lagged associations is of special importance because they allow for timely prediction of the impacts of these climatic anomalies. Hence action can be taken to ameliorate 125

deleterious consequences of the weather or to take advantage of favourable conditions.

The above discussion clearly illustrates the emphasis which has been placed on ENSO - rainfall relationships in this country. Considerably less attention has been paid to associations with temporal aspects of rainfall occurrence (i.e. rain day frequency). Although Court (1979), Louw (1979) and Harrison (1986) did describe the contribution of individual weather systems to the frequency of rain days in the Orange Free State, to this author's knowledge, no attempt has been made to determine possible links between the SOl and hail occurrence in South Africa. This seems to be a serious shortcoming since such information would indicate whether SOl teleconnections influence those systems which are associated with convective thunderstorms or with the less severe non-hail producing systems. As such, this study is purely exploratory and aims to determine whether significant zero-lagged and lagged, inter- and intra-annual relationships exist between the SOl and hail occurrence in the Transvaal. No attempt has been made to propose a physical mechanism to explain the results.

6.4.2 Data and Method

Southern Oscillation Index values for the 1970 - 1983 summer months (September ­ April) as calculated by Lindesay (1988a) were used. The HDF data sets comprised the relevant monthly HDFs for the CSIR hail observer network area as well as the annual HDFs time series for the Transvaal as a whole.

Zero-lag correlations were determined for HDF and SOl values on monthly as well as annual time scales. Both Pearson's and Spearman's Rank correlation coefficients were calculated. In addition, three-monthly moving averages were calculated from the July to December SOl data set. These were used to examine lagged SOl - HDF associations. This was done because Trenberth (1984), showed that the signal-to­ noise ratio of a SOl can be improved by using a weighted moving average. Correlation coefficients were subjected to a two-tailed t-test for significance. 126

6.4.3 Results and Discussion

6.4.3.1 Inter-annual SOl - HDF associations

The 1960/61 to 1985/86 point HDF values for the Transvaal are ranked in descending order in Table 6.7. This table reveals a strong association between Pacific Cold Events (La Ninas) and low HDF years. Five out of the seven Cold Events that occurred between 1960and 1984/85 were associated with below normal HDF in the Transvaal. Warm events, on the other hand, appeared to be mainly associated with periods when the annual HDF approached long-term mean and median values. It is notable that the anomalous Warm Event (El Nino) which coincided with a low HDF (i.e. in 1976/77), was also associated with above normal rainfall in the South African summer rainfall region (Van Heerden et al., 1988), as well as with above normal. monsoon rains in India (Rasmusson and Carpenter, 1983). Moreover, the 1964/65 Cold Event coincided with both high HDFs in the Transvaal and with dry conditions in the summer rainfall region (Van Heerden et al., 1988).

The contemporaneity of wet conditions in the summer rainfall region and high SOl phases i.e. Cold Events in the Pacific, together with the temporal association between low-hail years and Pacific Cold Events, shown above, seems to substantiate the inverse rain-hail relationship found previously. Moreover, the fact that Warm Events did not coincide with high hail years (HHY) suggests that the low-SOl ­ HHY - dry-year relationship is not as well defined as is the high-SOl - LHY - wet­ year association. No satisfactory explanation can be postulated for the temporal coincidence of Warm Events with mean HDF periods instead of with HHYs.

No significant linear relationship was apparent between the 1960-1983 sal and the HDF time series for the Transvaal (r = 0.09). Low correlations were also found between sal values and HDFs for the Pretoria-Witwatersrand area (r = 0.22). Yet Figure 6.9 indicates that the sal and HDF trends are often out of phase i.e. when the SOl increases, HDFs decrease and vice versa. This is so for 14 out of the 22 years under investigation. During the last twelve years (1970-1982) this relationship occurred eight times. Thus despite the low r-values, it seems clear that some kind of inverse relationship (albeit non-linear) did exist between the SO and HDFs in the 127

Transvaal during the 1960 - 1983 period. It thus seemed appropriate to investigate SOl - HDF relationships for individual months.

Table 6.7' Ranked annual point HDFs (Jul - Jun) for the Transvaal (1960/61 - 1984/85) and the associated Cold and Warm Events in the Pacific. .

Rank Year !I0F Rank Year !I0F Rank Year !I0F

1 1981-82 2,75 9 1968-69 1,95 17 1978-79 C 1,59 2 1964-65 C 2,74 10 1974-75 1,92 18 1979-80 1,55 3 1967-68 2,61 11 1963-64 II 1,90 19 1961-62 C 1,44 4 1971-72 2,47 12 1965-66 II 1,83 20 1962-63 1,39 5,5 1969-70 ~ 2,40 13,5 1972-73 1,76 21 1976-77 II 1,33 5,5 1984-85 2,40 13,5 1983-84 II 1,76 22 1973-74 C 1,13 7. 1970-71 C 2,23 15 1982-83 II 1,73 23,S 1975-76 C 1,08 8 1960-61 1,99 16 1966-67 C 1,64 23,S 1977-78 1,08 25 1980-81 1,06

C = Cold Events II = lIarm Events (Van !Ieerden !!. !!.!.., 1988 after Van Loon & Sher, 1985:

6.4.3.2 Intra-annual SOl - HDF relationships

The correlation coefficients obtained from analysis of monthly SOl values and the corresponding HDFs for the CSIR network area, are given in Table 6.8.

Except for November and December, there was no significant linear relationship between the sal and HDF at zero-lag. Only December correlations were significant at a better than 5% level. Nevertheless, the signs of the correlation coefficients show that, while the association was positive for November, an inverse relationship held for December. Ifrainfall is inversely related to HDF, (as has been demonstrated in this thesis) it would follow that in wet years, when the sal is high, there should also be less hail in December. The opposite would apply for dry years. The positive correlation between the Sal, rainfall and HDF during November, seems to imply that the SO influences convective thunderstorm activity then. Conversely, it is the rainfall which occurs on non-hail days that is affected by the SOl during December. It must be kept in mind, however, that these correlations are rather weak; the Sal explaining approximately 30% of the variance in the December HDF. 128

o OUT OF PHASE

3

......

-1 ::::} ...... •......

- 2 :-::::

Figure 6.9 (a) Point HDF (Transvaal) and (b) Southern Oscillation Index values for 1960-1984; illustrating the inverse nature of the relationship

Table 6.8 Correlation coefficients for monthly SOl and HDF values (1970 - 1983; Pretoria-Witwatersrand area).

~onLh SepL OCL ~ov Dec Jan Feb March APri~

t: + 0,02 - 0,25 + 0,42' - 0,56"· - 0,11 + 0,26· + 0,11 + 0, I'

n - 14 level of significance - 0,2': 0,1" : 0,05·" 129

6.4.3.3 Intra-annual lagged relationships.

Both Pearson's and Spearman's Rank correlations were calculated for three­ monthly running means of the sal values vs. HDFs, for each of the months September through April. These are summarized in Table 6.9. The monthly HDFs were those of the Pretoria-Witwatersrand hail observer network area.

Only the September-October-November (SON) index and November HDFs correlated significantly at a better than 5% level (r = 0.55).. Weak (a= 0.20) linear associations were .also found between the October-November-December (aND) index and November and December HDFs. The general lack of significant linear associations between sal and HDF is not entirely unexpected - the high level of noise precluding the probability of showingclear relationships.

On the other hand, Spearman's Rank correlation coefficient indicated the presence of a greater number of SOl - HDF relationships, albeit at relatively low levels of statistical significance. When HDF data for only the Pretoria area were used, an additional statistically significant correlation was found, namely, between the SON index and February HDF (r = -0.49; a = 0.10).

Table 6.9 Pearson's (r) and Spearman's Rank (r ) correlation coefficients for three-monthly mean SOl values and monthly HDFs f5r the Pretoria-Witwatersrand area (1979 - 1983). .

SF.PT OCT "OV DF.C JA" t'EB HARCn rrhree-aonthly runninR .eans tor 501 r r r r r r r r r r r r r r s s s s s s ., " lJJA 0.07 0,14 -0,27 -0,51 0,11 -0.01 -0,23 -0,24 0,01 0,04 0.19 0,08 -0.24 -0, 2 11 lJAS 0,05 0.06 -0,29 -0.52 0,19 0.16 -0,29 -0,33 -0,07 -0,11 0,21 0,11 -0.25 -0.2·l ASO -0.11 -0,13 -0,25 -0.46 0,33 0.27 -0,30 -0.34 -0,19 -0.15 0.21 0.08 -0.18 -0.2J .. , " I SOil -0,22 -0.34 0,07 -0.17 0.55 0.50 -0.29 -0.39 -0.40 -0.10 ·0.09 -0,23 0.20 n.ou O"D -0.17 -0,21 -0,05 -0,35 0,43 0.31 -0.39 -0.31 -0.18 -0,12 0,21 0,01 0.01 -o.lli Leyel ot significance - 0.2": 0.1": 0,05'" I Pta 1I0F data: r --0,48": r · O. 5 1 " · *for s JJA: JUDe July August:. et:c. I

Despite the overall weakness of these SOI-HDF relationships, these results suggest that it might be possible to use lagged sal index values to predict HDFs for October (and to a lesser extent February). Moreover, it appears highly probable 130

that at least the sign of the departure from the mean can be forecast with a fair degree of accuracy. Although February hailstorms cause extensive crop loss, October hail is not particularly damaging. A more serious shortcoming is the fact that those relationships which exhibit the greatest amount of statistical significance (i.e, November and December HDFs) have little or no predictive value. This is because the three-monthly SOl period overlaps with that for which the HDFs are to be predicted.

6.4.4 Summary and Conclusion

o 25 E

F::::::::) A50 SO I - 0 EC R § A50 501 - JA NR [[IJ JJ A SO I- FEB R ,..../ (223 SON 501 - MARCH R /" i /

o 25

Figure 6.10 Composite map showing significant (95%) SOl-rainfall correlation fields in the Transvaal for the period 1935- 1986 (Van Heerden, et al., 1988, modified) Isolines represent r = 0,25; -0,05.

The above analysis has shown that a number of zero-lag as well as lagged SOl ­ HDF associations occurred in the Pretoria-Witwatersrand area during the 1960 to 131

1983 period. Although these were of limited predictive value, it should be noted that the lagged sal indices explain a greater proportion of HDF variance than they do for rainfall. This can be inferred from the magnitude of the Sal-rainfall correlations found by Van Heerden et al. (1988). A composite map (Fig. 6.10) showing the location of significant correlations in the Transvaal was compiled from Figures 2a - d; Van Heerden et al.,1988, 583-586. From this it is evident that the only significant sal - rainfall associations in the Pretoria-Witwatersrand area, were those between the ASO sal and December rainfall as well as the JJA sal and the February rainfall. This compares unfavourably with the results presented in this chapter. Therefore, considering the number of SOl-rainfall studies which have been undertaken in recent years, it is clear that Sal-hail research is extremely important and as such deserves much more attention. Since hail occurrence is associated with severe convective activity, its timely prediction could signal (future) humidity, temperature and wind trends as well as indicating the likelihood of droughts, floods and hail damage. The advantages which would result are too numerous to list. 132

ANALYSIS: PART ill THE GEOGRAPHY OF HAIL DAMAGE WITH REFERENCE TO MAIZE IN THE TRANSVAAL· 133

CHAPTER 7 SPATIAL AND TEMPORAL HAIL DAMAGE PATTERNS

7.1 INTRODUCTION

Crop hail damage is a widespread phenomenon especially in countries such as Argentina, the USA, Canada, Switzerland, Japan, the USSR, Italy and South Africa (Carte, 1977). Enormous crop losses were already recorded in the USA and Canada during the early 1960's. For instance, in southern Dakota, crops worth an estimated $2 808 932 were lost due to hail damage in 1960, while in Saskatchewan, Canada, this averaged $41,2 millionfyr during the 1961-1966 period (Frisby, 1964; Paul, 1980). Recent estimates of annual losses to weather hazards in the United States indicate that damage worth $750 million was the result of hail - with crop loss accounting for $680 million while $70 million originated from property damage (Kessler & White, 1983). According to Carte (1977), an insurance cooperative estimated the 1976/77 hail damage to South African crops to have been in the order of R144 million, i.e. 8,7% of the total crop production. Maize is the crop which suffers most hail damage - with approximately 4% of the crop being destroyed by hail annually (Carte, 1977; Gillooly, 1978). This implies a maize yield loss of about 290 000 tons (R69 million) in terms of the 1986/87 maize production figures (Republic of South Africa, 1988a).

Notwithstanding these enormous losses, drought accounts for an even greater proportion of the total annual crop loss; For this reason most agroclimatic research has centred around the drought problem. This study attempts to redress this imbalance by describing some geographical aspects of hail damage. Due to the importance of maize in the agricultural production and its susceptibility to hail damage, the study focuses on identifying high hail risk zones for maize production in the Transvaal.

According to Roth (1949), the long-term hail damage to crops depends on (1) the frequency and severity of hailstorms according to location and date and (2) the type of crop, including its stage of growth. Therefore aspects such as the intensity, spatial extent and the seasonal occurrence of hailstorms in the Transvaal need to be examined. However, due to the lack of adequate data relating to the former two aspects, indirect 'measurements' of these have to suffice. Consequently, indices 134

reflecting the magnitude of damage resulting from these hail parameters have been devised (Table VII.1, Appendix VII). This chapter thus describes temporal characteristics of hail damage; spatial hail damage patterns; and finally, delineates hail risk zones for maize production in the Transvaal.

7.2 DATA

Hail insurance and hail damage data were obtained from Sentraoes Insurance Co. for the 1981/82 to 1986/87 period. The data comprised accumulated weekly summaries of hail damage claims and liability (policies) records for each magisterial district in the Transvaal (excluding self-governing and independent National States). Weekly data records, commencing on 1 April of each year, included the following information: the number of claims processed per week (Nclaims)' the value of these (R claims)' the number of hail insurance policies taken out (Npolicies) and their worth (Rpolicies)' as well as the area of cropland covered by the insurance policies (hainsured)·

In this study it was assumed that:

(a) Rclaims reflects the extent of hail damage; (b) .Rpolicies gives an indication of the expected maize yield. Thus the ratio of loss paid (Rclaims) to the amount insured (Rpolicies) reflects the ratio of crop yield destroyed to the expected crop production (Roth 1949); and (c) Nclaims is a measure of the frequency of hailstorms and/or the spatial extent of these storms.

District hail damage data are not directly comparable for a number of reasons - inter alia - the magisterial districts (as shown in Fig 7.1) differ in size, as do the areas allocated to maize production. Furthermore, not all farmers take out hail insurance each year - and even if they do - there is no guarantee that they will insure through Sentraoes Insurance Co. However, since the latter is the largest bail insurance company in the southern hemisphere, it is the largest in this country and therefore it is assumed that the majority of farmers do make use of this firm. Figure 7.2 indicates the degree to which the data are representative of true conditions. It depicts the ratio of the area insured at Sentraoes to the area in which maize is cultivated in each district. From this it is evident that, in general, more maize 135

O·

Figure 7.1 Magisterial districts in the study area INSURANCE COVER

o No data g less than 10.00 ODJ]] 10.00 - 20.00 ~ 20.00 - 35.00 ~ 35.00 - 50,00 _ more than 50,00 ~

o 100

~ W Figure 7.2 Representativeness of insurance data. Expressed as percentage area insured 0'\ at Sentraoes (ha)/area of maize land (ha) 137

farmers in the south eastern and south western Transvaal take out hail insurance at Sentraoes than do their counterparts in other areas. In the Piet Retief, Amersfoort, Ermelo, Standerton, Balfour, Lydenburg, Van der Bijlpark and Ventersdorp districts more than 50% of the maize which was cultivated, was insured against crop loss due to hail during the 1981/82-1986/87 period. It seems likely that as the relative importance of maize (as an agricultural crop) decreases to the north and east (see Table VII.2, Appendix VII), so too does the extent to which farmers take out maize bail insurance. It can only be assumed that farmers in the high maize production areas in the western Transvaal either make use of other insurance companies or do not insure against .hail damage. The low values in the southern Transvaal coincide with the more densely populated PWV area, where greater emphasis is placed on other economic activities such as mining and manufacturing.

"Insurance data alone can only give a picture of the climatology of agricultural hail damage and not the true hailstorm climatology" (La Dochy, 1985, 16).

This statement is especially applicable to temporal patterns of hail damage because the extent of the damage is a function of crop susceptibility - and hence depends upon the growing season of the crop.

