Natural and Anthropogenic Influences on the Water Quality of the Orange River, South Africa

Home , Boegoeberg Dam, Gariep Dam, Orange River, Prieska, Vanderkloof

Natural and anthropogenic influences on the water quality of the Orange River, South Africa

Kelly Bucas

MINOR DISSERTATION

Submitted in partial fulfilment of the requirements for the degree

MASTER OF SCIENCE

ENVIRONMENTAL SCIENCE

in the

FACULTY OF SCIENCE

at the

UNIVERSITY OF JOHANNESBURG

SUPERVISOR: PROF. J.M. HUIZENGA

DECEMBER 2006

Abstract The natural condition of the Orange River should be evaluated in order to develop baseline information so that it can be used for comparison, monitoring and informed decision-making. This will also allow for further research to take place. Although there is a lot of data available on the Orange River, little work has been done on the evaluation of the natural conditions that influence the inorganic water chemistry

Inorganic data, from 1986 to 2006, obtained from the Department of Water Affairs and Forestry (DWAF) was evaluated for six sample stations along the Orange River (D1H009Q01, D3H008Q01, D3H012Q01, D3H013Q01, D7H008Q01 and D7H005Q01). Climate data (1986-2006) was obtained from the South African Weather Service.

The following water quality data was used in the evaluation: pH and the concentrations of major elements (all in mg/L). The major elements include sodium (Na + ), potassium (K + ),

2+ 2+ 4+ − 3− calcium (Ca ), magnesium (Mg ), silica (Si ), fluoride (F ), orthophosphate (PO 4 ),

− − 2− chloride (Cl ), total alkalinity (TAL) assumed to be bicarbonate (HCO 3 ), sulphate (SO 4 ),

− − − nitrate (NO 3 ) (assuming that NO 3 >>> NO 2 ) and the total dissolved solids (TDS). Various geochemical techniques were used to analyse the data.

The results of this study show that the water chemistry of the Orange River is controlled by: 1. Chemical weathering of siliceous sediment, intrusive igneous rocks and metamorphic

+ + 2+ 2+ − − 4+ rocks (Na , K , Mg , Ca , HCO 3 , F and Si ). 2. Input from agricultural and urban activities affecting, in particular, the concentrations

3− − 2− − of PO 4 , NO 3 , SO 4 and Cl .

There is an increase in cation and anion concentrations from 1986-2006. The concentration of cations and anions increases downstream from D1H009Q01 to D7H005Q01 i.e. from a colder wetter climate to a drier hotter climate.

Based on the chemical characteristics, two groups were identified. The stations in each group include: Group 1: D1H009Q01, D3H013Q01, D3H012Q01 and D3H008Q01 and Group 2: D7H008Q01 and D7H005Q01.

i For group 1 the degree of pollution is generally consistent (between 10 and 30 percent). The element concentration was plotted against the total annual runoff and the visual trend shows a

+ + 2+ 2+ − − decrease in Na , K , Mg , Ca , HCO 3 and F as the annual runoff increases. This is because the dilution effect is stronger than the release of cations and anions due to chemical weathering. This decrease is typical for weathering of rock types such as granites, shale and metamorphic rocks. Si 4+ shows an increase in concentration as total annual runoff increases. This indicates that feldspar is the dominant mineral that is being weathered. Chemical weathering of feldspars (specifically Na- and K-feldspars) releases more Si 4+ compared to

3− 2− − − other species. PO 4 and SO 4 show no visual trend and Cl and NO 3 show a possible increase in concentration with an increase in total annual runoff. This is most likely due to greater input of these species from the surrounding agricultural and urban areas when the runoff increases.

For group 2 the degree of pollution is generally higher and shows a greater fluctuation compared to group 1. The visual trend for the concentration of Na + , K + , Mg 2+ , Ca 2+ , Cl − ,

2− − − SO 4 , HCO 3 and F shows a sharp decline at low runoff, dilution is the dominant process. The concentration of these then increases as chemical weathering becomes more dominant, the effect of chemical weathering becomes greater than the effect of dilution. After this the effect of dilution becomes greater and the species concentrations show a steady, slight decrease, similar to group 1. Si 4+ shows a sharp increase as total annual runoff increases to 5000 million m3, thereafter it decreases slightly. The increase of the Si 4+ concentration is due

− to chemical weathering which is stronger than the dilution effect. The increase in NO 3 and

3− PO 4 is most likely due to agricultural activities and urbanisation in the immediate area. As surface runoff increases it increases transport of these chemical species into the river.

Both the agricultural/urban input and the release of cations and anions by chemical weathering are severely influenced by the stream runoff. Any evaluation of the inorganic chemistry from the Orange River should include runoff. The variation of the annual runoff affects the percentage of pollution, especially for the lower Orange River.

ii Acknowledgements I would like to thank the University of Johannesburg for funding this study and the Department of Geology for their support and allowing me to carry out this study.

I would also like to thank Prof. J.M. Huizenga for his endless patience, encouragement and guidance. There was never a dull moment.

Jenny, thank you for hanging in there with me and convincing me that everything is going to be fine!

Lastly to my family for their patience, love, support and motivation, and my faith for guiding me to this point.

iii TABLE OF CONTENTS Pg ABSTRACT i ACKNOWLEDGEMENTS iii LIST OF FIGURES vi LIST OF TABLES vii

1. Introduction 1 1.1 Statement of the problem 3

1.2 Methodology 4

2. Description of study area 5 2.1 Study area 5 2.2 Factors influencing surface water chemistry 10 2.2.1 Chemical weathering 10 2.2.2 Climate 10 2.2.3 Soil and soil forming processes 15 2.2.4 Sources of solutes in the atmosphere 16 2.2.5 Influence of humans 17

3. Data collection and evaluation 19 3.1 Data collection 19

3.2 Accuracy of chemical analysis 23 3.3 Data methodology 24 3.3.1 Stiff diagrams 24 3.3.2 Piper diagrams after Piper (1944) 24 3.3.3 Gibbs diagrams after Gibbs (1970) 25 3.3.4 Mixing Diagrams after Gaillardet et al. (1999) 26 3.3.5 Degree of pollution 27

iv 3.4 Results 28 3.4.1 Stream runoff per year 28 3.4.2 Stiff diagrams 30 3.4.3 Piper diagrams 32 3.4.4 Gibbs diagrams 33 3.4.5 Mixing diagrams 35 3.4.6 Degree of Pollution 37

4. Interpretation of results 38 4.1 Group 1 39 4.2 Group 2 43

5. Conclusion 46

References 49

APPENDIX 1: 52

Total annual runoff each of the stations from 1986-2006 53 APPENDIX 2: Average annual summer and winter temperature and rainfall APPENDIX 3: 55 Overall average composition of cations and anions for each station

LIST OF FIGURES Figure 1: Generalised relationship between climatic factors and water chemistry 2 Figure 2: The Orange River catchment 6 Figure 3: The average annual precipitation of the Orange River Catchment 6 Figure 4: Simplified geological map of the Orange River and surroundings showing the major 8 lithologies Figure 5: Simplified geological map of the Orange River showing water sampling stations and weather 19 stations Figure 6: Electro Neutrality calculated for data 23 Figure 7: Variation of the weight Na + / (Na + + Ca 2+ ) as a function of the total dissolved solids of 26 surface waters

+ 2+ − Figure 8: Na normalised diagram of Ca and HCO 3 showing lithological end-members for silicates 27 carbonates and evaporates Figure 9: Total runoff for each sample station over the last 20 years 29 Figure 10: Stiff diagrams 31 Figure 11: Piper diagrams 32 Figure 12: Gibbs diagrams 34 Figure 13: Saturation index for D1H009Q01 and saturation index against runoff. D1H009Q01 is 35 undersaturated, there is generally no precipitation of calcite. Figure 14: Saturation index for D7H005Q01 and saturation index against runoff. D7H005Q01 is 35 generally oversaturated, there is precipitation of calcite. Figure 15: Mixing diagrams 36 Figure 16: Degree of pollution 37 Figure 17: Element concentrations against total annual runoff 40 Figure 18: Relationship between TDS and annual runoff for various lithologies 41 Figure 19: Relative abundance of chemical species in water as a result of mineral weathering 41 Figure 20: Pollution against runoff. 42 Figure 21: Element concentrations against total annual runoff. Annual median values for different 44 chemical species was calculated and plotted against total annual runoff Figure 22: Pollution against runoff 45 Figure 23: Stiff diagrams indicating the overall composition of cations and anions for each station 53 Figure 24: Piper diagrams for each station indicating the overall relative composition of cations and 54 anions.

LIST OF TABLES

Table 1: Water requirements met by the Orange River 9 Table 2: Average world river composition in unpolluted fresh water and potential sources 11 Table 3: Relative chemical erosion rates for various lithologies 14 Table 4: Detailed description of the six stations studied along the Orange River 21 Table 5: Total annual runoff for the six stations from 1986- 2006. 51 Table 6: Average annual winter temperature (ºC) and average annual winter rainfall (mm) (original 52 data obtained from the South African Weather Service). Table 7: Average annual summer temperature (ºC) and average annual summer rainfall (mm) (original 52 data obtained from the South African Weather Service). Table 8: Median values for pH, TDS and concentrations of major elements (all in mg/L) for all the 53 stations from 1986 to 2006.

vii Chapter 1

Introduction

Water is the most essential resource and without water it is impossible to maintain biodiversity or promote social and economic development (Ashton, 2002). The worldwide demand for water has increased rapidly due to population growth as well as growing urbanisation and industrialisation (King, 2004).

