Data Collection

The research for this study followed both a quantitative and a qualitative methodological approach. Primary quantitative data include meteorological (rainfall) data, evaporation data, water flow data and water quality data for the period from July 2000 to June 2012. National land cover data were also obtained for the years 1994, 2000, 2005 and 2009.

4.1.1 Secondary, Tertiary and Quaternary Catchments

The Upper Vaal WMA was subdivided according to the DWA’s drainage regions into secondary catchments and tertiary catchment areas as discussed in Chapter 2 (DWAF, 2004a). Secondary as well as tertiary catchments were investigated as to establish a holistic view of the Upper Vaal WMA’s water quality status as well as land cover. This research quantified and predicted hydrological changes in terms of water quality in the event of land cover change for all tertiary catchments as well as selected quaternary catchments in order to limit the generalisation or bias of these relationships as well as to ensure accuracy of predictions.

A total of 8 quaternary catchments were selected for this research. Del Barrio et al. (1996) have

process which can be subjective and rely on intuitions which are not always made clear. Due to the present significant anthropogenic impacts or influences on the Upper Vaal WMA as described in Chapter 2 and 3, this research selected quaternary catchments according to anthropogenic pressure. The clearance of land, agricultural development, urbanisation, industrial point discharges, water abstraction, flow alterations, artificial barriers as well as the introduction of species have resulted in noticeable changes to biological characteristics of the freshwater ecosystem (Chadderton et al., 2004). These human factors can therefore be viewed as disturbance pressures which affect the natural heritage of a region. By determining the level of pressure will in turn provide an indication of the intensity of human-induced disturbances affecting the natural conditions of a region. Systems or regions characterised by low levels of human-induced disturbance pressure are most probably characterised by higher levels of natural conditions.

A total of seven pressure variables can be used to determine the level of anthropogenic pressure within a region as listed in Table 4.1. Due to this research focussing on the intricate hydrological responses in terms of water quality in the event of land cover change, water quality was deemed as to be the dominant indicator of anthropogenic pressure and was consequently the primarily data sources used in the selection of quaternary catchments.

Quaternary catchments were ranked according to their type of catchment cover, level of urbanisation, land use intensity, the presence of being downstream of a dam and lastly major point source discharges. The fish passage and exotic fish variables were not included in the ranking process due to it falling outside the scope of the research. This research consequently selected the top 10% of the quaternary catchments within the Upper Vaal WMA according to the mentioned variables. These selected quaternary catchments were therefore selected due to their high levels of anthropogenic pressures.

Table 4.1: Anthropogenic pressure measures developed for waters of national importance (Chadderton et al., 2004).

VARIABLE IMPACT

Catchment cover Loss of natural- and riparian vegetation as well as in-stream cover will lead to an increase of sedimentation and stream temperature as well as modify the hydrological function of the catchment.

Urbanisation Changes in hydrological function, increased levels of heavy metals, sedimentation, loss of riparian cover and connectivity, lowered water table.

Land use intensity Increased levels of nutrient sedimentation and pesticides; loss of cover, riparian connectivity and shade; increased channelization; changes in hydrological functioning.

Fish passage Barrier to fish passage.

Downstream dam Impeded sediment transport, alterations in flow and fine sediment deposition rates; loss of habitat.

Exotic fish Predation and competition with native communities, trophic cascades, altering physical habitat and water quality.

Major point source discharges Changes in Dissolved Oxygen, pH, nutrients; increases in toxic heavy metal levels, biological oxygen demand, pesticides, flocculants, biocides and hydrocarbons; increasing water temperatures.

A description of the data collected and used for this research now follows.

4.1.2 Data

The data that were collected were therefore structured and developed into databases according to these catchments in order to give sound results on various catchment scales and for the Upper Vaal WMA as a whole.

The data in question were obtained from various sources. The locations of the sampling points were established to determine firstly, whether these places were indeed located within the Upper Vaal WMA, and secondly, whether the distribution of these sampling points was adequate for the analysis. The Geographical Information System (GIS) used for this research included Arc GIS 10.1 software package. This GIS was used to map all of the mentioned variables in the Upper Vaal WMA and its various catchments. The distribution of these sampling points, especially the water quality sampling stations, was found to be suitable for the study as the sampling points were adequately represented within the Upper Vaal WMA. A description of the data used as well as illustrations of the relevant sampling stations locations now follows.

4.1.2.1 Rainfall

The rainfall data were obtained from the South African Weather Service for the period of July 2000 to June 2012. Daily readings at 08:00 a.m. were used to calculate the average value for the month. The average monthly rainfall figures were obtained for each of the nine weather stations listed and described in Appendix 1, while their locations in the study area are indicated in Figure 4.1. Sample stations which had long periods of no recorded data were excluded to ensure good quality data and sound results.