7.3 TEMPORAL HAIL AND HAIL DAMAGE PATIERNS

7.3.1 Hail seasonality

In general, the hail season in South Africa usually starts during October, reaches a peak in November, after which it gradually decreases through summer so that it is effectively over by March/April (Schulze, 1965). Some discrepancies have been observed during dry years when the hail season peaks slightly later than in wet years. Furthermore, it was found that the peak hail season varied from early November in the northern and eastern Transvaal to a later, December peak, in western Transvaal (Olivier, 1989). According to Schulze (1965) an even later February hail peak occurs in areas further to the west. This spatial variation is, however, complicated by bimodal tendencies in various places (See Chapter 4). 138

7.3.2 Hail damage seasonality

The hail damage season is restricted to the time between crop emergence and harvest and hence differs from the true hail season. Conditions prevailing during the different phenological stages of the plant have an important bearing on the ultimate crop yield. According to Roth (1949), the critical two week period when maize is tasselling and silking is when it is most susceptible to hail damage. It is generally accepted that maize is usually sown in late spring after a minimum of 25 mm rain has fallen within 5 days (DuPisani et al., 1982). Sowing dates thus vary between September/October in the eastern parts of the Transvaal in a wet spring to December/January in the drier western parts (Pioneer Seed Co. Field Trial Records 1970-1983). It usually takes about six to ten days for the shoots to emerge, and the critical tasselling and silking stages occur about 66 - 84 days after sowing ­ depending upon the cultivar and the prevailing weather conditions. Harvesting dates are also dependent upon cultivar-type but most often occur after March (Nel & Smit, 1976; Pretorius, 1979).

According to reliable sources, hail claims are processed within 1 week of the date of submission (it is assumed that farmers submit claims without delay) and hence a good approximation of temporal patterns of hail damage could be obtained.

7.3.2.1 District hail-claim values (Rclaims)

An indication of the period during which hail damage occurred was obtained from spatially averaged monthly hail claim values (Rclaims). As mentioned, the Sentraoes data commence on 1 April and thus monthly values were obtained by summing data for the following weeks: November: weeks 31 - 35; December: weeks 36 - 39; January: weeks 40 - 44; February: weeks 45 - 48; and March: weeks 49 - 52. These totals were adjusted in order to equalize the lengths of all months. It appears that, for the 1981/82-1986/87 period, the hail damage season commenced during November (Fig. 7.3a). The earliest claims were recorded during week 32, (i.e. the second week of November) in Bethal (1985), Groblersdal (1984) and Warmbad (1986). These early-season damage claims show no spatial coherence. In each of the Novembers of 1982 and 1983 hail damage was confined to one district only, namely, Ermelo in 1982 and Waterberg in 1983; in 1984 the damage was limited to the Heidelberg and Groblersdal districts; 1985 hail claims were paid out in the 139

Waterberg, Bethal, Groblersdal, Klerksdorp and Randfontein districts, while in 1986, the claims originated from eight districts, namely, Ermelo, Piet Retief, Potgietersrus, Warmbad, Pietersburg, Barberton, Brits and Thabazimbi. It therefore seems that early-season hail damage was more prevalent in the post-1985 part of the data set.

As expected, peak hail damage (Fig. 7.3a) occurs considerably later than does the meteorological hail season (reproduced as Fig. 7.3b for convenience). This may be due to a number of reasons. Firstly, it must be remembered that the wet season does not start at the same time in all parts of the study area. The late commencement of the rainy season in the west necessarily implies an absence of hail storms during the early summer months. This would result in lower average Rclaim as well as Nclaim values during November and December. Secondly, it could indicate that the hailstorms in the late-season are more severe than the early ones, but substantial evidence exists to the contrary (Held 1974). Another possible interpretation involves differences in the relative sensitivity to hail damage during various phenological stages. Assuming the planting date to be around mid-October in the south eastern Transvaal, the critical silking and tasselling stages will be reached during January. However, according to Figure 7.3a, hail damage reaches a maximum during February, with losses in March still exceeding those of January. It therefore seems likely that maize is not just sensitive to hail damage during the flowering stages but also during the subsequent green mealie, soft - and hard dough stages. In the drier western parts, where the planting dates are nearer to mid­ November, the critical flowering and seed-filling stages only occur during February and March. This probably contributes most to the high hail damage values during this period.

7.3.2.2 Hail-claim frequency (Nclaims)

The seasonality of Nclaims (Fig. 7.3c) shows "that most hail claims are submitted during March. This late summer peak in the Nclaims accords with Changnon's (1967) comment that "hailstorms in late summer produced crop damage more frequently than those that occurred earlier in the crop season" (Changnon, 1967, 537). 140

{al AVE RQAMS (IN 1OOOI/MONTH/DISlRlCT (bl MONTHLY HAIL DAY. FREQUENCIES

20 tlO ::.;.:...... , 90 ...... , ...... 80 15

cr UJ 60 ::; '":::> Z 10 50 ...l-c :::> )( z - z c: 40 -e u. o 30 ;~ 5 20 B Q •••.••• 1

NOV DEC JAN FEB MAR SEP NOV JAN MAR MAY OCT DEC FEB APR JUN

MONTHS

AVE NCLAIMS I MONTH/DISTRICT Cd) AVE MONTHLY RCL.AIMS/NCLAIMS

5000

4000

30 3000 c:

20 2000

10 1000 .•...

.. :{: ••••• I'CN DEC JAN FEB MAR I'CN DEC JAN FEB MAR

Figure 7.3 Hail and hail damage seasonality. Mean monthly (a) RCL\IMS p~r ~istrict (in thousands); (b) poin.t h~iI day frequency; (c) NCL\IMS per districts; (d) RCL\IMJNc IAlM s per district 141

The areally-averaged monthly Nclaims for the Transvaal indicates that the incidence of damage-causing hailstorms does not accord with the HDF seasonality. It therefore seems likely that these late season hail storms have a greater spatial extent than those' of the early season. Furthermore, the number of farmers with hail insurance increases throughout the season - hence increasing the number of farmers who are able to claim hail damage. It is also possible that the greater damage resulting from the late season storms may induce more farmers to submit damage claims.

The influence of the late start of the rain (and hail) season on hail claims in the western Transvaal has been mentioned. In fact, spatial analysis of the seasonality of Nclaims shows that the Nclaims season does start significantly later in the western Transvaal. This could partly account for the low November and December Nclaims values.

The seasonal pattern of Nclaims also differs from that of Rclaims' Although it is expected that the monthly Nclaims pattern should be similar to that of Rclaims this is not necessarily so. The latter reflects the amount of damage caused by a hailstorm while the former indicates the frequency and/or the spatial extent of the storm.

7.3.2.3 Intensity - susceptibility index

The ratio of Rclaims :Nclaims gives a relative measure of the damage caused during a particular storm since it is assumed that a farmer will submit a hail damage claim whenever some crop damage is sustained - irrespective of the amount of the damage. Therefore the greater the Rclaim :Nclaim ratio, the greater the intensity of the storm or the more susceptible the plants to hail damage. This ratio (x 100) is thus called the intensity-susceptibility index (1-5 index). It is not affected by the frequency of the storms and only minimally by their spatial extent.

Analysis of the 1981/82-1986/87 1-5 Indices (Fig. 7.3d) shows that the most damaging storms occur in November, December and February. The high 1-5 values in November and December may be due to the greater severity of these early storms (Held 1974) but may also reflect the difference in planting dates in different parts of the study area. Very early sowing might result in tasselling and silking occurring during late December, while in the drier western areas the plants might only reach 142

this stage during February/March. The extremely low number of Nclaims values early in the season might also affect the relative size of these claims. Hence it seems likely that.the high early-season values reflect low Nclaim values as well as high hailstorm intensity while the late-season high can be attributed to the greater susceptibility of crops to hail damage.

7.3.2.4 Crop-loss and crop loss-intensity indices

The relative extent of the damage incurred during a hail season is given by the crop­ 2 loss index (C-L index). This is given by the ratio of Rclaims : Rpolicies (x 10 ) and indicates the proportion of the expected yield which is lost due to hail damage. This index is influenced by the frequency of the storms; their intensity; the stage of development of the crop - and its concomitant level of susceptibility to hail damage; as well as by the areal extent of the storm.

Because the average monthly values of Rpolicies do not reflect yield expectation during that month, an analysis of seasonality of the C-L index would be meaningless.

Instead, Figure 7.3a,which shows monthly R claim values, can be used as an adequate indicator of the seasonality of crop loss.

The C-L index is in essence analogous to Changnon and Stout's (1967) 'Intensity Index' and therefore, a similar method to theirs may be employed to render data comparable on an inter-district basis. Accordingly, Changnon's technique was followed to calculate the Crop-Lass-Intensity Index (C-L-I index). The median of the 1981/82-1986/87 monthly R claims values was determined for selected districts from different parts of the study region. These were standardized by dividing each month's median by the district's mean annual Rpolicy value.

C-L-I Index = Me monthly Rclaims X lat mean annual Rpolicies

Crop-Lass-Intensity indices were then plotted for these districts (Fig. 7.4).

From this figure it is evident that the seasonal pattern of crop-loss varies over the Transvaal. Evidently a relatively smaller proportion of the maize crop is destroyed 143

by hailstorms in the western Transvaal when compared with that in the south eastern parts. This is probably due to spatial differences in the relationship between HDF seasonality and stage of development of the plant. Lydenburg (in the north east) suffers most crop loss during January while most of the other regions exhibit a February peak crop damage season. A January-March bimodal C-L-I pattern occurs in Bethal. This may be due to both long and short season cultivars being grown in this region resulting in critical stages being reached in January (for the short season cultivar) and in March for the longer season cultivar. A wider selection of cultivar types is possible in the more eastern parts of the Transvaal because of the early start to the rainy season.there.

90

BETHAL $TANDERTON 80 WOLMERANSSTAD LYDENBURG SCHWEIZER-RENEKE /\ 70 I\

60 I \

~ / 52 \

)( 50 I ..,'" \ ...§ ce0 / \ <,., 2i 40 \ -c 1\./ <>... • II: \ :::E.. /f\ \ /\• /1 \ 30 \ i \ \ ,/j ;..\. \ \ \ // / \ \ I \ \ 20 / \ . ,II •.... \ / .... \ !' .... \ / "" .... ' I . ..-, 10 'I .- ,\ \ ...... " I•1 / .... ". \., 1 •/ ._ .-. .,.,,, \ ... <» l/ ...... ~ ...... •...... Oct Nov Dec Jan Feb March April

Figure 7.4 Crop-Loss - Intensity Index for selected districts 144

The preceding analysis highlights the temporal and spatial variability in the amount of damage resulting from hailstorms. This variability may in part be due to the seasonal incidence of hailstorms - but it also reflects the importance of the phenological stage of the crop. Since planting dates are dependent upon the advent of the rainy season, they differ from one season to the next and from one district to the next. Therefore plants in different regions will not reach the same level of development simultaneously and, moreover, these dates will vary from one year to the next. A more accurate assessment of the role played by the phenological stage of the crop (in the overall damage incurred) would be possible if crop loss indices could be determined for post planting periods instead of simply on a monthly basis. Such information could then be used to evaluate the hail risk associated with a hailstorm occurring during a certain stage in the plant'S life-cycle and could be incorporated in yield prediction models. Such a plant development stage - crop loss evaluation is discussed in the following section.

7.3.3 Hail risk seasonality and planting date

It was previously noted that maize is usually sown in spring after the first 25 mm of rain has fallen within a 5 day period. This of course is just a general rule and may not necessarily be adhered to by all farmers. However, it was not feasible to determine the exact planting date for each maize farmer in the Transvaal for each of the seasons under investigation, and it was therefore assumed that the above rule was followed by the majority of farmers. Furthermore, due to the large volume of data which had to be processed in order to determine probable planting dates for each season, the study was limited to the south eastern Transvaal (where the HDF is also the highest. See Chapter 3).

Daily rainfall data for the 1981-1985 period were obtained from the South African Weather Bureau for those major towns in the south eastern Transvaal for which good quality data were available. It was assumed that these data were representative of rainfall conditions in the district as a whole. The rainfall records were perused for the start of the rainy season - and hence planting dates. The latter could not be determined for those places where the criterion of more than 25 mm of rain, recorded within a five-day period during October and November, was not met. The results of the analysis are presented in Table 7.1. 145

Table 7.1 Onset of spring planting rains in the south eastern Transvaal (1981/82 - 1984/85).

Pq.NTING DATES

Districts 1981/82 1982/83 1983/84 1984/85

Balfour 8/10 12/10

Bethal 9/10 22/10 17/10 16/10

Bronkhorstspruit 9/10 16/10 12/10 8/10

Delmas 7/10 18/10 10/10 11/10 Erme10 9/10 15/10 25/10 7/10

Heidelberg 9/10 16/10 13/10

Middelburg 25/11 24/12 25/10 19/10

Standerton 9/10 17/10 12/10 19/10

Although some time probably elapsed between the advent of these 'spring planting rains' and actual sowing, this was not taken into account in the subsequent analyses because it was assumed that it would be cancelled out by the lag between the occurrence of hail damage, the submission of a claim and its processing by Sentraoes. Using the dates given in Table 7.1 as the beginning of week 0 (during which sowing and germination occurred) the proportion of the total crop destroyed by hail was calculated for each week of each growing season, for the above districts. These weekly crop-loss indices were then averaged over all 8 districts. The resulting values thus give a quantitative assessment of the crop loss which could be expected if a hailstorm occurs during any particular stage of the plant's development and also indicates the relative sensitivity of the crop to hail damage during its different phenological stages.

The results of the analysis are summarized in Table 7.2. During the 1981/82­ 1984/85 seasons approximately 3,35% of the expected maize yield (in these 8 districts) was lost as a result of hail damage. 'This is in line with the expected 4% (Theron et al., 1973; Carte, 1977; Gillooly, 1978), but still varies between R81000,OO (0,00019x 1 585 928 tons x R270/ton) and R1 764 000,00 (0,00412 x 1 585 928 tons x R270/t) lost per week at 1986/87 prices and first yield estimates (Republic of South Africa, 1988a). (See Table VII.3 in Appendix VII for 1986/87 production estimates for all Transvaal districts.) Most of the damage was caused by those hailstorms which occurred between 112 - 140 days (16 - 20 weeks) after 146

Table 7.2 Weekly crop-loss during the growing season (expressed as percentage of the estimated yield destroyed and ofthe total crop lost). --T------l 1,,,reekS % of est. r i c l d Weekly crop loss Cumulnli\'(' lafte r dest.royed I (% of t.o t.a L crop loss /rluHting 1981-19El5 I loss) i 7 0,071 I- 2,110 2,1 1 I 8 0,019 0,570 2,68 9 0,152 4,520 7,20 I 10 0,205 6,120 13,32 I I 11 0,038 1,140 14,46 12 0,059 1,747 1(),21 I 13 0,067 2, OO/~ 18,21 I 11. 0,151 4,505 22,72 ! 15 0,125 3,732 2(" t.5 I 16 0,336 10,039 36,49 I 17 0,209 6,250 42,74 I i 18 O,ld :2 12,300 55,04 i ;I 19 0,330 9,855 64,90 I 20 0,1.00 11 ,932 76,ID i 21 0,2 /.8 7,410 84,24 ! I I 22 0,182 5,1.42 89,69 I 23 0,2')') R,9J7 I ')8, (,0 I 2/, 0,047 1,400 100,00 '

~],OTA L YIELlJ !1.0ST 3,315% 100 ! I l

"~ '''1I c, 010--' ~ -I o i z , o I ~ o.os o . JI ll. I ~ I I i o L!---.,...... -----...-----.--'--'--'-..--"--...... ----.,..-- o 5 10 15 20 25 30 WEEKS AFTER Pl..AHTJNG

Figure 7.5 Susceptibility ofmaize to hail damage (in terms ofcrop lost per age of plant) 147

planting. Despite crop losses being relatively high for the two week period extending from day 63 to day 76 post-planting, maximum crop losses were recorded during weeks 18 and 20, that is, around 126 to 147 days post-planting (Fig. 7.5). Although this confirms Roth's (1949) findings that maize is sensitive to hail damage during tasselling and silking, it also indicates that extensive crop loss can result from hail during later phenological stages such as the green-mealie, soft- and hard dough stages.

7.3.4 Inter-annual hail damage patterns

La Dochy (1985) found that Canadian farmers tend to take out more hail insurance in the season following high hail damage. This does, in part, seem to be the case in South Africa. Figure 7.6a shows that the number of policies (Npolicies) taken out remained more-or-Iess constant (around 7 000) between 1981/82 and 1986/87. One exception was the relatively high number of policies (8 056) which were taken out during 1984/85. The time series of the amount of damage incurred due to hail, expressed in terms of the crop loss index, (Fig. 7.6b) shows that more than twice as much damage occurred in 1983/84 in comparison with the previous two years. This lends support to La Dochy's findings.

Table 7.3 Hail day frequency (HDF) and cumulative HDFs in the Transvaal (1981/82 • 1986/87).