Resource depletion and pollution has a negative effect on the availability of water. Meeting the demand for water is especially difficult in arid regions of the world like north and south- western Africa where water scarcity and an increase in water pollution impedes social and economic development. Water scarcity is also linked to poverty, hunger and disease that exist in these areas (Ashton, 2002). Throughout Africa water is seasonably variable and unpredictable and global climate change as well as local and regional water shortages can worsen this (Ashton, 2002).

Southern Africa is generally an arid to semi-arid country and has an average rainfall of less than 500 mm per annum. The rainfall is uneven and the areas of highest demand receive little rainfall (O’Keeffe et al., 1992). The high evaporation rates results in the runoff to rainfall ratio being amongst the lowest for any populated region in the world. As a result of this water is a very scarce resource in most of southern Africa. Water is often the limiting resource in development (O’Keeffe et al., 1992). Engineering techniques have been used to store and distribute water with catchments that have abundant supplies and low demand to areas where demand is high and supplies are scarce (e.g. the Lesotho Highlands Water Scheme) (O’Keeffe et al., 1992).

South Africa is a country that is rich in natural resources but freshwater is the exception. There are hardly any freshwater lakes in South Africa and water supplies are confined to rivers, artificial lakes (behind dams) and groundwater. The warm Agulhas current on the east coast, the cold Benguela current on the west coast and the topography have resulted in an overall arid climate. South Africa’s total runoff is estimated at 53 500 million m3 per annum and only about 33 000 million m3 of this can be exploited. The recharge estimate of the groundwater reserves are uncertain, thus so is the renewable resource value (O’Keeffe et al., 1992).

1 The available freshwater resources are already nearly fully-utilised and under stress. The demand for water does not always relate to the distribution of water. It is unlikely that the projected demand on water resources will be sustainable at the current projected population growth and economic development rates and this will impede on future socio-economic development (Walmsley et al., 1999).

The three main driving forces affecting South Africa's freshwater environment are (Walmsley et al., 1999): • The natural conditions, especially climate, which combines low rainfall with high evaporation rates and results in a low availability of runoff. South Africa is situated in the desertic climate where total dissolved solids are high and the major cations and

2+ 2+ + − − anions include: Ca , Mg , Na , Cl , and HCO 3 (Figure 1). • Rapid population growth and need for development through economic activities leads to greater water demand and increased pollution of the resources that are available. • Policy regarding management of water resources, this determines the approach taken to managing the resource by the appropriate government departments and directly impacts other driving forces and pressures.

Figure 1: Generalised relationship between climatic factors and water chemistry (adapted from Plant et al., 2001).

2 1. 1. Statement of the problem

There are two main drainage zones in Southern Africa: the plateau and the coastal or marginal area. The plateau system can be divided into the eastward draining Limpopo-Sabie system and the westward draining Orange system. The Orange River system drains about 48 percent of the total area of the country and carries about 22 percent of the total South African down-flow (Swanevelder, 1981). The climate at the source is cool and temperate but becomes more arid as the river moves towards the west (Tooth and McCarthy, 2004). According to DWAF (1997) the Orange River basin is highly developed and water is mostly used for irrigation. The water from the river is a crucial resource for South Africa, especially in the arid areas.

The Water Research Commission funded a study on the increasing salinity of irrigation water that is threatening the productivity of grape farmers along the Orange River between Boegoeberg and Onseepkans. The Lower Orange Water Management Area is located in an arid region and has limited rainfall and high evaporation losses. The tributary flows in the area are intermittent and only contribute to the river flows during periods of high precipitation in the Orange River basin. According to van Vuuren (2006) in a report on the study the Lesotho Highlands Water Project has resulted in large volumes of low salinity water being diverted from the Orange River into the Vaal River catchment. This has lead to an increase in salt levels in the Gariep and Vanderkloof dams. Expansion in the table grape industry in the upper part of the Lower Orange River system could add pressure to the system by decreasing the available volume of water and increasing irrigation return flow to the river (van Vuuren, 2006).

Water supply and water composition is influenced by the amount of rainfall, rate of rainfall, runoff and evaporation. The naturally occurring solid reactants found in water are controlled by geological processes. The elements found in the rocks can be present in the final solution depending on the structure and composition of the rocks. Biological and biochemical processes influence the way in which chemical reactions occur in water as well as the result of these reactions. The factors that influence surface water chemistry include: climate, geological effects, soil and soil forming processes, sources of solutes in the atmosphere and the influence of humans (Hem, 1989).

This study will focus on the natural conditions that influence the inorganic water chemistry of the Orange River. The natural condition of the Orange River should be evaluated in order to develop baseline information so that it can be used for comparison, monitoring and informed decision-making. This will also allow for further research to take place. Although there is a lot of data available on the Orange River, little work has been done on the evaluation of these data.

1.2 Methodology

Inorganic data, from 1986 to 2006, obtained from the Department of Water Affairs and Forestry (DWAF) will be evaluated for six sample stations along the Orange River (D1H009Q01, D3H008Q01, D3H012Q01, D3H013Q01, D7H005Q01 and D7H008Q01). The following programmes will be used to evaluate data: Microsoft Excel, AquaChem 5.0® and PHREEQC (http://www.usgs.gov/).

4 Chapter 2

Description of study area

2.1 Study area

The Orange River originates in the Lesotho Highlands, where the river is known as the Senqu River, at 3 300 m above sea level and flows westward for 2 300 km to the Atlantic Ocean at Alexander Bay (DWAF, 1997). It ranks within the four most turbid rivers in the world (Bremner et al., 1990). The Orange River drains a maximum potential catchment of about 1 000 000 km2 (DWAF, 1997). In Figure 2 the location of the Orange River as well as its catchment area can be seen.

The climate varies greatly from the source to the mouth of the river. The climate at the source is cool and temperate but becomes more arid as the river moves towards the west (Tooth and McCarthy, 2004). The average annual precipitation in the Lesotho Highlands is 1 800 mm and the average annual potential evaporation is 1 100 mm. At Alexander Bay the average annual precipitation decreases to 50 mm and the average annual evaporation increases to 3 000 mm. The average daily temperature varies from 12 ºC in the Lesotho Highlands to 22 ºC near the mouth. Extreme temperatures of -10 ºC in the Lesotho Highlands can be experienced and some areas experience more than 200 days of frost a year. The temperatures along the banks of the lower Orange River can reach up to 50 ºC and higher (DWAF, 1997).

Figure 3 shows the average annual precipitation of the Orange River Basin. A clear decrease in average annual precipitation can be seen from the source of the river towards the mouth of the river at Alexander Bay.

Figure 2: The Orange River catchment (after Bremner et al., 1990)

Figure 3: The average annual precipitation of the Orange River Catchment (Source: DWAF, 1997, http://www.dwaf.gov.za/ orange/climate.htm).

6 A belt of green vegetation is present along the banks of the Orange River that is otherwise a hot and dry desert. The Orange River Mouth Wetland is a delta type river mouth where a braided channel system is present in low flow months. The Orange River mouth consists of sand banks or channel bars that are covered with pioneer vegetation, a tidal basin, a narrow floodplain, pans, the river mouth and a salt marsh on the south banks of the river. The river mouth together with the salt marsh and pans supports a variety of birds in the sheltered shallow water. A wide variety of rare and endangered birds are supported during breeding or as they migrate annually from one hemisphere to another (DWAF, 1997).

The Orange River crosses various geological structures and lithologies. Above Prieska (Figure 4) in the upper and middle section of the Orange River the lithologies include the late Achaean to Mesozoic sedimentary and igneous rocks of the Ventersdorp, Transvaal and Karoo Supergroups (Tooth and McCarthy, 2004). These rock types include unconsolidated sand or soil, sandstone, siltstone, shale, conglomerate, dolomite, limestone, iron-formation, quartzite, lava and dolerite (Figure 4).

Below Prieska the river crosses Proterozoic metamorphic and igneous rocks of the Kaapvaal Craton and Namaqualand mobile belt. The rock types found in this section include: gneiss, amphibolite and granulite, schist and quartzite. Sedimentary rocks found along the river are the same as those mentioned above: unconsolidated sand or soil, sandstone, siltstone, shale, conglomerate, dolomite, limestone, iron-formation (Figure 4). Changes in the river valley and channel morphology occur as the Orange River flows over these different lithologies. Upstream of Upington (Figure 4) the river crosses Brakbosch and Dagbreek faults as well as interlayered granites, granitic gneisses and a ridge of resistant quartzite (the Neus Berg) (Tooth and McCarthy, 2004).