4.1.2.2 Water Flow and Evaporation

Water flow, as well as evaporation data, were obtained from the DWA for the period, July 2000 to June 2012. The daily average water flow rate in cubic metres per second was measured at all of the river water flow sample stations over this time period. Monthly water flow values were calculated for each the stations listed in Appendix 1 and their locations are shown in Figure 4.2.

Monthly evaporation values were calculated for the stations listed in Appendix 1, with their locations shown in Figure 4.3. In total, 42 river water flow stations and 14 evaporation stations were used for this study. Sample stations which had large data inconsistencies, as well as long time periods of no recorded data, were excluded in order to improve accuracy and the quality of the data.

4.1.2.3 Water Quality

Water quality data were obtained from all of the available Rand Water as well as DWA sample points situated in the Upper Vaal WMA for the period, July 2000 to June 2012. The Rand Water sampling stations are distributed across the Vaal River, Wilge River and Vaal River Barrage catchments. Owing to the absence of Rand Water sampling stations in the Kromdraai catchment, data were obtained from the DWA for the same period. A total of 118 water quality sample stations, as listed in Appendix 1, provided a complete dataset of water quality data and were consequently used for this research. The locations of these stations in the Upper Vaal WMA are set out in Figure 4.4.

The water quality data obtained from these sampling points were measured monthly, weekly and, in some cases, daily throughout the year, but at no scheduled time and on no fixed day or week.

A monthly, quarterly and yearly average were calculated for each of the water quality parameters used at each station, as well as for all of the other variables described thus far.

Figure 4.1: Location of the South African Weather Service’s meteorological stations in the Upper Vaal WMA.

Figure 4.3: Locations of the DWA evaporation stations.

As stated previously, some sampling stations were excluded from this research due to inadequate data recordings. Sampling stations were therefore excluded from this research in the case of the station having recorded less than four measurements within a year. Some water quality sampling stations were also not included in this research due the station having measured less than four parameters with less than four recorded measurements within a year. The ‘Four by Four’ (4x4) rule was therefore followed as recommended by the Canadian Council of Ministers for the Environment (CCME, 2001). This rule ensures that only sampling stations that regularly monitor the relevant parameter are included and eliminates stations that have only three monitoring phases per year. This research was therefore limited to these sampling stations which had adequate replication in an attempt to ensure high quality data and representation within the Upper Vaal WMA.

4.1.2.4 Land Cover

Importantly, it should be noted that land cover and land use are closely related but are not identical. Land cover refers to all natural and human-made features that cover the Earth’s immediate surface whereas land use typically refers to the human activity that is associated with a specific land unit in terms of utilisation, impacts or management practices (Gregorio, 1996;

Thompson, 1996). Land use therefore focuses on the function, where a specific use can be defined in terms of a series of activities which are undertaken to produce one or more goods or services.

Owing to these characteristics, one portion of the Earth’s surface can be associated/defined by only one land cover type but by several types of land use (e.g. grasslands, which may be used for communal grazing within a conservancy area). Therefore a change in an area’s land cover may influence the range of potential types of land use in the area, whereas a change in land use may physically alter the land cover by converting or modifying it in order to perform a different function (Latham, 1996).

National land cover data were obtained from the Agricultural Research Centre (ARC) for 1994, 2000, 2005, while land cover data for 2009 were obtained from the South African National

Biodiversity Institute (SANBI). These land cover datasets were used in order to establish land cover changes for the Upper Vaal WMA over the selected time period.

A database was therefore created for all of the rainfall, water flow, evaporation and water quality sampling stations located within the Upper Vaal WMA to be used within the data analysis phase.

A land cover database for the Upper Vaal WMA as well as its associated secondary, tertiary and identified quaternary catchments was also created. These databases were structured accordingly to enable data analysis to be completed. However, before data analysis could be completed the water quality database needed to be refined in terms of water quality parameters. A brief discussion regarding the water quality guidelines and standards used, the selection process of water quality parameters as well as the selected water quality parameters for this research now follows.

4.1.3 Water Quality Guidelines and Parameters

Any amount of water quality parameters can and have already been used by previous research as indicators of water quality however, there is no single parameter that can be used to describe the overall water quality for any water body. A wide variety of water quality parameters can and should be used in order to obtain a holistic and accurate view of a water body’s water quality in terms of environmental and human health (UNEP, 2007). Water quality parameters to be used in this research were selected according to a selection process and were therefore selected according to the following rules:

1. The water quality parameter needs to have available national and regional water quality indices or guidelines.

2. The water quality parameter must have been commonly measured and reported by the Rand Water and the DWA water quality sampling stations within the Upper Vaal WMA.