SEASONS 1981/82 1982/83 1983/84 1984/85 1985/86 1986/87 HDF Cum HDF Cum HDF Cum HDF Cum HDF Cum HDF Cum

CSIR Network:

Sept 3 3 1 1 1 1 3 3 4 4 0 0 Oct 4 7 8 9 8 9 13 16 6 10 4 4 Nov 6 13 5 14 14 23 13 29 7 17 10 14 Dec 13 26 13 27 14 37 7 36 13 30 15 29 Jan 17 43 10 37 11 48 9 45 12 42 14 43 Feb 6 49 5 42 15 53 6 51 8 50 8 51 Total for season 60 57 63 61 62 64 TVL point HDF/Station 2,75 1,73 1,76 2,40 2,15 I 148

(a) N POllOES (b) alOP-LOSS

130

~. 8000 ~ 120 ...... )( .. c:::;:- 0/ : 0/ '"\II : : !oJ '7:" .. : w 7000 : ~ Q"' : -, 40 : : a: ~ : : : : : ~ z : : 30 :.. r : .. : -c : d 6000 ~ : : a: 20 :.. : : : : : : : : : 1) : .. : .... -, -, 5000 0 •••••• ~v ~ ~I ~I ~I ~1/ ~ ~I ~I 861 W 87 SEASONS SEASONS (e) N CLAIMS (dl RClAIMS IN CLAIMS 160

140 0 0 0 0 0 120 12 0 zs

;:'" 1) zs 100 -c ;: ...o "'-c 80 z 8 U <, en zc ;: 60 -c 6 U 40 c:: 4

20 n··· 2 r~~;i~ 0 .,. 0 1981/ 82/ 831 84/ 351 36/ 19811 821 831 841 851 861 62 63 84 65 86 87 82 83 84 85 86 87 SEASONS SEASONS

(el Ra.AIMS/HA

90

80

70 ......

60

50 :::::: ......

Figure 7.6 Inter-annual hail damage patterns. Annual time series

1981/82 - 1986/87 for (a) NPOU CIES; (b) crop-loss; (c) NcLAlMs; (d) RcIAIMsfNclAIMS; (e) RcuwJha 149

An additional factor which might account for the relative increase in the number of policies taken out during 1984/85, is the seasonality of the hail. It is obvious from Table 7.3 that the total annual HDF in this season did not influence the number of policies taken out. However, analysis of the early season (September to December) HDF, as was recorded in the PWV area, may explain this increase. The cumulative HDF totals indicate that relatively more hail days occurred during September, October and November of 1984/85 than for the corresponding months of the previous few years. It therefore seems likely that some farmers take out hail insurance only after the first hailstorms of the season have occurred.

The exceptionally low hail cover during the 1982/83 season probably reflects the influence of the extended drought experienced in most of the summer rainfall region.

Furthermore, there is no sign of a decreasing trend in Npolicies as is expected from the decline in the relative importance of maize as expressed by the size of the maize farming area (Fig. VILl, Appendix VII).

It is interesting to note that there was an exponential increase in the number of hail damage claims (Nclaims) submitted to Sentraoes over the study period (Fig. 7.6c). The N~Iaims time series for the 1981/82-1986/87 is given by:

Y = 2,74Xo,21 or log Y = log 2,74 + 0,21 X where Y = Nclaims and X = time (years) with r = 0,96; c = 0,001).

This trend is also evident in the Nclaims : Npolicies ratio (r = 0,96; c = 0,001).

The average damage incurred per hail event - as expressed by RclaimsiNclaims - does not exhibit any inflation-price linked temporal pattern (Fig. VII.2, Appendix Vll). The value of maize crop has increased steadily throughout this period while the RcIaims/NcIaims value peaked in 1983/84 (Fig. 7.6d). This indicates that the hailstorms were particularly destructive during that season, with each hail claim averaging RIO 179,00. As previously mentioned, the damage might have been due 150

to one or more of the following reasons : a high hailstorm frequency; high hail intensity; great spatial extent of storms; or the hailstorms might have occurred during a hail-sensitive phenological stage of the maize. The relatively low HDFs recorded during January and February of 1984 seem to exclude this factor as a possible reason for the high 1983/84 values, but it must be kept in mind that the monthly HDF given in Table 7.3 are those for the Pretoria-Witwatersrand area and do not necessarily reflect conditions in the rest of the study area. It is evident that the high 1983/84 value of the hail claims (Rdaims) does accord with the extent of the crop loss experienced during this season, when about 3,9% of the total maize crop was destroyed by hail (Fig. 7.6b).

The extremely high crop loss which occurred in the 1986/1987 season, when 12,6% of the maize crop was destroyed, is not reflected in the average claim value (only R4872/claim). However, Table 7.3 values show that the critical December, January and February HDFs were higher in this season than in preceding years. This, together with significantly higher Nclaims indicate that the damaging hailstorms occurred more frequently during this season - especially during the critical phenological stages.

7.4 SPATIAL HAIL DAMAGE PATTERNS

In section 7.2 it was pointed out that the hail insurance data obtained for this study are not comparable on aninter-district basis because, inter alia, they are not equally representative of the true conditions prevailing in all districts. The proportion of the maize land which was insured against hail damage at Sentraoes was shown on Figure 7.2. Thus, in order to render data spatially comparable, values were either standardized or were given in terms of indices such as the crop-loss - or intensity­ susceptibility index etc. In the following sections, the spatial characteristics of various relative indicators of hailstorm activity and the resulting crop damage is described. Table VII.2 (in Appendix VII) lists these values for all magisterial districts in the Transvaal. Quintiles have been used to define classes on all maps in order to delineate areas with very high, high, medium, medium-low and low values, respectively, and to allow comparison between maps. 151

7.4.1 Relative hailstorm frequency and spatial extent

The ratio of Nclaims : Npolicies gives an index of the relative storm frequency which causes hail damage to maize during the growing season. This index also reflects the areal extent of a damage-causing storm because it is likely that a large storm would cause damage to a number of farms - hence increasing the number of hail damage claims submitted to the insurance company. Therefore the Nclaims/Npolicies ratio will henceforth be referred to as the F-E index. It is assumed that a farmer will submit a hail damage claim whenever any crop loss occurs - irrespective of the extent of the loss. Neither the intensity of the hail, nor the phenological stage of the crop influences the number of claims submitted - but only their value.

Comparison of district F-E index values (Fig. 7.7) shows a concentration of high values in and around the south eastern and southern Transvaal. These districts coincide with areas with the highest point HDF i.e. where the long-term (and thus the expected) point HDF exceeds 4 days/year (see Fig. 3.2). It is also likely that storms in these areas are larger in areal extent than those occurring elsewhere. Low F-E values occur along the fringes of the Transvaal - notably in the Lowveld, extreme northern, north western, western and south western Transvaal and in the vicinity of Pietersburg. This implies that (maize) crop damage in these regions is mainly due to the intensity or the seasonal incidence of the hailstorms.

7.4.2 Intensity - susceptibility index

As previously mentioned, the ratio of Rclaims : Nclaims reflects the extent of damage caused to crops by each hailstorm. Intensity-Susceptibility Index values are indicative of the relative intensity of the hailstorm and/or the susceptibility of the crop to damage, - but do not indicate variability in hailstorms frequencies. The size of the storm-damaged area may also influence the I-S index. However, it is likely that a large-area storm will affect more than one farmer and hence will increase the number of claims submitted. This will - to some extent, but not completely ­ counteract the increase in I-S index value.

In contrast to Figure 7.7, the most intense and seasonally inopportune hailstorms occur in an east - west zone extending from Swartruggens to Barberton (Fig. 7.8). It is interesting to note that only one of the eight districts with very high I-S values F-E INDICES

o No data g less than 0.15 mIll 0.15 ­ 0.27 ~ 0.27 ­ 0.53 ~ 0.53 - 0.B4 - more than 0.B4 ~

o 100 ..... U1 tv Figure 7.7 Spatial characteristics of I-S Indices (NCIAIMS/NI'OLlCIES) 1-8 INDEX

o No data ~ less than 1105.00 IIIIIJJ 1105.00 - 1822.00 ~ 1822.00 - 2508.00 ~ 2508.00·- 4426.00 - more than 4426.00 ~

o 100

-" Figure 7.8 Spatial characteristics of I-S Indices (RCI.AIMslNcl.AIMS) VI W 154

. coincides with that of the same category F-E value, namely Springs. Similarly, if regions with index values of zero are ignored, the only district with a very low I-S index value.which also has a very low F-E value, is Bloemhof. This apparent mutual exclusivity of I-S and F-E indices seems to indicate that those districts with hail damage resulting from high hailstorm frequency or from large storms are less likely to also experience high intensity storms. Furthermore, hailstorms in these districts occur during a stage when plants are less susceptible to hail damage. This inverse relationship between I-S and F-E indices in the Transvaal is shown by a X2 value of 6.32, which is significant at the 2.5% level (see Addendum VII). (The rand rs values of -0,32 and -0,30 respectively, are not statistically significant.) It is thus apparent that different hailstorm characteristics are measured by these two indices.

7.4.3 Crop hail damage

There are two indices which reflect crop hail damage. Firstly, the ratio of Rclaims : Rpolicies which gives the proportion of the expected yield which is destroyed and secondly, Rclaims/ha' which simply shows the extent of the hail damage occurring per unit area. According to Figure 7.9, crops sustain most hail damage in the Belfast, Bethal, Pretoria and Krugersdorp districts. During the study period, more than 5% of the estimated yield was destroyed annually by hail. The adjacent districts of Middelburg, Delmas, Balfour, Rustenburg and Potchefstroom also lost in excess of 3,5% of the total expected crop. Despite these seemingly low values, they do represent substantial financial losses. Based on the estimated production figures for the 1986/87 season, 52 765 tons of maize were destroyed by hail during this period (Dept. of Development and Planning). At 1986/87 maize prices - this amounts to a staggering loss of more than R14 million in these districts alone. Estimated production figures and the yield lost as a result of hail damage, have been summarized in Table 7.4.

The hail damage pattern, as given by Rclaims/ha (Fig. 7.10), is essentially similar to that of crop loss (r = 0,92), with the east - west zone of maximum damage running through the central and south eastern Transvaal. Average-to-high hail damage thus occurs in a zone extending from about 25CS to 26V2OS and from 27'E to the Swaziland/Mozambique border. Anomalously low values of crop hail damage occur in the Bronkhorstspruit and Witbank districts. The former may be explained by the CROP LOSS

o No data g less than 0.61 [ill] 0.61 - 1.26 ~ 1.26 - 2.26 ~ 2.26 - . 3.50 - morethan 3. 50 ~

o 100

Figure 7.9 Spatial patterns of Crop-Loss Indices (as percentage of RCI.AIMS/RPOUClEs) ..... U1 U1 156

low I-S and F-E index values while the ostensibly low value in the Witbank area is in fact an aberration caused by the selection of the class limits on the maps.

Table 7.4 Estimated cost or hail damage to maize (1987).

District ave. % Est. yield Yield (tons) crop lost (tons)* lost (1981/82-1986/87) Feb. 1987

Balfour 4,37 113 386 4 954,97 Belfast 5,60 80 851 4 527,66 Benoni 3,50 70 2,45 Bethal & Hoe- tveldrif - 5,10 299 182 15 258,28 Delmas 4,71 143 436 6 755,84 Krugersdorp 5,91 2 719 160,69 ~iddelburg 4,05 374 273 15 158,06 Potchefstroom 3,64 52 653 1 916,57 Pretoria 5,02 11 330 568,77 Randfontein 3,55 56 555 2 007,70. Rustenburg 3,67 4 583 168,20 Warmbad 3,95 32 576 1 286,75 TOTAL = 52 765,94 R270/t** R14 246 803

* From Dept Development and Planning : maize estimates ­ 1987 Area & Crop, RSA development maize ** Dept Agricultural Economics and Marketing : 1988 Abstract of Agricultural Statistics

Correlating district F-E and I-S indices with district crop-damage (%) give r values of 0,68 and 0,35, respectively. The former correlation coefficient is significant at the 99,9% level of significance and explains a larger proportion of the variance in crop damage than does the I-S index. This overall low correspondence between the I-S and crop damage patterns seems to indicate that intensity and seasonal incidence of hailstorms are not the most important factors as far as crop loss is concerned.

On the other hand, the correlation between the 1982-1986 point HDF in each district and the district crop damage values is not statistically significant either ­ which clearly establishes the relatively more important role of the spatial extent of the hailstorm in causing crop damage. These apparently conflicting results suggest that the interrelationships between storm frequency, intensity, spatial extent and the crop susceptibility factors on crop loss are more complicated than expected, necessitating more attention. CROP DAMAGE o No data less than 4.33 '~ 4.33 -. 11. 00 ~ 11.00 - 17.00 m1ll) 17.00 - 28.00 - more than 28.00 ~

o 100 200km ..... Ul Figure 7.10 Spatial crop damage patterns (RCIAIMsihaINSUREO) -...J 158

The relatively small impact of hail seasonality on crop damage may be explained vis­ a-vis the spatial differences in peak hail season, (as discussed in Chapter 4) together with the seasonal characteristics of hail damage (Fig. 7.3). It has been shown that early hail occurs most often in the eastern parts of the Transvaal but shifts to peak later over the western parts. It is these January and February hailstorms which affect yield most (see Fig. 7.3a). From this it seems likely that the early peak hail season in the extreme southern and south eastern parts of the Transvaal precedes the critical phenological period - hence resulting in less crop damage in these potentially high-yield areas. It therefore seems probable that different relationships could exist between the 1-5index and crop loss in" different parts of the region.

.\ S• SE TV\..OISTRCTS EJ

AN. W TV\.. 0lSl1'lICT5 CJ • CENTRAL TVL ~

9

Prerona .\ Belfast • 8e1hal.\

War""""

SwartruQllOnS A

2 5 6 8 9 13 ..

1-5 ,..;ex IN 1000

Figure 7.11 Relationship between Crop-Loss and Intensity­ Susceptibility indices for all Transvaal districts (Insert shows sub-regions used for analyses)

That this is indeed the case is clearly shown by Figure 7.11. Here the district C-L indices have been plotted against district 1-5 values. It is evident that the districts 159

seem to cluster into three zones, each of which shows some linear tendency. With minor exceptions; all those districts with relatively high C-L/I-S ratios (around 1 : 1000) occur in the southern and south eastern Transvaal, while those with the lower ratio are mostly contiguous in the northern and western Transvaal. A central zone comprising districts extending in a SW - NE band across the study area has a C-L/I­ S ratio of approximately 1 : 2000. Swartruggens is the only obvious outlier.

Accordingly, the Transvaal districts were subdivided into three subsets as indicated on the insert in Figure 7.11. Pearson's product moment correlation coefficient was calculated and regression analysis performed on each in order to determine the strength and nature of the relationship existing between hailstorm intensity, crop susceptibility and the amount of crop loss sustained in each subregion. It seemed probable that those districts that were particularly poorly represented by the data (l.e. <50 policies in six seasons) and/or in which less than 30 claims were recorded during the study period, could cause statistical bias. They were thus excluded from the analyses.

The resulting calculations showed strong links between the I-S index and crop loss in each of these three zones. In the south and south eastern as well as the central Transvaal, correlation coefficients of 0,7 were found. These are significant at the 1% level. A highly significant ( Ct = 0,001) linear relationship exists between I-S and C-L inthe western and northern Transvaal (r = 0,92). Thus despite earlier findings (for the entire province) it is clear that a strong linear relationship does exist between the I-S index and crop loss, but that its nature differs from one region to the next.

To determine the relative importance of these two indices (I-S and F-E) in explaining the variance in crop loss, district I-S, F-E and C-L values, for each of the three zones separately, were subjected to stepwise multiple regression analysis. The results show that the I-S and F-E indices together explain 75%, 90% and 91% of the variation in crop loss in the central, northern and western and south-south eastern parts of the Transvaal, respectively. The multiple linear regression formulae are:

Central TVL: C-L = 1,7077 + 3,81 F-E + 0,0006I-S. R2 = 74,91% N & W TVL : C-L =-0,673 + 0,000426 S-I + 2,507F-E. R2 = 90,45% S & SE TVL: C-L =2,706 + 3,90 F-E + 0,0011 I-S. R2 = 91,4%. 160

It must be remembered that all index- as well as crop loss values are the statistical means of six seasons' data. Anomalies in anyone season may thus influence the mean to such an extent so as to give an ostensibly 'bad fit' between the index and crop loss values.

7.5 HAIL RISK ZONES

The purpose of this chapter is, in part, to delineate high hail risk areas for maize production in the Transvaal. For this purpose, either the proportion of crops destroyed by hail i,e. the crop-loss index, or the damage incurred per unit area can be used as estimators of the total crop damage which occurs within a district. However, 'hail risk' pertains to the hail damage which can be expected in a certain area - and hence can only be derived from long-term trends. Because the above analyses are based on short-term (1981/82 - 1986/87) data, Figures 7.9 and 7.10 showing crop loss, cannot be used to define 'hail risk' areas per se. Therefore, hail risk was calculated for each district by dividing the crop loss (RclaimslRpolicies) by the average number of storms which occurred per year during the 1981/82-1986/87 period (thus determining the crop lost/hail event) and then multiplying this with the long-term mean annual HDF.

