Thesis on Okavango Water Quality

7/27/2019 Thesis on Okavango Water Quality

1/74

1

Chapter 1

Introduction

The availability and quality of fresh water is inextricably linked to development

(Ashton and Braune, 1999). Population growth affects water demand, which has a direct

impact on water-quality (Biswas, 1992). El Obeid and Mendelsohn (2001) found that the

population of the Kavango Region of Namibia has increased rapidly since the 1950s, with

the most rapid growth between 1970 and 1981 at a rate of 7.5 % per year. They suggested

that the Kavango Region has the greatest impact on water resources along the Okavango

River. The effect of the increasing population on the Okavango River needs to be

evaluated in order to develop a management plan for water utilization in the region.

An indirect impact of increased population is often land cover change. The annual

rate of land clearing between 1972 and 1996 was 4 % (el Obeid and Mendelsohn, 2001).

In more recent years, most of the clearing has been inland along relic sand dunes

(omarumba), as there are very few suitable sites left for clearance along the river. Therehas been relatively little land clearing in the Angolan headwaters of the river, due to

instability caused by war. However, there are plans to develop the upper catchment of the

Okavango River for agriculture, which may have a significant impact on the water-

quality (Brown, pers comm., 2002; Miller, 1997). The development of a model linking

land use and water-quality is required, so that the impacts of agricultural development

can be projected (Butcher, 1999).

An international project Every River has its People Project has been developed to

manage the Okavango River watershed as a whole. The main goals of this project are to:

promote sustainable management of natural resources in the Okavango River Basin

and to increase the capacity of communities and other local stakeholders to participateaffectively in decision making about natural resources, particularly those related water

resources, at local, national and regional levels(Jones, 2001a, p1).

Local people have recognized that the quality of the water and fish resources is

decreasing, and have an interest in understanding how to protect these resources (Jones,

2001b). It is therefore necessary to understand the relationships among population, land

use change and water-quality, in order to increase the understanding of the Okavango

River system at a local and regional level.

Research Objective

The objective of this research is to examine the effects of increased population andland use/land cover change on the water-quality of the Okavango River in Namibia.

In order to determine the impacts of land use change on the water-quality, satellite

imagery was used to identify land cover change, and correlated with archive (Bethune,

1991; Hays et al., 2000) and recent water-quality data from 2002. A land cover

classification scheme was developed to determine the land cover change from 1973 to

1993.


2/74

2

Study Site

Location

The Okavango River flows through three southern African countries, Angola,

Namibia and Botswana (figure 1.1). It is a major regional freshwater resource and is

Namibias largest perennial river (Schneider, 1986). The study area is concentrated

within a 470 km long section of the Okavango River that defines the northern Namibianborder and crosses the Kavango Region before flowing into Botswana where it forms an

inland delta. The study section of the river includes the confluence with the major

tributary, the Cuito (figure 1.2). Water samples were collected at seven sites along the

Okavango River in Namibia. From west to east these are Nkurenkuru, Mupini, Nkwasi,

Mupapama, Katere, Mukwe and Ngepi (figure 1.3).

Figure 1.1: The location of the Okavango Drainage within Southern Africa. The red box

is subset in figure 1.2


3/74

3

Figure 1.2: The Okavango River Drainage System, subset from figure 1.1 The

Kavango Region of Namibia is subset in figure 1.3

Figure 1.3: Location of Sampling Sites along the Okavango River, within the Kavango

Region of Namibia.

Hydrology


4/74

4

The Okavango River originates in Angola as the Rio Cubango and flows south until it

reaches the Angolan\Namibian border, where it turns east for some 415 km until it turns

south and flows south into Botswana where it forms an inland delta (figures 1.1 and 1.2).

The Okavango Delta has been designated a World Heritage Site (Pallet, 1997). The

Cubango drains approximately 88,700 km2 and originates on crystalline rock

approximately 1700 m above sea level, in an area that receives a mean of 983 mm annualprecipitation (Bethune, 1991), of which approximately six per cent reaches the Cubango

(Miller, 1997). The river flows 930 kilometers from its source to the confluence with its

major perennial tributary, the Cuito. The Cuito also originates in Angola on Kalahari

sands, 715 km from the confluence and drains approximately 60,600 km2. Its source

region receives a mean annual precipitation of 876 mm. Together, the Cubango and Cuito

Rivers drain from an area of almost 150,000 km2 (Ellery, 1997). The flow from the

Okavango/Cubango is much more variable than the Cuito, so during the floods of the wet

season (January through May) it contributes considerably more water to the delta. This

situation is reversed during the other seven months of the year (el Obeid and

Mendelsohn, 2001).