3. The water quality parameter needs to have a representation percentage of a minimum of 80% (50% in the case of biological parameters), within the Upper Vaal WMA.

4. Water quality parameters which are characterised by the occurrence or measurement of non-detectable values needs to be excluded due to the possibility of bias.

Firstly, there is no globally accepted composite index of water quality, but some countries and regions have used, or are still using, aggregated water quality data in the development of water quality indices or guidelines. These water quality standards or guidelines are dependent on normalising or standardising data for each parameter according to expected concentration. These parameters are then weighted according to their perceived importance or significance which enables the establishment of unique and country or region specific water quality standards or guidelines (Stambuk-Giljanovic, 1999; Pesce & Wunderlin, 2000; Sargaonkar & Deshpande, 2003; Liou et al., 2004; Tsegaye et al., 2006).

This research made use of relevant international, national and regional water quality standards and guidelines. Water quality parameters were therefore excluded on the basis of the availability of international, national and regional guidelines. The following water quality guidelines and standards were used:

 International Water Quality Guidelines from the following international associations:

o World Health Organisation (WHO), o European Union,

o United States Environmental Protection Agency (US-EPA) and, o Food and Agriculture Organisation (FAO)

 South‎Africa’s‎National‎DWA‎Water‎Quality‎Guidelines‎for‎the‎following‎water‎uses:

o Domestic use, o Aquatic Ecosystems, o Recreational use, o Irrigation and, o Industrial use

 In-stream Water Quality Guidelines for the following main catchments within the Upper Vaal WMA:

o Vaal Dam,

o Vaal River Barrage and, o Kromdraai

Water quality parameters were further selected according to how commonly they have been measured or reported in the developed water quality database. A total of 47 water quality parameters were identified. Further refinement of the database was however needed in order to ensure that the selected parameters were adequately represented across the whole Upper Vaal WMA (UNEP, 2007). A minimum regional coverage for each parameter was consequently needed and chosen to refine the water quality database. This research required a parameter to be represented at 70% within the Upper Vaal WMA to be included in the water quality database.

Furthermore, due to the importance of biological parameters as indicators of environmental health and possible human health issues, this research chose a minimum regional coverage of 50%. The further refinement process reduced the amount of water quality parameters to a total of 24.

Lastly, water quality parameters were assessed and selected according to non-detects and zero values due this possibly attributing to bias in the equation (UNEP, 2007). Parameters which were characterised by non-detectable values due to concentrations within the water body being below the detection limit (either method or instrumental) were identified and excluded from the water quality database. The possibility of bias in terms of specific water quality parameters were therefore addressed with the exclusion of these from the water quality database.

Subsequently a total of 16 water quality parameters² were identified, according to the described rules, for this research and include the following:

 Physical parameters: pH and Electrical Conductivity;

 Chemical parameters: Alkalinity, calcium, chloride, sodium, magnesium, nitrate, sulphate, ammonia, phosphate and Chemical Oxygen Demand;

 Biological parameters: Chlorophyll a, Faecal coliform, Dissolved Oxygen and Dissolved Organic Carbon.

A brief discussion regarding the selection of land cover classes now follows.

4.1.4 Land Cover Classes

The national land cover data used the ‘Standard Land Cover Classification Scheme’ as the reference system to compile a 49-class legend. This full classification scheme, as defined by Thompson (1996), is based on a hierarchical framework designed to suit the South African environment and incorporates known land cover types, which can be identified in a consistent and repetitive manner from high resolution satellite imagery such as LandSat TM and SPOT.

In their turn, the class definitions ensured that the data were standardised, and that broad generic classes were subdivided into more specific user-defined subclasses. The classification used by the land cover data was designed to conform to internationally-accepted classification standards and conventions to ensure cross-border compatibility and integration with existing national and international land cover classification systems and datasets.

The classes based on these land cover datasets consists of Level I classes as defined within the

‘Standard Land Cover Classification’, and only a selected subset of Level II subclasses (Refer to Appendix 3 for Level I and II classes as well as seven and five class land cover).

2 For reference, the water quality guidelines and standards for each of the identified water quality parameters are listed in Appendix 2.

This study made use of the seven class legend of land cover for the years 1994, 2000 and 2009 as well as the five class legend for the year 2005 in order to describe the land cover of the Upper Vaal. The seven class legend for the land cover of 1994, 2000 and 2009 was converted to the five class legend used for 2005 during the data analysis in order to obtain uniformity. The five class legend land cover was consequently used in order to establish the percentage of land cover change over the time period, to determine relationships between water quality and land cover as well as to determine relationships between the land cover classes themselves. The seven and five land cover classes were based on the Level I and II classes as listed in Table 4.2.

Table 4.2: The seven and five land cover classes are based on the Level I and II classes³.