DISlRICfHAILRISK = Crop Loss x HDF HDF - Long-term 1981/82-1986/87

District values for the latter factor were obtained by interpolating long-term point HDFs for all Transvaal stations (extracted from SAWE 1986 i.e. WE40).

Inaccuracies due to the effect of the cyclical pattern of annual HDFs which occur in some parts of the Transvaal (chapters 4 & 5) as well as short-term hail day anomalies would thus be eliminated.

Figure 7.12 depicts areas with high, medium and low hail risk. In the interpretation of hail risk it must be borne in mind that a high hail risk area is not one in which a high HDF necessarily occurs - but one in which a high crop loss can be expected in the long-term. According to Figure 7.12, most maize damage due to hail can be expected in the south eastern and southern parts of the Transvaal. This decreases towards the north, north west and the Lowveld. The spatial hail risk pattern is in HAIL RISK

o No data ~ less than 0.20 mm 0.20 ­ 1.20 ~ 1.20 ­ 2.00 m 2.00 - 4.00 - more than 4.00 ~

o 100

-> Figure 7.12 Hail risk zones for maize production in the Transvaal 0'\ -> 162

many respects similar to that of the F-E index and the long-term HDF pattern. Deviations which occur in the Belfast and Volksrust districts might be due to anomaloushail incidence during the 1981-1986 period.

RISK-INDEX o 0 - 2.9 o 3.0 - 15.9 El 16.0 - 34.9

0:>35.0

'00 20011,"

Figure 7.13 Spatial distribution ofagricultural (all crops) hail risk zones

There are a number of differences between high hail risk areas for maize production and those of high agricultural (all crop) hail risk (Fig. 7.13). This is especially obvious in the Piet Retief, Lichtenburg, Cullinan, Groblersdal and Lydenburg districts as well as in the Lowveld areas (Pilgrirnsrest and Witrivier) where crops such as tobacco, cotton, sunflowers etc. are relatively more important than maize. As pointed out previously, Gillooly (1978) and Carte (1977) found that these crops are also highly susceptible to hail damage.

7.6 SUMMARY

The geography of hail damage with reference to maize production in the Transvaal was described in this chapter. Because hail frequency is not the sole cause of crop damage, factors such as hail intensity, spatial extent of hail storms and hail seasonality with respect to the phenology of the plant, were examined for the 163

1981/82-1986/87 period. By virtue of the nature of the data available, certain assumptions were made, namely: that Rclaims were representative of the extent of the hail damage; Nclaims == frequency of hail storms; Rpolicies == size of the expected yield.

Furthermore it was shown that the data were not equally representative of conditions prevailing in the districts and hence were not comparable. This problem was eliminated by devising various indices or by standardizing the data.

The most frequently used indices were the crop loss index (Rclaims/Rpolicies • 102) which reflects the proportion of the expected yield which is destroyed by hail; the frequency-extent index (F-E index = Nclaims/Npolicies) which gives a relative indication of either the frequency of damage-causing hail storms or their spatial extent; and the intensity-susceptibility index (I-S index = Rclaims/NclaimJ All three indices reflect relative and not absolute values and can thus be used for inter­ district comparison purposes.

Temporal and spatial patterns of hail damage were investigated. The former comprised analysis of seasonal and inter-annual hail damage patterns. It was found that:

1) The seasonality of hail damage and claim submissions peaked significantly later in the season than did the HOF. It was suggested that this deviation could possibly reflect differences in the susceptibility of crops to hail damage during specific phenological stages. Both the flowering (silking and tasselling) as well as the later stages were found to be particularly sensitive to hail damage. Moreover, the factors responsible for the damage during the early season was likely to have been the intensity of the storms while the late season damage could be attributed to the crop-susceptibility factor.

2) By calculating hail damage seasonality in terms of the planting date, it was found that, although the crop was sensitive during the tasselling and silking stages, it was more so during the seed-filling stage. 164

3) Inter-annual variations in the hail damage parameters show that:

(a). the number of hail damage claims increased exponentially in the period between 1981/82 and 1986/87; and (b) the high crop loss sustained in 1983/84 was probably due to extremely severe thunderstorms and not the storm frequency. The converse was true for 1986/87.

Analyses of spatial hail damage patterns revealed the following:

1) F-E index: storms occurred most frequently and had the largest areal extent in and around the southern and south eastern Transvaal. 2) I-S index : most damage due to high hail intensity or to the seasonal incidence of the storms occurred in an east - west zone extending from Swartruggens to Barberton. 3) Most crop loss was recorded in the Belfast, Bethal, Delmas, Balfour, Middelburg, Pretoria, Krugersdorp, Rustenburg and Potchefstroom districts. The damage was mostly the result of the high intensity of the storms. The spatial extent of the storms was a less important damage-causing factor. The F-E and I-S indices together accounted for 75%, 90% and 92% of the variation of crop-loss in the central, northern and western, and south-south eastern parts of the Transvaal, respectively.

Hail risk was determined by finding the long-term expected crop loss III each district. The formula used was: CROP LOSS· long term HDF HAIL RISK = HDF during study period

It was found that those districts in which maize is at risk occur mostly in the central, southern and south eastern Transvaal. They are : Potchefstroom, Krugersdorp, Pretoria, Heidelberg, Balfour, Boksburg, Brakpan, Bethal, Ermelo and Carolina. Furthermore, the districts with maximum maize hail risk do not coincide with those in which the agricultural hail risk is greatest. This highlights the susceptibility of other crops to hail damage and clearly establishes the importance of this type of analysisto agricultural planning. 165

CHAPTERS SUMMARY AND CONCLUSION

Research on natural hazards in the United States of America revealed that that country is becoming increasingly vulnerable to natural disasters and that disaster­ caused losses are rising (White & Haas, 1975). These results are probably also applicable to the Republic of South Africa. Hail is one of the most destructive weather hazards known to man. It impinges on numerous economic activities such as the transport sector (especially aviation), the building and manufacturing industries, forestry and has even resulted in loss of life. Moreover, with the increasing population and demand for food, crop losses due to hail have become more important. Already crop damage amounting to millions of Rand is sustained each year. It is thus crucial that research be conducted which could lead to more effective adjustment of hail.damage. According to Newark (1989), a knowledge of the climatology of hail and its impact on society forms the basis to understanding this hazard. This involves research directed at:

• understanding the behaviour of severe storms in time and space (i.e, their geographic characteristics); • analysing the relationship between severe storms and other climatological variables "in order to better define their spatial and temporal likelihood" (Newark, 1989, 17); and • determining their impact on the economy.

The aim of this thesis was to address these aspects.

The study was divided into three main sections, each focussing on a particular aspect of hail in the Transvaal. The spatial and temporal hail day frequency (HDF) patterns were described in Chapters 3 and 4 respectively. The following two chapters (5 and 6) investigated hail - rainfall relationships on an annual, seasonal and daily basis. Anomalies occurring during dry and wet periods were analysed, as were the atmospheric characteristics prevailing on hail days, rain days and dry days. The meso- and macroscale hail controls were given attention in Chapter 6. The last aspect examined, dealt with the impact of hail on maize production and as such comprised a geographic study of hail damage. This was presented in Chapter 7. A 166

map showing hail risk areas for maize production in the Transvaal concluded the study.

The chapters are summarized and the most important findings of each section are listed below.

I SPATIAL AND TEMPORAL HDF PATTERNS (1960 -1986)

1) The highest HDF in the study area was found to occur in the south eastern Transvaal highlands. From here it decreased northwards and with decreasing altitude.

2) The relationships between latitude and altitude and HDF were found to be more curvilinear than linear and could best be described in terms of exponential equations. According to these, latitude explained about 45% of the variation in HDF while variations in altitude accounted for at least 70%. of it.

3) Prior to the analysis of the temporal aspects of HDF, it was considered necessary to subdivide the Transvaal into homogeneous hail regions. This was achieved by using Principal Components Analysis. By mapping those components on which stations had significant loadings, seven homogeneous regions were delineated. In most cases the boundaries of these regions coincided with topographic features such as mountain ranges or river valleys, emphasizing the influence of altitude on hail day patterns.

4) The hail season in the Transvaal was mostly confined to the summer months (September to April), and peaked during November. However, during years with particularly high hail activity (HHYs), hail occurred more frequently during January and less frequently during December.

5) The peak hail season differed from one part of the Transvaal to another. For instance, it occurred during early November in the northern Transvaal; during November/December in the south - central parts and later towards the west The incidence of late season hail thus increased westwards while 167

the November HDF showed a concomitant decrease. The monthly HDF in the Lowveld did not exhibit any clear seasonality pattern.

6) Analysis of diurnal HDF patterns revealed that hailstorms usually started between 12:00 and 20:00 while peak occurrence was confined to the period between 17:00 and 18:00SAST. It transpired that there were relatively more nocturnal hailstorms in the Transvaal that elsewhere in the South African summer rainfall region.

7) Both spatial and seasonal variations were identified in diurnal hail incidence patterns. In the northern Transvaal, the afternoon (12:00 to 16:00) peak was more pronounced while in the Lowveld, more hailstorms occurred between 16:00 and 20:00. In the remainder of the province, the temporal hail pattern coincided with that of the rest of the summer rainfall region. During October and November hail occurred most often between 18:00 and 24:00 while in December and January it peaked between 16:00 and 18:00 and again just before midnight (20:00 - 24:00). From February till June hail was relatively rare, but when it did occur, it was usually confined to the 14:00 to 18:00 period. July and September hailstorms occurred between 14:00 and 16:00 and again from 20:00 - 24:00.

8) The spatially averaged HDF time senes exhibited a clear pattern of alternating high and low HDF spells of approximately nine years each. Before 1962/63 the HDF was below normal but between 1963/64 and 1971/72 it was above normal. This was followed by a low hail period which lasted from 1972/73 until 1980/81; after which the annual HDF was again above normal. However, this temporal pattern varied between homogeneous regions. Although there were signs of quasi-periodicity in the southern, central and south western parts of the Transvaal, the peaks and troughs did not occur contemporaneously in these different regions.

II RAIN· HAIL INTERRELATIONSHIPS AND THEIR CONTROLS.

The relationships which existed between rainfall and hail were investigated on annual, seasonal as well as daily time-scales in Chapter 5. ' 168

1) Annual rain-hail relationships:

It was found that, like the HDF time series, annual rainfall also showed alternating periods of wet and dry spells lasting for approximately eight to nine years. However, in most cases, a wet spell coincided with below normal HDFs and vice versa. This inverse relationship suggested that dry-year precipitation frequently occurred in the form of bail-producing thunderstorms while during wet years, widespread general rains were more likely.

2) Seasonalrelationships:

Comparison of high-HDF year (HHY) and low-HDF year (LHY) precipitation characteristics revealed significant differences. For example during HHYs:

(a) The hail season was shorter than was the case during LHYs, with most hailstorms occurring during the October to February period.

(b) The HDF was higher during October, November, January and February, and lower in December. However, more rain occurred during October, November and December and less during January. With regard to the November to January precipitation patterns, it is evident that the hail season exhibited a seasonality shift towards the fall while the rainfall peaked earlier in the season.

(c) The majority ofthe weather stations in the Transvaal recorded less rain during HHYs. The 'drought' was evident throughout the summer months except for November and April.

3) Dailyprecipitation relationships:

Rain days were divided into those with hail (HDs) and those without (NHDs). The analyses showed that:

(a) There were generally more NHDs than there were HDs per year. (b) Rainfall was more intense on HDs than on NHDs. 169

(c) More stations received rain on HDs than on NHDs and thus the precipitation area was spatially more extensive on HDs. (d) The. total rain day frequency did not differ significantly between wet (LHY) and dry (HHY) years. However in Pretoria;

* during wet years there were fewer HDs and more NHDs, * the rainfall on both HDs and NHDs was higher during wet years and * more stations reported rain on HDs during wet years than during dry ones.

These results pointed to fundamental differences in the precipitation characteristics of HDs and NHDs and in the precipitation-producing mechanisms of dry (HHY) and wet (LHY) years. This prompted an analysis (albeit a cursory one) of atmospheric dynamics and kinematics on hail days, non-hail rain days (NHRDs) and dry days (DDs). In addition, large-scale precipitation controls active during wet and dry conditions were investigated. These analyses were confined to the Pretoria­ Witwatersrand area and were described in Chapter 6. It was found that:

4) In general, scattered showers occurred most frequently followed by isolated showers and widespread rain. However, the meso-scale systems active on HDs and NHRDs differed from that described above and from each other, in that scattered > general > isolated on HDs while on NHRDs, isolated > scattered > general.

5) The instability and humidity characteristics of HDs, NHRDs and DDs (given by Tsurface - Tsco and [l/«T - Td)surface + (T - Td)700)]' respectively), showed that:

(a) conditions were more humid and unstable on HDs than on NHRDs than on DDs; and (b) the humidity index was the better indicator of the presence or absence of hail.

It is interesting to note that the stability and humidity characteristics of the atmosphere on HDs at Irene were remarkably similar to those of violent thunderstorm days in Greece - despite their difference in latitude and altitude. This was also the case for DDs at Irene and at Athens. 170

6) Vertical equivalent potential temperature profiles showed mid-level minima on all category days (i.e. HDs, NHRDs and DDs) at Irene but on wet days, these occurred at greater altitudes (lower pressure levels) than on DDs. Furthermore, it was found that on HDs, the 500 hPa air was considerably colder, and the difference between the surface and 500 hPa 8e greater, than on either NHRDs or DDs.

7) A cursory analysis of atmospheric kinematics did not reveal particularly large or consistent differences between conditions on HDs, RDs and DDs. However

(a) low level wind (below 600 hPa) was generally weak north easterly on HDs. Above this it reversed direction to become south westerly. The wind speed increased markedly with height.

(b) Similarly, on NHRDs, north easterly surface winds prevailed but these were replaced by south westerlies at the 700 hPa level. Wind speeds were generally weaker on NHRDs than on HDs.

(c) The DD wind profile showed considerable directional vanation, Weak surface north westerlies veered to very weak north easterlies at 800 hPa: Between this level and the 400 hPa level, south easterlies prevailed while in the upper air, the wind direction was mainly south westerly. Wind speeds were generally low at all levels.

8) Thermal wind strengths (Vt) were used as direct measures of atmospheric baroclinicity as well as the amount of wind shear occurring between two

pressure levels. No clear differences could be distinguished between Vt of HDs, NHRDs and DDs (except for slightly higher values in the surface to

400 hPa layer on HDs). However, mean monthly Vt strengths (500 - 800 hPa) revealed differences in the seasonality of baroclinicity and quasi­ barotropicity during LHY (wet) and HHY (dry) years. Analyses of vector wind strengths for the 1978 - 1982 period showed that during wet conditions (illY), baroclinic systems were present during the early summer months (November and December) but during HHYs, quasi-barotropic conditions 171

were already fully established by November-December. These differences in the onset of barotropicity corresponded with HHY - LHY changes in the seasonality of rainfall.

9) The relatively frequent occurrence of Pacific Cold Events during years with below normal HDF (wet years) corroborated to some extent the inverse rain­ hail relationship previously noted. Furthermore, it suggested possible HDF­ sea surface temperature teleconnections (and thus hail-global circulation links). Therefore zero-lag and lagged Southern Oscillation Index (SOI)-HDF associations were examined. Although no statistically significant linear relationships could be demonstrated on an annual scale, it was clear that some kind of inverse relationship existed between them since the HDF usually decreased when the SOl increased and vice versa. This would be expected assuming a direct rain-SOl relationship and an inverse rain-hail association. On a monthly basis, a significant relationships was identified for December SOl and HDF. Moreover, using three-monthly running means of SOl values, significant lagged relationships were found to occur between the September-October-November SOl and November HDF; the OND SOl and November HDs; as well as between OND SOl and December HDFs for the Transvaal as a whole. An additional SON SOl-February HDF relationship was identified for the Pretoria area.

Although the correlation coefficient values for these associanons were not particularly high, they still exceeded those found elsewhere for SOl • rainfall relationships.

Four important findings have emerged from parts I and II of the thesis, namely:

* An inverse relationship existed between HDF and rainfall in the Transvaal during the 1960 to 1986 period. This negative association was shown to occur on both annual and monthly time-scales, and at individual stations as well as in spatially averaged data.

* During HHYs, the nature of precipitation (intensity, spatial extent etc.), its seasonality and that of the associated baroclinic and barotropic systems, deviated significantly from the norm and that of LHYs. 172

* Precipitation characteristics such as the amount, intensity and spatial .extent of HDs, differed from those of NHRDs; as did the atmospheric conditions prevailing on HDs, NHRDs and DDs.