The flow regime is variable and the near-continuous low flows are interrupted by occasional large summer flood events (Tooth and McCarthy, 2004). The average natural runoff of the river, being the river flow without the influence of developments in the catchment, is approximately 12 000 million m3 per annum. The Orange River basin is highly developed and this means that the average annual runoff reaching the mouth of the river is less than half the natural runoff (DWAF, 1997).

Figure 4: Simplified geological map of the Orange River and surroundings showing the major lithologies (map compiled from the Geological Map of South Africa, Council for Geoscience).

8 The Orange River is highly regulated with dams having been built for irrigation, electricity generation, flood control, and water transfer (Tooth and McCarthy, 2004). The are two storage reservoirs on the Orange River in South Africa, the Gariep Dam and the Vanderkloof Dam, and one in Lesotho, the Katse Dam. The Gariep Dam is the largest reservoir in South Africa and the Vanderkloof Dam is the second largest. The Vanderkloof Dam controls the flow water between the dam and Alexander Bay for 1400 km. Heavy development (not specified) occurs downstream from the Vanderkloof Dam. The Gariep Dam and the Vanderkloof Dam are used to regulate river flow for irrigation and produce hydro-electricity during peak demand periods (DWAF, 1997).

Except for the Vaal River, which is the largest and most important tributary of the Orange River, not much of the water from the river is used for domestic or industrial purposes. The water demand supported by the Orange River is summarised in Table 1 and the use of water by the irrigation sector for agriculture is one of the highest. It is estimated that that the total water requirement met by the Orange River is 3500 million m3 per annum (DWAF, 1997).

Table 1: Water requirements met by the Orange River. (Adapted from DWAF, 1997, http://www.dwaf.gov.za/ orange/waterreq.htm). Description of water requirements Annual requirements (million m3) Irrigation Upstream of Gariep (36 400 ha) 395 Supplied locally in Riet and Modder (7 100 ha) 70 Directly from Gariep and Vanderkloof (24 300 ha) 278 To the Eastern Cape through the Orange/Fish Tunnel (52 700 ha) 627 Lower Orange River in South Africa (63 790 ha) 751 Lower Orange River in Namibia (2 270 ha) 39 Total from the Orange River (excluding the irrigation in the Vaal River basin 2160 upstream of the Vaal/Riet influence (179 640 ha) Urban/ Industrial Demands Orange River Upstream of Vanderkloof Dam 70 Eastern Cape demands through Orange/Fish Tunnel 20 Downstream of Vanderkloof Dam including Namibia 10 Total urban/industrial demands 130 Other requirements/return flows Environmental 300 River evaporation losses (varies with flow) 960 Return flow from irrigation - 50 Total other requirements 1 210 Total Orange River Requirements 3500

9 2.2 Factors influencing surface water chemistry

As previously discussed various environmental factors influence the chemical processes that take place in water. The supply of water is replenished by precipitation. The components of regional climate like the amount of rainfall, rate of rainfall, runoff and evaporation are all important factors in the control of water composition (Hem, 1989). The naturally occurring solid reactants found in water are controlled by geological processes. The elements found in the rocks can be present in the final solution depending on the structure and composition of the rocks (Hem, 1989). These factors influence reactant supplies but biological and biochemical processes influence the way in which chemical reactions occur as well as the results of these reactions. The factors that influence surface water chemistry include: chemical weathering, climate, soil and soil forming processes, sources of solutes in the atmosphere, rivers themselves and the influence of humans.

2.2.1 Chemical weathering

The fundamental source of dissolved ions in the water is from the mineral assemblage of the underlying or surrounding rock. Most, but not all, dissolved solids are produced from weathering as some may originate in rain. Secondary minerals and dissolved components are the two products of weathering. Chemical weathering of the bedrock geology is the most important natural factor that controls water chemistry (e.g. Domenico and Schwartz, 1990; Appelo and Postma, 1993; Stumm and Morgan, 1996). In Table 2 the chemical species and their possible sources can be seen. These sources include: feldspar, carbonate, gypsum,

− 2− − dolomite, pyroxene, olivine etc. Cl , SO 4 and NO 3 may be sourced from certain minerals as well as the atmosphere. The species normally found in the highest concentration are:

− 2+ 2− − , 2+ + HCO 3 , Ca , SO 4 , Cl , SiO 2 Mg and Na .

Rock composition is only one of the characteristics that may influence the composition of the water passing over or through the rock. Others include: purity of the mineral, crystal size of the minerals, rock texture, porosity, length of exposure time etc. Higher temperatures increase the solubility and rate of dissolution of minerals (Hem, 1989). According to White (2006) the rate of weathering reactions are also dependent on pH but this dependence can be complex. Generally the weathering rates increase with a decrease in pH and decrease with an increase in pH. The pH of weathering solutions depends on: dissolved CO 2 , the presence of organic acids, and the extent of weathering.

10 Table 2: Average world river composition in unpolluted fresh water and potential sources (Meybeck, 1985; Appelo and Postma, 1993). World river composition Species Normal range (mg/L) Source mg/L mmol/L meq/L Feldspar, rock-salt, Na+ 8.5 0.37 0.37 2 – 46 zeolite, atmosphere K+ 1.5 0.04 0.04 0.5 – 8 Feldspar, mica Carbonate, gypsum, Ca2+ 15.0 0.37 0.75 2 – 200 feldspar, pyroxene, amphibole Dolomite, serpentine, Mg2+ 3.9 0.16 0.32 1 – 50 pyroxene, amphibole, olivine, mica

+ NH 4 0.02 0.00 0.00 - -

SiO2 10.4 0.17 0.69 1 – 60 Silicate minerals F− - - - - Fluorite Organic matter, phosphate PO 3− 0.01 0.00 0.00 0 – 2 4 minerals (apatite) Cl− 10.5 0.30 0.30 2 – 70 Rock salt, atmosphere Carbonate minerals, HCO − 53 0.87 0.87 0 – 300 3 organic matter Atmosphere, gypsum, SO 2− 12.3 0.13 0.26 1 – 500 4 sulphide minerals Atmosphere, organic NO 2− 0.1 0.00 0.00 0.1 – 2 3 matter

Minerals are inclined to dissolve in water and dissolution (also known as chemical weathering) depends on the mineral composition and mineral structure (Appelo and Postma, 1993).

• Dissolution of minerals

To determine whether a solution is saturated, undersaturated or supersaturated a mineral may be observed in solution. If the mineral dissolves, the solution is undersaturated; if the mineral grows in size, the solution is supersaturated; and if nothing happens, the solution is saturated (i.e. at equilibrium). This can be done more simply by determining the composition of a solution and theoretically calculating the state of saturation (Anderson, 1996). The solubility property of a mineral can be quantified with the solubility product. The solubility product is an equilibrium constant for a reaction having a solid mineral on the left side and its constituent ions on the right (Anderson, 1996).

11 For example the mineral fluorite (CaF2) dissolves in water according to the dissociation reaction (Appelo and Postma, 1993):

2+ − CaF2 → Ca + 2F (2.1)

The solubility product for this is defined as:

2+ − 2 K fluorite = [Ca ] [F ] (2.2)

[Ca 2+ ] and [F-] represent the concentration of Ca 2+ and F- in solution (normally expressed in –10.57 mole per kg H2O or mole per litre). K fluorite has a value of 10 at 25ºC.

The chemistry of natural spring water can be described by calculating the Saturation Index (SI) of a number of minerals. The Saturation Index is a number that indicates how saturated, undersaturated or over saturated a solution is with respect to a particular mineral phase (e.g. Domenico and Schwartz, 1990; Appelo and Postma, 1993).

2+ − 2+ − 2 For example if a solution has a certain concentration of Ca and F , [Ca ] sol and [F ] sol respectively, the saturation state of the solution can be calculated with respect to fluorite as (e.g. Domenico and Schwartz, 1990; Appelo and Postma, 1993):

2+ − [Ca ]sol [F ]sol SI = log10 (2.3) K fluorite

The solution is saturated with fluorite if SI is 0, undersaturated if SI < 0 and oversaturated if SI

2+ − 2 > 0. The product [Ca ] sol [F ] sol is defined as the Ionic Activity Product of fluorite. The reactive concentration of the cations and anions involved should be considered and this makes solubility calculation complicated. Electrostatic shielding and complex formation influence the reactivity of a certain species (e.g. Domenico and Schwartz, 1990; Appelo and Postma, 1993). The programme PHREEQC is ideal to do these calculations.

12 • Chemical weathering of carbonates

The minerals typical of carbonate rocks include calcite, CaCO 3 , and dolomite, (Ca,

Mg)(CO 3 ) 2 . The dissolution of calcite can be expressed by the following equation:

2+ − CaCO3 + CO2 + H2O → Ca + 2HCO3 (2.4)

The amount of calcite that dissolves is dependent on the CO 2 in solution. Minerals that

+ − weather easily, like calcite, result in high concentrations of Ca and HCO 3 . During the

+ − precipitation of CaCO 3 the reverse reaction occurs and there is a decrease in Ca and HCO 3 (Appelo and Postma, 1993).

