small reservoir non-point source pollution …

i

SMALL RESERVOIR NON-POINT SOURCE POLLUTION IDENTIFICATION AND WATER QUALITY MONITORING FOR DOMESTIC, LIVESTOCK AND IRRIGATION USE IN

MZINGWANE CATCHMENT (ZIMBABWE)

By

CHIPO MASONA

A thesis submitted in partial fulfilment of the requirements of the degree of Master of Science in Soil and Environmental Management

Department of Soil Science and Agricultural Engineering

Faculty of Agriculture University of Zimbabwe

July 2007

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ABSTRACT

The use of soil amendments (manure and fertilizers), livestock production and small scale industries has led to increased concern on their environmental impacts, particularly on water quality. The main objective of this study was to assess the spatial and temporal water quality variation in small reservoirs as a function of usage, and to identify non point source pollution of the reservoirs’ watersheds. A study was carried out in Mzingwane subcatchment for eight months to determine the biological (total coliforms, faecal streptococci and faecal coliforms) and physico-chemical (pH, hardness, electrical conductivity, nitrate and chloride) water quality parameters of three small reservoirs (Avoca, Bova and Sifinini) and one medium sized reservoir Siwaze, for comparison. The study included non point source pollution identification of the respective four watersheds and a consumers’ water quality perception survey (taste, colour, soap consumption, frothing when boiling and smell).

The 10mg/l WHO (1984) guideline for nitrate in drinking water was exceeded in March and April for all four reservoirs but the water was within this guideline for the rest of the study months. pH ranged between 6.18 to 10.45 and was generally within the FAO irrigation guidelines of 6.5 to 8.5. Electrical conductivity ranged from 37 to 320.67µS/cm, which was within the FAO irrigation water guideline of 700µS/cm and within the derived WHO drinking water guideline of 1380 µS/cm. Total coliform count ranged from 12 to 1100+ counts /100 ml, streptococii ranged from 0 to 427 counts/100 ml. Most faecal coliform counts were above the WHO drinking water guideline of 0 counts/100ml and the DWAF (1996a) 10/100ml counts for total coliforms. There was a negative Pearsons’ correlation coefficient between rainfall and total coliform counts and between rainfall and faecal streptococci (r = -0.11552 and r = -0.04388) respectively. For all the water quality perceptions the majority of the respondents indicated that water quality was satisfactory during the wet season, 80.2%, 66.5%, 75.8 and 92.9% for colour, taste, smell and soap consumption respectively. Most of the respondents indicated that they use animal manure (75.1%) as soil amendments and only 21.1 % and 5.9% use Compound D and ammonium nitrate fertilizers respectively. Non-point source pollutants calculated as pollutant loading ranged from 13.5 mg/s to 117.3 mg/s for hardness, 2.9 mg/s to 96.7 mg/s for chloride and 95 to 132 mg/s for EC. The conclusion reached was that water quality in all the reservoirs is not suitable for drinking purposes but can be used for laundry, livestock watering and irrigation purposes. It was also concluded that non point source pollution, originating from homesteads and farming fields affects water quality in small reservoirs and pollution varies depending on the watershed area, and the activities within a specific watershed. It was recommended that the villagers should boil the water or use sodium hypochlorite (Jik) to purify the water before drinking. It was also recommended that further water quality studies including sediments and ground water should be carried out to confirm this conclusion.

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DECLARATION

I CHIPO MASONA hereby declare that this thesis is my own composition. I generated

the results presented except where clearly and specifically acknowledged at the Department of Soil Science and Agricultural Engineering, Biological Science Department

at the University of Zimbabwe and the Institute of Mining and Research.

Date: _______________________________________

Signed: _____________________________________

CHIPO MASONA

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DEDICATION

To my husband Tonderai Jamu, and my family as a whole.

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ACKNOWLEDGEMENTS

I would like to thank my supervisor Dr. A. Senzanje for guiding, assisting and

understanding me throughout the whole project.

Muchaneta Munamati, my sister in this thesis, thanks for giving me pressure, then I

thought you were just being difficult, but in the end I realised you were doing it for my

own good. Thanks sis.

To the very first MSc (2005-2007) group in the Department of Soil Science and

Agricultural Engineering (Charity Pisa, Juliana Mupini, Esther Masvaya, Mketiwa

Chitiga, Gabriel Soropa and Josiah Mukutiri) I say, thank you for being with me through

thick and thin. These two years were the best university years that I have ever had, may

The Almighty Bless You in abundance.

My project mates, Geoffrey Mamba, Sifiso Ncube, Roick Chikati and Ngonidzashe

Mufute, not forgetting Mr E. Chitopo for being with me in the field, your assistance is

gratefully appreciated. Thank you guys.

Mr Ncube and his family who were available every time, thank you for all the field

assistance, as well as the two enumerators, Zibonele Mahlangu and Thabo Nkomo. My

gratitude goes to Mr Tshuma of Denje who constantly supplied us with imibhida. The

Avoca community for making this project what it is, thank you.

T.J., Love, I will rather say my thanks to you for everything in person. My greatest thanks go to the Small Reservoir Project (SRP) within the Challenge Program

for funding this project.

Thank you Lord.

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TABLE OF CONTENTS

ABSTRACT ...................................................................................................................ii DECLARATION ............................................................................................................iii DEDICATION................................................................................................................iv

ACKNOWLEDGEMENTS..............................................................................................v

TABLE OF CONTENTS................................................................................................vi LIST OF TABLES .........................................................................................................ix

LIST OF FIGURES ........................................................................................................x

LIST OF APPENDICES ................................................................................................xi ABBREVIATIONS AND GLOSSARY..........................................................................xii CHAPTER ONE .............................................................................................................1

1.0 INTRODUCTION......................................................................................................1

1.1 JUSTIFICATION ......................................................................................................2

1.2 GENERAL OBJECTIVE ......................................................................... 4 1.3 SPECIFIC OBJECTIVES........................................................................ 4 1.4 HYPOTHESES ....................................................................................... 4 1.5 THESIS STRUCTURE ............................................................................ 5

CHAPTER TWO ............................................................................................................6

2.0 LITERATURE REVIEW ...........................................................................................6

2.1 BACKGROUND: SMALL DAMS IN ZIMBABWE .................................. 6 2.2 PHYSICAL WATER QUALITY PARAMETERS...................................... 7

Temperature ..............................................................................................................7 pH..............................................................................................................................7 Electrical Conductivity (EC).....................................................................................8

2.3 CHEMICAL WATER QUALITY PARAMETERS .................................... 8 Nitrate .......................................................................................................................8 Hardness....................................................................................................................9 Chlorides ...................................................................................................................9

2.4 BIOLOGICAL WATER QUALITY PARAMETERS ................................. 9 Faecal Contamination ...............................................................................................9

2.5 DRINKING WATER .............................................................................. 10 2.6 WATER QUALITY PERCEPTIONS...................................................... 11 2.7 WATER QUALITY MANAGEMENT POLICIES .................................... 11 2.8 NON-POINT SOURCE POLLUTION IDENTIFICATION....................... 12 2.9 EMERGING ISSUES................................................................................. 12

CHAPTER THREE.......................................................................................................13

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3.0 GENERAL MATERIALS AND METHODS ...........................................................13

3.1 STUDY AREA....................................................................................... 13 3.1.1 LIMPOPO RIVER BASIN...........................................................................13 3.1.2 MZINGWANE CATCHMENT.....................................................................14

Physical factors in the Mzingwane catchment that may influence water quality. ..15

CHAPTER FOUR.........................................................................................................17

SUITABILITY OF SMALL RESERVOIR WATER FOR DOMESTIC, IRRIGATION AND LIVESTOCK USE AS DETERMINED BY PHYSICO-CHEMICAL AND BIOLOGICAL

WATER QUALITY PARAMETERS IN MZINGWANE CATCHMENT..........................17

4.1 INTRODUCTION .................................................................................. 17 4.2 MATERIALS AND METHODS.............................................................. 18

4.2.1 SAMPLE COLLECTION ...........................................................................18 4.2.2 ANALYSIS OF PHYSICO- CHEMICAL WATER QUALITY PARAMETER ...18 4.2.3 MICROBIOLOGICAL ANALYSES ..................................................................19

4.3 RESULTS ............................................................................................. 20 4.3.1 BIOLOGICAL PARAMETERS....................................................................20

Bacterial Coliforms.................................................................................................20 4.3.2 PHYSICO-CHEMICAL PARAMETERS ...........................................................21

EC ...........................................................................................................................21 pH............................................................................................................................22 Hardness..................................................................................................................23 Chloride...................................................................................................................23

4.3 DISCUSSION........................................................................................ 25 4.4 CONCLUSION...................................................................................... 28

CHAPTER FIVE...........................................................................................................29

VILLAGERS’ WATER QUALITY PERCEPTIONS (COLOUR, SMELL, TASTE, SOAP CONSUMPTION AND FROTHING WHEN BOILING) OF SMALL RESERVOIRS IN MZINGWANE CATCHMENT........................................................................... 29 5.1 INTRODUCTION .................................................................................. 29 5.2 MATERIALS AND METHODS.............................................................. 29 5.3 RESULTS ............................................................................................. 30

5.3.1 PROFILE OF THE RESPONDENTS ................................................................30 5.3.2 WATER QUALITY PERCEPTIONS ...........................................................30 5.3.3 ANIMAL MANURE AND FERTILIZER USE.............................................32 5.3.4 RESERVOIR WATER USES .......................................................................33

Avoca ......................................................................................................................33 Bova ........................................................................................................................35 Sifinini.....................................................................................................................36 Siwaze .....................................................................................................................36

5.3.5 WATER AVAILABILITY IN RESERVOIRS........................................................38 5.4 DISCUSSION .......................................................................................... 39 5.5 CONCLUSION.......................................................................................... 41

CHAPTER SIX .............................................................................................................42

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NON POINT SOURCE POLLUTION IDENTIFICATION AND POLLUTION LOADING IN AVOCA GROWTH POINT, MZINGWANE CATCHMENT. .....................................42

6.1 INTRODUCTION .................................................................................. 42 6.2 MATERIALS AND METHODS.............................................................. 42

6.2.1 POLLUTION LOADING ...................................................................................42 6.2.2 NON-POINT SOURCE POLLUTION IDENTIFICATION ........................43

6.3 RESULTS ............................................................................................. 43 6.3.1 POLLUTION LOADING CALCULATIONS...............................................43

6.3.2 NON -POINT SOURCE POLLUTION IDENTIFICATION....................................45

6.4 DISCUSSION........................................................................................ 47 6.5 CONCLUSION...................................................................................... 48

CHAPTER SEVEN.......................................................................................................49

GENERAL DISCUSSION CONCLUSION AND RECOMMENDATIONS ....................49

7.1 GENERAL DISCUSSION.......................................................................................49

7.2 CONCLUSION .......................................................................................................51

7.3 RECOMMENDATIONS..........................................................................................51

REFERENCES.............................................................................................................53

APPENDICES..............................................................................................................58

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LIST OF TABLES

Table 3.1: Reservoir location, age, catchment area and estimated volume of reservoirs……………………………………………………………………………..16 Table 5.1: Profile of respondents for the reservoirs Avoca, Bova, Sifinini and Siwaze………………………………………………………………………………...31 Table 5.2: Villager’s water quality perceptions of the small reservoirs……………...31 Table 5.3: Percentage use of Compound D, Ammonium Nitrate, and Animal manure in

each of the four watersheds………………………………………………….….33 5.4: Months in which the water is available in the reservoir……………….………...38 Table 6.1: Mean concentration of ( Cl-, Hardness, and EC) and average flow rates for

stream leading to Avoca, Bova, Sifinini and Siwaze reservoirs)………………..44 Table 6.1: Pollution loading of (NO3, Cl-, Hardness, And EC)……………………...44

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LIST OF FIGURES

Figure 3.1: Limpopo River Basin Map 13 Figure 3.2: Mzingwane Map 14 Figure 3.3: Map showing the location of Siwaze in relation to the small reservoirs Avoca, Bova, Sifinini 16 Figure 4.1: Variation of mean total coliforms counts and average rainfall with time

(months) in reservoirs Avoca, Bova, Sifinini and Siwaze 20 Figure 4.2: Variation of mean faecal streptococci counts and average rainfall with time

(months) in reservoirs Avoca, Bova, Sifinini and Siwaze 21 Figure 4.3: Variation of mean EC with time for reservoirs Avoca, Bova, Sifinini and

Siwaze 22 Figure 4.4: Variation of mean pH with time for reservoirs Avoca, Bova, Sifinini and

Siwaze 23 Figure 4.5: Variation of mean hardness with time for reservoirs Avoca, Bova, Sifinini and Siwaze 24 Figure 4.6: Variation of mean chloride with time for reservoirs Avoca, Bova, Sifinini and