* The association between the Southern Oscillation Index and HDF in the Transvaal was stronger than that of the SOl and rainfall.

These findings suggested that atmospheric conditions and circulation patterns differ between periods with enhanced and diminished HDF - at scales ranging from local to global. Furthermore, it seems evident that a much clearer picture of these differences could be achieved if the rainfall data set were subdivided into, for example, convective and non-convective subsets prior to analysis. Such research could be extremely useful for medium to long-term climatic forecasting.

Much remains to be done to elucidate the geography and climatology of hail since this study seems to have raised more questions than it has answered.

III SPATIAL AND TEMPORAL HAIL DAMAGE PATTERNS

The third part of the thesis focused on the impact of hail on the agricultural sector. Specifically, the study concentrated on the geography of hail damage with respect to maize production in the Transvaal. The following section summarizes the main findings of Chapter 7.

Because hail (day) frequency is not the sole cause of crop damage, factors such as hail intensity, spatial extent of hail storms and hail seasonality with respect to the phenology of the plant, were examined for the 1981/82-1986/87 period. By virtue of the nature of the data available, certain assumptions were made, namely:

that Rclaims were representative of the extent of the hail damage; Nclaims = frequency of hail storms; Rpolicies = size of the expected yield.

Furthermore it was shown that the data were not equally representative of conditions prevailing in all districts and hence were not comparable. This problem 173

was eliminated by devising various indices or by standardizing the data. The most frequently used indices were the crop-loss index (RclaimsiRpolicies) which reflects the proportion of the expected yield which is destroyed by hail; the frequency-extent index (F-E'index = NclaimsiNpolicies) which gives a relative indication of either the frequency of damage-causing hail storms or their spatial extent; and the intensity­ susceptibility index (I-S index = RclaimsiNclaims)' All three indices give relative ­ and not .absolute values, and could thus be used for inter-district comparison purposes.

The analysis of temporalhail damage patternsrevealed that:

1) The seasonality of hail damage peaked significantly later in the season than did the HDF. This deviation probably reflects differences in the susceptibilityof crops to hail damage during specific phenological stages.

2) Although the crop was sensitive during the tasselling and silking stages, it was more so during the seed-fillingstage.

Analyses of spatialhail damagepatternsshowed the following:

1) Storms occurred most frequently and had the largest areal extent in and around the southern and south eastern Transvaal.

2) Most damage due to high hail intensity or to the seasonal incidence of the storms occurred in an east-west zone situated between Swartruggens and Barberton.

3) Most crop loss was recorded in the Belfast, Bethal, Delmas, Balfour, Middelburg, Pretoria, Krugersdorp, Rustenburg and Potchefstroom districts. The damage was mostly the result of the high intensity of the storms while their spatial extent was a less important damage-causing factor. The F-E and I-S indices together accounted for 75%, 90% and 92% of the variation of crop-loss in the central, northern and western, and south-south eastern parts of the Transvaal, respectively. 174

Hail riskwas determined by finding the long-term expected crop loss in each district. The formula used was:

HAIL RISK = CROP-LOSS ~ long-term HDF HDF during studyperiod

It was found that those districts in which maize was at risk occurred mostly in the central, southern and south eastern Transvaal and included the Potchefstroom, Krugersdorp, Pretoria, Heidelberg, Balfour, Boksburg, Brakpan, Bethal, Ermelo and Carolina districts. Furthermore it was shown that the districts with maximum maize hail risk did not coincide with those in which the agricultural hail risk was greatest. This highlighted the susceptibility of other crops to hail damage and clearly established the importance of this type of analysis.

The question which now arises is what can be done to minimize hail risk? It is possible that research relating to, inter alia, the micro-physical processes involved in hail formation, may reveal ways in which hail can successfully be suppressed. Until this becomes a reality, however, the onus is on the farmer to make management decisions which will minimize his crop loss. For example, he could determine whether the yield loss resulting from hail damage is offset by the profit made from planting maize. If so, hail damage might be lessened if the planting date or cultivar type is varied so that the hail season in the area does not coincide with the critical phenological stages. Otherwise the viability of alternative crops such as sunflower, sorghum, etc., which are less sensitive to hail damage, might be considered. The use of non-contiguous lands and more comprehensive crop-hail loss insurance could also offset losses.

Hazard impact assessment and the identification of high risk areas are obviously necessary prerequisites for meaningful crop-related decisions. It is hoped that this study has contributed towards this.

However, much remains to be done. This study needs to be expanded both spatially, to include the whole of the Republic, and temporally, to assess the impact of climatic change on hail and hail damage patterns. Furthermore, the impact of hail on other crops needs to be evaluated, since, according to Hobbs (1977, 104), 'There 175

is a great reliance on assured levels of crop yields. Weather induced reductions in yield have become of crucial political, social and economic importance.....agricultural production will be used as a political tool on the bargaining table." 176

APPENDICES 177

APPENDIX I Sunday Tribune 4/1/87 178 NORTHERN NATAL, HIT BY Die Vader/and 23/1/87 nURRICANE Skade R60 miljoen ACKSBUKG - Haebt.ade van meet' as R60 miljoen Tribune Reporters is ,edurende die buidige somerseisocn aan gesaaides op A HURRICANE followed by a violent plsse in die land ungeriB. hailstorm tore aCf'OA Northern Natal MDT 1.m'enz Schuttc. boofbestuwdcr van die land­ yesterday, IeIYin« a trail of uprooted boa-ocsvenekC':'UTS. ~~, het bier ~ dat eise ~ 1IJlU~ and windows. In some! van IOWat R60 miljocn ty bullc ingedien is vir places 1t was stiU rain!ng heavily late a:m ~. bst nJg!ll hxhkade Die skade is op 3 <400 plase The Jame norm ,wept into the aanFrig· Transvaal. kUllnl two people and Die eisbedrtlf ~ ook. twec keer mccr as ~ vir die­ wnakin, tens of ~oUSJ.lnds of rands Idfdc tydpcrt. in die seDoen 1985186. damage in the toWD of Piet RetJef. V~ '-eek a.l]een bet Sentra-ocs 1510 CL1e ont- In Natal the small mininl t01ll'n of nftI· Hlobane was w:nt bit by the hurricane, C' .ftI#~~. 1RU ~ J.-Isbdc bocbaAklik k.om­ wbile Vrybeid enlbt till end of the ~~. ~.' ~ "e"';·Bo·tha'• -'_"l'l ' P' .. "~:-'.J • 't'k'Jec! storm. ' eo oeste ~I IS, IS YUle. arys, "'-I"OO11lKdU, A poliC'C! spoftet~an In Hlobane ~id LicbtenbwJ. Potcbc:fmoom en StanUlb tM town. satd the hall,t.,"", beste in baie jare en reen in die volgende twee weke sal uitstekende oeste verseker. WeT'1!blpff -.har. ~olf balls. He said c.-orru.ltf'd Iron roof',,~ !lheets hid bf'en iefl tntanlll..d In powerU,,". telephone poles had to,'..r: n~ out of the ground and some reo­ dl"t1t~ Wf'~ snll battling II) save lhl'ir hous~hold pr,s..~iom 3S the rain eon­ tinued to pour (fown.

Figure 1.1 Newspaper clippings reporting recent hailstorms in South Africa. 179

Die Volksblad 28/8/87

Verlles Dr. Van Rooyen se ondanks die omvang van oesssade en die ge­ volglike verlles het Sentraoes al sy elsver­ pligtlnge nagekom. Oit het 'n dreineren­ de uitwerking op die geldelilte reserwes van die maatsbppy en (lie reserwefonds het va., is verseser, aarvan DIE kooperaUewe oes­ R42 miljoen tot R28 was rnlelles, koring, ta­ miljoen afgcneem. versekeraar Sentraoes bait en so!".l1eblom dIe het die a!gelope sei­ belangrikste. . Bydrae soen 'n rekordelse-be­ Vrugteversekermg, dra~ van meer as RI10 asaok druiweveneke­ Dr. Van Rooyen se mlljoen aan boere vir ring, het in 'n redelike Sentraoes beskou dit as skade aan oeste uitbe­ tempo toegeneem. So­ 'n bydrae tot die sUbl­ taal. Dit is '0 gemid­ wat veertig persent llsering van die land­ deld van meer as van die land se kon­ boll. In die Itnellende R435000 per wf'rkdag lantge\Vasse soos vrug­ omstandlzhede waarin oor 'n jaar bereken, Sf' te, groente- en graan­ die IanJ60u bom be­ dr. Ivan van Rooyen, oeste, tabak en ka- vlnd, Is dit geen ge­ adjunk·hoofbestuurder ringe bydrae nie. van Sentraoes. toen word by Sen­ Hoewel die reserwe­ traces verseker. . fonds vermlnder het, Dr. Van Rooyen se bly Sentraoes kerni~ die meeste skade aan sand. Na die afgelope oeste ill deur hael ver­ seisoen Is die solven­ oorsaak. P.aelstorms siemarJe steeds meer hel wyd voor~e!tom, as derUg persent. Oil is Die Hoeveld, die Lae­ meer as die huidlge veld en Natal het dip wetlike minimum van ergste deurgeloop en tien persent, se dr. Van veral Itoring, rnielies Itooyen, en labak Is swaar ge­ tree. Die omv..ng van die else het tot gevolg ge­ had ciat Sentraoes die seisoen 'n groot onder­ urywingsverlies gely het. 'n Deel daarvan kon van herverseke­ raars verhaal word. 011. VAN ROOYEN maar Sentraoes ~Iuit Dr. Van Rooyen se noglans die {'aar af met 8 125else is die seisoen 'n netLo ver les van :;0­ Die Beeld 19/2/87 deur versekerde boere wat R14 miljoen. • teen Sentraoes inge­ stet. Meeste Mielie-oes het knou gekry Oeste van meer as HAELSKADE'in die oostelikedeel van Die land se beraarnde verbruik van R1 850 miljoen is denr Sentr.oes verseker. Dit die 1:100, oneweredige reenval en bale mieliesis sowat5 rniljoen Ion per jaar 'n Is die meeste nog in hoa dagtempcrature het die verwagte Oes van 7,8 miljoen Ion sui dus vanjaar Sentraou se bestaan. mielie-oes in die Iaaste twee weke 'n die invoer van mielies onnodig maak. Meer as negenUg ver­ knou gegee, se die Mielieraad in 'n skillende. gew!ssoorte Na verwagting sal genocg wil rnielies verklaring. gcoes word vir menslike verbruilt Dil Genoeg micJies sal nictemin vir Suid­ sal 'n eindc bring aan dlc noodwendlj!c Afrika sc eie behoeftcs geoes kan word, vermcnging van wit en. geel mleli~s. se mnt'. Hennie Davel, hooCbc:stuurder Suid.Afrilaanse verbrulkers verities van die read. produkte wal van wit mic1ies gemaak is. Die r:l.ad se koopcratiewe agenlc sc jongstc oesskatting is tussen 7,8 en Mnr. Davel sc die heerscnde kli· 8 miljoen lon. Die :lmple1ike oessk:utlng maalstoeslande is nog baie vlocibaar van die Dcpartcment van Landbou sal Volle sekerheid oor vanjaar se mielie· CCI'! bler in die sclsoen bekend gemaak ocs sal dus ecrs laler an die ,CISllen word. verby !oln word. Table 1.1 Gross value of individual agricultural crops (excluding horticultural and animal products) 1975/76 . 1986/87. (Source: Abstract of Agricultural Statistics, 1988,83) . ,

YEAR 1975/76 1976/77 1977/78 1978/79 1979/80 1980/81 1981/82 . 1982/83 1983/84 1984/85 1985/86 . 1986/87

FI ELD CROPS RI 000

Maize 500 370 734 220 833 007 862 588 I 310 007 1 767 710 I 189 399 770 447 1 '055 446 2 039 747 2 070 763 I 735 329 Wheat 188 895 272 927 228 408 230 071 385 654 313 765 556 765 706 208 481 324 691 911 503 789 820 001 Oats 8 122 7 344 6 043 6 319 9 663 8 443 12 202 15 416 14 855 15 118 2 055 8 876 8arley 6 685 7 786 11 598 15 089 15 871 10 146 20 259 26 696 37 712 46 220 52 716 58 106 Rye 397 422 427 383 1 103 851 1 669 1 792 1 475 1 369 456 531 Grein sorghum 23 749 35 401 48 669 34 384 68 657 57 597 44· 248 43 310 92 520 118 596 88 965 97 191 lIay 91 408 132 173 183 929 259 343 362 780 391 431 524 430 616 181 814 407 698 660 660 461 700 689 Lucerne seed 1 064 1 371 2 432 I 762 1 211 293 1 355 4 253 5 022 4 247 1 061 3 536 Dry beans 22 834 34 614 36 901 30 484 47 182 74 173 54 144 59 173 80 754 89 186 89 056 81 610 Dry peas I 895 2 169 2 028 2 691 2 876 .5 150 4 033 2 566 1 795 3 287 3 814 4 871 Lentils 694 989 933 1 206 993 940 980 866 637 1 216 I 677 2 042 Sugarcane 232 195 250 829 263 655 288 899 334 171 347 618 444 942 489 095 450 472 610 978 578 386 658 143 Chicory root 3 708 3 181 3 177 3 721 I 482 5 023 7 804 3 487 8 783 6 305 11 267 12 288 rrobacco 5~ 441 81 126 79 368 100 181 80 032 68 ')54 118 028 181 131 178 17') 202 016 195 856 174 855 Cotton 12 869 40 044 50 373 62 342 80 868 74 120 45 850 41 667 57 509 92 394 95 650 129 459 Groundnuts 29 637 61 125 80 250 54 415 113 145 114 787 50 251 41 425 41 479 118 172 44 545 51 339 Sunflower seed 45 065 98 875 78 621 67 201 85 950 142 135 73 037 61 758 59 358 84 819 108 715 189 349 Soya beans 2 848 6 096 7 753 5 988 9 793 7 875 6 615 9 760 13 992 15 720 14 302 14 365 Wattle bark 9 761 11 353 14 548 14 833 12 747 12 074 15 838 15 654 15 267 18 160 20 490 2') 475 Phormium tenax 991 933 966 821 940 748 830 404 54 0 0 0 Sisal 2 199 2 396 2 709 3 075 4 195 4 434 4 001 3 731 4 309 4 392 4 310 4 000 pther field crops 6 318 . 6 400 7 000 6 500 7 200 8 208 8 400 9 870 10 100 10 500 10 916 11 200

TOTAL FIELD CROPS I 248 145 1 791 774 1 942 795 2 052 296 2 936 520 3 416 535 3 184 988 3 104 890 3 425 449 4 873 013 4 559 250 4 787 255

.... ex> o 181

APPENDIX II 182

Table 11.1 Some locational and HDF characteristics of Weather stations. (Source: WB40,1986)

Station name Altitude Latitude Longi- Mean long (m) (oS) tude (oE) term HDF

Armoedsvlakte 1234 26°57' 24°38' 1 , 1 Barberton 772 25°47' 31°01' 4,5 Belfast 1950 25°39' 30°02' 1,8 . Bethal 1663 26°27' 29°29' 2,8 Bothaville 1280 27°24' 26°30' 1,2 Brits 1158 25°35' 27°49' 2,0 Carletonville 1500 26°20' 27°23' 3,8 Carolina 1689 26°04' 30°07' 8,5 Doornlaagte 1473 26°37' 26°06' 2,7 Frankfort 1517 27°16' 28°30' 4,5 Gemsbokfontein j 1661 25°45' 29°40' 4,9 I Giyani I 472 23°19' 30°43' 0,7 Goedehoop 1025 24°11' 27°57' 1,9 IHoedsprui t 513 24°22' 31°02' 0,5 lIrene ! 1524 25°55' 28°13' 4,6 IJan Smuts 1692 26°08' 28°14' 3,6 ILetaba 215 23°51' 31°35' 0,1 Levubu 610 23°05' 30°17' 0,7

I Lindleypoortdam 1175 25°29' 26°42' 1,2 I Loskopdam 1009 25°24' 29°22' 1,6 Lydenburg 1439 25°06' 30°28' 1,9

Macuville 522 22°16' , 29°54' 1,1 Mafikeng 1280 25°51 ' 25°39' 2,8 Makatini 73 27°23' 32°11' 1,9 ,Mara 894 23°09' 29°34' 1,3 Marico 1078 25°30' 26°21' 1,2 Marnitz 932 23°09' 28°13' 0,9 Messina 538 22°21' 30°03' 0,6 Nelspruit 660 25°26' 30°59' 5,1 183

Table II.1 (cont.)