• Chemical weathering of silicates

Chemical weathering of rocks dominated by silicate minerals is much slower than chemical weathering of carbonate rocks. Though silicate weathering is slow, it is still responsible for about 50 percent of total dissolved solid loads in rivers on Earth (Appelo and Postma, 1993). Mineral phases formed at a high pressure and temperature undergo chemical weathering more easily than minerals formed at low pressure and temperature. Minerals like olivine, pyroxene and Ca-feldspar which are formed in upper mantle rocks like basalts (Drakensberg) dissociate much quicker than mineral phases like quartz and K-feldspar that occur in continental crust rocks such as granites (Appelo and Postma, 1993).

Chemical weathering reactions of silicate mineral phases involves partial dissolution and the formation of a secondary mineral phase. This reaction can be expressed as:

reactant phase → weathering residue + dissolved ions

13 Secondary mineral phases produced during chemical weathering are insoluble aluminium-rich clay mineral phases like kaolinite, montmorillonite, gibbsite and iron-oxides. Partial dissolution reactions have the following general characteristics (Appelo and Postma, 1993; Bluth and Kump, 1994; Bowser and Jones, 2002): o Water and/or hydrogen proton (H + ) as a reactant (i.e. pH)

− o Hydrogen protons are normally supplied by carbonic acid (H 2 CO 3 ). This is

+ − the product of the reaction H2O + CO 2 = H 2 CO 3 , H 2 CO 3 = H + HCO 3 ),

CO 2 comes from the atmosphere, soil and vegetation. o Immobility of iron and aluminium (they stay behind as an insoluble residue).

− 2+ + + o The release of HCO 3 and cations such as Ca , K and Na

o A part of the silica gets dissolved in the form of H 4 SiO 4 (this is because

precipitation of SiO 2 normally does not occur. Silica is derived from mineral phases like Ca-rich feldspars, olivine, pyroxene, amphibole and biotite but not quartz and K-feldspar) the silica concentration in natural waters is a good indication of the extent of silica weathering because its source is only silica weathering. o The most common residue is the clay mineral kaolinite.

Meybeck (1987) determined the relative rate of chemical weathering for various lithologies normalised to granite (Table 3) and these relative erosion rates control the river chemistry. For example: igneous and metamorphic rocks occupy 34 percent of the global surface and contribute 12 percent of the dissolved loads of the rivers. Evaporates, however, occupy only 1.3 percent of the global surface but contribute 17 percent of the dissolved load (Meybeck, 1987). Table 3: Relative chemical erosion rates for various lithologies (Meybeck, 1987) Rock type/lithology Erosion rate relative to granite Granite 1.0 Gneiss/schist 1.0 Gabbro 1.3 Sandstone 1.3 Volcanic rock 1.5 Shale 2.5 Marble, amphibole 5.0 Carbonate rocks 12 Gypsum 40 Rock salt 80

14 2.2.2 Climate

Processes of rock weathering are influenced by temperature as well as the amount and distribution of precipitation. Climatic patterns produce characteristic plants and soil types and these can influence the composition of water. Certain ionic components are more strongly influenced than others by climate. Bicarbonate is usually predominant in areas where vegetation thrives. Certain metals can be accumulated by vegetation and can reach peak concentrations when plant decay cycles lead to an increase in the amount of these metals entering the water. Humid temperate climates and warm, wet climates easily accommodate growth of vegetation (Hem, 1989).

Climates that are dominated by wet and dry seasons may have periods where the amount of soluble inorganic matter is higher in certain seasons than others. The volume of flow in these rivers can fluctuate and there may be a wide variety in the chemical composition (Hem, 1989). The low temperatures of cold climatic regions inhibit weathering reaction rates. Most of the precipitation in these cold regions is in the form of snow and thus the water is in a solid state and most surface runoff is usually low in solute concentrations (Hem, 1989). Low temperatures also lower the reaction kinetics (Appelo and Postma, 1993). A study done by Millot et al. (2002) shows that the highest weathering rates are associated with rivers that have a hot and humid climate. It was also shown that weathering rates for basalts depend on runoff and for a given runoff value, chemical weathering increases with temperature. Both runoff and temperature act positively on weathering rates (Millot et al., 2002).

2.2.3 Soil and soil forming processes

Quality and quantity of soil in South Africa is mainly determined by climate and geology (Laker, 2003). South Africa is dominated by underdeveloped soils due to the low inefficient rainfall that most of the country receives. In the dry western areas soils are poorly developed and shallow (usually less than 30 cm in depth) and they overly rock or rock plus lime (calcrete) (Laker, 2003). Northern and north-western regions are dominated by shallow, red, sandy soils on lime or rock and lime (Laker, 2003).

15 The carbonates underlying the shallow soils have an impact on agriculture, this is important when implementing an irrigation system. More than eight percent of soils in the drier areas of South Africa below the western escarpment overlay silicrete, which is an extremely hard layer cemented by silica, this also has to be taken into consideration by farmers. Deeper soils are found in the eastern parts of South Africa as these regions receive a higher rainfall per annum (Laker, 2003).

Soils of high productivity are usually rich in organic matter. In most of these soils the mineral- species distribution obtained from the parent rock has been altered and the minerals themselves may also have changed. A large part of the atmospheric precipitation that reaches the earth’s surface falls on soil surfaces. This water can appear as runoff or as groundwater and can be present in the soil as soil moisture. The chemical composition of soil moisture and the chemical processes that take place in soil to dissolve or precipitate minerals can also influence the composition of the water that will eventually reach rivers (Hem, 1989).

The factors influencing the chemical composition of soil moisture are: dissolution of alteration of silicate and other minerals, the precipitation of soluble minerals like calcium carbonate, removal and circulation of nutrient elements by plants, biochemical reactions that produce carbon dioxide, concentration of minerals by evaporation, sorption and desorption of minerals by mineral and organic surfaces and the conversion of gaseous nitrogen to a form that is useful for plant nutrition (Hem, 1989).

2.2.4 Sources of solutes in the atmosphere

Gases like H2O, SO2, NH3, N2O, NO2, HCl, CO and CO2 are produced from burning fossil fuels, metallurgical processes and other anthropogenic activities. Biochemical processes in soil and water as well as volcanic and geothermal activity can also contribute to the production of gases. The chemical properties of rainwater can be affected by the presence of these gases in the atmosphere (Hem, 1989). Naturally occurring particulate matter in the atmosphere consists of terrestrial dust carried by the wind or dust emitted from volcanoes and salt that is picked up from wind agitation of sea water. There are also particles released as a result of anthropogenic processes like discharge from industrial plants, vehicle emissions and other sources (Hem, 1989).

16 These particles form nuclei for the condensation of water and are a source of solutes in precipitation, and influence surface-mediated chemical reactions (Hem, 1989).

2.2.5 Influence of humans

Humans have a major impact on the environmental factors that influence the composition of water. Solutes can be directly added to water by disposal of wastes or they can be removed by water treatment or the recovery of minerals. The hydrology and ecology of a whole drainage basin can be changed by developing plantations in the catchment area. Movement of water and the rate of solute circulation can be changed by the development of structures and paved surfaces on land as urbanisation continues to grow (Hem, 1989). Mining and agricultural activities as well as the influence of urban areas on the composition of water will be discussed in more detail below:

Mining Mining, processing and use of mineral resources uses enormous amounts of energy and can cause land disturbances, soil erosion and air and water pollution (Miller, 2005). Moving groundwater can change the geological environment and cause geological hazards. The interaction between the geological environment and groundwater can cause deformation and can affect the strength of rock. There could be a change in stress fields, recharge through-flow and discharge conditions of groundwater. Earthquakes, landslides, flooding of mines, ground engineering hazards, instability of dams, the collapse of cavities in carbonate rocks and subsidence can occur (Wu, 2003).

Agriculture In agricultural areas water is depleted and silt enrichment occurs due to runoff from cleared land. Soil is degraded as fertility is lost, soil is eroded and salinisation can occur. Unlimited abstraction of water eventually effects the functioning of an ecosystem that is found in that water body. Irrigation consumes a lot of water from these water bodies and in South Africa the intensity of this has been aided by the subsidised water tariffs on government water schemes and irrigation board schemes (Rabie and Day, 2000).

17 Loss of riparian vegetation effects a rivers flood resistance and flow variation. This can cause soil erosion and an increase in silt load and this leads to a reduction in a storage capacity of dams. Sediments from soil erosion, the use of pesticides and fertilisers can lead to water pollution (Rabie and Day, 2000).

Nitrate, phosphate, sulphate and potassium originate mainly from fertilisers. Irrigation in arid areas leads to an increase in salt loads. Water may evaporate but the salt present in the water stays in the soil (as it cannot evaporate) and accumulates in the soil. This then gets washed into the rivers and groundwater and causes the water to become polluted (Rabie and Day, 2000).