Siwaze 24 Figure 5.1: Villagers’ water quality perceptions for colour, taste, smell, soap

consumption and frothing when boiling for the four reservoirs during the dry and wet seasons) 32

Figure 5.2: Water sources uses in the dry and wet season and % households interviewed

in the Avoca watershed (N = 18) 34 Figure 5.3. Woman abstracting water along a river during the 2006/07 rainy season 35 Figure 5.4: Water sources uses in the dry and wet season and % households interviewed

in the Bova watershed (N=8) 36 Figure 4.3: Water sources and uses in the dry and wet season and % households

interviewed in the Sifinini watershed (N = 12) 37 Figure 4.4: Water sources and uses in the dry and wet season and % households

interviewed in the Siwaze watershed (N = 16) 38

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Figure 4.6. Mean monthly rainfall for the 2006/2007 rainy season, adapted from ZINWA Siwaze station 39

Figure 6.1: Small reservoir location in Siwaze watershed 45 Figure 6.2: Figure 6.2 Study site location in relation to Mzingwane Catchment 46 Figure 6.3: Figure 6.3: Non point source pollutants in each reservoir’s (Avoca, Bova, Sifinini and Siwaze) watershed. 46

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LIST OF APPENDICES APPENDIX 1 Number and capacity of dams per province in Zimbabwe 54

APPENDIX 2 Effects of chloride on the health of livestock (DWAF, 1996) 55

APPENDIX 3 Questionnaire to investigate the villagers’ water quality perception 56

APPENDIX 4 Analysis of Variance tables 61 APPENDIX 5 Pollution loading calculations 64

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ABBREVIATIONS AND GLOSSARY

BMP Best Management Practices DWAF Department of Water Affairs and Forestry (Republic of South Africa) FAO Food and Agricultural Organisation IPC Integrated Pollution Control ICM Integrated Catchment Management ICOLD International Commission on Large Dams MDG Millennium Development Goals NPS Non-point source SAZ Standards Association of Zimbabwe WHO World Health Organisation

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CHAPTER ONE

1.0 INTRODUCTION

Water is a vital resource to support all forms of life on earth. Unfortunately, it is not evenly distributed

over the world by season or location. Some parts of the world are prone to drought, making water a

scarce and precious commodity, while in other parts of the world it appears in raging torrents causing

floods and loss of life and property. Due to the scarcity of clean water, financial constraints and the

variability of the water sources, rural communities resort to using untreated water directly from the

water sources such as rivers, reservoirs and boreholes (Pirages, 2005). This poses environmental and

health hazards to the concerned communities. There is therefore need for temporal and spatial water

quality analysis and monitoring of the major rural water sources.

Water quality of surface or ground water is defined as a function of either or both natural influences

and human activities. The natural influences that determine water quality are weathering of bedrock

minerals, atmospheric processes of evapotranspiration and the deposition of dust and salt by wind,

natural leaching of organic matter and nutrients from soil, hydrological factors that lead to runoff, and

biological processes within the aquatic environment that can alter the physical and chemical

composition of water (GEMS/Water Program, 2006). Generally water has multiple uses and as such

water quality requirements and consumer perceptions differ for different water uses, such as domestic

or agriculture, therefore water quality should be determined according to different uses. Water quality

requirement for a particular use plays an important role in the management of water resources and in

turn forms an integral part of water quality management (Parsons and Tredoux, 1995).

Reservoirs are usually constructed for several purposes. According to ICOLD (1998), 48% of the

world reservoirs are for irrigation, and 20% for hydropower generation. The rest are mainly for flood

control, domestic and industrial water supply, and recreation. Of the small reservoirs in Southern

Africa (excluding South Africa), 86% are found in Zimbabwe, which constitutes only about 6.8% of

the geographic area in the region (Sugunan, 1997). An initial analysis of the reservoirs in Zimbabwe

indicated that about 60% of them are less than 1 million m3 (Chimowa and Nugent, 1993). These

small reservoirs were mainly developed in the former large scale commercial farms and communal

areas, each sector constituting 61% and 39% of the total number of small reservoirs respectively. It is

however estimated that there are approximately 1 000 small reservoirs in the semi arid Limpopo River

2

Basin (Sawunyama, 2005). The importance of these reservoirs lies mainly in their multiple uses,

which include domestic use, livestock watering, small-scale irrigation, fishing and brick making.

Small reservoirs also provide the surrounding communities with the Cypress spp, reeds that they use

for thatching (Rusere, 2005).

Despite the multiple uses of small reservoirs and their abundance, there has been an urban bias

regarding water quality studies in Zimbabwe, which is unfortunate given that about 60% of the

country’s population lives in rural areas (FEWS NET, 2004). Water quality in small reservoirs can be

contaminated or polluted through different activities, such as small rural industries for instance brick

making, and through runoff and through flow from agricultural lands, pastures and blair toilets. The

pollution or contamination can be direct (point source pollution) where the source of pollution is

known, or indirect (non point source), where the source of pollution is diverse and diffuse. Hussein et

al., (2000) found direct contamination from agricultural chemical contamination of water sources in

Seke, Zimbabwe. Indirect contamination through discharge from dip tanks into nearby water bodies

has been reported (Mandizha, 1995), which raises the potential danger posed to aquatic ecosystems,

livestock and the people using the water. It is essential to identify non- point source pollution as it

results in the estimation of all the possible pollutant sources in the reservoirs and aids in the water

quality management of these reservoirs.

1.1 JUSTIFICATION

In water scarce arid and semi arid regions, small reservoirs serve as a drinking water, irrigation and

livestock-watering source. Global estimates suggest that nearly 1.5 billion people lack safe drinking

water and that at least 5 million deaths per year can be attributed to waterborne diseases (Scheelbeek,

2005). The ability to properly track progress toward minimizing impacts on reservoir water quality

and improving access of humans to safe water depends on the availability of baseline data that

document trends both spatially and temporally. Therefore continuous monitoring of reservoir water

quality is necessary to reduce negative impacts on human health, irrigation and livestock quality.

In the arid and semi-arid regions, livestock commonly use poor or marginal quality drinking water for

several months of the year. These supplies originate from small reservoirs, canals and streams. The

small reservoirs in Zimbabwe’s Matabeleland South province were constructed mainly for mitigation

of drought effects and are mainly used for livestock watering (Chimowa and Nugent,1993) . Poor

3

water quality for livestock watering normally results in reduced water and feed consumption,

physiological upset or even death in livestock. The reduced water and feed consumption is usually

caused by a water imbalance rather than related to any specific ion. Water quality parameters that

affect the palatability of water for livestock include total dissolved solids (TDS), nitrates, pH and

microorganisms (Bolyes, undated). These parameters may occur naturally in water or are as a result of

human influence.

The water quality requirements for irrigated agriculture unlike that for domestic and livestock use

depend mainly on physical and chemical parameters such as TDS (total amounts as well as the type of

the salts), calcium, sodium chloride and boron. Irrigation water quality or suitability for use therefore

depends on the potential severity of problems that can be expected to develop during long-term use.

These problems depend on soil type, climatic condition of the area and crops grown. As a result,

suitability of water for irrigation use is determined by the conditions of use which affect the

accumulation of the water constituents and which may restrict crop yield. The soil problems most

commonly encountered and used as a basis to evaluate water quality are those related to salinity, water

infiltration rate and toxicity. For example salts in soil or water reduce water availability to the crop to

such an extent that yield is affected (FAO, 2003).

The urban bias in terms of water quality studies in Zimbabwe has resulted in the rural communities to

rely more on their perceptions of water quality for uses such as domestic uses. Consumer water

quality perceptions give an indication of the baseline information on the water quality from one

season to another. However, these perceptions are based on human senses, for example taste and

smell, which are themselves subjective as they are dependant on an individual senses. In order to

determine the suitability of the water source for a particular use (domestic, irrigation and livestock),

there is need to integrate these consumer perceptions with laboratory water quality analysis. Hoko

(2005), in a study in Gokwe, recommended that studies linking the water quality both measured and

perceived should be carried out to find the effect of geology and pollution on rural water quality.

Water quality in small reservoirs is therefore an important aspect of water resources management in

arid and semi arid regions. It is a key catalyst for development and conservation because it determines

the spatial and temporal dynamics of aquatic organisms and drives various water uses (Mwaura,

2000). Determining water quality of small reservoirs aids in integrated pollution control (IP

C) and integrated catchment management (ICM) because small reservoirs water sources depend on

4

catchment runoff. Water quality management therefore contributes both directly and indirectly to

achieving the targets set out in the Millennium Development Goals (MDG’s), specifically MDG target

goal 7, “To ensure environmental sustainability”.

1.2 GENERAL OBJECTIVE

To assess the spatial and temporal water quality variation in small reservoirs as a function of usage,

and to identify non-point source pollution of the reservoirs’ watersheds.

1.3 SPECIFIC OBJECTIVES

The specific objectives were:

1. To determine the suitability of small reservoir water quality for domestic, irrigation and livestock

use, by analysing selected physico-chemical parameters (chloride, nitrate, water hardness, pH,

temperature, and electrical conductivity).

2. To determine biological water quality of small reservoirs based on the presence of selected

pathogenic organisms (total coliforms, streptococci and faecal coliforms).

3. To determine villagers’ water quality perceptions (taste, odour, soap consumption and colour),

using structured interviews.

4. To identify non-point source pollution in the small reservoirs (Avoca, Sifinini, Siwaze and Bova)

watersheds.

1.4 HYPOTHESES

1. There is spatial (in relation to watersheds) and temporal (seasonality) variation of water quality of

small reservoirs.

2. Water in small reservoirs is not suitable for small-scale livestock watering, irrigation and domestic

use according to World Health organisation (WHO), Food and Agricultural Organisation (FAO)

and Department of Water Affairs and Forestry (DWAF) standards.

3. There is correlation between analysed laboratory water quality parameters and the community

water quality perceptions

4. Non point source pollution significantly affects water quality in small reservoirs.

5

1.5 THESIS STRUCTURE

The first chapter gives an introduction and justification of the thesis. Literature on water quality in

small reservoirs is reviewed in Chapter 2. Chapter 3 presents an outline of the general materials and

methods of the project. The laboratory water quality is discussed in chapter 4. Chapter 5 is the

consumer water quality perceptions results and discussion. This chapter includes the agricultural and

reservoir information in the watersheds. Non point source pollution identification and pollution

loading into the watersheds is explained and discussed in chapter 6. The final chapter, chapter 7

discusses the whole thesis giving detail on the major findings and recommendations for further study.

This chapter also includes the conclusion.

6

CHAPTER TWO

2.0 LITERATURE REVIEW

2.1 BACKGROUND: SMALL DAMS IN ZIMBABWE

Small reservoirs like other reservoirs are storage structures used to store and capture runoff water;

however categorization of a reservoir as being large or small varies widely across the world. In

Zimbabwe small reservoirs are defined as storing less than 1 million cubic metres of water and have

less than 8 metres height (Kabell, 1986). Small reservoirs impound, partial and temporal precipitation

from a given watershed (the land area that is drained by a particular river, stream or creek), which is

then used for multiple purposes, such as, irrigation, livestock watering, brick making, domestic use

and recreation (Sugunan, 1997; Keller et al., undated).

The multiple uses of small reservoirs make them very important for the improvement of livelihoods of

the rural society (Stevenson, 2000). Livelihoods can be defined as the means people use to support

themselves, to survive, and to prosper. Livelihoods can also be viewed as an outcome of how and why

people organize to transform the environment to meet their needs through technology, labour, power,

knowledge, and social relations (Wim van der Hoeck, 2001). Livelihoods are therefore shaped by the

broader economic and political systems within which they operate. Water is the essential element in

rural livelihoods because of the food security and income options it generates in rainfed and irrigated

crop production, industry, domestic use, livestock and recreation. Safe water and sanitation also

influence the health of the community through potable water supply, safe food preparation, hygiene

and improved nutrition. (Wim van der Hoeck, 2001).

In the early 1990s, Zimbabwe experienced severe droughts and the semi arid Matabeleland South

Province was greatly affected. The droughts resulted in the development of more reservoirs in

Zimbabwe. The main objectives of developing small reservoirs in Zimbabwe were to mitigate the

drought effects by providing sources of water for domestic uses, creation of new irrigated areas and

recharge groundwater (Chimowa and Nugent, 1993). About 10% of rainfall is lost as runoff in semi-

arid areas of Zimbabwe. This runoff is sufficient to fill small to medium reservoirs in which rural

7

communities depend on in most years except the years when there is little or no runoff (Mugabe et al.,

2004). Matabeleland South, which is a province concentrating mainly on livestock rearing, had a very high

density of dams in 1993 (23% of the national total (9818), but these represented only 11% of the total

water capacity of Zimbabwe’s dams (Table 2.1) because most of the dams in this area are relatively

small and are built mainly for livestock watering (Chimowa and Nugent, 1993).