Station name Altitude Latitude Longi- Mean long (m) (OS) tude (OE) term HDF

Nooitgedacht 1694 26°31 ' 29°58' 3,8 Nylsvley 1090 24°40' 28°43' 1,4 Oudestad 953 25°11' 29°20' 2,0 Phalaborwa 427 23°56' 31°09' 0,4 Piet Retief . 1263 27°02' 30°48' 3,6

Pietersburg 1230 23°52' 29°27' I 1,5 Potchefstroom 1345 26°44' 27°05' 3,3 Potgietersrus 1116 24°11 ' 29°01' 0,5 Pretoria (Forum) 1330 25°44' 28°11' 1 , 2 , Pretoriuskop 600 25°10' 31°16' 0,3 Roodeplaat 1164 25°35' 28°21' 2,7 Rooibokkop 853 I 24°53' 29°22' 1,0 Rustenburg 1157 25°43' 27°18 3,0

Sandpan 794 23°14' 27°44' I 0,5 Skukuza 263 24°59' 31°36' I 0,6 Standerton 1581 I I 26°56' 29°14 I 4,4 Thabazimbi 1026 24°37' 27°24' I 1,4 Thohoyando 762 22°53' 30°29' 0,3 Towoomba 1143 24°54' 28°20' I 1,6 Tzaneen 716 23°50' 30°09' 1,0 Vereeniging 1440 26°41' 27°55' 4,8 Volksrust 1652 27°22' 29°53' 3,7 I Wakkerstroom 1777 27°21' 30°09' 2,9 Welverdiend 1140 27°28' 30°50' 3,2 Zwartkoppies 1514 26°21 28°04' 2,9 184

o o ;> .... on on N 8LOCJ: 145 N JI'OO'

3:H-+-t-+-t-t-+-H--r+-t-+--H++-+-+-I-+-+-H-4-~-4-~ l"Hf-++-H+H-t-+-t-t-+-1r++H+H++-~-I-4~ e

rc~i-:H-+-+-HH++Hr-+-+-+HH--4-+-.J-1-44-t-+-H-4--4880

f~ j07 ~o. 145/707

78

2ftt-HH++-HH++Hit++HH-++-+-1-44-+-+-H-+.-1

3

90 210 330 450 570 690 810 900

Figure II.I Allocation of station numbers to rainfall stations (adapted from SAWB, 1981) 185

Table B-2 Codes for thunderstorms used by the SAWB since 1982. * denotes hail codes. (Source: SAWE, 1981)

~ode Meaning

80 Rain shower(s), slight 81 Rain showe(s), moderate or heavy 82 Rain sho~er(s), violent 83 Shower(s) of rain and snow mixed, slight 84 Shower(s) of rain and snow mixed, moderate or heavy 85 Snow shower(s), slight 86 Snow shower(s), moderate or heavy *87 Shower(s) of snow pellets or Small! slight hail, with or without rain or rain 88 and snow mixed moderate or heavy -::-89 Sh o Wer ( S ) of hail, with or without slight rain or rain and snow mixed, not *90 !associated with thunder moderate or heavy , 91 Slight rain at time of observation 92 Hoderate or heavy rain at time of Thunder­ observation storm during *93 Slight snow, or rain and snow the prece­ mixed, or hail at time of obser­ ding hour vation but not at *94 Moderate or heavy snow, or rain and time of ob­ snow mixed, or hail at time of ob- servation servation , 95 Thunderstorm, slight or moderate, with- ~ out hail, but with rain and/or snow at time of observation *96 Thunderstorm, slight or moderate, with hail at time of observation Thunderstorm 97 Thunderstorm, heavy, without hail, but at time of with rain and/or snow at time of obser­ observation vation 98 Thunderstorm combined with duststorm or sandstorm at time of observation *99 Thunderstorm, heavy, with hail at time of observation 186

Table II.3 Time codes for hail (SAST).

CODE TIHE I CODE TIME

39 14:00 - 16:00 86 14:00 - 16:00 40 16:00 - 18:00 87 16:00 - 20:00 45 12:00 - 16:00 88 20:00 - 24:00 46 16:00 - 20:00 89 00:00 - 08:00

47 20:00 - 24:00 92 08:00 - 10:00 50 18:00 - 20:00 93 10:00 - 12:00 94 12:00 - 14:00 187

Table 11.4 Annual point HDF (1960 - 1986) at selected Transvaal stations. (Year given as Julian year e.g, 1960 = July 1960 - June 1961)

Station 19601 19851 61 61 6i 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 86

.4.rllloed.sylakte • 2 2 2 5 1 o 2 1 3 1 0* 0 2 1000000 o 0 o O· Barberton 2 4 2 1 2 1 1 1 3 2 2* o 3 0* Belfast o o 0 0 o o o 0 1 0 o 5 4 2* Bethal 1 0* 1 3 3 1 3 16 1 3 3 Z 1 1 1* 1 1 0 l' 2 2 2 2 Bothaville 1 o 3 3 6 1 2 1 1 1 1 1 3 4 o o o 0 2 o 0 0* Brits 1 2 I 2 3 16' I 6 1 15? 0* 2 1 2 3 2 3 1· 2 3 1 2 0 o O· Carletonville ·9 2* 3 3 3 2 o 0 o 0 o 1 0 0 o o 1 2 0 o 0 6 10 4 2' Carolina 9 8 6 12 12 7 10 14 9* 18 7 4* 1 4 2 3 o 3 1 6 0* Doornlaa, te 1* 7 3 3 1 3 5 1 1 1 o 2 0 0* 0 5 3' 4 11 Frankfort 3 o 1 6 5 o 2* 5 5 6 10 4 5 3 7 6 o 2 1 4 5 O· Ge::asbokfontein 1 o 0 0 o o o 8 13 1 15 40 1 2 o o 3 2* Giyani 0* 1 o o 1 1 1* 0* 0 o 2 o Gouldville o o o o 2 0 l' 5 3 1 1 2 1 1 1* 1 Z 1 2* Z 2 2 3 1 0 Groendraai 1 2 . 2·1 O' Hlatikulu 3 o o 5 o o 0 1 5 o 3 Z 0 3 0 0 0* Hoedsprui c 0* 0 1 0 0·' 1 Q a O· Lr e ne 1* 0 o 1 6 1 1 5 4 6 6 Jan Smuts '4 4 5 6 5 2 1 2 2 3 1 1 0 3 0 1 1 5 3 2 5 2 4 ~ 3' Letaba o o 0 0 o o o 0 0 1 o o 0 0* Levubu O· a O· o 1 2 0 0 O· 2 3' 0 0 1 o o 0 o 0 o I' Lindleypoort O· o 0 0 2 o 1 1 1 1 2 3 0 0 o o o i- Loskopdalll 1 1 1 2 12 o o 4 1 1 1 1 2 0 o o 1· Lydenburg 1 o 4 5 5 4' 0 0 1 o 1 0 0 0* 1 o ~acuyille o 0 0 0 o o 1 1 0 0 ~ait.keng I 1 1* 2 2- 4 0 1 2 3 o 4 3 3* 2*1 :iakacini 0* 0 1 0 1 1 6 1 ~alkerns I 2 c- C 3 1 1 2 5 2 0 o 1 O' 0 ~ara o o 0 1 1 1 0 2 0 1 O' 3 1 0 o 1 1* :-farico o o 0 0 o 1 o 0 0 0 o 1* :iarnicz o o 0 O' 1 o o 0 0 O' 0* O· 0*\ Xbabane 5 6 0 0 2 o 2 3 2 4 4 2 Xpisi o 4 1 0 o o l' o xe Le p ru.i e 3 4 4 4 3 o 2 2 2 0 1 6 o 4 3 2 ~h.lanio 3 1 1 0 4 2 o 0 2 2 o Yooitgedacht 3 2 0 3 9 3 1 1* 9 11 4 o 5 5 I :fylsvley 3 2 1 1 1 o O' Oudescad. o 4 3 l' Phalaborv" o o 1 ~ o o o 2 0 1 o o 0 o J I J' Piet Retief 9 4 3 0 o O' 0 3 0 4* 2 0 C· I P1etersburg o 2 2 1 1 o 0 0 0 1 o 0 1 .. I = 0* PliSs Peak o o 0 0 o o o 26 0 0 3 Pilanesberl 0* 6 0* 1 2* O· 0- O· O· O· 1 Pocchefstrooll 2 2 5 3 9 3 3 1 1 2 6 J I,.

Potg1ecersru5 o o 0 0 o o o 0 0 1 J 3 I o I o c 0'1 Precarla 5 4 2 6 2 3 8 6 0 3 o I 0 1 ~ 1· : I Pretoriuskap C o 1 0 o 1 o 0 1 0 I 1 Rooelepl.at 1 4 1 2 4 4 2 4 1 4 ! I Roo1bokltop o o 0 2 o 1 o 0 0 3 1.1 , Rustenburl 2 5 2 2 7 2 4 1 1 1 1 3 ro .1 Sabie 4 o 0 2 2 o 2 1 1 1 o Sandpaa O· 1 1- l' 1 1* 0 O· I Sitek1 0* o 0 0 o o o 1 0 0* o I' 0* 0* O· 9 O' I I Skukuza o o 0 0 o o a 1- O· 1 2* o 1 0 2 0 1 O' 0 0 4' o 0 .2 3·' Stand.ectoa. 3 3 1 1 9 5 5 4 3 0 3* 3 0* 2 1 6 9 O' 9 1 3 1 I Thabaziab1 1 10 o 1 1 1 1 1 1 1 :* :I: 002 o 6 1 1 1 0 o Ts",.10pele 0* o 1 1 9 rzaneen. o 2 0 0 o 3 0 2 7 1 0 1 1* 0 0 0 O' 0 3 1 1 1 0 0* Vaalhoeit o 2: 2- ',"ereen1i1nl 7 2 7 7 4 2 3 6 4 3 1 7 2 6 3 4 0 S· 'lolksc"J.sc o o 0 1 1 II 1 3 S 6 4 3 i 8 I) 1 :. 1- ...kkecscrooa 6* o 0 0 3 o 1 0 1 0 o 030 o 0* ~elyerd.1ead o 0 0 o 4 1 5 4 1 3 5 5 0 o O' "isselrode O· 0 O· 2* 0* 002 0 2 000 1 1 0 l' Z",artkopjes o o 0 0 o 8 3 7 5 6 4 1 0 1233010 o l'

1nco.plate 188

APPENDlxn PRINCIPAL COMPONENTS ANALYSIS

PRINCIPLES AND TECHNIQUES

Principal Components Analysis is concerned with the interrelationships - specifically the interconnectedness - between multivariate data (Rummel, 1970).

It is a well-known fact that in plotting the relationship between two variables and N observations, using an orthogonal co-ordinate system with one variable denoted as the abscissa and the other by the ordinate, the shape of the resulting scatter diagram indicates the strength of the relationship between the variables. If a circular shape results, there is no correlation; if a straight line results - a perfect correlation exists; and if an ellipse is produced, there is some correlation. This latter shape is the one which is most often produced and consequently will be dealt with here.

In the case of an ellipse, there is a major and a minor axis. The major axis is similar to a regression line measuring the greatest common variance among the two variables. The minor axis measures the variance at right angles (orthogonal) to the first and is consequently statistically independent of the first.

The degree to which the major axis accounts for the variance is a measure of the correlation between the two variables. The minor axis measures the residual variance, independent of the main axis. The principal axes are thus the minimum orthogonal dimensions required to linearly reproduce (define, generate or explain) the original data (Rummel, 1970). The major axis can be called Principal Component I and the minor axis, Principal Component 2.

A component may thus be written as.: PC1= all Xl+ c i z XZ+ •••••• +(l1 p x p where ex is the original variable and X is the Principal Component coefficient.

In reality there are seldom only two variables involved. The methods used are still analogous but not as easy to visualize. In effect, if there are N variables, N independent Principal Components (eigenvectors) can be obtained. 189

The analytical method usually proceeds in a stepwise fashion with the determination of the first Principal Component which explains the largest proportion of variance. The second component is orthogonal to the first and explains the second largest amount of variance and so on. The second component is determined from the residual correlation between variables once the influence of Component I has been removed.

rij.l = residual correlation

rij = original correlation between variables i & j

L1i = loading for variable on component I

Lij = loading for variable j on component 1 (Johnston, 1980).

It is usually found that the first few components (eigenvectors) account for the major portion of the variance. The dimensionality of the data matrix can be reduced if the components accounting for little variance are discarded as representing 'noise' or unimportant variance (Gregory, 1975; Dyer, 1979,40).

Many reference works exist which describe the analytical procedure in detail ego Rummel, 1970; Johnston, 1980 etc.

Variables, their correlations with each other, and Principal Components can also be represented graphically as in Figure II.2.

DEFINITION OF TERMS

Three indices can be extracted from the relationship between a component and the original variable. They are: 190

(a) the angle (a) between the component and each original variable

(b) the cosine of this angle. This represents the correlation between the component and the variable and is called the loading. The loading indicates which variables are involved with which eigenvector and to what degree

(c) the square of the loading. This indicates the proportion of the variance associated with the component. The sum of the loadings squared is called the eigenvalue and indicates the degree to which the new variable (component) replaces the original variables, i.e, -

Where

j=l

Ai = eigenvalue of component i ~j = loading for variable j on component i

Component scores measure the relationship between the observation and the component. Component scores can be defined as weighted summed values for the observations over the variable, the weights being the component loading, i.e. -

where Sik = score of observation i on n component k Sik l:DijoL = j k Dij = the standardized value for j=l observation i on variable j

Ljk = loading of variable j on component k

Data is usually arranged in the form of a matrix - i.e. a rectangular array of rows and columns containing variables. Table Il.S summarizes 4 different types of matrices used during PCA a b c

X 2

x,

',90' x) , X , 6 ,, X~ ](9"f'It If the correlation coefficient Is found C Is the first Principal Com- TIle second Principal Component ell is I between two, var Lab t e s (XI)' the angle lIollellt. Itll position 011 the orthogonal (lit right nngl e a) to tilE! between them '(0) cnn he calclllated hy f Ind-: dl.1grnm Is obtn Ined hy meuus of first and' Is calculllted from the Ing Hie arc t:ns of r , A" v.rr t ab l e s call the following procedure r residunl correlation matriK remain­ then he reprrsented IIrllphlcl1lly as llhoWtl J) The corr e l at l ous for each iug once the effect of the first above. varlahle are aunmed separately. component 11/15 heen nlmovE!.I. 2) The se values lire ad,lc.1 up Cor 1111 the varlnhles to I lud the t ota I Slim of co r r e lnt Ions. ]) The ratio Slim of correlat Ions for a Vllri ab Ie Is COIIl1.1 total sum of correllltiolls 1111<1 the answer converted hack Lo all Dllgle.

Figure 11.2 Geometrical representation of variables and Principal Components (after Johnston, 1978)

~ 1.0 -'-l 192

Table 11.5 Matrix elements

COLUMN HEADING ROW HEADING ELEMENT

1) variah1e observation raw data

2) variable variable correlations or covariance

3) component variable loading

4) component observation score

PRINCIPAL COMPONENTS ANALYSIS AND FACTOR ANALYSIS

Anyone familiar with mathematical techniques will recognise the similarity between Principal Components Analysis and Factor Analysis. In essence PCA is a type of Factor Analysis but whereas FA recognises an error term, PCA assumes that all of the variance in the original variables is being explained. This results in two obvious differences:

In the correlation matrix, the values along the prime diagonal equals unity in PCA but in FA the values are less than one.

There are as many Principal Components as there are variables, but there are fewer Factors than there are variables.

ROTATION

Often all the variables have high loadings on the first component. A technique, called rotation, was developed to redistribute and maximize the variance explained by the components without changing the total variance explained. The rotated components are simply linear transformations of the unrotated components. In Varimax rotation, orthogonality is maintained between the components, that is, each component still represents some situation that is uncorrelated with whatever circumstances are represented by the others.

Rotation often gives increased information about the data being analysed and tends to remove ambiguities (Dyer, 1975, 1979; Rummel, 1970). 193

SELECTION OF SIGNIFICANT COMPONENTS

As previously stated, there are as many components as there are variables. Since peA is a method of reducing the amount of data needed, one has to decide on how many components can be regarded as being significant. Preisendorfer and Barnett (1971) showed that the classical theory of selecting eigenvectors using statistical significance tests is invalid unless the ratio of the number of observations to the number of variables is of the order 10 - 100 or greater (Gray, 1981).

Decisions must therefore be made using some other criterion.

There are numerous methods of selection, for example:

(a) The number of new variables can be selected on the basis of some arbitrary, satisfactory percentage of the total variance accounted for ego 80% (Dyer, 1979).

(b) All the components with a variance (eigenvalue) greater than one can be selected. Kaiser, (1960) and Blasing (1975) both suggested that the last eigenvector should contribute more than any individual variable. This is equivalent to using eigenvectors with eigenvalues of more than one (Dyer, 1975; Gray, 1981).

(c) Beale, Kendall and Mann (1967) suggested that only eigenvectors with eigenvalues much smaller than the rest should be discarded (Craddock, 1973).