The Omatako River in Namibia is thought to be a fossil catchment as there are no

records of it flowing more than 400 km from its source (the source is 635 km away from

the confluence). It potentially drains about 55,700 km2. However it forms an important

backwater when the Okavango River flood waters flow up the lower part of the drainage

(Bethune, 1991; el Obeid and Mendelsohn, 2001). The Okavango River probably gains

water from aquifers in the Kalahari sediments. These are mainly recharged from elevated

areas to the south. Near the river the aquifers are less than 20 meters deep el (Obeid and

Mendelsohn, 2001).

Floodwaters reach Rundu in January or February and arrive at the delta in June or

July. The highest waters in Rundu usually occur in April. Floods usually raise the

Okavango river level 3-5 m above its lowest levels that occur in November (Bethune,

1991).

Climate

The climate of the Okavangos catchment area in Namibia and Botswana is sub-

tropical, with a long, dry cool season and a short, hot wet season (Hines, 1997, el Obeid

and Mendelsohn, 2001). The summers are very hot with a mean maximum temperature of

34 OC, and winters are mild with a mean minimum temperature of 6 OC. Occurrence of

frost is rare. The Kavango Region has a mean annual rainfall of 600 mm, although it is

highly variable, both between and within years (Hines, 1997). The greatest evaporationoccurs in September and October and exceeds precipitation by a factor of three, (Ellery,

1997, el Obeid and Mendelsohn, 2001). Farmers take into account the high variability of

rainfall by staggering ploughing and planting of crops through the rainy season. Wind

velocity is generally low, averaging around 3 kilometers per hour, and the prevalent

direction ranges from north east to south. In January some winds may blow from the west

(el Obeid and Mendelsohn, 2001).

Geology and Soils


5/74

5

Most of the water that flows into the Okavango Delta flows over Kalahari Sands. This is

an environment of aeolian sands, which is an ancient erg extending from the Northern

Cape in South Africa to the Congo; initial sand deposition started in the mid-Tertiary

(Skinner, 2000). The Kalahari is underlain by thick geologically ancient Precambrian

granitoid rocks, with poor surface exposure due to the thick sands (Scholes and Parsons,

1997).

Two principal physiographic regions dominate the Kavango region. The first is the

riverine landscape, comprised of the main Okavango River channel, floodplains with

braided channels, and a fluvial terrace with alluvial deposits. The second region consists

of Kalahari sands dominated by linear dune systems and undulating plains (Hines, 1997,

Schneider, 1986, Simmonds, 1997). The dune systems are flat to gently undulating with

the dune ridges and slacks (omurambas) trending east-west. The substratum consists of

calcareous sand and gravel from the Kalahari beds which are mainly of aeolian origin.

The Nosib Formation, laid down 850 to 700 million years before present, occurs at

shallow depths east of Rundu and is comprised of conglomerate, phyllite, and quartzite

(Dierks, 1994; Ellery, 1997). The Kalahari sands were deposited on Tertiary calcretes andhave been eroded and partially reworked by wind and water (Simmonds, 1997).

The principal soil types are related to the physiographic regions. Fluvisols occur in

the Okavango and Omatako floodplains. These soils are developed in alluvial deposits,

and are flooded regularly along the Okavango River. These are the most fertile soils of

the region and are exploited for crop production (Mendelsohn, et al., 2002). On the south

and west banks of the Okavango River terrace system, the fluvisols can be divided into

three general soil sub-types in the area: Clovelly, Oakleaf and Hutton, using the South

African Soil Classification System (Schneider, 1986). These exhibit physical, chemical

and mineralogical properties typical of arid-region soils, with orthic topsoils and apedal

(no structure) B horizons (Mpumalanga Soil Mapping Project, 2003). They display a

moderate to high base saturation, which results in slightly acid to slightly alkali soils (pH

6.8-7.6). The cation-exchange-capacity is low and kaolinite is the most abundant clay

mineral (Schneider, 1986; Simmonds, 1997).