Urban areas Seepage from urban refuse-disposal sites contains a variety of contaminants. This causes water pollution when pollutants from these sites find their way into rivers, dams and groundwater (Rabie and Day, 2000). Urbanisation leads to increased runoff from surfaces that are polluted and this can also find its way into rivers and cause water pollution. Large amounts of sewage are generated and if this is untreated it may give rise to effluents that are high in salts, phosphates and nitrates (nutrients). When this nutrient rich effluent reaches rivers it leads to accelerated eutrophication and reduction of fauna (Rabie and Day, 2000). The impacts of urbanisation on water quality include (Baer and Pringle, 2000): o Massive sediment pulses o Increase pollutant wash-off o Nutrient enrichment o Bacterial contamination o Increased organic carbon loads o Higher levels of toxics, trace metals and hydrocarbons o Increased water temperature o Trash/debris jams

18 Chapter 3

Data collection and evaluation

3.1 Data collection

Water quality data (1986-2006) was obtained from the Department of Water Affairs and Forestry (www.dwaf.gov.za) for the following stations along the Orange River: D1H009Q01, D3H008Q01, D3H012Q01, D3H013Q01, D7H008Q01 and D7H005Q01 (Figure 5). The station ID, location, coordinates, years that the samples were taken as well as the number of samples for each station can be seen the Table 4.

Figure 5: Simplified geological map of the Orange River showing water sampling stations (D1H009Q01,D3H013Q01, D3H012Q01, D3H008Q01, D7H008Q01 and D7H005Q01) and weather stations (1= Gariep Dam, 2= Vanderkloof Dam, 3= Pieska, 4= Boegoeberg and 5= Uppington) (map compiled from the Geological Map of South Africa, Council for Geoscience).

19 Climate data (1986-2006) was obtained from the South African Weather Service (www.weathersa.co.za). The weather stations closest to the water sampling stations along the Orange River were used: Gariep Dam (D3H012Q01), Vanderkloof Dam (D3H013Q01), Boegoeberg Dam (D7H008Q01) and Uppington (D7H005Q01). The weather station at Prieska was also included as there was no data available for D3H008Q01 and this data was between D3H012Q01 and D7H008Q01. The average annual maximum temperature as well as the average annual rainfall was calculated from the monthly averages obtained. The overall average for the last 20 years was then calculated. As can be seen in Table 5 and Table 6 (Appendix 1) the average winter and summer temperature is the lowest at D3H013Q01 and the temperature increases downstream towards D7H005Q01 where the average annual temperature is the highest. The average annual rainfall for both winter and summer shows the opposite pattern. It is the highest at D1H009Q01 and decreases towards D7H005Q01 where it is the lowest overall.

Chemical data was analysed using Microsoft Excel and AquaChem 5.0 and PHREEQC. The following water quality data was used in the evaluation: pH and the concentrations of major species (all in mg/L) including sodium (Na + ), potassium (K + ), calcium (Ca 2+ ), magnesium

2+ 4+ − 3− − (Mg ), silica (Si ), fluoride (F ), orthophosphate (PO 4 ), chloride (Cl ), total alkalinity

− 2− − (TAL) assumed to be bicarbonate (HCO 3 ), sulphate (SO 4 ), nitrate (NO 3 ) (assuming that

− − NO 3 >>> NO 2 ) and the total dissolved solids (TDS). Various geochemical techniques were used to analyse data and these will be discussed below.

20 Table 4: Detailed description of the six stations studied along the Orange River (source: Google Earth, 2006, http://earth.google.com/).

Station ID D1H009Q01

Location Oranjedraai

Coordinates 30º 20’ 11.0” S 27º 21’ 31.0” E

Years 1/6/1986- 2/1/2006

Number of samples 363

Station ID D3H013Q01

Location Roodepoort

Coordinates 30º 35’ 05.0” S 25º 25’ 15.0” E

Years 1/1/1986- 2/1//2006

Number of samples 632

Station ID D3H012Q01

Location Dooren Kuilen

Coordinates 29º 59’ 28.0” S 24º 43’ 13.0” E

Years 1/8/1986- 2/3/2006

Number of samples 374

Station ID D3H008Q01

Location Marksdrift

Coordinates 29º 09’ 42.0” S 23º 41’ 47.0” E

1/24/1986- Years 2/15/2006

Number of samples 626

Station ID D7H008Q01

Location Boegoeberg Reserve

Coordinates 29º 01’ 48.3” S 22º 11’ 14.2” E

8/9/1986- Years 2/13/2006

Number of samples 604

Station ID D7H005Q01

Location Upington

Coordinates 28º 27’ 28.5” S 21º 14’ 21.2” E

1/6//1986- Years 3/6/2006

Number of samples 423

22 3.2 Accuracy of chemical analysis

During chemical analysis errors may occur one of these being accuracy errors. Accuracy errors or systematic errors show systematic deviations caused by faulty procedures or interference during analysis. Systematic errors can be tested by analysing reference samples and by interlaboratory comparison of results. The accuracy of the analysis for major ions can be approximated from the Electro Neutrality (E.N.) conditions as the sum of positive and negative ions should balance (Appelo and Postma, 1993). This can be calculated by (Appelo and Postma, 1993):

Σ cations + Σ anions E.N. (%) = 100 × (3.1) Σ cations − Σ anions

The ions used are expressed as meq/L (Appelo and Postma, 1993). The E.N. calculated for this data set (Figure 6) shows that the large majority (80 percent) of the data falls within ± six percent.

Figure 6: Electro Neutrality calculated for data used in this study indicating that most of the data falls within ± six percent. .

23 3.3 Data evaluation

Various geochemical diagrams were used to characterise and evaluate water quality. These include Stiff diagrams, Piper diagrams, Gibbs diagrams and mixing diagrams.

3.3.1 Stiff diagrams after Stiff (1951)

Stiff diagrams show the patterns of anions and cations in the water in meq/L. Different water types show different shapes and the absolute concentrations can be visualised by the width of the figure. These diagrams are constructed from the recalculation of cations and anion concentrations (normally given in mg/L) into milliequivalents per litre (Appelo and Postma, 1993). The shape formed by the Stiff diagrams will identify samples that have similar compositions (AquaChem, 1998). Stiff diagrams can be used to compare the water chemistry of different river systems or the variation of a single river system in time and space (Appelo and Postma, 1993). Four Stiff diagrams were created for each of the six sample points. The medians were worked out for the following groups of years: 1986-1990, 1991-1995, 1996- 2000 and 2001-2006. These values where then imported into AquaChem and Stiff diagrams were created.

3.3.2 Piper diagrams after Piper (1944)

A Piper diagram plots the major ions as percentages of milliequivalents on two different triangles. The total cations and the total anions are set equal to 100 percent and the data points in the two triangles are projected onto an adjacent grid. These triangles show the relative composition of the cations and anions. The diamond-shaped diagram combines the composition of cations and anions. The plot reveals useful properties and relationships for large sample groups. Piper diagrams are useful in showing distinct water quality populations

− 2− 2+ + + − (Appelo and Postma, 1993). The median values for Cl , SO 4 , Ca , Na , K , HCO 3 and Mg 2+ for each year were calculated and these were plotted on a Piper diagram to indicate the overall composition and trends for the sample points over all the years.

24 3.3.3 Gibbs diagrams after Gibbs (1970)

According to Gibbs (1970) the major cations that characterise the end-members of the world’s surface water are Ca 2+ for freshwater bodies and Na + for high-saline waters. To distinguish different mechanisms that control the chemistry of natural surface water Gibbs plotted Na + / (Na + + Ca 2+ ) against total dissolved solids (TDS) and this was used to separate rivers into three classes. These three classes include precipitation dominated, rock dominated and evaporation-crystallisation dominated. Any river composition should plot within the shaded area (Figure 7).

Rainfall has low TDS and high Na + / (Na + + Ca 2+ ), i.e. rivers with these characteristics are derived from precipitation. Weathering increases TDS and lowers Na + / (Na + + Ca 2+ ) and rivers with these properties show water that interacted more extensively with rock. Evaporation increases TDS and because calcite and gypsum eventually precipitate from water when evaporative concentration occurs, the Na + / (Na + + Ca 2+ ) ratio increases. • The precipitation dominated end-member is controlled by the amount of dissolved salts from precipitation. This occurs where the area is thoroughly leached with a low relief, the rate of supply of dissolved salts to the river is very low and the rainfall is high. The dissolved salts supplied by the rainfall are much higher than those supplied from the rocks (Gibbs, 1970). • The water associated with the rock dominated end-member is mostly in partial equilibrium with the materials in the basin. The position within the group depends on relief, climate and composition of the material in each basin (Gibbs, 1970). • The evaporation- fractional crystallisation dominated end-member extends from Ca- rich medium salinity (freshwater) rock source end-member to the Na-rich high salinity end-member. These rivers and lakes are usually located in hot arid regions. The change in concentration and composition in these rivers is usually due to evaporation

that increases salinity and precipitation of CaCO 3 from solution that increases the proportion of Na to Ca (Gibbs, 1970).

Figure 7: Variation of the weight Na + / (Na + + Ca 2+ ) as a function of the total dissolved solids of surface waters in order to characterise the mechanisms that control water chemistry (modified from Gibbs, 1970).