(d) Craddock and Flintoff (1970) advocated the use of the LEV (Log eigenvalue) graph. Here the natural logarithm of the eigenvalues are plotted against their ordinal numbers. * The truncation point is near the point at which the LEV graph becomes almost straight (Craddock, 1973).

When plotted graphically, the first few eigenvectors exhibit a curvilinear path but the rest follow a straight line. By repeated experiments it was observed that those eigenvalues WhICh follow the straight line (geometric progression), are the ones which represent noise. The eigenvalues which deviate from the straight line are thus the significant ones. 194

(e) Cattell (1966) suggested that if a graph is plotted with the percentage explained variance against component coefficients, those components which lie on the 'scree' or tail can be discarded. The first component on the 'flat portion' of the curve is the last significant component. This is known as the 'Scree Test' (Willmott, 1978).

THE DATA MATRIX peA usually proceeds from a basic data matrix which is then changed into a correlation or a covariance matrix. Any data matrix consists of a rectangular array of elements arranged in rows and columns. The columns are represented by the variables and the rows by cases. According to Rummel (1970) any phenomenon can be described along three dimensions, namely, entities, characteristics and occasions. Figure 11.3 shows Rummel's well known Data Box or Data Cube.

E.'lT!!IES eg. hall recording stacions

C1II\RACTDUSnCS of entities ie. \ variables. att.ributes \\ etc. - in this case HOF \ a datum cell eneicJ--'61

characteristi~ ?cClsion

Figure 11.3 Rummel's data cube or data box (after Rummel, J.970 ) 195

Although phenomena are three dimensional, only two dimensions can be analysed at a time.

''The specification, therefore, consists of cutting a slice out of the data cube. This slice will then define the data matrix to be analysed" (Rummel, 1970, 192).

The six possible data slices which can be obtained from .this data cube are summarized in Table II.6.

Table JI.6 Mode of analysis resulting from different slices ofa data cube.

CONSTANT VARIABLE CASES MODE OF DIMENSIONS (columns) (rows) ANALYSIS (technique)

occasion characteristics entities R (time)

entities characteristics Q

entity characteristics occasions P

occasions characteristics 0

characte- occasions entities T ristics

entities occasions S

APPLICATIONS

The most important applications of PCA are to:

(a) Bring about a useful reduction in the amount of basic data that need be processed, that is, in the number of variables needed (Bryson, 1960; Craddock and Flood, 1969; Craddock, 1973; Dyer, 1975; Tyson et al, 1975; 196

Willmott, 1978). This is achieved by replacing the measured and intercorrelated variables by a smaller number of uncorrelated variables.

(b) Group the original variables which are correlated with each other and which, when linearly combined on a component, form a Principal Component. In this way groups ofintercorrelated variables can be identified.

(c) Rewrite the original data set in an alternative form with characteristics not possessed in its original state. The many original variables among which intercorrelation exist can be replaced by fewer Principal Components which are weighted combinations of the former variables and which are independent of each other (Johnston, 1980).

A CRITIQUE OF THE METHOD

(a) Use ofthe correlation matrix:

Some geographers ego Craddock & Flood (1969), Willmott (1977, 1978) etc. advocate the use of the covariance - instead of the correlation coefficient matrix. During the standardization process in the calculation of the correlation coefficient, r, only deviations from the mean and not the mean itself are used. This means that entities with highly different normals or averages but similar patterns of deviation can be identified as being similar.

It is widely accepted that if one works with many variables with different units, the correlation coefficient must be used, but not necessarily if there is only one variable or if all the units are the same.

(b) Assumptions regarding the relationship between variables ./

The use of a correlation coefficient and PCA assumes a linear relationship between variables. This is not necessarily true in climatological work - the relationship is more often curvilinear or some power - or logarithmic function. 197

(c) Spatial autocorrelations

Climatological processes at one location are not independent of those at a neighbouring one. This could present a problem in areas where the same processes operate. However, Principal Components are by definition orthogonal to each other and hence highlight dissimilar climatic controls. A climatic classification based in PCA using climatic parameters would thus tend to maximize between-group differences and minimize Within-group differences.

(d) Achieving simple structure

According to Johnston (1981),the effectiveuse of PCA presupposes the clear identification of components by their loadings. In an orthogonal solution, all loadings should be either zero or one. However, such results are rarely achieved and the use of loadings of 0,7 or 0,8 may cause an instability in the results of the analysis.

"If one does get stability, the results are not necessarily valid ­ it may only indicate consistent overinterpretation" (Johnston, 1981,217).

Notwithstanding these criticisms, PCA has an advantage over the other orthogonal polynomial analytical methods in that the eigenvectors are derived from the data being studied directly and thus has a strong re­ semblance to the important features of the data (Stidd, 1967).

Also, numerous climatologists have applied this procedure and compared their results with those obtained when using other methods. A good

/ correlation has been found (Gregory, 1975; Willmott, 1977, 1978). Harrison (1983, 6) lends credence to the use of Principal Components Analysis by stating:

"....this (procedure) provides evidence supporting the validity of the interpretation of the component loading field.... Principal Components are representative of the true series." 198

The use of peA is facilitated by the availability of numerous computer programme 'packages' ego the BMDPAM* routine which was used in this research. 199

APPENDIXm 200 :

Table 111.1 Test for difference in area with < 2HD/annum: 1950· 1964 and 1960· 1986. (Method used: UNISA APPLIED STATISTICS STA 102 1978. Compiled by Prof CF Crouse in co-operation with Mrs L Strydom, Mr P J Becker and Dr C A van der Merwe, Dept Statistics and Operations Research, UNISA, Pretoria.)

This test was performed in order to determine whether the proportion of the Transvaal (in terms of surface area) which received fewer 'than 2 HD per annum during the 1950-1964 period differed significantly from that of the 1960-1986 period.

Hvpothesis

p proportion of TVL with <:2HD/yr 1950-1964 q proportion of TVL with <2HD/yr 1960-1986 Test statistics

z where 0 0 Y1 number of 1/2 x 1/2 blocks with 2 HDI annum 1950-1964 = 40 YZ as above but 1960- 1986 = 70 m = n = 100 blocks.

40 70 100 100

40 + 70 ( 1 _ 40 + 70)( 1 + 1) 200 200 100 100

0,55 (1 - 0,55) (0,02)

-4,286

Decision rule

Critical region (W) for HI : p = q is (z : z ~.- z or z >z ) Z = 2,58 for the 0,01 level of significance. (l/2 -- (X/2

Conclusion

_~ Since -4.286 -2,58, Ho is rejected and hence the size of the area with <: 2 HD/annum during the 1950-1964 period differed significantly from that between 1960-1986. 201

Table 111.2 X2 test for independence between HDF and latitude.

LAT 22-24 24-26 26-28 Total

1 8 4 0 12 2,6 5,2 4,1 Key r-s-l observed ~ expected frequency HDF 1-2,5 4 13 3 20 4,4 8,7 6,9

2,5 a 7, 16 23 5,0 10,0 8,0

Total 12 24 19 55

Hvpothesis

Ho : there is no relationship between latitude and HDF HI : there is a relationship Test: ,,(0 - E)2 X2 =LJ-E- where 0 = observed frequency E = expected frequency

X2 = 11,22 + 0,28 + 4,1 + 0,04 + 2,13 + 2,2 + 5 + 0,9 + 8 = 33,87 Decision rule 2 2 2: Ho rejected if W (X :X > X 2 2 fora. = 0,005 W (X :X > 7,879)1) Conclusion: since 33,87 > 7,879 H is rejected and hence there is a significant relationship bgtween latitude and HDF (at a. 0,005). 202

Table 111_3 peA loadings: Based on annual HDF characteristics of various hail recording stations in the Transvaal. (peA: correlation matrix: unrotated solution)

FACTOR 1 FACTOR 2 FACTOR 3 FACTOR 4 FACTOR 5 FACTOR 6 FACTOR 7 FACTOR 8

Jan Smuts STl9 78* 10 -17 0 16 -44" 18 9 Lydenburg STJ6 70* 33* 3 -32 6 0 -25 -13 Carletonville STl7 66* -17 - 6 49" 4 -17 - 4 37* Bothaville STl 62* 26 - 3 -18 19 48" -12 -24 Pietersburg ST44 60* -23 53" 7 14 -16 - 8 22 Groendraai ST42 58* 9 8 -36- 42* 17 41- - 1 Vereeniging STlO 58" -46* 29 -21 6 -17 5 29 Rustenburg ST26 53" 21 -10 7 -20 24 -43* -26 Nelspruit ST38 53" -36" 49* 13 -11 -19 - 8 11 Doornlaagte ST8 48" 14 8 -46* -26 16 35- 12 Standerton STU 42* 40" -12 6 9 40" -16 -31 Potgietersrus ST43 -52* 42" 24 16 24 -30 35- -17 Welverdiend ·ST5 -53" - 3 40" -B - 8 48" -13 44' Zwartkoppies STl8 -53* 36' 43' 6 -27 -13 2 -21 Skukuza ST41 -61' 10 7 6 42- 3 55- - 7 Volksrust ST3 -62* 4 -29 -31 35- 25 -32' - 5 Nooitgedacht ST12 - 5 80' -10 17 25 -17 - 6 16 Carolina ST21 17 80' -10 - 5 -12 -23 16 36' Bethal ST20 19 68- 49* - 9 4 - 6 5 -16 Halkerns STl4 -30 63- - 4 - 9 U -29 -45' 28 LoskopdaD ST35 48* 62- 5 3 33' 27 - 7 12 Pot.chefstrooa ST9 -45- 51' 35' 9 16 37 - 7 -14 GelisboJc.fontein STJO -30 -21 83' 3 2 - 1 -29 II Lindleypoortda.. STJ4 -13* 36- 82* 7 10 3 -27 - 4 Levubu ST49 -34* -12 70* 1 - 3 - 7 -27 -27 Tovoa.ba ST39 -18 19 53* 37' -14 26 - 5 31 Hafikeng ST25 39* 7 42* -39' 39* -30 -21 - 8 Nhlango ST6 -37- -36- -46- 32' 41- 1/ - 7 26 Roodeplaatda... ST29 - 5 51- -56' -20 -12 39- - 5 34 • Piet Retie'f STl3 6 -40- 5 81- 23 -19 11 8 Wakkerstroolll ST4 35- 2 -14 72- 15 31 - 4 31 Mbabanbe ST22 -11 29 5 64- -39- -37" 1 19 I Wisselrode STl6 2 32* 21 56' 38- 8 9 -34- Barberton ST32 3 33- -28 50' -40' U 8 18 Sabie STJ7 33" - 5 - 6 49' 33- 7 19 25 8rits ST27 -28 39- -30 -43' 18 -28 -32' 42' Phalaborva ST46 10 1 35' -59- - 6 -19 JJ 56- I I Harnitz ST47 - 7 -26 -29 - 2 68- - 9 7 -43- I Hlatikulu STl5 4 32- 0 32 65- -16 -29 27 Rooibokkop ST40 -29 - 5 -21 -23 64- -41" 13 5 Mphi ST23 -13 26 -14 17 -70- -32" - 5 -33' Hara ST48 -16 15 -14 9 0 73" - 2 -22 Siteki ST24 -40" 14 22 1 - 8 61" 53" .23 Belfast ST31 -27 -39" 35' - 1 8 43" -40" 15 Tzaneen ST45 -51" . 51" -10 - 1 -13 -57" -12 - 4 Araoedsylakte ST7 35" 4 9 8 -21 -29 55' -38" Frankfort ST2 -31 28 39" . 13 54" 5 54' -14 Pretoria ST28 13 30 6 6 -45" 23 50' 6 I Piga's Peak STJ3 -23 - 9 12 -34" 4 19 46' 35" I I SIgnIficant at ) 95% le.el. 203

APPENDIX IV 204

Table IV.I Year with the highest hail frequency (HHY) for each Transvaal station.

STATION HEAVIEST HAIL DAY STATION HEAVIEST HAIL DAY STATION HAIL FREQUENCY STATION HAIL FREQUENCY YEAR YEAR - BOTHAVILLE/BALKFONTEIN 1964 6 BELFAST 1971 5 FRANKFORT 1970 10 BARBERTON 1961 4 VOLKSRUST 1965 11 LINDLEYPOORT 1971 3 WELVEROIEND 1967 5 PILANESBERG 1961 6 ARMOEDSVLAKTE 1963 5 LOSKOPOAM 1964 12 DOORNLAAGTE 1952 7 LYOENBURG 1953 5 POTCHEFSTROOM 1970 6 NELSPRUIT 1971 6 VEREENIGING 1971 7 WARMBAO/TOWOOMBA 1970 3 NOOITGEOACHT 1959 11 NYLSVLEY/AMSTERDAM, CRECY 1950 3 PIET RETIEF 1960 9 ROOIBOKKOP 1974 4 ZWARTKOPPIES 1966 8 SKUKUZA 1970 2 JAN SMUTS 1963 6 GROENDRAAI/GOEDE HOOP 1953 BETHAL 1967 16 5 POTGIETERSRUS 1970 3 CAROLINA 1969 18 TZANEEN 1963 7 MAFIKENG 1962 4 PHALABORWA 1967 2 RUSTENBURG/BUFFELSPOORT 1964 7 LETABA 1969 1 BRITS 1969 15 MARA 1972 3 PRETORIA-FORUM 1953 6 GEMSBOKFONTEIN 1968 13 205

APPENDIX V 206

Table V.1 Summary ofrain and hail data for the Pretoria and Witwatersrand areas for 1977/78 - 1978/79 and 1978/79 - 1979/80, respectively.

PRETORIA AREA , TOTALS HAIL DAYS NON-HAIL RAIN DAYS

rrear Month ROF R No. HOF R No~ RI RI R No. RI RI (mm) st. (mm) st. I/O STI NHOF (mm) st. NHO ST/ HO NIlO

1977 07 0 0 0,0 0 - - 0 0,0 0 - - 08 5 79,1 23 0 0,0 0 - - 5 79,1 23 15,8 3,4 09 10 514,6 54 2 201,1 16 100,6 12,6 8 313,5 38 39,2 , 8,3 10 17 1 095,7 89 0 0,0 0 0,0 0,0 17 1 095,0 89 64,4 12,3 11 18 1 019,6 102 6 629,6 50 104,9 12,6 12 390,0 52 32,5 7,5 12 17 688,8 87 3 184.7 25 61,6 7,4 14 504,1 62 36,0 8,1 1978 01 25 4 799,5 - 202 2 1 103,5 18 551,8 61,3 23 3 696,0 184 160,7 20,1 02 23 1 315.6 .125 5 147,4 28 29,S 5,3 18 1 168,2 97 64,9 12,0 03 22 386,1 78 3 176,6 20 58,9 8,8 19 209,5 58 11,0 3,6 04 16 686,6 67 2 38,S 14 19,3 2,8 14 648,1 53 46,3 .12,3 05 3 7,0 10 0 0,0 0 0,0 0,0 3 7,0 10 2,3 0,7 06 1 6,6 10 0 0,0 0 1 6,6 10 6,6 0,7 ------1978 07 2 0,6 3 0 0,0 0 -- 0 0,0 0 -- 08 6 150,8 34 0 0,0 0 - - 0 0,0 0 -- 09 10 361,9 25 0 0,0 0 - - 10 361,9 25 36:2 14,5 10 22 802,0 109 10 . 705,6 71 70,6 9,9 12 96.4 38 8,0 2,5 11 24 662,2 106 9 344,2 48 38,2 7,2 15 318,0 58 21,2 5,5 12 18 539,4 95 8 252,0 49 31, 8 5,2 10 285,4 46 28,5 6,2 1979 01 20 829,3 114 6 327,5 44 54,6 7,4 14 501,8 70 35.8 7,2 02 18 358,1 113 2 43,2 15 21,6 2,9 16 314,9 98 19,7 3,2 03 17 910,8 201 2 27,6 8 13,8 3,5 15 883,2 193 58,9 4,6 04 11 327,2 43 5 282,1 32 56,4 8,8 6 44,9 11 7,5 4,1 05 7 176,2 40 0 0,0 0 - 0,0 7 176.2 40 25,2 4,4 06 0 0,0 0 0 0,0 0 0 0,0 0 - -

WITWATERSRAND AREA

TOTALS HAIL DAYS NON-HAIL RAIN DAYS

ear Month ROF R No. 1I0F R No. RI RI R No. RI RI (mm) st. (mm) st. HO STI N1I0F (mm) st. 1/110 STI HO NHt