An estimated distribution of the two main soil types, fluvisols and arenosols in the

Kavango Region is shown in figure 1.4. Upon the examination of figure 1.5 (vegetation

type) the fluvisols occur with riverine forests, floodplains, Omatako drainage and dry

tributaries to the Okavango River.


6/74

6

Figure 1.4: Estimated Soil Map of the Kavango Region (from Mendelsohn, et al., 2002).

The soils of the sandveld surrounding the riverine environment are comprised ofarenosols. These are developed in sediments of aeolian origin, and have very high sand

contents. This results in rapid infiltration of water and little retention of nutrients, which

makes them infertile and difficult to cultivate (Mendelsohn, et al., 2002). In the north

eastern stabilized Kalahari sand dunes, where there is a deep sand mantle but little or no

relief, the soils are the loose grey sands of the Sandspruit series. Where relief and

drainage are more defined there is a catenary sequence of soils. These include red sands

on elevated slopes, yellowish-brown sands on mid-slopes, and grey sands or heavier

darker soils at the base. In the omarumbas, grey sandy loams are found, which result in

impeded internal drainage and salinization if irrigated. Where the terrace system is

discontinuous, the soils are red loamy sands with inclusions of grey coarse sandy loams

(Simmonds, 1997).

VegetationThere is a great diversity of flora within the Kavango region (figure 1.5), with 869

species in 88 families being identified (Bethune, 1991; Hines, 1997). The vegetation in

the Kavango Region is a mosaic of small units, although each landform has a

characteristic vegetation assemblage. Tall deciduous woodlands, consisting ofBurkea-

Teak woodland and shrubland, generally occur in relic dune systems where there is

marked variation in soil between the sandy dunes and clay soils between the dunes. The

floodplains and riverine forests are associated with the drainage of the region. Generally

the river valley is characterized by medium to tall riparian woodland with RhodesianTeak (Baikaea plurijuga), Dolfwood (Pterocarpus angolensis), Chivi (Guibourtia

coleosperma) Yellowwood (Terminalia sericea) and various acacias. Herbs and grasses

are extensive, even in overgrazed areas (Schneider, 1986).


7/74


8/74

8

Figure 1.6: Estimated Population Density Integers (people per square kilometer) for 2000

(from el Obeid and Mendelsohn, 2001)

As can be seen from figure 1.6, the majority of the population lives along the river in

the riparian zone of the Okavango River, along the dry drainages and major roads.

Farming activity is an important source of income, with 96 % of the households engaged

in both crop and livestock farming activity, while 71 % are dependent on farming as an

income source (figure 1.7).

Figure 1.7 The floodplain (left), just east of Rundu, near Nkwasi (site 3) and household

with a field of mahangu.

Land Use/Land Cover

The principal land use in the Kavango region is communal grazing and small scale

farming (usually crop cultivation). The rest of the regions land use is composed of


9/74

9

conservation areas, government farms and other private farms (figure 1.8). Most of the

land is controlled by tribal authorities (el Obeid and Mendelsohn, 2001).

Figure 1.8: Landuse in the Kavango Region, (after el Obeid and Mendelsohn, 2001)

The most intense period of crop farming occurs from September to February, when

the fields are cleared, ploughed and planted. Most crops are not irrigated, with the

exception of large scale and government agriculture farms. Planting is staggered through

the raining season, and is undertaken after there has been a good rainfall event. This

increases the chance of crop survival during the hot, dry periods. The use of fertilizer is

low and limited to the government and large scale agriculture farms. Livestock farming

is dominated by cattle and goats, although there are some sheep, pigs and donkeys. Theyare not kept within fields but are moved between sources of water (usually the river) and

grazing. Along the river, fishing provides another resource (figure 1.9). Figure 1.10

shows the pressure placed on natural resources (from el Obeid and Mendelsohn, 2001)


10/74

10

Figure 1.8: Children fishing at Mupini Health Center (site 2)

Figure 1.9: Pressure on natural resources in Kavango: sum of people, cattle and goat

densities (from el Obeid and Mendelsohn, 2001)

The Kavango Region is currently showing an increase in human population and land

clearing for crops and livestock, which is putting more pressure on the natural resources,

including the Okavango River (el Obeid and Mendelsohn, 2001). With the increase in

stability in Angola and the likely migration of people into the catchments of the Cubango

and Cuito Rivers, an examination of the water-quality of the Okavango River and its

relationship to land use and land cover change is necessary, so that decisions can be made

with an understanding of the present and probable future impacts.


