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Water SA

On-line version ISSN 1816-7950
Print version ISSN 0378-4738

Water SA vol.35 n.5 Pretoria Oct. 2009

 

Heavy daily-rainfall characteristics over the Gauteng Province

 

 

Liesl L Dyson*

Department of Geography, Geoinformatics and Meteorology, Geography Building 2-12, University of Pretoria, Pretoria 0001, South Africa

 

 


ABSTRACT

Daily rainfall over the Gauteng Province, South Africa, was analysed for the summer months of October to March using 32-yr (1977 to 2009) daily rainfall data from about 70 South African Weather Service stations. The monthly and seasonal variation of heavy rainfall occurrences was also analysed. Three 24-h heavy rainfall classes are defined considering the area-average rainfall. A significant rainfall event is defined when the average rainfall exceeds 10 mm, a heavy rainfall event when the average rainfall exceeds 15 mm and a very heavy rainfall event when the average rainfall exceeds 25 mm. January months have the highest monthly average rainfall as well as the highest number of heavy and very heavy rainfall days. The month with the second-highest number of heavy and very heavy rainfall days is February followed by March and October. December has the second-highest monthly average rainfall and the most days with rain. However, it is also the month with the lowest number of heavy and very heavy rainfall days. The highest 24-h rainfall recorded at a single station during the 32-yr period was 300 mm in December 2006. However, rainfall exceeding 115 mm at a single rainfall station in the Gauteng Province is very rare and does not occur every year. January months receive these events more than any other month but this only transpires in approximately a third of years. The central and north-western parts of the Province experience the most events where the rainfall at a single station surpasses 75 and 115 mm. With regard to seasonal rainfall, the 1995/96 summer rainfall season had the highest seasonal rainfall during this 32-yr period followed by the 1999/2000 season. The 1995/96 season had above normal rainfall in both early and late summer but the 1999/2000 season was dry in early summer and very wet in late summer. Significantly high seasonal rainfall is associated with above-normal rainfall in late summer.

Keywords: widespread heavy rainfall, Gauteng, daily rainfall, weather forecasting, summer rainfall, climatology of heavy rainfall


 

 

Introduction

Rainfall resulting in flooding occurs from time to time over the Gauteng Province. The heavy rainfall events may take place over most of the province and last for days, resulting in widespread flooding and disruption of infrastructure and even loss of life. Examples of such events took place during January and February 1996 (De Coning et al., 1998) and February 2000 (Dyson and Van Heerden, 2001). However, heavy rainfall may also occur in isolated areas over the Gauteng Province from so called mesoscale weather systems, resulting in flash flooding. In these instances the heavy rainfall may be of a short duration (but intense) and is often associated with strong winds and hail (Viviers and Chapman, 2008; Ngubo et al., 2008).

Gauteng Province (hereafter Gauteng) is situated on the interior plateau of South Africa and receives most of its rainfall in summer. The annual average rainfall in Gauteng varies between just over 700 mm on the Witwatersrand (approximately 1 700 m a. m. s. l.) and just over 600 mm north of the Magaliesberg (approximately 1 100 m a. m. s. l.) (SAWS,1998). Most of Gauteng falls into the climate region: Moist Highveld Grassland, and is relatively cool with average annual maximum temperatures of about 22°C in the south but increasing to 25°C in the north. There are about 100 d with rain in Johannesburg and 85 in Pretoria (Kruger, 2004). The extreme northern parts of Gauteng fall into the Central Bushveld climate region. According to Kruger (2004) the maximum rainfall over Gauteng occurs during the December and January months.