1978 07 3 6,4 8 0 0,0 0 - - 3 6,4 8 2,1 0,8 08 11 498,6 88 2 180,0 28 90,0 6,4 9 318,6 60 35,4 5,3 09 15 1 122,1 137 2 420,8 23 210,4 18,3 13 701,3 114 54,0 6,2 10 22 2 469,8 321 13 2 300,5 272 177,0 8,5 8 169,3 49 21,2 3,5 11 29 2 254,2 270 15 1 014,1 159 67,6 6,4 14 1 240,1 111 88,6 11,2 12 25 2 328,5 268 5 1 026,7 73 205,3 14,1 20 1 301,8 261 65,1 5,0 1979 01 24 2 725,6 311 8 1 135,5 126 141,9 9,0 16 1 590,1 185 99,4 8,6 02 26 2 287,0 292 8 1 518,0 148 189,8 10,3 18 769,0 144 42,7 5,3 03 22 2 336,6 283 6 1 124,3 110 187,4 10,2 16 1 212,3 173 75,8 7,0 04 22 1 342,5 182 7 1 129,0 122 161,3 9,3 15 213,5 60 14,2 3,6 05 14 725,8 116 2 41,8 19 20,9 2,2 12 684,0 97 57,0 7,1 06 8 70,3 26 0 0,0 0 - - 8 70,3 26 8,8 2,7 ------'. ------1979 07 12 696,3 88 1 186,5 26 186,5 7,2 11 509,8 62 46,4 ·a,2 08 17 2 168,3 266 3 250,6 47 83,S 5.3 14 1 917,7 219 137,0 8,8 09 11 1 143,9 124 3 370,5 37 123,5 10,0 8 773,4 87 110,5 8,9 10 25 3 171,0 259 9 1 781,4 142 197,9 12,6 16 1 390,1 117 86,9 11,9 11 27 7 180,0 481 10 2 822,0 276 282,2 10,2 17 4 358,0 205 256,4 21,3 12 28 3 594,3 339 15 2 613,6 225 174,2 11,6 13 980,7 114 75,4 8,6 1980 01 29 7 426,9 473 10 4 837 ,4 231 483,7 20,9 19 2 589,5 242 136,3 10,7 02 28 4 601,2 417 6 1 712,3 124 285,4 13,8 22 2 888,9 293 131,3 9;9 03 18 2 222,2 230 10 1 757,4 160 175,7 11,0 8 464,8 70 58,1 6,6 04 10 738,1 119 4 448,9 64 112,2 7,0 6 289,2 55 48,2 5, 05 4 110,2 31 1 90,5 24 90,5 3,8 3 19,7 7 6,6 2; , 06 0 0,0 0 0 0,0 0 - - 0 0,0 0 -- 207

APPENDIX VI Table VI.I Dates of hail days, rain days and dry days in the Pretoria­ Witwatersrand area, 1981/82. Widespread, scattered and isolated precipitation categories have been distinguished.

HAIL DAYS NON-HAIL RAIN DAYS DRY DAYS

Dates W S I Iv S I

1981:0ct 1. !!.. 1!!. •. 3, 8, 9. 21 1 , 5, 10. 19 6. 7. 11 •. 12, 13, 20, 22, 23. 14. 15. 16, 17, 25 18, 27, 28, 29, 30, 31

:Nov 21 13, 16. 28 12, 12, 14, 17. 18. 15, 20" 24, 2 , 3 • 4 • 7 , 1 , 5 • 6. 8, 10, 22. 23 25. 27 9, 19, 26 11. 30 :Dec 1. !!., 2. ~' 29 30 14. 15. 23 6, 9. 22. 27 2, 10, 11, 1. 18, 25. 26 2• .§, 11, 12, 17, 19, li, 20, 31 11, 28 1982:Jan 1, !!., ~' ,2., n,l!! 11, 20 2, 3, \10, 5, 29 8, 18, 21, 19, 23, 26 2, 11, li, 11 24, 27 12, l2., 17, 25, 30, n : Feb ~, li, 25 9, lQ, 24 19 5, 11 , 16, 7, 8, 20, 21 , 1, 2, 3, 4, 12, 18 23 13, 14, 15, 22, 26, 27, 28

:Mar 15, li, 22, 18 4, 5, 21 19, 20 3, 6, 17, 24, 1, 2, 7, 8, 9, 23 29 10, 11, 12, 13, 14, 25, 26, 27, 28, 30, 31

v .. Widespread showers 1. e. 66,7% of stations received rain I .. Isolated showers i.e. 15% of stations received r a in S .. Scattered showers i . e . 15-66,7% of stations received rain Jn d e r lined dates indicate those occasions on wh Lc h hail occurred in the Pretoria area.

N o co 209

APPENDIX VII Table VII.t Hail damage indices: summary and meaning.

INDEX FACTORS WHICH AFFECT INDEX

Storm Plant Name Abbrev Formula Frequency Intensity extent stage (size)

4 R x 10 claims /R ~ ~ .; ~ Crop - loss C-L policies R Crop - damage claims/ ha ..; .; .; .;

N 1 . c alffis/ x x Frequency - Extent F-E Npolicies '''; .; R 1 . Int~nsity c al:ffis/ x ..; x .; - Susceptibility I-S Nclaims V

N ...... o .211 Table VII.2 Maize hail damage data given as the total for the six seasons: 1981/82 - 1986/87.

Average Maize prod. Size of Ave no. Long- R Rclaims N'claims policies Npo- ha in- area (ha) district HD term licies Bured 1986/87 (in 100s) 1982- mean (ha) 1986 HDF

Amersfoort 1 425 594,3 945 32 136 443,1 694 8 905,7 23 047 1 825 3,0 3,4 Balfour 4 t:97 983,2 1 950 102 961 858,0 1 610 26 849,9 42 995 2 372 2,5 3,9 Barberton 82 727,5 6 3 316 098,8 49 417,1 1 398 3 420 2,5 4,0 Belfast 1 164 476,7 694 20 777 772,0 379 4 541,9 12 658. 2 981 2,5 1,8 Benoni 32 425,3 23 924 964,8 17 235,1 1 763 274 4,0 2,8 Bethal 10 770 960,0 3 001 212 025 245,0 2 800 49 553,9 83 184 2 461 3,1 2,8 Bloemhof 4 721,5 8 5 896 933,7 77 2 077,0 17 772 1 788 5,0 1,7 Boksburg 40 709,9 5 1 375 920,0 21 398,1 4 923 194 2,0 3,9 Brakpan 41 740,0 65 7 827 610,0 84 1 538,1 4 444 228 3,0 2,8 Brits 112 410,1 105 5 126 896,6 355 678,7 3 407 2 592 2,0 1,9 Bronkhorstspruit 259 613,7 150 20 518 053,8 289 4 477 ,4 39 574 2 900 3,7 1,8 Carolina 1 041 927,6 542 29 877 529,0 520 6 852,6 28 796 3 991 3,0 7,0 Christiana 20 641,8 21 3 413 279,9 72 947,0 14 525 1 779 5,0 1,6 Coligny 1 431 647,5 • 504 125 612 233,9 1 453 32 562,9 91 799 1 612 6,0 2,1 Cullinan 101 \119,5 21 4 130 204,0 59 938,5 6 164 2 543 3,0 1,7 Delareyville 419 746,3 208 76 476 169,0 1 121 28 195,9 158 027 3 231 5,5 1,9 Delmas 3 221 057,4 756 78 293 570,0 1 036 17 037,1 37 470 1 238 4,0 2,6 Ermelo 4 380 676,9 1 954 127 838 586,0 2 044 33 600,5 62 200 7 726 2,8 3,9 Groblersdal 444 410,9 107 19 348 822,8 681 2 244,7 2 054 2 297 1,6 1,3 Heidelberg 966 335,7 413 28 482 453,1 441 7 111,2 23 568 1 260 2,3 3,9 Johannesburg 0,0 0 135 660,0 5 26,3 2 416 590 2,1 3,7 Kempton Park 45 863,6 14 5 189 700,6 92 1 150,1 2 088 437 3,3 2,9 Klerksdorp 1 763 785,3 1 020 152 040 146,9 2 478 44 851,2 108 555 3 735 4,0 3,0 Koster 837 433,3 664 88 050 836,2 1 257 23 846,4 61 881 2 862 5,0 2,1 x.rugersdorp 978 551,4 125 16 549 540,0 195 3 544,3 7 205 1 445 3,0 3,4 Letaba . 0,0 0 280 985,0 9 35,5 874 5 386 0,5 0,8 Lichtenburg 3 827 647,5 1 418 304 048 374,9 3 659 86 331,1 252 684 5 380 5,5 2,0 Soutpansberg 4 396,7 16 2 175 110,0 26 260,0 2 933 10 042 2,8 0,9 Lydenb'urg 732 325,7 663 42 283 063,9 1 265 7 406,4 10 525 5 062 1,9 1,5 Harico 75 111,1 40 9 629 133,8 216 2 871,4 18 520 6 584 4,5 1,3 Messina 0 0 230 560,0 5 14,3 18 5 625 0,0 0,0 Hiddelburg 5 923 203,0 1 177 146 130 649,2 1 422 33 400,5 103 043 5 969 2,5 1,7 Nelspruit 14 507,3 2 910 517,0 31 102,3 897 2 229 2,5 5,0 Nigel 1 439 761,0 336 42 358 238,0 555 10 127,9 23 353 982 3,5 2,9 Waterb~r8 584 257,6 133 55 455 177,0 906 10 896,9 26 347 15 716 3,5 1,8 Oberholzer 656 689,1 191 27 462 619,8 360 6 694,6 10 143 554 4,1 3,8 Pelgrimsrust 0,0 0 2 117 686,3 21 403,0 1 113 6 201 1,5 3,0 Pietersburg 172 018,6 78 10 032 379,0 249 1 217,0 7 306 5 328 1,5 1,4 Piet Retief 526 353,9 183 41 913 870,0 t 041 8 252,9 11 912 4 786 2,5 2,9 Patchefstroo. 3 585 741,6 1 969 98 557 392,0 2 313 28 409,2 68 761 3 699 2,3 3,5 Potgie.tersrus 642 310,2 152. 42 213 200,0 949 7 051,2 19 849 15 194 3,0 0,7 Pretoria 275 512,0 75 4 897 995,0 85 861,5 4 956 1 379 3,5 2,8 Randfantein 2 585 699,0 404 72 784 099,0 986 16 743,3 24 984 831 5,0 2,9 Roodepoort 11 562,9 6 1 362 360,0 22 275,0 1 503 239 2,5 3,0 Rustenburg 133 114,7 15 3 627 414,4 114 759,8 3 659 1 897 4,5 2,3 Schveizer-Reneke 375917,9 206 88 508 546,0 1 186 31 611,6 156 719 4 255 5,0 1,9 Springs 678 375,5 99 6 107 684,0 79 1 307,3 4 690 347 4,0 2,7 Standerton 4 655 745,0 2 717 177 011 790,1 3 243 48 877,5 69 440 4 849 3,0 4,0 Swartruggens 39 289,6 3 3 274 230,0 61 Hl,l 7 656 1 828 4,5 1,3 Thabazi.bi 52481,7 24 7 363 425,9 139 891,3 13 914 11 356 4,3 1,5 Van der Bylpark 589067,1 401 25 934 34'3,0 549 8 106,5 15 358 967 2,3 3,4 Ventersdorp 4 460 383,3 2 274 198 101 046,2 3 055 53 158,8 91 936 3 530 5,2 2,8 Vereeniging 266 769,5 160 13 878 332,0 202 4 067,5 21 584 1 613 2,0 3,9 Volksrust 425 364,5 365 13 539 364,0 367 4 317,3 17 013 1 960 2,8 3,6 Wakkerstroo. 299 273,3 97 18 593 796,0 246 4 486,1 9 507 2 450 2,5 3,1 War.bad 1 111 231,7 115 28 143 971,0 520 4 154,8 17 890 4 621 2,5 1,5 Waterval-Boven 8 386,5 25 640 359,0 26 95,7 570 1 021 2,6 6,0 Westonaria 412 836,7 109 25 816 842,0 196 6 053,9 5 502 570 3,5 3,5 Wi~bank 1 339 738,0 396 66 293 299,0 669 14 093,2 37 094 2 878 2,8 1,9 Wltrivier 0,0 0 153 733,0 5 13,4 299 1 600 2,3 0,4 Wolaaransstad 955 492,7 381 103 224 496,7 1 572 32 482,0 148 242 5 272 4,3 1,8 212

Table VII.3 First maize production estimates for the 1986/87 season. (Source: Maize Estimates: 1987 Area and Crop: RSA· Development Regions, Dept. of Agricultural Economics and Marketing, Pretoria) ..

DISTRICT PRODUCT. DISTRICT PRODUCT. EST. (TONS) EST. (TONS

Alberton 0 Middelburg 374 273 Amersfoort 48 699 Nelspruit 4 958 Balfour 113 386 Nigel 117 759 Barberton 15 300 Oberholzer 56 766 Belfast 80 851 Pelgrimsrus 340 Benoni - 70 Piet Retief 93 170 Bloemhof 19 294 Pietersburg 24 545 Boksburg 1 420 Potchefstroom 52 653 Brakpan 1 462 Potgietersrus 31 75 Brits 9 948 Pretoria 11 330 Bronkhorstspruit 129 209 Randburg 38 Carolina 82 382 Randfontein 56 555 Christiana 13 463 Roodepoort 0 Coligny 166 953 Rustenburg 4 583 Cullinan 6 801 Schweizer-Reneke 270 205 Delareyville 144 941 Soutpansberg 7 771 Delmas 143 436 Springs 2 471 Ermelo 291 641 Standerton 175 527 Germiston 22 Swartruggens 3 290 Grobblersdal 7 052 Thabazimbi 622 Heidelberg 69 274 Vanderbijlpark 31 666 Hoeveld & Bethal 299 182 Ventersdorp 213 975 Johannesburg 54 Vereeniging 28 411 Kempton Park 18 642 Volksrust 16 536 Klerksdorp 181 403 Wakkerstroom 19 161 Koster 113 167 W.armbad 32 576 Krugersdorp 2 719 Waterberg 87 241 Letaba 7 744 Waterval-Boven 30 Lichtenburg 333 095 Westonaz:-ia 2 504 Lydenburg 65 786 Witbank 185 834 Marico 12 864 Witrivier 789 Messina 1 170 Wolmaransstad 278 565 Wonderboom 14 213

SOUTH AFRICAN MAIZE PRODUCTION

Area planted with maize 15000 (excluding homelands and independant states) • Production ,I ,I (includil19homelands and independant states) 14 000 " ," , 5000 , , 13 000 , I, I 12000 I , ""J , , ::J , I 11000 0 1 ~ •I , s0 0 I 0 , I 10000 6 0 • I Z n • Z ~ 1\ II I 9000 II .' I I <: ,, ,I C lJ.J ~ C c: 0 4000 I -c ,,,I,, " , 8000 = ,, I I , 'I ,, I , 7000 " ,I I , , ~ I ,I \ , ,-~ ,I" I I , , 6000 I I ,I,~-.I ,' , , \ " , I I ,,,,,. " , 5000 ,~,. ~. , I "., /. " ~.. " '... 4000 3000 3000

~ 'D co 0 C\l <0 co 0 C\l o .,. LO LO <0 <0 <0 <0 <0 ..... e-, CD co <, <, <, <, <, ~ ~ "­ <, LO ..... OJ M 0> "- OJ M LO LO LO ;:0 <0 c.o <0 to r:: ..... CD $!l PRODUCTION SEASONS FigureVII.l SouthAfrican maize production (tons) and maizeproduction area (ha) 1955/56 - 1983/84 (Source: 1988Abstract of Agricultural Statistics) 214

1955/56 1955/66 1975/76 1965/66

Figure VII.2 Maize Price Index time series 1955-1985 (Source: 1988 Abstract of Agricultural Statistics) 215

VII.3 x2 test for significance of relationship between I-S and F-E indices

Hypothesis: H o there is no relationship between 1-5 and F-E there is a relationship between 1-5 and F-E indices

Test: Observed values

1-5 classes 1-S classes 1- 2 3 4 5 1+2 3+4

1 1 0 1 2 4 1+2, 1 10 11 F'FE

2 0 0 6 3 1 4+5 11 9 20

4 __ F-E 3 2 3 1 2 12 19 31 classes Expected values 4 2 3 1 4 2 , 4,26 6,74 5 1 5 4 2 1 7,74 12,26

(1-4,26)2 (10-6,74)2 (11-7,74)2+(9-12,26)2 = 4,26 + 6,74 + 7,74 12,26

= 2,5 + 1,58 + 1,37 + 0,87 6,32 2 Critical value: -x.. 0,025;1 5,02

Decision: Reject H: There is a less than chance of the o 2,5% frequency distri bu tion being random. "I'hu s there is an inverse relationship between the F-E & I-S indices. 216

REFERENCES 217

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Dr. G Held, Division of Earth, Marine and Atmospheric Sciences and Technology, CSIR, Pretoria.

Prof. J Van Heerden, Department of Meteorology, Faculty of Engineering, University of Pretoria.