11/74

11


12/74

12

Chapter 2:

Literature Review

Water as a Resource

Water is the most essential natural resource for human survival, agricultural

production and economic development (Biswas, 1992; Duda and El-Ashry, 2000). Eighty

percent of the rivers in Sub-Saharan Africa are transboundary (Duda and El-Ashry,

2000). These rivers have a high resource potential for socio-economic development

through fisheries, tourism and recreation, irrigation schemes and hydropower generation.

They also facilitate inter-country cooperation, meeting the goals and objectives of the

African Union (UNECA, 2000). As a continent, Africas proportion of freshwater

resource is comparable to its portion of the global population. However the distribution of

freshwater and population is not equal. The Congo River Basin holds thirty percent of the

continents total water resource and only ten percent of its population. In fact Africa is the

third driest continent in the world (UNECA, 2000). In arid and semi-arid regions, where

water availability is limited, the water resource value is exceptionally high (Helmscrotand Flugel, 2002). Ali (1999) suggests that water resource issues deserve singular

attention to avoid potential conflicts in southern Africa, where currently there is no

agency to deal with water issues. This is important as conflicts arise over water usage.

The Southern African Developing Community (SADC) has not been able to manage

these conflicts. One such example was the proposal of a water diversion project (Eastern

National Water Carrier, ENWC) from the Okavango River in Namibia to its capital

Windhoek, to combat water shortages (Pallett, 1997). Botswana opposed this proposal

due to threats to the Okavango Delta. The case had to be brought to the International

Court of Justice in order to resolve the problem. In 1997 the ENWC project was

postponed as there was sufficient rainfall to fill the reservoirs with enough water for two

years consumption (Ramberg, 1997).

Wetlands are important water resources as they provide many hydrological,

ecological, economical and social benefits. For example, they support human population

by supplying agricultural land for both crops and grazing, fishing, and water resources

(Thompson and Polet, 2000). In floodplain wetlands, the intermittent floods provide

nutrients into the side channels, allowing the biota population to increase and diversify.

This provides an important food resource to people who live in the area, as well as

alluvial deposits that make the plains fertile for dry season agriculture (Johnson and

Richardson, 1995; Thompson and Polet, 2000).

Water-qualitySafe drinking water is unavailable to approximately 1.1 billion people world wide

(Gadgil, 1998). The decreasing fresh water availability in many countries provides

motivation for the development of water-quality remediation projects to improve the

water-quality and therefore increase water availability (Deksissa et al., 2001).

In order to utilize a water resource sustainably it is necessary to understand the status

of the water-quality (van Ree, 1999). Water-quality is defined as the physical, chemical

and biological status of the water body(Wang, 2001, p25). The abundance and diversity


13/74

13

of biota in a water body is dependent on the chemical and physical characteristics. The

main physical/chemical parameters that are typically measured in water-quality studies

are electrical conductivity, pH, temperature, suspended solids and nutrients (van Ree,

1999; Wang, 2001). Electrical conductivity is the ability to conduct an electrical current

and provides information on the abundance of dissolved solids (Ministry of Environment,

1998). Temperature is important, because of its impact on the biological and chemicalcomponents of the water. Dissolved oxygen is also an important parameter because biotic

life cannot survive without it. It also affects the solubility and availability of nutrients. A

water body with dissolved oxygen levels less than 5 mg/l puts serious pressure on the

biota, while 4 mg/l is the limit to avoid acute mortality (Ministry of Environment, 1998).

In flowing water a pH between 6 and 8 is expected, depending on the watersheds

geology (van Ree, 1999; Wang, 2001). Lethal effects on aquatic biota occur at pH below

4.5 or above 9.5 (Ministry of Environment, 1998).