3.3.4 Mixing Diagrams after Gaillardet et al. (1999)

Gaillardet et al. (1999) constructed a Na + normalised diagram by using water quality data from 60 of the world’s rivers. The normalised molar ratios were used because absolute concentrations are dependent on dilution and evaporation processes i.e. normalisation automatically corrects for these effects. Rivers can be classified according to their end- member lithology through which they drain and polluted and non polluted rivers can be also be differentiated using this diagram. The rivers are classified by using a diagram that plots the

2+ + − + mole ratio of Ca /Na against the mole ratio of HCO 3 /Na (Figure 8). Waters draining carbonates show Ca 2+ and Mg 2+ dominated reservoirs and the end-member of waters draining silicates have lower Na + normalised ratios. Na + has a higher solubility than Ca 2+ so lower Ca 2+ / Na + molar ratios are expected in the dissolved load of rivers draining silicates.

+ 2+ − Figure 8: Na normalised diagram of Ca and HCO 3 showing lithological end-members for silicates carbonates and evaporates. The white dot shows average world river composition. The dark grey area shows where some polluted rivers in Europe plotted (from Gaillardet et al., 1999).

3.3.5 Degree of pollution

Chlorite, sulphate and nitrate are considered to be from atmospheric and anthropogenic sources and are thus defined as pollutants. Cl − can originate from the application of fertilisers (dominantly as KCl) as well as industrial and domestic effluent. Besides decay of organic matter, sulphate is mainly derived from air pollution and the application of fertilisers and fungicides. Nitrate is derived from atmospheric gases and aerosols by washout of combined

+ nitrogen (NO x , NH 3 , NH 4 ) as well as fertilisers (Van der Weijden and Pacheco, 2006).

− HCO 3 is a natural “pollutant” as it originates from natural processes like chemical weathering. By using the molar concentration of these pollutants Van der Weijden and Pacheco (2006) defined the degree of pollution (%) by:

− 2− − [Cl ]+ 2[SO4 ]+[NO3 ] pollution = 100× − 2− − − (3.2) [Cl ]+ 2[SO4 ]+[NO3 ]+[HCO3 ]

27 3.4 Results

3.4.1 Stream runoff per year

The total annual runoff graphs were constructed from data obtained from the Department of Water Affairs and Forestry (www.dwaf.gov.za/Hydrology/) (Appendix 1). These graphs show minimum and maximum values over the last 20 years (Figure 9). All the stations show the highest peak around 1988 and 2002 (except for D3H008Q01 where no data was available). The high peaks correlate with the seasons of highest rainfall and lowest temperature (Appendix 2).The runoff, therefore, is a good summarising parameter for climate.

The minimum runoff for all the graphs shows lowest annual runoff values for 1992, 1993 and 1995 as well as between 2003 and the end of 2005. The low peaks correlate with seasons of highest temperature and lowest rainfall (Appendix 2) relating to periods of drought in South Africa (Laing, 1994). D7H008Q01 and D7H005Q01 show the largest fluctuations in runoff. These two stations are found in the hottest, driest areas of all the stations (Appendix 2).

Figure 9: Total runoff for each sample station over the last 20 years. D7H008Q01 and D7H005Q01 show the greatest fluctuations in runoff. Note that for D3H008Q01 data were not available for the years 1988-1991.

29 3.4.2 Stiff diagrams

2+ 2+ + − The Stiff diagrams for all six stations have a similar shape (Ca > Mg > Na and HCO 3 >

2− − SO 4 ≈ Cl ), all concentrations on meq/L, indicating that there is no change in the relative concentration (Figure 10). The Stiff diagrams show that there is a lower concentration of cations and anions at the source of the river and the concentration increases downstream. For

− each individual sample point there is a slight increase in the HCO 3 concentration from 1986 to 1995, which is due to the increase of the pH from about 7 to 8. For each station the Stiff diagram gets wider from 1986 to 2006, indicating that there is an increase in cation and anion concentrations.

Figure 10: Stiff diagrams created for each sample point showing the values for the cations and anions. Four diagrams were created for each station by calculating the median values for the years 1986-1990, 1991-1995, 1996-2000 and 2001-2006.

31 3.4.3 Piper diagrams

The Piper diagrams (Figure 11) created for the major ions of the six stations (over the last 20

2+ 2+ − + years) shows that all the stations have high Ca , Mg and HCO 3 values and low in Na ,

+ − 2− + + − 2− K , Cl and SO 4 values. The Na , K , Cl and SO 4 concentrations increase downstream.

Figure 11: Piper diagrams created for each station. The Piper diagrams show the relative composition of cations and anions for all the data.

32 3.4.4 Gibbs diagrams

All the station data are situated in the rock dominant field (Figure 12). Sample stations D7H008Q01 and D7H005Q01 show a tendency of increasing TDS and Na + / (Na + + Ca 2+ ) values towards the evaporite dominated field. These sample stations are situated at Boegoeberg and Uppington where the average temperatures are the highest of the six stations and the average rainfall the lowest (Appendix 2). This reflects a change in climatic conditions (hotter and more arid conditions) which results in the precipitation of calcite. During the

2+ − precipitation of calcite (according to reaction 2.4) Ca and HCO 3 are consumed resulting in an increase of Na + relative to Ca 2+ .

Calcite precipitation can be demonstrated by calculating the Saturation Index for calcite. The

SIcalcite was calculated for sample stations D7H005Q01 and, to compare, D1H009Q01.

For D1H009Q01 (Figure 13) the saturation index is generally below 0, i.e. undersaturated in calcite. The saturation index varies with annual runoff, it increases to about 3000 million m3 where after it decreases (and becomes undersaturated) as runoff increases.

For D7H005Q01 (Figure 14) the saturation index is greater than 0 after 1993, i.e. oversaturated in calcite. It is the lowest around 1987/1988 and it increases to oversaturated from there on. D7H005Q01 is not as undersaturated at its lowest point as D1H009Q01 is. Calcite precipitation takes place at D7H005Q01.

Figure 12: Gibbs diagrams showing the area in which the data for each station plotted. The white area (which indicates where 90 percent of data plotted) shows that the data plotted in the rock dominant field. The black line outlines the area in which all the data fell.

Figure 13: Saturation index for D1H009Q01 (annual median values) and calcite saturation index against runoff. D1H009Q01 is undersaturated, there is generally no precipitation of calcite. Note for the runoff vs. SIcalcite diagrams data from the years 1986-1988 were not included to exclude the effects of the low pH (6.5-7.5) in these years.

Figure 14: Calcite saturation index for D7H005Q01 (annual median values) and calcite saturation index against runoff. D7H005Q01 is generally oversaturated, indicating precipitation of calcite. Note for the runoff vs. SIcalcite diagrams data from the years 1986-1988 were not included to exclude the effects of the low pH (6.5-7.5) in these years.

3.4.6 Mixing diagrams

In the mixing diagrams (Figure 15) created for each station the population moves towards the evaporites from D1H009Q01 to D7H005Q01. D1H009Q01 to D3H012Q01 plot between silicates and carbonates and this indicates that water chemistry is mainly controlled by chemical weathering of silicate and carbonate rocks. The last two stations (D7H008Q01 and D7H005Q01) move closer to the evaporites and this indicates that there is a decrease in

2+ + − + Ca /Na mole ratio and a decrease in the HCO 3 / Na mole ratio. However, the Orange River does not drain evaporites (Figure 4) and the trend towards evaporites can, therefore, only be explained by calcite precipitation according to reaction 2.4. (similar to the Gibbs diagrams). The majority of the data for all sample stations plots within the non polluted area of the diagram.

Figure 15: Mixing diagrams (after Gaillardet et al., 1999) for each of the six stations. All the data for each station were used in construction. The sample population moves towards the evaporites. This could be due to the presence of evaporites or the precipitation of calcite.

36 3.4.6 Degree of Pollution

The degree of pollution was calculated using equation 3.2 and plotted against the years of each (Figure 16). The pollution index for stations D1H009Q01 to D3H013Q01 is between 10 and 30 percent. D1H009Q01 fluctuates before 1990 and becomes more consistent thereafter.

D7H008Q01 and D7H005Q01 show pollution indexes that fluctuate between 25 and 60 percent. These stations are situated in hot areas where the rainfall is the lowest of all the stations (Appendix 2) and the degree of pollution is consistently irregular.

Figure 16: Degree of pollution for each station. D7H005Q01 shows the most irregular pattern of all the stations.

37 Chapter 4

Interpretation of results

The results of all the stations can be grouped into two visual trends. The stations included in each group include: • Group 1: D1H009Q01, D3H013Q01, D3H012Q01 and D3H008Q01 • Group 2: D7H008Q01 and D7H005Q01

Both groups have typical chemical characteristics as can be seen in the Gibbs and mixing diagrams. The typical climate characteristics of each group are reflected in the runoff of each group.

Group 1: The total annual runoff in the first group shows similar peaks and lower concentrations of cations and anions. The samples are situated in the rock dominance field (Figure 12) of the Gibbs diagrams and are slightly closer to the carbonates in the mixing diagrams (Figure 15). The degree of pollution is also generally consistent (Figure 16), apart from the few peaks during 1990-1995 for D1H009Q01. The stations in this group have lower average temperatures and higher average rainfall than the stations found in the second group.

Group 2: The second group shows fluctuation with distinct highs and lows and the concentration of cations and anions is slightly higher overall. The samples move closer to the evaporation dominance field in the Gibbs diagram and they move closer to the evaporites in the mixing diagrams. This is a result of calcite precipitation. The degree of pollution shows a greater fluctuation in this group. The two stations in this group have the lowest average rainfall and highest average temperature of all the stations.