2.2 PHYSICAL WATER QUALITY PARAMETERS

Temperature

Temperature may be the most important single factor affecting the occurrence and behaviour of the

life and chemicals in surface water. It affects practically every physical factor that is of concern in

water quality management in that it alters the density, viscosity, vapour pressure and surface tension

of water. It also affects the rate of biological and chemical reactions (Ellis, et al., 1989). Some water

quality parameters, such as electrical conductivity and dissolved oxygen vary in concentration with

temperature. For example an increase in temperature accelerates the process by which aerobic

microorganisms decompose organic material in the water, which, in turn, increases the demand for

oxygen. As temperature rises the amount of oxygen that the water can hold decreases although the rate

at which atmospheric oxygen is able to re-dissolve into de-oxygenated water increases (Ellis, et al.,

1989).

pH

pH is an unstable important variable in water quality assessment as it influences many biological and

chemical processes within a water body (Chapman, 1996). At a given temperature, pH indicates the

intensity of the acidic or basic character of a solution and is controlled by the dissolved chemical

compounds and biochemical processes in a solution. The general state of a reservoir can be estimated

by pH (Tilman et al., 1982). Water pH in reservoirs range from 5 to 10 but it can fluctuate upwards or

downwards as a result of changes in photosynthetic activity (Moehl and Davies, 1998). Factors such

as the taste of water, its chlorinating efficiency and the solubility of metal ions are influenced by pH.

For example at low pH water may have a sour taste, while at high pH the water may have a soapy

taste (Kempster and van Vliet, 1991). Small changes in pH often causes large changes in the

8

concentration of available metallic complexes and can lead to significant increases in the availability

and toxicity of most metals (DWAF, 1996c).

Electrical Conductivity (EC)

The electrical conductivity (specific conductance) of water is an expression of its capacity to conduct

a current and is related to the concentration of free ions such as Ca2+, Mg2+, NO-3, Fe2+, Na+and AL3+

and to water temperature (Goldman and Horne, 1983). The type of bedrock and soil in the watershed

affects conductivity. It is also affected by human influences, for example, the use inorganic fertilizers

results in agricultural runoff high in phosphate and nitrate. Conductivity provides a convenient

estimate of the Ca2+ and Mg2+ content and thus the quality of water. If the levels of Ca, Mg and

chlorides, as a group or alone, are too high in soils, they result in reduced crop growth. The effect of

high EC in the soils is similar to drought-stressed effects; due to this an osmotic gradient on salty soils

is formed. Water uptake by plant roots is increasingly restricted as the concentration of soil salts

increases. Conductivity measurement is expressed in microsiemens per centimetre (µS/cm) at 25

degrees Celsius. Conductivity measures can be converted to total dissolved (TDS) values by

multiplying EC by a factor that varies with the type of water. A suggested range is 0.55-0.9 for this

factor (Sawyer et al., 1994).

2.3 CHEMICAL WATER QUALITY PARAMETERS

Nitrate

The nitrate ion is the common form of combined nitrogen found in natural waters. Natural sources of

nitrate to surface waters include igneous rocks, land drainage and plant and animal debris. In rural and

suburban areas, the use of inorganic nitrate fertilizers is a major source. When influenced by human

activities, surface water can have nitrate concentration up to 5 mg l-1 NO3-N but often less than 1 mg

l-1 NO3-N. However the World Health Organisation (WHO) recommended limit for NO3- in drinking

water is 50 mgl-1 (Chapman, 1996). The determination of nitrate in surface water gives a general

indication of the nutrient status and the level of organic pollution. Nitrate does not cause direct toxic

effects, but in the reduced form, nitrite it is 10-15 times more toxic than nitrate. Nitrate oxidises

haemoglobin to methaemoglobin, which unlike haemoglobin cannot transport oxygen in body tissues.

Suffocation due to a lack of oxygen in the tissues then occurs. This condition normally occurs in

babies and is called the methaemoglobinemia, blue baby syndrome (DWAF, 1996b).

9

Hardness

The term "hardness" is an indication of the presence of usually calcium and magnesium carbonates

that reduce the lathering of soaps (Chapman, 1996). Water hardness gives a reflection of the geology

of the geology area. At times it gives a measure of the influence of human activity in the area, for

example acid mine drainage as water hardness also includes Fe 2+. However hardness is more of a

reflection of the amount of Ca and Mg entering the reservoir through the weathering of rocks such as

limestone (Kreger, 2004). Approximately 22% of the earth’s fresh water is ground water, and as it

flows through soil originating from limestone rocks, it picks up minerals Ca and Mg carbonates.

(National Consumer Water Quality Survey, 1997).

Chlorides

Effects of chloride on human health may occur at very high levels above 1 200mg/l by disturbance of

the electrolyte balance and nausea. Infants are susceptible and fatalities due to dehydration may occur

(DWAF, 1996a). High levels of chloride in water may render it unpalatable for most livestock.

Poultry, pigs and sheep are more susceptible to excess chloride as indicated by Table 2.2 in

appendices.

2.4 BIOLOGICAL WATER QUALITY PARAMETERS

Faecal Contamination

A high health risk is associated with the consumption of drinking water that is contaminated with

bacteria and parasites from human and animal excreta. This is a major cause of diarrhea. Worldwide

diarrhea hits 1.5 billion people per year and kills five million, mainly children under five (Scheelbeek,

2005). The main pathogens are E.coli bacteria and Cryptosporidium and Giardia parasites.

Indicator organisms

General coliforms, E. Coli, and Enterococcus bacteria are the "indicator" organisms generally

measured to assess microbiological quality of water. However, these are only used to indicate the

presence of pathogenic microorganisms and are themselves not harmful. It is difficult and expensive

to detect some of the pathogenic microorganisms and it is therefore common practice to use microbial

indicators as an indicator of recent faecal pollution and the potential risk of infectious diseases from

the water (WRC, 1998). Indicator microbes are generally selected for the following reasons:

10

They are initially abundant in the sampling material (water, soil) to be assayed.

A relatively rapid, accurate, and cost effective analytical method for enumerating the indicator

organisms exists or can be readily developed.

A reasonably strong correlation exists between the presence/absence of the indicator and a

particular pathogen or group of pathogens. The strength of the correlation will determine the

effectiveness and accuracy of the indicator as a measure of pathogen occurrence.

General coliforms indicate that the water has come in contact with plant or animal life. General

coliforms are universally present. They are of little concern at low levels, except to indicate the

effectiveness of disinfection. At very high levels they indicate there is what amounts to a lot of

compost in the water, which could easily include pathogens (Oasis design, 1997).

Faecal coliforms are a collection of relatively harmless micro-organisms that live in large numbers in

the intestines of humans and other warm-blooded animals where they aid in the digestion of food.

Escherichia coli bacteria normally inhabit the intestines of all animals and humans, but a minority of

the strains may cause human illnesses with severe cramping (abdominal pain) and diarrhoea,

especially in young children and elderly (Scheelbeek, 2005). Faecal coliforms are used to indicate the

presence of bacterial pathogens such as Salmonella spp., Shigella spp. and Vibrio Cholerae. These

organisms can be transmitted via the faecal/oral route by contaminated or poorly treated drinking

water and may cause disease such as gastroenteritis, salmonellosis, dysentery, cholera and typhoid

fever (DWAF, 1996a).

2.5 DRINKING WATER

Water that is directly ingested by human beings, without any treatment requires the highest water

quality standards (Scheelbeek, 2005). Illnesses that can occur by drinking contaminated water are very

diverse, but most of the times symptoms like diarrhoea and vomiting occur. At any given time, about

half the population in the developing world is suffering from one of these diseases associated with

water supply and sanitation. About 400 children below the age of five die per hour in the developing

world from waterborne diarrhoeal diseases (Gadgil, 1998). Due to the scarcity of clean water, financial

constraints and the variability of the water sources, rural communities in Avoca resort to using

untreated water directly from small reservoirs. This results in increased incidences of water borne

11

diseases, therefore there is need to analyse water quality in these water sources to reduce the risk of

these water borne diseases.

2.6 WATER QUALITY PERCEPTIONS

Water quality perceptions of a water body are best obtained through the participation of primary

stakeholders. Water quality perceptions provide background water quality of the water body and

include perceptions such as colour, smell/odour, taste frothing when boiling and soap consumption.

The perceived colour of water determines the depth to which light is transmitted. The colour can be

measured as true or apparent colour. Natural minerals such as ferric hydroxide and organic substances

such as humic acids results in true colour of the water while apparent colour is caused by coloured

particulates and the refraction and reflection of light on suspended particulates. Therefore polluted

water tends to have a strong apparent colour.

Water odour or smell is usually the result of labile volatile organic compounds and may be produced

by aquatic plants or decaying matter. The smell/odour of water can be measured I terms of the greatest

dilution of a sample, or the number of times a sample of water has to be halved with odour free water

that yields the least definitely perceived odour.

2.7 WATER QUALITY MANAGEMENT POLICIES

Untreated or insufficient treated industrial and municipal wastewater, inappropriate agricultural

practices and poor quality mining and industrial effluent constitute the main causes of water pollution

in southern Africa (Moyo and Mtetwa 2000). However the main causes of surface and ground water

pollution in rural areas in Zimbabwe are poor quality mining and inappropriate agricultural practices.

The main tools of water quality management that should be incorporated include, receiving water

quality objectives, effluent discharge standards, planning tools, best management practices and whole

effluent toxicity approaches and biomonitoring. Though only a few of these tools are applicable in a

rural setup such as Avoca Growth Point where there are no large scale industrial and mining activities,

management of rural water supplies in Zimbabwe is not formally written down into a policy document

and the government is responsible for development, operation and maintenance of the water supplies.

Effective water quality management policies require the cooperation of the government, industries and

12

the general public. Where these policies are not implemented at a national level, other international

water quality guidelines such as the WHO, DWAF and FAO are used.

2.8 NON-POINT SOURCE POLLUTION IDENTIFICATION

The main sources of reservoir pollution originate mainly from point sources (direct discharges into the

water bodies) and diffuse sources (chemicals, bacteria and nutrients from runoff). The area of land

that drains into a stream or reservoir is called a watershed and this watershed influences the non-point

source (NPS) pollution of a water body (Naranjo, undated). The most common NPS pollutants are

soils (sediment) and nutrients picked up by runoff as it flows over watersheds to surface waters. These

pollutants may come from agricultural land and other open spaces, urban areas, construction sites,

roads and parking lots. Organic wastes and fertilizers can introduce nutrients such as nitrogen and

phosphorus, into runoff. When polluted runoff enters reservoirs, nutrients can cause algal blooms and

dense weed growth that disrupt the balance of aquatic ecosystems. The algal blooms result in oxygen

depletion, which can cause odour and taste problems. However, in semi arid rural areas where the

main form of agriculture is livestock rearing, the main source of NPS is organic matter (manure)

including pathogens (bacteria and viruses). In order to produce a complete pollution load assessment,

information on the extent of the watershed basin, type of population (urban or rural), land use, climate

(rainfall), vegetation, soil types is required.

2.9 EMERGING ISSUES

There is an urban bias to water treatment and water quality studies in Zimbabwe, though the majority

of Zimbabweans live in the rural areas (60 %) and rely mainly on open water sources such as open

wells, rivers and reservoirs. Coupled with this, water quality regulations in Zimbabwe are mainly

centred on urban areas; rural communities therefore end up using untreated water for livestock

watering irrigation and domestic uses, increasing health risks and decreasing rural livelihoods. There

is need therefore for continuous water quality monitoring in Zimbabwean rural sources.

13

CHAPTER THREE

3.0 GENERAL MATERIALS AND METHODS

3.1 STUDY AREA

3.1.1 LIMPOPO RIVER BASIN

The Limpopo River basin comprises portions of four countries (Botswana, Mozambique, South Africa

and Zimbabwe). The basin is located between 19.5° and 26.5° South latitude and between 25.5° and

34.5° East longitude. The basin has a total area of approximately 282,000 km2 (Moyce et al., 2006).

Figure 3.1: Limpopo River Basin Map (Adapted from Mwenge, 2004)

14

3.1.2 MZINGWANE CATCHMENT

The Mzingwane catchment (Fig 3.2), forms part of the Limpopo River and is divided into four sub

catchments, namely Shashe, Upper Mzingwane, Lower Mzingwane, and Mwenezi (Saunyama, 2005).

The catchment is made up of Mzingwane River and the Ncema, Inyakuni and Insiza tributaries

(Moyce et al., 2006). The study site is part of the Upper Mzingwane sub catchment specifically Avoca

Bussiness Centre in Filabusi.

Figure 3.2. Mzingwane catchment map

15

Physical factors in the Mzingwane catchment that may influence water quality.