Gauteng is responsible for over a third of South Africa's Gross Domestic Product (GDP) and a tenth of Africa's GDP. Geographically, Gauteng is the smallest province in South Africa, covering approximately 16 500 km2, but nearly 20% (9.6 m.) of South Africa's population reside in Gauteng. It is estimated that Gauteng will be home to 14.6 m. people by 2015. There were 405 informal settlements in Gauteng in 2006 (Gauteng Department of Housing, 2006); the overcrowding in these settlements has reached extreme proportions with as many as 24 people sharing a living space of approximately 40 m2 (Beavon, 2004). The vacant land on the river banks in the informal settlements has also become populated by shacks and these communities are especially vulnerable to flash flooding. This study focuses on Gauteng due to its importance to the economy of South Africa and its high population density, but also due to the availability of observed meteorological data in the province. In addition it is also one of the regions for which weather forecasts are issued on a daily basis.

In an attempt to understand more about the characteristics of heavy rainfall over Gauteng, observed daily rainfall data were analysed for the summer months (October-March) for a period of 32 years. In this paper early summer refers to October to December and late summer to January to March. The rainfall at individual stations is investigated, but the emphasis is on the areal average rainfall over Gauteng. One of the forecasting challenges for Gauteng is that the type of weather system responsible for precipitation, and indeed heavy rainfall, differs considerably from early to late summer. During early summer the atmosphere has a distinct extra-tropical nature when weather systems such as cut-off lows are frequent (Singleton and Reason, 2007). However, in late summer (January and February) tropical circulation systems are much more prevalent over South Africa (Dyson and Van Heerden, 2002). In this paper the emphasis is not on the weather systems responsible for the heavy rainfall but rather concentrates on rainfall statistics over Gauteng. The results from this paper form the basis of ongoing research investigating the atmospheric variables and synoptic circulation patterns associated with heavy rainfall over Gauteng.

An example of heavy rainfall 'climatology' in the scientific literature is by Maddox et al. (1979), who described aspects of flash flooding over the USA. More recently, Brooks and Stensrud (2000) created an hourly rainfall climatology over the USA, followed by Schumacher and Johnson (2006), who described characteristics of extreme rain events over the eastern two-thirds of the United States. They found that extreme rain events (where the 24-h precipitation total at 1 or more stations exceeds the 50-yr recurrence amount for that location) are most common in July, and that in the northern USA these events transpire almost exclusively in the warm season. They also concluded that most of these events (66%) are associated with mesoscale convective systems while synoptic and tropical systems play a larger role in the south and east. Chen et al. (2007) used a similar statistical approach to investigate heavy rainfall in Taiwan. They found that heavy rainfall occurs with a pronounced afternoon maximum over Taiwan and that the orographic effects are important in determining the spatial distribution of heavy rainfall.

A better understanding and knowledge of the climatology of heavy rainfall will facilitate the forecasting of these extreme events. The main aim of this paper is to make weather forecasters aware of how likely heavy rainfall events are over Gauteng during a particular summer month. Understanding the spatial and temporal distribution of heavy rainfall events is a key aspect in furthering this aim. As flood-producing heavy rainfall events are infrequent, knowledge of the climatology of these events could therefore also aid inexperienced weather forecasters, by providing guidance as to how likely heavy rainfall might be during a particular time of the year.

The data used in this analysis are discussed first and some of the problems encountered in the dataset are highlighted. Information is consequently supplied about the seasonal, monthly and daily rainfall characteristics in Gauteng. Three different heavy rainfall classes are defined for average daily Gauteng rainfall and the monthly characteristics of these events are examined. Lastly, heavy rainfall characteristics at individual stations are discussed for each of the summer months.