Water-quality Degradation

Since water is such a basic necessity to the daily living of every organism, the quality

of freshwater resources needs to be monitored and maintained. However, the quality ofsurface water has decreased on a global scale, which limits fresh water availability and

puts even more pressure on a stressed resource (Gyau-Boakye, 1999; Helweg, 2000;

Schulze, 2000). In fact, most large rivers across the globe have been greatly affected by

human activity (Johnson and Richardson, 1995).

Individual sources of water-quality degradation can be classified into two main

categories. These are point and non-point sources. Point sources are where there is one

location which is impacting the stream and usually have a spatial response, in that

immediately downstream from the pollution source the water-quality is degraded. These

impacts decrease further downstream as nutrients are taken up by biota and diluted as the

pollutant is dispersed throughout the river. Non-point sources have a larger spatial scale

impacts, and it is difficult to determine the exact cause and extent of the decrease in

water-quality. Examples of point sources include waste water treatment plants and

industrial parks. Non-point sources include run off over agricultural fields and

atmospheric deposition (Smith and Alexander, 2000).

Physical changes in land use/land cover and population density within a rivers

watershed usually have an impact on the hydrology and water-quality of the river. These

changes are not constrained to the riparian zone (Johnson and Richardson, 1995; Schulze,

2000). The hydrology of a region with limited water availability will dictate the

distribution of pattern of population and land use. In these regions any increase in wateruse will upset the equilibrium between availability and demand (Thompson and Polet,

2000). Pegram and Bath (1995) found that the Mgeni River catchment (South Africa) was

highly stressed, due to pressure to supply the increasing population of the

Pietermaritzburg/Pinetown/Durban urban areas, rural domestic use, agriculture,

environmental and recreational water demands. Eighty five percent of the contamination

was from non-point sources.

Previous Studies of the Okavango River


14/74

14

The Ministry of Fisheries and Marine Resources of Namibia is developing baseline

procedures to manage systems for sustainable utilization of its resources, upon which

people are indirectly or directly dependent (Hocutt et al., 1991). As people are directly

dependent on the Okavango River falls, a concerted effort is being made to monitor the

biological quality of the Okavango River. The authors formed a conceptual basis for the

development of an Index of Biological Integrity (IBI) for the Namibian section of theOkavango River. The IBI relies on structural and functional components of a fish

community to reflect the health of an aquatic system.

Hays et al.,(2000) conducted a fish survey from 1992 to 1999, to produce guidelines

for sustainable management of fisheries in the Okavango River. They also collected

water-quality data from the autumn of 1992 to the winter of 1997. Bethune (1991)

conducted a comprehensive study of the hydrology, water-quality, vegetation, and fauna

in the wetlands associated with the Okavango River. So far there does not seem to have

been an excessive exploitation of the water resources in the Kavango Region. Presently

the Okavango River is not affected by water scarcity, but by 2025 Duda and El-Ashry

(2000) predict that the watershed will have serious water shortages. This projected watershortage is likely to lead to further international disputes over such an important water

resource. With population growth, more pressure is being exerted on water resources.

Since the Okavango River is a life-sustaining resource, it should be carefully managed to

benefit the people of region (Bethune, 1991).

The population in the Kavango Region has increased rapidly in extent since the

1950s, with eighty-five percent of the population live in the riparian zone (el Obeid and

Mendelsohn, 2001; and Hocutt et al., 1997). Riparian zones are areas of land that adjoin,

influence or are influenced by a body of water (Waterways and Wetland Manual, 2003).

Associated with the population growth, there has been an increase in livestock, fire

frequency and area of land cleared for crops and fuel (el Obeid and Medelsohn, 2001).

Hocutt et al., (1997) suggest that due to the present population increase rates, the

associated land use change, and the increasing chance of drought, a water-quality

monitoring protocol is essential for the Okavango River.