Geology Limpopo Belt gneisses underlies the southern half of the sub-catchment, except for the area around

Mazunga, which is underlain by Karoo basalts. The northern half of the Mzingwane sub-catchment is

underlain by the Zimbabwe Craton: Bulawayo Greenstone Belt, Gwanda Greenstone Belt, Filabusi

Greenstone Belt and granitic terrain (Ashton et al., 2001).

Soils

Soils in the Mzingwane sub-catchment can be divided into four groups:

• Moderately shallow, coarse-grained kaolinitic sands, derived from the granites;

• Very shallow to moderately shallow sandy loams, formed from gneisses;

• Very shallow to moderately shallow clays, formed from the Greenstone Belts; and

• Very shallow sands, derived from the basalts

However the soils in Avoca Business Centre are mainly moderately shallow, greyish brown, coarse-

grained sands throughout the profile to similar sandy loams, over reddish brown sandy clay loams,

formed on granitic rocks (DRSS, 1979).

Rainfall and Temperature

Generally, rainfall in the sub-catchment is erratic and decreases from the north to south. From

Gwanda northwards, the sub-catchment is in Natural Region IV, with low (under 650mm) and

unreliable rainfall, and poor soils. South of Gwanda is in Region V, with poor soils, rainfall under

600mm and in other places under 450mm (ZSG, 1997). The mean maximum daily temperatures in the

sub-catchment vary from about 30-340C in the summer to 22-260C in winter. The mean daily

temperature in most areas lies between 18-22 0C in the summer and 5-100C in the winter (FAO and

UNCTAD, 2003). On average 10% of the rainfall ends up as runoff in the rivers. This 10% is

sufficient to fill small and medium sized reservoirs in which the communities depend on for multiple

purposes (Munamati, 2005).

Land Use

Land use in the northern part of the sub-catchment is commercial farming, private and resettlement

land, while in the southern part there are communal lands, where agriculture is limited to mainly

livestock (ZSG, 1998).

16

Table 3.1: Reservoir location, age, area catchment and estimated volume of Avoca, Bova,

Sifinini and Siwaze.

*YC-Year of Construction *DDF – District Development Fund

Figure 3.3: Map showing the location of Siwaze reservoir in relation to the small reservoirs (Avoca, Bova and Sifinini)

Reservoir Location *YC

Area

(m2)

Catchment area

(km2)

Estimated

volume

(m3)

Longitude

East

Latitude

South *DDF Measured

Avoca 29º 31.41 20º 48. 59 1947 51170 4 4.438 41031

Bova 29º 30.46 20º 49. 86 1980 22955 7 6.792 14160

Sifinini 29º 33.51 20º 49. 46 1980 19657.

5

4 3.574 11582

Siwaze 29º 29.37 20º 50. 81 1992 54 2.235*106

17

CHAPTER FOUR

SUITABILITY OF SMALL RESERVOIR WATER FOR DOMESTIC, IRRIGATION AND LIVESTOCK USE AS DETERMINED BY PHYSICO-CHEMICAL AND

BIOLOGICAL WATER QUALITY PARAMETERS IN MZINGWANE CATCHMENT

4.1 INTRODUCTION

Small reservoirs are valued in the rural communities as a source of drinking water, irrigation,

livestock watering, brick making, fishing, supply of reeds used for thatching and for recreational

activities. The quality of water necessary for each water use varies as do the criteria used to assess

water quality, for example the highest standards of purity are required for drinking water

(GEMS/Water Program, 2006). Due to the significance of reservoirs in the community, management

of small reservoirs and the subsequent watersheds is very important. Effective reservoir watershed

management requires information on water and sediment quality (Juracek and Ziegler, 2006). The

quality of water is typically determined by monitoring microbial presence, especially faecal coliform

bacteria, and physico-chemical properties (Gray, 1994; DWAF, 1996; USA-EPA, 1999). Degradation

of water quality erodes the availability of water for humans and ecosystems, increasing financial costs

for human use and decreasing species diversity and abundance of resident communities.

The objective of this chapter was to evaluate the physico-chemical parameters (chloride, nitrate, water

hardness pH, and electrical conductivity) in small reservoirs in Avoca Growth Point, with respect to

domestic, livestock and irrigation use. The study intended to determine if the water quality parameters

were within the set standards for domestic use, small scale irrigation and livestock use according to

WHO, FAO and DWAF respectively. This chapter also determines the presence of microbial

organisms (total coliforms, faecal coliforms and faecal streptococci) in the reservoirs using biological

water quality measures. These water quality parameters were selected because, while nitrate is

important for livestock and human health, total coliforms, EC, calcium and magnesium are regarded

as four of the five determinants for most developmental studies (Hoko, 2005). The chapter intends to

integrate the findings in the previous water quality perceptions chapter with the analysed laboratory

water quality parameters.

18

4.2 MATERIALS AND METHODS

4.2.1 SAMPLE COLLECTION Water samples were collected from three small reservoirs namely Bova, Sifinini and Avoca, and

compared with Siwaze; a medium sized reservoir. The samples were collected during an eight-month

period (March, April, May, July, August, October December 2006 and February 2007). The samples

were collected at points were the communities collect water for domestic and irrigation uses and

livestock watering using the grab sampling method, which does not require the classification of the

water into temperature and nutrient zones. 500 ml sterilised containers were used to collect samples

for biological analysis and 300 ml containers were used to collect samples for physico-chemical

parameters. Three replicates were collected for each of the following parameters, NO-3, hardness, EC,

Cl-, temperature, pH and bacterial coliforms (total coliforms, faecal coliforms and faecal streptococci)

for each of the four reservoirs. After collection, the samples were placed in a cooler box with ice while

being transported to the laboratory for analysis.

4.2.2 ANALYSIS OF PHYSICO- CHEMICAL WATER QUALITY PARAMETER

Temperature was determined in the field using laboratory thermometer and pH was determined in the

laboratory at the University of Zimbabwe using a 3510-pH meter model. Electrical conductivity and

TDS were determined using an EcoScan Con5 conductivity meter. TDS of the water samples was

estimated by multiplying the temperature normalised electrical conductivity by 0.55-0.9 (Sawyer et

al., 1994), which is the widely accepted range. The average of this range (0.725) was used to calculate

TDS. The following equation was used:

TDS (in mg/L or ppm) = 0.725x EC25 (in micromhos/cm) eqn4.1

Chloride determination (Mohr method) A filtered water sample (10 ml) was pipetted into a 250ml Erlenmeyer flask and diluted by adding

65ml of distilled water. Potassium chromate 0.25M (1 ml) was added into the flask, and the solution

was titrated with a 0.075M standard of silver nitrate (AgN03) and the end point was indicated by the

presence of a persistent red-brown colour. The same procedure was carried out with a blank solution

19

and the Cl- concentration was determined by subtracting the volume of AgN03, for the blank from the

average used for the sample (Clesceri et al., 1989).

Nitrates

Nitrate and ammonia (NH4+) was determined by pipetting 5 ml of sample into a flask, 5 granules of

NaOH and a pinch of Devarda’s alloy was added to the solution. The solution was distilled into a vile

containing 5 ml of 0.02 M HCl. About 38 ml of distillate was collected and 1 ml of 6% EDTA was

added to the distillate. Sodium Nitroprusside (4 ml) and 2 ml of buffer solution was then added and

mixed thoroughly. Colour was allowed to develop for 1 hr and the N03- and (NH4

+) determined on a

UV spectrophotometer at 667nm. The N03- was determined from prepared standards. The (NH4

+) was

determined using the above procedure and the N03- value was obtained by subtracting the (NH4

+) from

the N03- + (NH4

+) value.

Total hardness

Total hardness was determined by pipetting the water sample (50 ml) into a conical flask. Sodium

hydroxide solution (2 ml) was added to the water solution using a dispenser. Approximately 0.2g of

Murexide/ NaCl indicator was then added. The resultant mixture was titrated with 0.01 EDTA mixing

continuously until the colour changed from pink to purple. The volume of EDTA used was then used

to calculate the calcium and the calcium hardness (Basset et al., 1978: Van Loon, 1982).

4.2.3 MICROBIOLOGICAL ANALYSES

Faecal coliform, total coliforms, and faecal streptococci counts were performed using the 3-tube

MacConkey Method, Most Probable Number (MPN) (Oblinger and Koburger, 1975). The MPN

method uses a test tube full of media with a smaller inverted test tube inside which captures carbon

dioxide gas released from the growth of coliform bacteria. A series of dilutions and replicates are set

up, and those producing gas in 24 hrs at 35 0C are counted. A statistical analysis was used to

determine the most probable number of bacteria cells present.

20

4.3 RESULTS

4.3.1 BIOLOGICAL PARAMETERS

Bacterial Coliforms

Figure 4.1 shows the arithmetic mean of monthly total coliforms counts (CFU) populations and

rainfall data (obtained from the Siwaze Dam Met Station) for the reservoirs Avoca, Bova, Sifinini and

Siwaze for eight months (2006/07). Figure 4.2 shows the mean monthly faecal streptococci and

rainfall data for the same months. Generally the highest mean number of coliform counts was in the

winter period, which coincides with low rainfall, and the lowest number of coliforms counts was

detected in the summer. Pearson's correction coefficient showed that there was a little of no

correlation between the rainfall data and the total CFU (r = -0.11552), and between faecal streptococci

and rainfall data (r = -0.04388) for all sites, indicating that high levels of coliform counts were

associated with both high and low rainfall.

050

100150200250300350400450500

Mar

-06

Apr

il

May

July

Aug

ust

Oct

ober

Dec

embe

r

Feb-

07

Months

Rai

nfal

l mm

0

200

400

600

800

1000

1200

Tot

al c

olifo

rm c

ount

s/1

00m

l

Siwaze Sifinini Bova Avoca Average rainfall

Figure 4.1: Variation of mean total coliforms counts and average rainfall with time in four reservoirs (Avoca, Bova, Sifinini and Siwaze), in Mzingwane Catchment

21

0

100

200300

400

500M

ar-0

6

Apr

il

May

July

Aug

ust

Oct

ober

Dec

embe

r

Feb-

07

Months

Rai

nfal

l mm

-20

30

80130

180

230

feac

al st

rept

ococ

cico

unts

/100

ml

Bova Avoca Sifinini Siwaze Average rainfall

Figure 4.2: Variation of mean faecal streptococci counts and average rainfall with time in four reservoirs (Avoca, Bova, Sifinini and Siwaze), Mzingwane Catchment.

4.3.2 PHYSICO-CHEMICAL PARAMETERS EC

Electrical conductivity (EC) values for the reservoirs were generally low with Bova ranging from 125

µS/cm in March to 320.67 µS/cm in October, 140.5 µS/cm in March to 309 µS/cm in October for

Avoca, 200 µS/cm in May to 409.5 µS/cm in October for Sifinini and 161.1 µS/cm in March to 258

µS/cm in October for Siwaze. These EC values for all reservoirs were below the FAO (1985)

irrigation guidelines of 700 µS/cm (Figure 5.3). There is no restriction to use water with EC values

less than 700 µS/cm, for irrigation purposes. Mwarura (2006) detected EC values ranging from 37

µS/cm to 101 µS/cm in small plateau reservoirs in Kenya, which are low compared to those obtained

in this study.

22

Figure 4.3: Variation of mean EC with time (months) for reservoirs Avoca, Bova, Sifinini and Siwaze.

pH

The pH values for all reservoirs were generally in the range of 6.7 to 10.45 (Figure 4.4), with Bova

reaching the highest pH (10.45) in the month of April, the pH values obtained in this study were

generally higher than those obtained from a similar study in Kenya (Mwarura, 2006) which ranged

from 6.9 to 7.9. The higher pH recorded in the months of March and April 2006 was probably due to

higher organic matter and suspended solids after the high rains which fell in December and January

2005.

There was a significant difference between the interaction of months and reservoirs (p = 0.008).

Therefore the pH levels in the reservoirs differ with months and between the reservoirs. The

difference in pH with months and between the reservoirs may be caused by the fact that these

reservoirs lie within different watersheds, with different areas, which may differ in both point source

and non- point source pollution sources.

01 0 02 0 03 0 04 0 05 0 06 0 07 0 08 0 0

Mar

-06

Apr

il

May

July

Aug

ust

Oct

ober

Dec

embe

r

Feb-

07

M o n t h

Mea

n EC

uS/

cm

B o v a A v o c a S if in in i S iw a z e

F A O i r r i g a t i o n g u i d e l i n e

L s d = 1 8 . 2 2t i m e * s i t e s

23

Figure 4 .4: Variation of mean pH with time (months) for reservoirs Avoca, Bova, Sifinini and Siwaze.

Hardness

Water hardness in all the reservoirs was in the range of 5.33 mgl-1 to 114.67 mgl-1 (Figure 4.5), with

the highest value recorded in the month of February 2007 in the Avoca reservoir. This range was

within the WHO (2003) drinking water guideline of 500mg/l (as calcium carbonate), which is mainly

based on taste and household use considerations, however no health-based guideline value for

hardness has been established. With the consideration of water hardness only the water in the

reservoirs does not interfere with its drinking water uses.