 

Data and methods

Daily rainfall data were obtained from the South African Weather Service (SAWS) for all summer months (October to March) from 1977 to 2009. SAWS rainfall stations report 24-h accumulated rainfall in the morning (0800 South African Standard Time). All the rainfall stations over Gauteng were investigated for their suitability for use in this analysis. Only stations where data were available for at least 75% of the period were considered. There were 58 stations with a record length spanning between 90-100% of the period and another 10 stations with record lengths between 75-90%. However, data from selected rainfall stations with records spanning shorter parts of the period were also included. This was done mainly to capture data in cases where rainfall stations were replaced by new stations, with only slightly different locations, within the period under consideration. An example is the rainfall station at OR Tambo International Airport which closed on the 31 May 1989 while another station opened on 1 June 1989 at almost the same location. Data from both these stations were then used in the analysis. Data for 5 locations were combined in this way resulting in a total number of 73 stations available for analysis over Gauteng. Not all of the rainfall stations were operational every day (i.e., there are discontinuities within the time series of some stations) and consequently the number of rainfall stations which were available for analysis varied between 55 and 73 on any given day within the 32-yr period.

Figure 1 depicts the location of the SAWS rainfall stations over Gauteng. The rainfall stations are generally spatially well-distributed throughout the province, with the exception being the north-eastern extremes where no rainfall stations were available. There is a higher concentration of rainfall stations in the major metropolitan areas of Gauteng (Pretoria in the north and Johannesburg, about 50 km from Pretoria to the south).

 

 

Quality control of rainfall data

The quality of the rainfall data used, especially in such a large dataset, is of significant concern, and a considerable amount of time was spent performing quality control on the data. Some obvious errors were easy to identify and were removed from the data set. However there were some questionable data values where it was close to impossible to determine the reliability of the observations. The raw rainfall data from SAWS include possible error information, with the daily rainfall values labelled as 'Normal', or 'Error' or 'Accumulated' (if accumulated over more than 1 d). If the rainfall value was labelled anything other than Normal it was not used in the analysis for that particular day. As this research focuses on heavy rainfall it was important to have confidence in the high 24-h rainfall values. Brooks and Stensrud (2000) explain how difficult it is to distinguish between 'rare interesting' rainfall events and 'bad data', as these often look similar. Therefore all rainfall events where 24-h rainfall at a specific station exceeded 115 mm were investigated for possible errors. As will be explained later, 115 mm was identified as a 'single very heavy rainfall event' and represents the 99th percentile of daily maximum rainfall over Gauteng. It does happen from time to time that rainfall which was accumulated is not identified as such in the raw data set. This was relatively easy to identify in the data set when there was missing data for 1 or more days followed by a day reporting very high rainfall. This high rainfall value was then rejected only after comparison with rainfall from surrounding stations, in the process discussed below.

A 2nd set of errors removed from the data was all the cases where a station reported very heavy rainfall for several days in a row while there was no indication from surrounding stations that this did indeed occur. When rainfall at any station over Gauteng exceeded 115 mm on any particular day, the rainfall values at other stations over Gauteng were also analysed. If there were other stations reporting significant rainfall on that day or if there was a high percentage of rainfall stations over Gauteng reporting rainfall the value was accepted as correct. The last error check was to compare the events remaining, after the elimination of events considered by the previous checks, against other meteorological data and journals such as the SAWS newsletters and website and archived Meteosat 2nd Generation data. This was done in order to identify if there was a physical cause for a heavy rainfall event to occur. The real difficulty was in attempting to detect errors in the much larger number of rainfall events where the daily rainfall at a single station was more than 50 mm but did not exceed 115 mm (later defined as a 'single significant rainfall event'). There were too many of these events to hand-check and it would be difficult to determine the accuracy as there were no other data with which to compare it. It is therefore possible that the daily rainfall dataset created as part of this research does contain some errors which may have led to some inaccuracies in the results. However, the impact of this would be limited as the research focused on the heavy rainfall events.