The discussion will be in two parts: D1H009Q01 will be used to represent the first group and D7H005Q01 will be used to represent the second.

38 4.1 Group 1 Element concentration variations with runoff The element concentration was plotted against the total annual runoff for D1H009Q01

+ + 2+ 2+ − − (Figure 17) and the visual trend shows a decrease in Na , K , Mg , Ca , HCO 3 and F as total annual runoff increases. Si 4+ shows an increase in concentration as total annual runoff

3− 2− − − increases. PO 4 and SO 4 show no visual trend and Cl and NO 3 show a possible increase in concentration with an increase in total annual runoff (identified by the dashed line) (Figure 17).

+ + 2+ 2+ − − 4+ The general decrease of Na , K , Mg , Ca , HCO 3 and F and increase in Si (Figure 17) is due to reaction kinetics of the chemical weathering reactions. The decrease in concentration is caused by slow reaction kinetics (disequilibrium), i.e. the dilution effect is stronger than the release of cations and anions due to chemical weathering (Appelo and Postma, 1993; De Villiers et al., 2000). This is typical for rock types such as granites, shale and metamorphic rocks (Figure 18). The increase in Si 4+ indicates that feldspar is the dominant mineral that is being weathered. When chemical weathering of feldspars takes place (specifically Na- and K-feldspars) the release of Si 4+ is greater than that of the other species (Figure 19). This may explain the slight increase in Si 4+ with increasing runoff, i.e. the increase of the Si 4+ concentration due to the chemical weathering is slightly greater than the dilution effect.

Figure 17: Element concentrations against total annual runoff. Annual median values for different chemical species was calculated and plotted against total annual runoff. The visual trend (indicated by the solid line) shows a decrease in concentration with an increase in runoff except for Si 4+ , Cl − − 3− 2− and NO 3 which increases with runoff. PO 4 and SO 4 show no visual trend. A possible trend is indicated by the dashed line.

Figure 18: Relationship between TDS and annual runoff for various lithologies showing a decrease in TDS with and increase in runoff (adapted from Walling, 1980), in particular for the granites, shale and metamorphic rocks due to slow reaction kinetics (i.e. dilution is the main factor that controls the concentration).

Figure 19: Relative abundance of chemical species in water as a result of mineral weathering which results in the formation of kaolinite (adapted from Garrels and Mackenzi, 1971). The alkali feldspar releases more Si relative to other cations and anions.

41 Degree of pollution against runoff By plotting the degree of pollution (%) (equation 3.2) against runoff we see an overall increase in pollution as runoff increases for all the stations (Figure 20) which can be

− − explained by increasing Cl and NO 3 with the annual runoff. The pollution stabilises around 10000 million m3/a.

Figure 20: Pollution against runoff. The degree of pollution was calculated and plotted against the total annual runoff. The degree of pollution (annual median values) increases with an increasing total annual runoff.

42 4.2 Group 2 Element concentration variations with runoff

+ + 2+ 2+ − 2− − The visual trend for the concentration of in Na , K , Mg , Ca , Cl , SO 4 , HCO 3 and F − (Figure 21) shows a sharp decline as total annual runoff increases to about 5000 million m3, dilution is the dominant process. The concentrations of these then increase with increasing annual runoff to about 10000 million m3 as chemical weathering becomes more dominant, the effect of this is greater than the effect of dilution. After this the effect of dilution becomes greater and the species concentrations show a steady, slight decrease, similar to group 1.

Si 4+ shows a sharp increase as total annual runoff increases to 5000 million m3, thereafter it decreases slightly with increasing annual runoff (Figure 19). The increase of the Si 4+ concentration is due to chemical weathering which is stronger than the dilution effect. Similar

4+ − 3− to group 1, the increase in Si could be due to weathering of feldspar. NO 3 and PO 4 show

− 3− an increase in concentration as total annual runoff increases. The increase in NO 3 and PO 4 is most likely due to agricultural activities and urbanisation in the immediate area. As surface runoff increases it increases transport of these chemical species into the river.

Figure 18 shows the decrease in total dissolved solids for various lithologies with an increase in annual runoff. This general decrease correlates with the decrease in concentration of

4+ − 3− elements seen in group 2 (Figure 21), excluding Si , NO 3 and PO 4 . The fluctuations where runoff is low are influenced by dilution as well as chemical weathering. Temperature and erratic rainfall may also influence this as this group has a higher temperatures and lower rainfall in comparison to the first group.

Figure 21: Element concentrations against total annual runoff. Annual median values for different chemical species was calculated and plotted against total annual runoff. The visual trend (indicated by the solid line) shows a decrease in concentration with an increase in runoff except for Si 4+ , − 3− which increases with runoff. NO 3 and PO 4 that have a less clear visual trend but a possible trend (indicated by the dashed line) shows a positive trend.

44 Degree of pollution against runoff The degree of pollution (%) (equation 3.2) was plotted against total annual runoff for D7H008Q01 and D7H005Q01 (Figure 22). A decrease in pollution can be seen with decreasing annual runoff, until annual runoff reaches 5000 million m3. As annual runoff becomes greater than 5000 million m3 the pollution increases to around 10000 million m3 and thereafter it stabilises with increasing annual runoff. The fluctuation in the beginning of

− 2− Figure 24 correlates with the fluctuation of the Cl and SO 4 (Figure 21).

Figure 22: Pollution against runoff. The degree of pollution was calculated and plotted against the total annual runoff. The degree of pollution (annual median values) increases with an increasing annual runoff according to equation 3.2.

45 Chapter 5

Conclusions

The Orange River system drains about 48 percent of the total area of the country and carries about 22 percent of the total South African down-flow (Swanevelder, 1981). The climate at the source is cool and temperate but becomes more arid as the river moves towards the west (Tooth and McCarthy, 2004). The Orange River basin is highly developed and the use of water for irrigation is one of the highest. The water from the Orange River is an important resource for South Africa, especially in the arid areas (DWAF, 1997). The Department of Water Affairs and Forestry has a lot of inorganic data available for the Orange River, and for the first time, this study evaluates these data.

The results of this study show that the water chemistry of the Orange River is controlled by: 1. Chemical weathering of siliceous sediment, intrusive igneous rocks and metamorphic

+ + 2+ 2+ − − 4+ rocks (Na , K , Mg , Ca , HCO 3 , F and Si ). 2. Input from agricultural and urban activities affecting, in particular, the concentration

3− − 2− − of PO 4 , NO 3 , SO 4 and Cl .

There is an increase in cation and anion concentrations from 1986-2006. The concentration of cations and anions increases downstream from D1H009Q01 to D7H005Q01, i.e. from a colder wetter climate to a drier hotter climate.

Both the agricultural/urban input and the release of cations and anions by chemical weathering are severely influenced by the stream runoff. The stream runoff, which is measured by and available from DWAF, is an easy parameter to measure and is a good reflection of the overall climate. Any evaluation of the inorganic chemistry from the Orange River should, therefore, include runoff.

Based on the chemical characteristics, two groups were identified. The stations in each group include: Group 1: D1H009Q01, D3H013Q01, D3H012Q01 and D3H008Q01 and Group 2: D7H008Q01 and D7H005Q01.

46 Group 1: the degree of pollution is generally consistent (between 10 and 30 percent). The element concentration was plotted against the total annual runoff and the visual trend shows a

Group 2: the degree of pollution is generally higher and shows a greater fluctuation compared

+ + 2+ 2+ − 2− to group 1. The visual trend for the concentration of Na , K , Mg , Ca , Cl , SO 4 ,

− − HCO 3 and F shows a sharp decline at low runoff, dilution is the dominant process. The concentration of these then increases as chemical weathering becomes more dominant, the effect of chemical weathering becomes greater than the effect of dilution. After this the effect of dilution becomes greater and the species concentrations show a steady, slight decrease, similar to group 1. Si 4+ shows a sharp increase as total annual runoff increases to 5000 million m3, thereafter it decreases slightly. The increase of the Si 4+ concentration is due to

− chemical weathering which is stronger than the dilution effect. The increase in NO 3 and

3− PO 4 is most likely due to agricultural activities and urbanisation in the immediate area. As surface runoff increases it increases transport of these chemical species into the river.

The variation of the annual runoff affects the percentage of pollution (equation 3.2), especially the lower Orange River. Pollution shows a strong increase when the annual runoff is < 2000 m3 or around 10000 m3.

3− Currently the eutrophication of the Orange River is not a problem (PO 4 is below 0.1 mg/L).

3− However, it should be noted that with an increase in runoff PO 4 concentration increases while the saturation index for calcite decreases (Figure 13 and Figure 14).

47 3− When calcite precipitates it could decrease the concentration of PO 4 (House, 2003) but

3− because the saturation index of calcite decreases this mechanism of PO 4 removal will not

3− work. Increasing PO 4 input from agricultural and urban activities into the lower Orange River in the future may thus pose a potential eutrophication threat.