Chloride

Chloride values for all the reservoirs ranged from 0.06mg l-1 to 106 mg l-1 with highest values being

recorded in the month of July 2006 as shown in Figure 4.6. The levels increased from the summer to

the winter because minerals in reservoirs tend to be concentrated by evaporation and altered by

chemical and biological interactions in the reservoirs, when there is minimal dilution from runoff and

rainfall. There was a significant difference between the interaction of months and reservoirs (p =

0.001). Therefore the chloride levels in the reservoirs differ with months and between the reservoirs.

0

5

1 0

1 5

Mar

ch

Apr

il

May

July

Aug

ust

Oct

ober

Dec

embe

r

Febr

uary

M o n t h

pH

0

2 0

4 0

6 0

8 0

1 0 0

1 2 0

Rai

nfal

l mm

B o v a A v o c a S if in in iS iw a z e A v e r a g e r a in f a ll

L s d 0 . 4 t im e * s i t e s

F A O i r i g a t i o n g u i d e l i n e

24

Figure 4. 5: Variation of mean hardness with time for reservoirs Avoca, Bova, Sifinini and Siwaze

Figure 4. 6: Variation of mean chloride with time for reservoirs Avoca, Bova, Sifinini and Siwaze

02 04 06 08 0

1 0 01 2 01 4 01 6 0

Mar

-06

Apr

il

May

July

Aug

ust

Oct

ober

Dec

embe

r

Feb-

07

M o n t h

Mea

n H

ardn

ess

mg/

l

B o v a A v o c a S if in in i S iw a z e

S l i g h t l y h a r d u p p e r l i m i t

L s d = 1 7 . 4 6 t i m e * s i t e s

Lsd 3.955 time * s ite

-401060

110160210260

Mar

-06

Apr

il

May

July

Aug

ust

Oct

ober

Dec

embe

r

Feb-

07

Months

Mea

n C

onc

mg/

l

Bova Avoca Sifinini Siwaze

WHO drinking water guideline

25

4.3 DISCUSSION

The negative correlation between total coliforms and rainfall (r = -0.11552), and between faecal

streptococci and rainfall data (r = -0.04388) indicated that there was little or no association between

rainfall and coliform counts (- 0.3 to + 0.3). High levels of coliform counts were associated with both

high and low rainfall amounts. The results obtained by (Bezuidenhout et al., 2002) in the Mhlathuze

river in South Africa showed a positive correlation between rainfall and total coliform counts (r =

0.646). This is however expected because according to Mau Pope (1999), faecal coliform

concentrations are typically much greater in streams during runoff conditions because of non-point-

source pollution from the watershed. These contributions can originate from deposition of faecal

material by livestock and wildlife or from the use of manure as a soil amendment. However, the

contributions of streams to reservoirs in terms of faecal coliforms is minimal due to the fact that

watersheds tend to have a filtering mechanism and also because bacteria transported into the reservoir

by tributary streams are subject to die off and decrease in population because of predation by other

organisms.

During the winter season, livestock in the watersheds have limited watering sources and rely mainly

on the reservoirs for drinking water. This results in an increase in total faecal coliform contamination

from the livestock intestines into the reservoirs. In Zimbabwe the 2006/07 rainy season has been

declared a drought year, therefore there was minimal rainfall to contribute to faecal contamination of

reservoirs through runoff, except for flush floods, hence the changing concentration of total coliforms

during the summer and wet season.

The WHO irrigation guideline for total coliforms is 1000 counts per 100ml, and that for faecal

coliforms is 100 counts per 100ml. The total coliform counts in the reservoirs were generally within

these ranges, therefore with regard to total coliform counts, the reservoirs may be used for irrigation

purposes. However the water in the reservoirs is not suitable for drinking purposes because according

to WHO (2002), water that is intended for drinking purposes, should not have any detectable

thermotolerant coliforms in 100 ml sample.

Electrical conductivity mainly gives an indication of the following ions, Ca2+, Mg2+, NO-

3, Fe2+, and

Na+and AL3+. The villagers in Avoca growth point use minimal inorganic fertilizers, as shown in

Chapter Four, section 4.3.2, and the bedrock is of granitic origin (DRSS, 1979) which tends to have a

26

lower conductivity because granite is composed of inert materials that do not ionize easily in water

(US EPA, undated), hence the low electrical conductance of the reservoir water. Therefore most of the

electrical conductivity recorded in the reservoirs may have originated from animal manure, which is

used in substitution of inorganic fertilizers. Statistical analysis showed that there was a significant

difference in EC between the interaction of months and reservoirs with a p value of <0.001. Electrical

conductivity was generally higher in Sifinini reservoirs compared to the other reservoirs, which

coincide with the higher percentage use of animal manure (91, 7 %) shown in Chapter Five.

The WHO drinking water guidelines do not stipulate the EC guidelines but that of TDS, which is

1000mg/l. TDS can be correlated to EC using a 0.725 correlation factor (Sawyer et al., 1994), this

results in an EC value of approximately 1380 µS/cm (Hoko, 2005). All the reservoirs water did not

exceed this EC limit of 1380 µS/cm during the eight-month study. Therefore with regard to EC and

TDS the water in the reservoirs does not affect the waters’ use for drinking purposes. With regard to

electrical conductivity, the reservoir water can also be used for irrigation purposes as it conformed to

the set FAO (1985) irrigation guidelines of 700 µS/cm.

The general water quality of all the reservoirs with regard to pH was slightly alkaline, which is similar

to findings from Thornton (1980) who recorded pH values of 6.4 to 9.1 in Zimbabwe. Highest values

were recorded in March and April 2006. The higher pH in these two months may have been as a result

from high rainfall in the preceding months of January (110.1mm) and February (85mm) and

consequently runoff transporting NPS from animal manure. However, the reservoir water during the

study was within the typical pH range in reservoirs of 5-10, though short-term variations can occur

(Moehl and Davies, 1993). According to Chapman (1996), pH is an unstable important variable in

water quality as it influences many biological and chemical processes within a water body and can

therefore be used to indicate the general quality of the water body. Statistical analysis showed that

there was a significant difference in pH between the interaction of months and reservoirs (p = 0.008).

The differences cannot be attributed to rainfall because the reservoirs fall within the same hydro

geological zone and therefore receive the same amounts of rainfall. The difference may however be

explained by the fact that the reservoirs have different watershed areas (Chapter Three, Table 3.1),

which differ in point and non point source pollution.

27

The Department of Water Affairs and Forestry does not stipulate a pH guideline for livestock.

However, according to Bagley et al., (1997) the preferred pH for dairy animals is 6.0 to 8.0 and for

other livestock is 5.5 to 8.3. This pH ranges were only exceeded in March and April 2006, which

recorded pH values as high as 10.45. Highly alkaline waters may cause digestive upsets, diarrhoea,

poor feed conversion and reduced water/feed intake.

World Health Organisation (1993) pH aesthetic drinking water guideline is < 8 and the DWAF

(1996a) target water quality range for pH in water for domestic use is 6 – 9. Generally the pH in the

reservoirs fell within these ranges, therefore the pH of the reservoirs may be considered not to be

affecting the reservoirs’ use for domestic use.

Chloride is a common constituent in water and because of its highly solubility tends to accumulate in

nature. It is found only as chloride in the form of sodium, potassium, calcium and magnesium

chloride. It can only be removed from water by electrolysis (DWAF, 1996a). Effects of chloride on

human health may occur at very high levels above 1 200mg/l by disturbance of the electrolyte balance

and nausea. The levels recorded in all the reservoirs were lower than this range. However, according

to the WHO guidelines (2003), there is no health-based guideline value proposed for chloride in

drinking water, but chloride in excess of 250mg/l can give rise to detectable brackish salty taste in

water. Infants are susceptible and fatalities due to dehydration may occur (DWAF, 1996a).

The effect of high levels of chloride (1500mg/l) in water for livestock use is unpalatability. The

common livestock in Avoca is mainly cattle, donkeys and goats. Poultry, pigs and sheep are more

susceptible to excess chloride as indicated in Appendix 2. The DWAF guidelines are based on the

toxicological and palatability effects of chloride in water used for livestock (DWAF, 1996b). The

chloride values obtained during the study period, where within the DWAF (1996b), livestock

standards and therefore when considering chloride alone, the water in the reservoirs can be used for

livestock watering. The water quality in all the four reservoirs was also within the FAO (1999)

chloride irrigation guideline of 250mg/l, and therefore with regard to this parameter alone the water

can be used for surface irrigation.

28

4.4 CONCLUSION

The water in all the reservoirs was found not to be suitable for drinking purposes according to the

WHO drinking water guidelines. However the water was found to be suitable for irrigation and

livestock use according to the FAO and DWAF guidelines respectively.

The water in all the reservoirs can be used for irrigation purposes throughout the year as it conformed

to the set FAO (1985) irrigation guidelines for EC (700 µS/cm), and generally within the pH irrigation

guidelines of 6.5 to 8.5 except for the first two study months. The water in all the reservoirs was also

within the chloride irrigation water guidelines of 250 mg/l with chloride values in the reservoirs

ranging from 0.06 mg l-1 to 106 mg l-1. The water in the reservoir can also be used for livestock

watering purposes as the water quality conformed to a pH guideline of 5.5 to 8.3 and to the DWAF

chloride guideline for 1500 mg/l.

However, although the reservoir water was within the WHO, pH, EC and chloride drinking water

guideline, it did not conform to the same standard guideline for total coliforms. Therefore cannot be

used for drinking purposes, as water that is ingested by humans without treatment requires the highest

water quality standards to reduce incidences of illnesses such as cholera, salmonella spp and

dysentery.

Therefore the hypotheses that water in small reservoirs is not suitable for small irrigation, domestic

use and livestock watering according to the FAO, WHO and DWAF standards respectively, could not

be accepted when considering irrigation and livestock watering, but it was accepted when considering

drinking water use.

29

CHAPTER FIVE

VILLAGERS’ WATER QUALITY PERCEPTIONS (Colour, Smell, Taste, Soap Consumption and Frothing when Boiling) OF SMALL RESERVOIRS IN MZINGWANE

CATCHMENT.

5.1 INTRODUCTION

Water quality of surface or ground water is defined as a function of either or both natural influences

and human activities. The natural influences that determine water quality are weathering of bedrock

minerals, atmospheric processes of evapotranspiration and the deposition of dust and salt by wind,

natural leaching of organic matter and nutrients from soil, hydrological factors that lead to runoff, and

biological processes within the aquatic environment that can alter the physical and chemical

composition of water (GEMS/Water Program, 2006). Water quality is important not only to protect

public health but water provides ecosystem habitats, is used for irrigation, livestock watering and

contributes to recreation and tourism. Water quality requirements and consumer perceptions therefore

differ for different water uses, such as domestic or agriculture, therefore water quality should be

determined according to different uses. Water quality requirement for a particular use plays an

important role in the management of water resources and in turn forms an integral part of water

quality management (Parsons and Tredoux, 1995).

The objective of this chapter was to determine the villagers’ water quality perceptions (colour, soap

consumption, taste, frothing when boiling and smell) in the Avoca community through questionnaires.

The selected perceptions relate to human senses of smell, taste and sight and can be linked to the

selected physico-chemical parameters in Chapter Four. This chapter is a follow up study to the socio-

economic study done in the same area by Sithole (2005). The target group of this study chapter was

the communities that use the reservoirs for multiple uses. Communities within a reservoir watershed

were interviewed for their water quality perceptions of the respective reservoir.


A structured questionnaire (Appendix 3) with fill in the blank and binary type (for example yes/no) of

questions intended to obtain information on consumer water quality perceptions (colour, taste, soap

30

consumption and frothing when boiling) of the villagers’ was administered in the Avoca, Bova,

Sifinini and Siwaze watersheds in Avoca growth point, Filabusi. A total of 54 households were

interviewed, 18, 8, 12 and 16 households for Avoca, Bova, Sifinini and Siwaze watersheds

respectively in February 2007. The survey was conducted with the assistance of two enumerators, for

translation purposes, as the people in the study area are Ndebele speaking. The questionnaire also

generated information on the time in which water is abundant in the reservoirs, farming information

and water management aspects.

5.3 RESULTS

5.3.1 PROFILE OF THE RESPONDENTS

Stakeholder participation has been identified as key aspect in enhancing the sustainability of

water supply facilities throughout the world (Vhevha and Manzungu, 2007). The respondents in

all the reservoirs constituted of individuals within households who were above the age of 20

years and constituted both males and females (Table 5.1). The highest percentage of

respondents was generally in the 20 -35 and the 66 – 85 age groups. This age limit was selected

because it was considered that people above this age group were cable of answering the

questions in the questionnaire.