 

Calculation of average daily rainfall

Using the rainfall data from the selected stations, an average daily rainfall value for Gauteng was calculated. Additionally, the percentage of rainfall stations recording more than 0 mm of rainfall was calculated and the highest rainfall measured at any of the stations was also noted. The average Gauteng rainfall was computed using a weighted average method proposed by Tennant and cited in Marx et al. (2003). This method takes the geographical position of each station relative to the other stations into consideration. The following weighting function was applied to the daily rainfall values of all the stations:

where:

Wgt = weight assigned to station
= sum of the distance between the specific station and all other stations
rmax = maximum distance between the specific station and any other station
N = total number of stations

When rainfall stations are distributed evenly over an area, this method renders results which are very close to the mathematical average (the total rainfall at all the stations divided by the number of rainfall stations). The use of the weighting method becomes important when the rainfall stations are not distributed evenly over an area, as is the case for Gauteng. A rainfall station which is geographically distant (close) to other stations will have a larger (smaller) weight factor and will therefore contribute more (less) in the computation of the average rainfall. The stations with the largest weights were over the western extremes of Gauteng (Fig. 1). Hekpoort in the north-west had a weight of 0.573 and Welverdiend in the south-west 0.568. Other stations with weights larger than 0.5 were Rus De Winter in the extreme north (0.55) and Devon (0.506) and Nigel (0.507) in the south-east. These stations contributed more to the calculation of the average rainfall than the stations over central Gauteng such as Irene (0.390), Pretoria (0.414) and OR Tambo International Airport (0.398). The results from the 2 averaging methods are very similar to those of the weighted average method, generally giving slightly higher daily average values. Of the 5 673 d analysed, the weighted average method produced higher (lower) values than the mathematical average on 753 (560) d. The largest difference occurred on 27 January 1978 when the weighted average was 67 mm and the mathematical average 59 mm. This was a particularly wet day as 23 stations measured more than 50 mm of rain and 11 more than 115 mm. Stations over western Gauteng in particular measured high rainfall values, e.g. Randfontein (100 mm), Krugersdorp (116 mm) and Hekpoort (66 mm). From Fig. 1 one can see that these stations also have higher weights in the calculation of the average and therefore the weighted average was higher than the mathematical average for this day. On 18 December 2006 the weighted average rainfall was 18 mm but the mathematical average was 23 mm. On this day there were only 4 stations with rainfall of more than 50 mm over southern Gauteng, but with 300 mm at Viljoensdrift. Due to the isolated nature of the heavy rainfall, the weights assigned to the stations resulted in the weighted average being lower than the mathematical average; the influence of the extreme rainfall at a single station is therefore de-emphasised.

The average Gauteng daily rainfall was henceforth used to calculate the average Gauteng monthly rainfall and the data standardised in order to identify wet and dry months. Moreover, the rainfall at the individual rainfall stations was investigated in order to identify those locations in Gauteng where heavy rainfall occurs most frequently. This was done by dividing Gauteng into eight 0.5° by 0.5° grid boxes and calculating the number and percentage of stations that exceeded certain thresholds for each grid box for each 24-h period considered.

 

Defining heavy rainfall

An extreme precipitation event is usually defined by using a daily amount exceeding a certain threshold (Zhang et al., 2001). However, different threshold values apply for different parts of the world. One approach is to define heavy rainfall by considering when the areal average rainfall exceeds a particular threshold. For example, Houze et al. (1990) define a 'major rain event' as one in which more than 25 mm rain falls over an area greater than 12 500 km2 in 24 h. In a South African study, Poolman (contributing to Dyson et al., 2002) defines a heavy rainfall event when more than 25 mm occurs in 24 h in an area of at least 20 000 km2.

The Gauteng Province is approximately 16 500 km2 in size. When the average daily rainfall is at least 25 mm over Gauteng it would fall into the major rain event definition provided by Houze et al. (1990). However, analysis of the daily average rainfall data over Gauteng for the 32-yr period reveals that 25 mm is exceeded only 1% of the time. Over the 32-yr period the daily average rainfall exceeded 25 mm on only 65 occasions. In order to capture 10% of the heaviest rainfall events, a 'significant rainfall event' is defined here by using the 90th percentile, which is 9 mm. A further classification is made, with a 'heavy rainfall event' defined as the 95th percentile, in this case 13 mm, and a 'very heavy rainfall event', similar t