Water-quality Issues

Mattikalli and Richards (1996) found that the quality of surface water has decreased

in many countries over the past few decades, and that the agriculturally dominated

watersheds in England are affected by soil erosion and suspended sediment load in the

river. In the United States there is concern about the potential contamination, overuse and

development of scenic rivers (Scott and Udouj, 1999). This study found that land usechange within the Buffalo National River, Arkansas, may have impacted the water-

quality of the region. In South Africa a Riverine Health Program (RHP) is being

developed to monitor the biological quality of riverine ecosystems (Roux et al., 1999). It

is planned to link the outcomes of the RHP monitoring outcomes with water resource

management decisions. Smith and Alexander (2000) identified five main sources of

nutrient loading to streams in the Unites States. These are point sources, fertilizer, animal

agriculture, atmospheric deposition and non-agricultural run-off.


15/74

15

The quality of surface water is affected by human activity throughout a rivers

watershed (Wang, 2001). Agriculture and industrial development within the watershed

can seriously affect how a river functions (Johnson and Richardson, 1995). It is necessary

to understand consequences of human activity on the water cycle and environment at all

relevant scales (Klocking and Haberlandt, 2002). Over the past several thousand years the

impact of humans on the environment has increased. In the last century water-quality andsoil fertility have been severely degraded as a result of the growing pressure (Ojima and

Galvin, 1994). The current population growth within many watersheds is placing greater

pressure on the water resource. As the demand for water increases there is an associated

escalation in effluent discharge, which has a negative impact on the water-quality (Gyau-

Boakye, 1999). There is a global consensus that water demand will rise over time. Duda

and El-Ashry (2000) suggest that two-thirds of the worlds population will experience

water stress by the year 2025 and that a billion people will have severe water stress.

Population growth not only increases the demand for water, but alters the landscape

within watersheds, through land clearance for settlements, agriculture and infrastructure.

Although anthropogenic impacts are not the only factors to effect the water-quality ofsurface waters, they have the greatest impact on ecosystem equilibria (Hocutt et al.,

1994), even compared to long term climate change (Schulze, 2000). One of the impacts

humans have on the water-quality is modifying the landscape within the watershed

(Ojima and Galvin, 1994). Hunsaker and Levine (1995) and Wear, et al., (1998) suggest

that land use change may be the single largest factor affecting water-quality. In the

Wabash River basin of south eastern Illinois, land cover types and their spatial

distribution accounted for between 40 % and 86 % of variance in water-quality,

depending on watershed sizes (Hunsaker and Levine 1995).

Land Use/Land Cover

A distinction needs to be made between land use and land cover. Land cover is the

biophysical state of the earths surface and includes cropland, forest, grassland and

settlements. Land use is how land cover attributes are manipulated, managed and

exploited (Shulze, 2000). Land-use changes are linked to economic development,

population growth, technology and environmental change. Not all impacts from land

use/land cover change are negative; they can increase productivity and sustainability

without degrading the environment (Ojima and Galvin, 1994). Land use change has

unintended, as well as intended, impacts on the environment. The clearing of natural

vegetation releases nutrients into the atmosphere and water cycle (Houghton, 1994). Land

management practices, such as grazing, fire and tillage, affect ecosystem composition,

nutrient cycling and organic matter distribution (Ojima and Galvin, 1994). Hydrologicalresponses are highly sensitive to land use change, although local scale abrupt changes

may be more significant than regional scale changes (Schulze, 2000). Land use/land

cover change is not uniform within a rivers drainage basin. In the Southern Appalachian

Highlands of North Carolina, areas with intensive land cover change had serious

implications for increased erosion, temperature regime change and decreases in dissolved

oxygen (Wear, et al., 1998).


16/74

16

Every river system has water-quality threats that are particular to the characteristics

that function within its watershed. Wang (2001) found that the greatest problem for

water-quality is growing urban areas, although wastewater treatment plants did not

negatively affect the quality. However the main threat to the Crocodile River in South

Africa is nutrient pollution from agriculture (Deksissa et al., 2001). In the Serengeti

ecosystem, the combined effect of deforestation, irrigation and water diversion decreasedthe flow of the Mara River to 0.5 m3s-1, compared to a peak flow of 1000 m3s-1 (Gereta et

al., 2002). The Mutshindudi catchment in South Africa has shown environmental

overloading resulting from population-related emissions (van Ree, 1999). A study

conducted in the Gucha catchment in Kenya concluded that continued high rates of

population growth posed a great danger to the water resource. They examined the impacts

of land use change on water-quality within (i) agriculture and rural, (ii) urban, and (iii)

other social, industrial and transportation categories. The main threats were identified as

industrial effluents, agricultural runoff, and municipal and domestic wastes (Ongwenyi,

et al., 1993).