5.3.2 WATER QUALITY PERCEPTIONS The majority of the respondents in all the four watersheds Avoca (89.5 %) as shown in Figure 4.1,

Bova (62.5%), Sifinini (75 %) and Siwaze (93.8 %) indicated that the water in the reservoirs had a

satisfactory colour during the wet season with responses such as clear, muddy, green and brown

given. The majority of the respondents Avoca (94, 7 %), Bova (87, 5 %) and Siwaze (87.5 %) except

for Sifinini (33.3 %) did not have a problem with the smell of the reservoir water during the wet

season as well. Generally the respondents did not have a problem with the water quality indicators

during the wet season as opposed to the dry season, as shown in Table 4.1. The highest soap

consumption was perceived in the dry season in Avoca and Bova watersheds (Table 4.1). Water in all

the reservoirs frothed more while boiling in the dry season than in the wet season, with responses such

as green, brown and muddy froth given.

31

Table 5.1 Profile of respondents for the reservoirs Avoca, Bova, Sifinini and Siwaze (N=54)

Age Group Reservoir Frequency Percentage

20 - 35 Avoca 6 31.2

Bova 2 25

Sifinini 3 24.9

Siwaze 2 12.5

36 -45 Avoca 0 0

Bova 2 25

Sifinini 2 16.6

Siwaze 4 25

46 -55 Avoca 3 15.9

Bova 2 25

Sifinini 0 0

Siwaze 2 12.5

56 -65 Avoca 3 15.9

Bova 0 0

Sifinini 3 24.9

Siwaze 5 37.5

66 -85 Avoca 5 26.5

Bova 2 25

Sifinini 4 33.2

Siwaze 3 18.75

Table 5.2: Villager’s water quality perceptions of the small reservoirs (Avoca, Bova, Sifinini and Siwaze)

*Respondents could not comment for that particular perception DS: Dry and WS: Wet season

RESERVOIR WATER QUALITY PERCEPTION (% SATISFACTORY) (N=54)

Colour Taste Smell Soap consumption Frothing when

boiling

DS WS DS WS DS WS DS WS DS WS

Avoca 21.1 89.5 84.2* 57.9 5.3 94.7 15.8 84.2 11.1 88.9

Bova 0 62.5 100* 75 12.5 87.5 37.5 87.5 12.5 62.5

Sifinini 41.7 75 41.7 58.3 16.7 33.3 91.7 100 8.3 58.3

Siwaze 50 93.8 50 75 37.5 87.5 75 100 25.1 62.5

32

020406080

100120

dry wet dry wet dry wet dry wet dry wet

colour taste smell soapconsump

frothingwhen

Bova water quality perceptions

% sa

tisfa

ctor

y

0

20

40

60

80

100

120



frothingwhen

Sifinini water quality perceptions

% sa

tisfa

ctor

y

0204060

80100120


colour taste smell soapconsumption

frothingwhen

Siwaze water quality perceptions

% sa

tisfa

ctor

y

Fig5.1: Villagers’ water quality perceptions for colour, taste, smell, soap consumption and frothing when

boiling for the reservoirs Avoca, Bova, Sifinini and Siwaze during the dry and wet season

5.3.3 ANIMAL MANURE AND FERTILIZER USE

In rural and suburban areas, the use of inorganic nitrate fertilizers is a major source of water pollution

(Chapman, 1996). The results from the questionnaires administered indicated that there is minimal use

of both Compound D and Ammonium Nitrate fertilizers in all the watersheds as shown in Table 4.2.

The most commonly used form of soil amendment is animal manure with percentage uses as high as

77.8 %, 62.5 %, 91.7 % and 68.7 % for Avoca, Bova, Sifinini and Siwaze watersheds respectively. On

0102030405060708090

100



frothingwhen

Avoca water quality perceptions

% sa

tisfa

ctor

y

33

average the application rate of Compound D fertilizer in the Avoca watershed is at least 26.6 kg/ha,

which translates to 6.6 kg/ha of N (nitrate) and K (potassium) and 13.28 kg/ha of P (phosphorus). The

respondents in the Bova watershed indicated that they use 12.5 kg/ha N and K and 25 kg/ha of P. The

other two reservoirs, Sifinini and Siwaze indicated that they do not use any fertilizer for soil

amendment and they rely mainly on land application of animal manure. The application rate of

nitrate-N in sandy soils is 100-120 kg/ha N (Seed Co, 2001).

Table 5.3: Percentage use of Compound D, Ammonium Nitrate, and Animal manure in each of the four watersheds (Avoca, Bova, Sifinini and Siwaze)

5.3.4 RESERVOIR WATER USES

Avoca

A survey conducted in the Avoca watershed indicated that most households in the watershed obtain

their water for irrigation (75 %) and livestock watering (88.8 %) from the reservoir during the dry

season as shown in Figure 5.2. However in the wet season when there is an increase in number of

water sources, the households obtain most of their irrigation water from open wells located close to

the gardens. Open wells and sand water abstraction/mining (Figure 5.3.) also become a common water

source for uses such as laundry and bathing in the wet season. During the dry season the households

mainly rely on the reservoir for most uses.

RESERVOIR % USE OF FERTILIZER (N = 54)

Compound D Ammonium Nitrate Animal Manure

Avoca 38.9 11.1 77.8

Bova 37.5 12.5 62.5 Sifinini 8.3 0 91.7 Siwaze 0 0 68.7

34

0

10

20

30

40

50

60

70

80

90

100

Dry Wet Dry Wet Dry Wet Dry Wet Dry Wet

Drinking Laundry Dishes Irrigation Livestockwatering

Water uses in the dry and wet season

% h

ouse

hold

s

Borehole Open well AvocaSand water mining Roof water harvesting RiverTap Other

Figure 5.2: Water sources uses in the dry and wet season and % households interviewed in the Avoca watershed (N = 18)

35

Figure 5.3: Woman abstracting water along a river during the 2006/07 rainy season (courtesy of G.C Mamba, 2007).

Bova

The results from the questionnaires administered in the Bova watershed indicated that the common

water source for most uses in the dry season (drinking, laundry, dishes, irrigation and livestock

watering) was obtained from the reservoir as shown in Figure 5.4. Water abstracted from river-beds

(sand abstraction) is widely used for domestic uses (drinking, 62.5%, laundry 50% and dishes 37.5%),

during the wet season to obtain filtered water. The households in this watershed also rely on tapped

water in both seasons for other uses except for livestock watering. The river becomes an added water

source for livestock watering (12.5%) during the wet season. The reservoir was found to be the most

common water source for most uses throughout the year.

36

0

20

40

60

80

100

120



Water sources in wet and dry season

% h

ouse

hold

s

Borehole Bova Sand water mining River Tap

Figure 5.4: Water uses in the dry and wet season and % households interviewed in the Bova watershed (N=8)

Sifinini

The respondents from the Sifinini watershed indicated that they obtain water for most of their uses

both in the dry and the wet season from the reservoir as illustrated in Figure 5.5. The highest water use

from this reservoir both in the dry and wet season was livestock watering (100%) followed by

irrigation 91.7% (in the dry season) and 81.8% (in the wet season). The respondents also use borehole

water but mainly for domestic uses (laundry, dishes and drinking). The reservoir is the most common

source of water for most uses in this watershed.

Siwaze

Siwazes’ watershed includes all the other three watersheds and the households that are located close to

Siwaze reservoir, like those from Sifinini indicated that the most common water source was the

reservoir. About 68.8% of the households interviewed used Shangwe reservoir for irrigation during

both seasons, which was closer to their homesteads than the Siwaze reservoir. About 25% and 18.8%

of the households watered their livestock at Shangwe during the dry and wet season respectively.

37

Tapped water is a common water source for domestic uses, as this reservoir is a medium sized

reservoir as shown in Figure 5.6.

0

20

40

60

80

100

120



Water sources in wet and dry season

% h

ouse

hold

s

Borehole Open WellSifinini Sand water miningRiver Roof water harvestingOther

Figure 5.5: Water uses in the dry and wet season and % households interviewed in the Sifinini watershed (N = 12).

38

0

10

20

30

40

50

60

70

80


Drinking Laundry Dishes Irrigation Livestock watering

Water uses in dry and wet season

% h

ouse

hold

s

Borehole Open well SiwazeSand water mining River TapRoof water harvesting Other

Figure 5.6 Water uses in the dry and wet season and % households interviewed in the Siwaze watershed (N = 16).

5.3.5 WATER AVAILABILITY IN RESERVOIRS Water availability in all the reservoirs, was found to be generally between November (or October if

the rains come early) and March/ April as shown in Table 5.4. Zimbabwe experiences a single annul

rainy season of five months (November to March) as shown in figure 5.7, which corresponds to the

period of water availability in the reservoirs. Table 5.4: Months in which the water is available in the four reservoirs (Avoca, Bova, Sifinini and Siwaze)

* The coloured section of the table indicates the period of water availability in the reservoirs.

ReservoirJan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

AvocaBovaSifininiSiwaze

Month

39

0.020.040.060.080.0

100.0120.0140.0160.0

Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Month

Mea

n m

onth

ly r

ainf

all m

m

2006/07 2005/06

Figure 5.7: Mean monthly rainfall for the 2006/2007 rainy seasons, (ZINWA Siwaze station).

5.4 DISCUSSION

The perceived colour (muddy, green and brown) of small reservoir water in both seasons may be

attributed to dissolved organic material and inorganic substances such as manganese, aluminium, iron,

copper and total dissolved solids. Although most of the respondents complained of highly coloured

water in the dry season, the consumption of this water will not necessarily affect health; however, this

is dependant on the concentrations and types of elements responsible for the colour in the water.

Colour is a subjective parameter since it is influenced by an individual’s eyesight. While taste and

smell/odour originates generally from inorganic substances (chlorides, copper, pH, sulphate and

manganese) present in concentrations much higher than those of organic substances (EPA, 1992).

However, when the water is perceived to have an odour/smell it also relates to a higher than normal

biological activity. The odour/smell of water is a simple test for the suitability of drinking water, since

the human sense of smell is far more sensitive to low concentrations of substances than human taste.

Organic matter also contributes to taste and smell, the major cause in water being metabolites

produced by algae and actinomyctes (EPA, 1992). Taste and smell/odour are also subjective to the

individual being interviewed.

40

Land application of animal manure has its own benefits, such as an increase in soil physical and

chemical properties. However, according to Risse et al (undated), land application of animal manure

results in potential surface water pollution, associated with runoff. Pollutants of concern include

organic materials (which contribute to reservoir colour and smell), nutrients and pathogenic

microorganisms (Risse et al., undated). Nutrients such as nitrogen and phosphorus are the most

common pollutants associated with animal waste. Several studies have documented that watersheds

concentrating on animal agriculture tend to have higher nutrient levels in their drainage systems,

which end up in reservoirs (Risse et al., undated).

Despite the fact that water in small reservoirs maybe potentially polluted, these water sources have

multiple uses which include livestock watering, domestic uses, irrigation, fishing, brick making and

recreation purposes (Rusere, 2005). The three small reservoirs under study served as a water source

for various uses both in the dry and wet season, but mainly in the dry season because of the reduced

availability of other water sources. However the interviews conducted on water quality perceptions

indicated that the water quality was unsatisfactory during the dry season, when it is in demand the

most. Small reservoirs have limited storage capacity, therefore they respond rapidly to precipitation

runoff, often refilling rapidly at the start of the rainy season (Keller et al., 2000). The high mean

maximum daily temperatures in the Limpopo River Basin vary from about 30-340C in summer to 22-

260C in the winter. These high temperatures result in high evaporation rates, which range from 1600

mm/yr to more than 2600 mm/yr (FAO and UNCTAD, 2003). Small reservoirs have high surface area

to volume ratio and this result in significant evaporation loss, hence the reduced water abundance in

the dry season. Losses due to evaporation from these reservoirs can be as high as 50% of their

impoundments in the arid and semi arid regions (Gleick 1993 and Sakthivadivel et al., 1997).

Small reservoirs improve human livelihoods and reduce poverty due to their multiple-purpose use,

which increase household incomes. There are, for instance, some 1500 multi-purpose small reservoirs

in Burkina Faso and these provide small-scale food and livestock production (Williams and Carriger,

2006). Most villagers tend to use a water source closer to their homesteads, and livestock tend to be

watered from a watering source close to where they graze, such that villagers who do not have a

borehole close to their homesteads, sand abstract drinking water from rivers close to their homesteads.

Water abstraction from sand was a common practice during the time of the survey (2006/2007) rainy

season (Figure 4.6) because there was below average rainfall, such that the farming season was

41

declared a drought season (Met Department, 2007). The highest rainfall for the season was recorded

during the month of November 2006 (148.7 mm).