The overall average composition using median values of cations and anions can be seen in Table 7 (Appendix 3). Stiff diagrams (Appendix 3) and Piper diagrams (Appendix 3) were created from the data and these can be used as a reference for future studies.

48 References

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51 APPENDIX 1 Total annual runoff each of the stations from 1986-2006

Table 5: Total annual runoff (in million m3) for the six stations from 1986- 2006 (original data obtained from the Department of Water Affairs and Forestry). Date D1H009Q01 D3H013Q01 D3H012Q01 D3H008Q01 D7H008Q01 D7H005Q01 1986 4267.1 6565 4042 4135 4167 4209 1987 3737.3 6646.9 5423 3388 5140 5366 1988 8232 17462 16951 * 26294 25951 1989 5666.1 10600 9512 * 13637 13188 1990 3108.7 3749.4 2919 * 3255 3330 1991 5203.6 7533 5804 * 6621 6096 1992 906.2 2494.8 3452 3034 2969 2913 1993 2854.35 1640 1722.9 1489.8 948.7 1256.1 1994 3317.13 4497 3056 2839 2563 2970 1995 2251.57 1850.8 3405.5 2025 1294.7 1663.1 1996 1417.5 5124 1710 2747 9993 10796 1997 474.2 6882 6452 6699 11238 * 1998 4933.9 6096 4876.6 4577 6408 7079 1999 1593.57 2708 2237 2357 1844 2504 2000 4427.4 4978 1722 2382 8432.5 7645.4 2001 6622.7 9224 7124 7538 8595 10129 2002 5482 8893 8582 8590 5837 10641 2003 1838 2581 2430.7 2371 1693.7 1902 2004 2412.5 2715 2127.8 1798.4 1550.7 1775.4 2005 1976.6 2852 2127.9 1767.7 1327.4 1667.3 2006 4686.4 6000 4736 2131 8130 9187 * Data not available

52 APPENDIX 2 Average annual summer and winter temperature and rainfall

Table 6: Average annual winter temperature (ºC) and average annual winter rainfall (mm) (original data obtained from the South African Weather Service). D3H013Q01 D3H012Q01 D7H008Q01 D7H005Q01 (Gariep Dam) (Vanderkloof Dam) (Prieska) (Boegoeberg Dam) (Uppington) Max T Rainfall Max T Rainfall Max T Rainfall Max T Rainfall Max T Rainfall Winter 1986 * * 22.1 12.1 23.6 4.3 23.8 5.6 24.2 2.1 Winter 1987 * * 21.3 23.8 22.9 11.0 23.4 7.3 24.0 4.8 Winter 1988 18.5 32.3 20.8 29.8 21.9 10.6 22.8 7.6 23.4 16.3 Winter 1989 19.1 14.3 21.4 5.1 22.6 6.2 23.6 10.6 23.8 5.6 Winter 1990 19.1 20.3 21.3 17.8 22.5 17.0 23.4 11.3 24.1 13.2 Winter 1991 19.5 9.4 22.6 8.9 22.7 12.5 23.6 6.2 24.2 7.1 Winter 1992 20.0 6.6 22.6 6.1 22.6 8.1 23.9 0.8 25.2 0.5 Winter 1993 20.0 12.0 22.5 5.8 23.3 7.3 23.9 7.0 25.3 9.1 Winter 1994 20.0 0.7 22.3 1.2 23.5 23.5 23.7 3.3 24.9 3.1 Winter 1995 19.7 3.4 21.9 7.9 22.9 6.6 23.7 1.3 24.7 2.1 Winter 1996 18.5 9.3 20.6 10.1 21.4 6.3 22.3 5.3 23.4 6.0 Winter 1997 19.3 21.9 21.4 9.9 22.4 5.8 23.5 3.8 24.6 2.2 Winter 1998 20.0 11.4 22.8 4.3 24.1 1.7 24.5 1.1 25.5 1.1 Winter 1999 19.9 12.1 22.0 10.8 22.9 10.0 23.7 13.1 24.4 10.5 Winter 2000 19.1 28.1 20.9 39.9 23.2 12.0 23.7 15.5 24.6 6.1 Winter 2001 18.4 40.1 20.8 23.9 21.9 31.2 22.7 22.7 24.3 7.3 Winter 2002 19.7 38.9 21.4 31.1 22.9 12.3 23.2 8.0 24.6 8.3 Winter 2003 19.8 14.8 22.4 9.6 22.8 5.0 23.8 7.3 24.9 4.8 Winter 2004 19.7 32.9 22.2 26.9 23.7 17.7 * * 25.4 5.8 Winter 2005 20.7 16.1 23.1 8.3 23.9 9.6 * * 25.3 12.8 Winter 2006 17.8 29.1 20.5 52.9 21.9 25.3 * * 24.5 22.8 Average 19.7 14.8 21.9 10.1 22.9 10.0 23.6 7.1 24.6 6.0 *Data not available

Table 7: Average annual summer temperature (ºC) and average annual summer rainfall (mm) (original data obtained from the South African Weather Service). D3H013Q01 D3H012Q01 D7H008Q01 D7H005Q01 (Gariep Dam) (Vanderkloof Dam) (Prieska) (Boegoeberg Dam) (Uppington) Max T Rainfall Max T Rainfall Max T Rainfall Max T Rainfall Max T Rainfall Summer 1986/87 * * 32.0 26.1 33.8 6.5 33.8 1.3 34.0 11.0 Summer 1987/88 29.6 120.2 31.2 66.7 32.8 50.2 32.7 26.3 33.7 22.8 Summer 1988/89 26.1 46.9 28.9 83.9 30.6 64.2 31.2 41.7 32.0 31.8 Summer 1989/90 28.0 51.6 31.7 23.2 32.7 15.8 32.7 22.6 33.7 10.5 Summer 1990/91 28.6 72.4 31.4 52.6 32.1 39.5 31.9 42.7 32.9 44.1 Summer 1991//92 29.4 50.9 32.6 44.8 32.5 34.7 32.8 38.8 34.2 29.8 Summer 1992/93 29.3 26.1 32.3 26.8 29.5 8.2 32.6 13.0 33.7 16.9 Summer 1993/94 27.6 70.0 30.8 69.4 32.5 38.9 32.6 35.2 34.4 27.9 Summer 1994/95 29.8 9.2 32.7 25.9 33.5 19.5 33.2 30.8 34.6 14.2 Summer 1995/96 27.6 37.6 30.7 37.5 31.8 26.4 31.7 48.5 33.0 28.5 Summer 1996/97 28.0 53.1 30.3 63.1 31.3 31.8 31.7 35.3 32.2 31.2 Summer 1997/98 29.3 28.7 31.9 22.4 32.8 24.8 32.7 12.7 34.1 13.1 Summer 1998/99 29.9 38.9 33.2 3.5 34.1 8.9 33.6 14.9 34.7 19.4 Summer 1999/00 28.0 45.9 31.1 59.0 31.7 45.0 32.0 52.5 32.7 53.8 Summer 2000/01 29.0 29.1 32.2 15.6 34.0 13.7 33.6 11.2 29.8 13.9 Summer 2001/02 28.2 28.2 31.4 41.1 33.0 33.0 32.6 40.7 33.8 40.1 Summer 2002/03 30.0 36.3 32.9 28.2 33.7 23.5 33.6 23.1 34.9 12.1 Summer 2003/04 29.4 56.7 31.3 47.4 33.4 33.2 * * 34.3 31.5 Summer 2004/05 29.9 43.1 33.4 9.8 34.0 15.5 * * 34.9 21.6 Summer 2005/06 28.1 72.7 31.2 50.3 32.4 60.1 * * 33.2 53.4 Average 28.7 48.3 31.7 39.8 32.6 29.7 32.7 28.9 33.5 26.4 * Data not available

54 APPENDIX 3 Overall average composition of cations and anions for each station

Table 8: Median values for pH, TDS and concentrations of major elements (all in mg/L) for all the stations from 1986 to 2006. Station ID D1H009 D3H013 D3H012 D3H008 D7H008 D7H005 pH 8.0 7.9 7.9 8.1 8.2 8.2 TDS 124.0 123.8 132.0 149.0 190.0 220.0 + Na 4.4 5.4 5.9 8.0 13.8 18.0 + K 0.8 1.3 1.3 1.4 2.0 2.2 2+ Mg 6.0 5.9 6.4 7.3 9.6 11.2 2+ Ca 17.5 16.7 17.7 20.0 23.6 25.3 + 0.0 0.0 0.0 0.0 0.0 0.0 NH 4 − Cl 4.8 5.0 5.0 6.0 13.7 17.5 2− 8.4 8.1 8.8 11.9 21.0 23.6 SO 4 − 67.6 65.1 70.6 80.0 91.3 104.3 HCO 3 − 0.3 0.6 0.5 0.5 0.3 0.2 NO 3 F − 0.1 0.2 0.2 0.2 0.2 0.2 4+ Si 8.6 8.1 8.0 7.4 6.7 6.7 3− 0.03 0.03 0.02 0.02 0.02 0.02 PO 4

Figure 23: Stiff diagrams indicating the overall composition of cations and anions for each station

Figure 24: Piper diagrams for each station indicating the overall relative composition of cations and anions.