5.5 CONCLUSION

Villagers’ water quality perceptions of the reservoirs differed depending on the season (dry or

wet), with most of the respondents being satisfied with the studied water quality perceptions

during the wet season. The perceived smell and soap consumption of the reservoir water proved

to be the most important water quality perceptions as they generally had higher percentages of

satisfaction in the wet season for most of the reservoirs.

Agricultural inputs are a continuous challenge especially for communal farmers in Zimbabwe such

that people in the semi arid Mzingwane Catchment for example those in the Avoca watershed use on

average as little as 6.6kg/ha N and depend mainly on animal manure for soil amendments. The animal

manure contributes to non-point source pollution in the form of agricultural runoff and to some of the

perceptions, taste and smell.

There is therefore a correlation between analysed laboratory water quality parameters and the

community water quality perceptions. Generally the water quality was perceived to be unsatisfactory

during the dry season, which can be linked to the analysed laboratory parameters, which recorded

higher concentrations of most parameters in the dry than the wet season.

42

CHAPTER SIX

NON POINT SOURCE POLLUTION IDENTIFICATION AND POLLUTION LOADING IN AVOCA GROWTH POINT, MZINGWANE CATCHMENT.

6.1 INTRODUCTION

Population growth results in an increasing demand for land and water resources. Over exploitation of

these resources through agricultural activities leads to environmental degradation including pollution

of streams and reservoirs. Non- point source agricultural pollution is one of the major factors in

polluting surface waters in agricultural areas (Dayawansa, 1997). The non-point agricultural pollutants

are organic and inorganic materials including sediments, plant nutrients, pesticides and animal wastes

entering surface and ground waters form non-specific or undefined sources in sufficient quantities to

contribute to the problem of pollution.

Pollution loading is a term used to indicate the amount of a pollutant entering or within a water

resource. Pollution loading differs with concentration in terms of how the amount of pollutant in the

water is expressed, and the time frame over which the pollutant is generated or released into the water

resource. Loading is the total amount of a pollutant generated from a specific area of land, or received

by a water resource, during a fixed period of time. Therefore pollution loading provides information

about the land area producing the pollutant, the time over which the pollutant enters the water

resource and the total amount of pollutant delivered (Leeds et al., 1992).

Non point source pollution loading and identification results in the estimation of all the possible

pollutant sources in the reservoirs and aids in the water quality management of reservoirs. The

objective of this chapter was to identify and estimate non-point source pollution loading in the

reservoirs Avoca, Sifinini, Siwaze and Bova watersheds.


6.2.1 POLLUTION LOADING

The pollution loading (PL) of the following pollutants (NO-

3, hardness, EC, Cl-, pH) in each stream

that drains into each of the reservoirs (Bova, Avoca, Sifinini and Siwaze) was determined using the

following equation:

43

PL = QC eqn 6.1 Where: PL = pollutant loading in mg/s

Q = flow rate (m3/s)

C = level of pollutant in (mg/l).

PL= m3/s * mg/l

= m3/s *mg/1000l

= mg/s

Electrical conductivity was converted to mg/l using equation 5.1 in chapter 5.

6.2.2 NON-POINT SOURCE POLLUTION IDENTIFICATION Non point source pollution was analysed using GIS technology. A digitised Land Use aerial

photograph of Avoca Growth Point, A digital Elevation Model and the results of ground truthing were

overlayed using ArcView software to produce the non point source pollution map. Land use was

classified as cultivated land, settlements (blair toilets, homesteads and kraals), pastures and business

centres.

6.3 RESULTS

6.3.1 POLLUTION LOADING CALCULATIONS Pollution loading of each element (N03, Cl−, Hardness and EC) into each reservoir was determined

using Equation 6.1 above. The average catchment area of the reservoirs, Avoca, Bova, Sifinini and

Siwaze was determined as 4.219 km2, 6.898 km2, 3.782 km2 and 54 km2. The 2006/7 rainy seasons

started in October and the mean monthly rainfall was 12.3 mm, of this only 10 % ends up as runoff in

the rivers (Munamati, 2005). Equation 6.2 below was then used to determine the volume, which was

then used to calculate the flow rate of the stream that feeds into each of the reservoirs (Avoca, Bova,

Sifinini and Siwaze) by converting the cubic metres per month to cubic metres per second. The flow

rates of the four reservoirs were 0.0019 m3/s, 0.00327 m3/s, 0.00171 m3/s and 0.02479 m3/s,

respectively (Table 6.2). The flow rates determined were used to calculate the pollution loading of

each element into the reservoirs as shown in Table 6.1. The stream that feeds into Siwaze had a

relatively a higher pollution load than the other reservoirs, this was because Siwaze (a medium sized

reservoir) has a larger watershed area compared to the other reservoir watersheds. No nitrates were

recorded for all the reservoirs as shown in Table 6.1 below. Hardness ranged from 13.5 mg/l to 117.3

44

mg/l, chloride ranged from 2.9 mg/l to 96.7 mg/l and conductance ranged from 95.0 mg/l to 131.8

mg/l with Siwaze (medium sized) recording the highest values.

Volume = Rainfall * Runoff coefficient * subcatchment Area eqn 6.2

Table 6.1: Mean concentration of ( Cl-, Hardness, and EC) and average flow rates for stream leading to Avoca, Bova, Sifinini and Siwaze reservoirs in October 2006.

Table 6.2: Pollution loading of (NO3, Cl-, Hardness, And EC) for Avoca, Bova, Sifinini and Siwaze Reservoirs.

Reservoir Hardness mg/l Cl mg/l EC mg/l Flow-rate m3/s

Avoca 48.6 1.567 57.75 0.0019

Bova 5.63 14.23 40.35 0.00327

Sifinini 7.8 13.6 54 0.00171

Siwaze 4.73 3.9 5.316 0.02479

RESERVOIR POLLUTANT LOADING mg/ s Nitrate Hardness - Ca

and Mg carbonates

Chloride EC

Avoca 0 92.3 2.9 109.7

Bova 0 18.4 46.5 132.0 Sifinini 0 13.5 23.6 95.0 Siwaze 0 117.3 96.7 131.8

45

6.3.2 NON -POINT SOURCE POLLUTION IDENTIFICATION

Siwazes’ watershed contains the other three small reservoir watersheds as shown in Figure 6.1

below. The main non point source pollution pollutants originated from homesteads with each

homestead comprising a livestock kraal, a blair toilet and being surrounded by a farming field

(Figure 6.3). Most of the households use a reservoir closest to their homesteads as shown in

Figure 6.3 below.

Figure 6.1. Small reservoir location in Siwaze watershed

46

Figure 6.2: Study site location in relation to Mzingwane Catchment

Figure 6.3: Non point source pollutants in each reservoir’s (Avoca, Bova, Sifinini and

Siwaze) watershed.

47

6.4 DISCUSSION

The results from the questionnaires administered indicated that there is minimal use of both

Compound D and Ammonium Nitrate fertilizers in all the watersheds as shown in Chapter 4, Table

4.2 above. On average the application rate of Compound D fertilizer in the Avoca watershed is at least

26.6 kg/ha, which translates to 6.6 kg/ha of N (nitrate) and K (potassium) and 13.28 kg/ha of P

(phosphorus). The respondents in the Bova watershed indicated that they use 12.5 kg/ha N and K and

25 kg/ha of P. The other two reservoirs, Sifinini and Siwaze indicated that they do not use any

fertilizer for soil amendment and they rely mainly on land application of animal manure. The

application rate of nitrate-N in sandy soils is 100-120 kgN/ha (Seed Co, 2001). The minimal use

fertilizer explains the low nitrate loading. However the low nitrate levels recorded is expected because

nitrate levels in natural waters seldom exceed a daily load of 0.1mg/l according to Chapman (1992).

The low specific conductance for all the streams that lead to respective reservoirs (5.316 mg/l to 57.75

mg/l) indicates low mineral salt and metal concentration. Low mineral salts translate to low levels of

calcium and magnesium carbonate hardness (Ntengwe, 2005). The bedrock in Avoca is of granitic

origin (DRSS, 1979), which tends to have a lower conductivity because granite is composed of inert

materials that do not ionize easily in water (US EPA, undated), hence the low electrical conductance

and hardness of the water in the streams that feed the four reservoirs. Therefore most of the electrical

conductivity recorded in the reservoirs may have originated from animal manure, which is used in

substitution of inorganic fertilizers.

Most non-point source pollutants originated from homestead, which are surrounded by farming fields,

and from the blair toilets and livestock kraals within each homestead. Bova includes a business centre

and livestock selling pens, which also contribute to non point source pollution. Homesteads utilizing

the Avoca reservoir contribute non point source pollutants to the Bova reservoir. Siwaze (medium

sized) recorded the highest pollutant loading compared to the other reservoirs, because it has a larger

watershed, which includes the other three watersheds.

48

6.5 CONCLUSION

Pollutant loading ranged from 13.5 mg/s to 117.3 mg/s for hardness, 2.9 mg/s to 96.7mg/s for chloride

and 95 mg/s to 132 mg/s for electrical conductivity. Therefore non point source pollution, originating

from homesteads and farming fields affects water quality in small reservoirs. Siwaze had a higher

pollutant load compared to the other three reservoirs as it has a larger watershed indicating that non

point source pollution varies depending on the watershed area, and the activities within a specific

watershed.

49

CHAPTER SEVEN

GENERAL DISCUSSION CONCLUSION AND RECOMMENDATIONS 7.1 GENERAL DISCUSSION Despite the uncertainties regarding the nutrient availability of animal manure, rural communities in

Avoca depend highly on animal manure for soil amendment with percentage usages in watersheds

being as high as 77.8 %, 62.5 %, 91.7 % and 68.7 % for Avoca, Bova, Sifinini and Siwaze watersheds

respectively. Animal manure is a source of plant nutrients such as N, P, K, however these plant

nutrients are not readily available in manure as they are in inorganic fertilizers. The availability of

animal manure nutrients is highly dependant on the time of application as well as the application rate.

Uncertainties regarding these two factors results in nutrients loss through leaching and surface runoff,

which results in surface and ground water pollution (Risse, undated). Reservoir pollution from animal

manure results in the colour, smell/odour and taste of the reservoir water reported by the respondents.

The respondents in all the watersheds under study indicated that they use minimal inorganic fertilizers

with application rates as low as 26.6 kg/ha, which relates to 6.6 kg/ha of N (nitrate) and K (potassium)

and 13.28 kg/ha of P (phosphorus) in the Avoca watershed.

The main source of bacterial coliforms in the semi arid reservoirs in Mzingwane subcatchment is from

livestock, agricultural runoff and through flow from blair toilets and livestock kraals. The factors that

influence bacterial growth and abundance in water bodies include light, salinity, rainfall, predation,

available nutrients and environmental pollutants (Pernthaler et al., 1998; Lobitz et al., 2000; Solo-

Gabriele et al., 2000). The negative correlation between total coliforms and rainfall (r = -0.11552),

and between faecal streptococci and rainfall data (r = -0.04388) indicated that there was little or no

association between rainfall and coliform counts (- 0.3 to + 0.3). High levels of coliform counts were

associated with both high and low rainfall amounts. The results obtained in this study are contrary to

those obtained in other studies, Bezuidenhount et al., (2002) found a positive correlation between

rainfall and bacterial coliforms in the Mhalathuze river in South Africa. However the negative

correlation obtained in this study can be explained by the fact that small reservoirs serve primarily as

livestock watering source. During the winter season, livestock in the watersheds have limited watering

sources and rely mainly on the reservoirs for drinking water, during the same time the small reservoirs

have reduced water abundance, this therefore results in increased concentration of bacterial coliforms

during the dry season. The other explanation is that the contributions of streams to reservoirs in terms

50

of faecal coliforms is minimal due to the fact that catchments and watersheds tend to have a filtering

mechanism and also because bacteria transported into the reservoir by tributary streams are subject to

die off and decrease in population because of predation by other organisms.

Based on the WHO guidelines for drinking water, the water in the reservoirs was within stipulated

guidelines for parameters such as Cl-, TDS/EC, and pH. However the reservoir water was not within

the WHO total coliform counts of 0 counts/100 ml, therefore the reservoir water is not suitable for

direct domestic use and may pose a health risk to the villagers concerned. The water can however be

used for irrigation purposes as it was within the FAO (1985) irrigation guidelines for EC (700 µS/cm),

Cl- (250 mg/l) and generally within the pH irrigation guidelines of 6.5 to 8.5 except for the first two

study months.

The Department of Water Affairs and Forestry does not stipulate livestock guidelines for pH and total

coliforms. However using the same livestock guidelines for chloride, which is 1500 mg/l, and the pH

guidelines according Bagley et al., (1997) the water in the reservoirs can be used for livestock

watering throughout the year. The reservoirs’ water was relatively of low salinity, and water recording

less than 1000mg/l TDS (1380 uS/cm EC) does not present any health problems to livestock and

poultry and thus can be used for livestock watering.

Most non-point source pollutants originated from homestead which are surrounded by farming fields

and from the blair toilets and livestock kraals within each homestead. Bova includes a business centre

and livestock selling pens, which also contribute to non- point source pollution. Siwaze (medium

sized) recorded the highest pollutant loading compared to the other reservoirs, because it has a larger

watershed, which includes the other three watersheds.

Small reservoirs are a major source of livelihood in the semi arid area of Avoca. Livelihoods include

market gardening, irrigation, and livestock rearing and to some extent selling bricks and fish, all of

which would not be possible without water from the reservoirs (Sithole, 2005), most of which require

water of substantial quality. Water quality is thus closely linked to water use and to the state of

economic development

51

7.2 CONCLUSION

The water in all the reservoirs was found not to be suitable for drinking purposes according to the

WHO drinking water guidelines, but was found to be suitable for irrigation and livestock use

according to the FAO and DWAF guidelines respectively. The hypothesis that water in small

reservoirs is not suitable for small irrigation, domestic use and livestock watering according to the

FAO, WHO and DWAF standards respectively could not be accepted when considering irrigation and

livestock watering. However the same hypothesis was accepted when considering the use of the

reservoir water for drinking purposes.

Villagers’ water quality perceptions of the reservoirs differed depending on the season (dry or wet),

with most of the respondents being satisfied with the studied water quality perceptions during the wet

season. The perceived smell and soap consumption of the reservoir water proved to be the most

important water quality perceptions as they generally had higher percentages of satisfaction in the wet

season for most of the reservoirs. There was a correlation between analysed laboratory water quality

parameters and the community water quality perceptions. Generally the water quality was perceived to

be unsatisfactory during the dry season, which can be linked to the analysed laboratory parameters,

which recorded higher concentrations of most parameters in the dry than the wet season.

Non point source pollution, originating from homesteads and farming fields affects water quality in

small reservoirs. Siwaze had a higher pollutant load compared to the other three reservoirs as it has a

larger watershed indicating that non point source pollution varies depending on the watershed area,

and the activities within a specific watershed.

7.3 RECOMMENDATIONS The 2006/2007 farming season which coincided with the study period was declared a drought year.

Therefore further water quality studies should be carried out in non-drought years to give a true

reflection of the reservoir water quality.

Relatively cheap methods of water purification/treatment such as boiling, and the use of dilute sodium

hypochlorite (Chlorination) should be used before the water is used for drinking purposes to reduce

52

water related diseases. Chlorination is effective for the following, reduction of bacteria and most

viruses, residual protection against contamination, available in Zimbabwe as the common Jik, easy to

use and is of low cost; however chlorination has potential objections by users because of taste and

odour problems.

In order to have an integrated watershed and water quality management, there is need for ground

water quality, sediment quality and non point source pollution modelling studies to complement the

surface water quality studies since water is a continuum. According to Hunt et al., (1999), the amount

of ions carried by ground water through seepage is typically much higher than surface water and could

have a profound effect on water quality. Small reservoirs have low water abundance in the dry season;

this problem however is exacerbated by the fact that the reservoirs are highly silted. Reservoirs’

sediments are a sink of nutrients, therefore mechanical de-sedimentation of the reservoirs could be

inevitable in order to maintain a good water supply in the rural areas. Non point source pollution

modeling results in the estimation of the pollutants that may be a threat to surface water bodies.

Although this could be an expensive venture it could greatly improve water quality by removing

nutrients from their sinks.

53

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APPENDICES

APPENDIX 1

Table 2.1: No. and capacity of dams per Province in Zimbabwe

Number of dams

Province Total

number of dams

Total capacity

th m3 >1 000

000 m3

<1000000>500 000

m3

>500 000 >100 000 m3

<100 000m3

BULAWAYO 32 9785 2 0 2 28 HARARE 50 13272 1 0 12 37 MANICALAND 679 148656 16 10 80 573 MASHONALAND CENTRAL 763 691113 29 34 164 536

MASHONALAND EAST 1363 292378 29 35 207 1092

MASHONALAND WEST 1413 1334765 45 38 255 1075

MASVINGO 1044 2339527 20 14 158 852 MATABELELAND NORTH 611 190498 18 13 97 483

MATABELELAND SOUTH 2243 873271 51 53 310 1829

MIDLANDS 1620 2098731 54 38 195 1333 TOTAL: 9818 7991996 265 235 1480 7838

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APPENDIX 2

Table 2.2: Effects of Chloride on the Health of Livestock (DWAF, 1996b)

○○○ Target Water Quality Range. No adverse effects ○○• Adverse chronic effects such as decreased feed and water intake and a decline in productivity may occur, but are unlikely. Averse effects that do not occur will most likely be temporary and normal production should continue once stocks are adapted. ○•• Adverse chronic effects such as decreased feed and water intake, weight loss and a decline in productivity may occur, but will most likely be temporary and normal production should continue once stock are adapted. ••• Averse chronic (as above), and acute effects such as osmotic disturbance, hypertension, dehydration, renal damage and salt poisoning may occur. May be tolerated for shorter exposure time depending on site-specific factors and adaptation. Stock may subsist under certain conditions, but production will in all likelihood declines.

Effects

Chloride

Range

(mg/l)

Sheep Cattle Dairy cattle,

pregnant

and

lactating

cattle

Ruminants Monogastrics Poultry

0-1500 ○○○ ○○○ ○○○ ○○○ ○○○ ○○○

1500-

2000

○○○ ○○○ ○○○ ○○○ ○○• ○○•

2000-

3000

○○○ ○○○ ○○○ ○○○ ○•• •••

3000-

4000

○○• ○○• ○○• ••• ••• •••

4000-

5000

○•• ○•• ○•• ••• ••• •••

5000-

6000

○•• ••• ••• ••• ••• •••

> 6000 ••• ••• ••• ••• ••• •••

60

APPENDIX 3

Villagers’ Water Quality Perception Survey

SMALL RESERVOIRS PROJECT (SRP) AVOCA GROWTH POINT The aim of the survey is to obtain villagers’ water quality perceptive of the reservoirs being studied,

which will be correlated to the analysed water quality results.

Interviewer

Ward

Respondent Characteristics/Demographic data

Name

Age

Sex 1.Male 2.Female

Marital Status 1.Single 2.Married 3.Divorced 4.Widowed

No of people in

household

5(b) No. under 5 years

Position in household

A: WATER USE

1. What time of the year is water abundant in the reservoir (specify in months)_____________

2. Where do you normally get your water from (Dry season(D), and the Wet season (W)

61

3. How close is the water source to your homestead in the wet season for the following uses?

Drinking______________________________________

Laundry_______________________________________

Dishes________________________________________

Livestock watering_______________________________

Irrigation_______________________________________

4. How close is the water source to your homestead in the dry season for the following uses?

Drinking______________________________________

Laundry_______________________________________

Dishes________________________________________

Livestock watering_______________________________

Irrigation_______________________________________

Drinking laundry Dishes

Livestock

watering Irrigation

D W D W D W D W D W

Borehole

Open Well

River

Small reservoir and

name

Other (specify)

62

B: SMALL RESERVOIR WATER USE PERCEPTIONS

1. During the wet season what is the water quality in small reservoirs like?

colour taste soap consumption Frothing when boilingBovaAvocaSifininiSiwaze

Name of reservoir Water Quality Perceptions

2. During the dry season what is the water quality in the following small reservoirs like?

colour taste soap consumption Frothing when boilingBovaAvocaSifininiSiwaze

Name of reservoir Water Quality Perceptions

C: WATER MANAGEMENT ASPECTS

1. Are there any rules and regulations regarding water quality in the reservoirs?

Yes No

2. If yes name them______________________________________________ _____________________________________________________ _____________________________________________________ ______________________________________________________ 3. Are there any conflicts related to the water quality of these reservoirs

Yes No

4. If yes, who resolves them explain______________________________ ____________________________________________________________________

63

5. Who is responsible for resolving the water quality conflicts?_______________________________________________________

____________________________________________________________________

D: FARMING INFORMATION

1. What type of crops do you grow and fertilizers used

Crops Grown Area How much fertilizer do you use

Maize Basal Top Manure

Groundnuts

Bambara nuts

Rapoko

Other

Livestock ownership Number Housing

Wet season Dry season

Cattle

Goats

Sheep

Donkey

Chicken

Others

2. What livestock-watering source do you use in the wet season?

Small reservoir name Other Specify

3. What livestock-watering source do you use in the dry season?

Small reservoir name Other Specify

64

4. How often do you water your livestock, in the wet season _______________ In the dry season________________________

5. What is the distance of your homestead to the watering source, state units ___________________

6. Are there any spillages that occur from the livestock housing?

Yes No

E: OTHER COMMENTS THAT MAY BE RELEVANT TO THE STUDY

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________

65

APPENDIX 4 Analysis of Variance (ANOVA) tables. Variate: Chloride

Source of variation d.f.(m.v.) s.s. m.s. v.r. F pr. Dam 3 459.001 153.000 26.08 <.001 Month 7 73384.252 10483.465 1786.94 <.001 DamxMonth 21 12180.954 580.045 98.87 <.001 Residual 61(3) 357.869 5.867 Total 92(3) 86234.438 Standard errors of differences of means

Table Dam Month Dam Month rep. 24 12 3 d.f. 61 61 61 s.e.d. 0.699 0.989 1.978 Least significant differences of means (5% level) Table Dam Month Dam x Month

rep. 24 12 3 d.f. 61 61 61 l.s.d. 1.398 1.977 3.955 Variate: EC

Source of variation d.f.(m.v.) s.s. m.s. v.r. F pr. Dam 3 87530.5 29176.8 233.92 <.001 Month 7 248545.8 35506.5 284.67 <.001 Dam x Month 21 51915.4 2472.2 19.82 <.001 Residual 63(1) 7857.9 124.7 Total 94(1) 364621.3 Standard errors of differences of means

Table Dam Month Dam x Month

rep. 24 12 3 d.f. 63 63 63 s.e.d. 3.22 4.56 9.12

66

Least significant differences of means (5% level) Table Dam Month Dam x Month

rep. 24 12 3 d.f. 63 63 63 l.s.d. 6.44 9.11 18.22

Variate: Hardness

Source of variation d.f.(m.v.) s.s. m.s. v.r. F pr. Dam 3 1216.2 405.4 3.54 0.020 Month 7 110606.2 15800.9 137.96 <.001 Dam x Month 21 10312.8 491.1 4.29 <.001 Residual 63(1) 7215.4 114.5 Total 94(1) 128881.6 Standard errors of differences of means

Table Dam Month Dam x Month rep. 24 12 3 d.f. 63 63 63 s.e.d. 3.09 4.37 8.74

Least significant differences of means (5% level) Table Dam Month Dam x Month

rep. 24 12 3 d.f. 63 63 63 l.s.d. 6.17 8.73 17.46 Variate: pH

Source of variation d.f.(m.v.) s.s. m.s. v.r. F pr. Dam 3 4.1192 1.3731 5.90 0.001 Month 7 53.5753 7.6536 32.90 <.001 Dam x Month 21 10.8501 0.5167 2.22 0.008 Residual 63(1) 14.6573 0.2327 Total 94(1) 82.5937

67

Standard errors of means

Table Dam Month Dam x Month

rep. 24 12 3 d.f. 63 63 63 e.s.e. 0.0985 0.1392 0.2785 Least significant differences of means (5% level) Table Dam Month Dam x Month

rep. 24 12 3 d.f. 63 63 63 l.s.d. 0.2783 0.3935 0.7870

68

APPENDIX 5

Pollution loading calculations.

Avoca Hardness Bova Hardness

PL = 0.0019 m3/s * 48.60003 mg/l PL = 0.00327 * 5.6333 mg/l

= 9.23 mg/s = 18.42 mg/s Sifinini Hardness Siwaze Hardness

PL = 0.001736 * 7.8 PL = 0.02479 * 4.7333 = 13.5 mg/s = 0.117.338 mg/s Avoca Chloride Bova Chloride PL = 0.0019 m3/s * 1.56667 PL = 0.00327 * 14.2333

= 2.9 mg/s = 46.54 mg/s Sifinini Chloride Siwaze Chloride PL = 0.001736 * 13.6 PL = 0.02479 * 3.9 = 23.61 mg/s = 96.681 mg/s Converted EC to TDS

TDS (in mg/L or ppm) = 0.725x EC25 (in micromhos/cm) Avoca EC Bova EC PL = 0.0019 m3/s * 57.758 PL = 0.00327 * 40.35 =109.7402 mg/s = 131.96085 mg/s

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Sifinini EC Siwaze EC PL = 0.001736 * 54.738 PL = 0.02479 * 5.3166 =95.025168 mg/s =131.798514 mg/s

small reservoir non-point source pollution …

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