Many studies use Geographical Information Systems (GIS) to investigate therelationship between land-use and water-quality (Mattikalli and Richards, 1996;

Hunsaker and Levine, 1995; and Scott and Udouj, 1999). Scott and Udouj (1999, p.95)

state that GIS technology can rapidly assess environmental change that has occurred

within a watershedand Mattikalli and Richards (1996, p.72) emphasize that GIS is vital

for this type of study as it provides the appropriate technology input, storage,

manipulation, and analysis of large volumes of land use data at different scales.

Mattikalli and Richards (1996) used an export coefficient model for the rapid assessment

of surface water-quality using remotely sensed land use data. The model accounts for

spatial variability of land-use within a watershed as it operates on individual and land use

patches and the nutrient loads are aggregated to the watershed outlet. They suggested that

this was an appropriate model for assessing the effects of various land use management

scenarios on water-quality. Hunsaker and Levine (1995) observed two watersheds in the

United States and found that the proportion of land use and its spatial pattern within a

watershed were useful for characterizing water-quality. The type and location of land use

were essential in order to model the water-quality of the river. They developed empirical

and statistical models to analyze the importance of proportion and spatial pattern of land

use, as well as its proximity to the water body. They found that they could accurately

predict water-quality in two watersheds in the United States.

Remote Sensing is a powerful tool for digital change analysis of land use/land cover

(LULC). This involves detecting, describing and understanding changes in the physicaland biological processes that occur within ecosystems (Mouat et al., 1993). Common

detectable changes are clearing of natural vegetation, increased cultivation and urban

expansion. LULC change can be analyzed using aerial photos and multispectral scanners,

such as Landsat Multi-Spectral Scanner (MSS), Landsat Thematic Mapper (TM),

Satellite Probatoire dObservation de la Terre (SPOT), and Advanced

Very High Resolution Radiometer (AVHRR). These changes are identified by using the

different spectral reflectance curves that are characteristic of LULC classes. Each pixel

has a digital number (a measure of reflectance) associated with each wavelength or band


17/74

17

that ranges from 0 to 255 in an 8-bit sensor. A higher digital number represents greater

reflectance. The reflectance at different wavelengths can be plotted in a spectral

reflectance curve and this can be used to identify spectral and information classes

(Richards and Kelly, 1984). Zomer et al., (2001) conducted a detailed landscape level

analysis of land use land cover change over 20 years in the forest Makalu Barun

Conservation Area, Nepal. They successfully used Landsat TM (1992) and Landsat MSS(1992) data to map the change and identify the riparian forest stands that are under

pressure and subject to disturbance and degradation.

The need for an understanding of the effect of land use/land cover change on water-

quality of the Okavango River is required so that there is improved understanding of

ecosystem function. Once this relationship is understood, improved policy decisions can

be made to alleviate (or at least not to exacerbate) the pressures on the Okavango River

due to increasing population and land use change within the watershed. To obtain this

understanding, a study into the effects of land use change on the physical, chemical and

biological aspects of water-quality of the Okavango River needs to be undertaken. This

study will analyze water-quality data and classified Landsat MSS and TM images todetermine relationships between land use/land cover change and the water-quality of the

Okavango River in Namibia.

Chapter 3:

Methodology

This research focuses on the changing land use/land cover (LULC) within theOkavango River drainage basin and its effects on the water-quality of the river. The

primary focus is the temporal and spatial pattern of water-quality. Water-quality data

were collected on three occasions between May and December 2002, and were compared

to data collected in 1984 and 1993/4 (Bethune, 1991; Hays, 2000). The recent and

archived data were then correlated with classified satellite imagery which had undergone

change detection analysis. The two years with satellite imagery coverage are 1973/5

(Landsat MSS) and 1993 (Landsat TM). Classification is the process of analyzing pixel

spectral signatures (their reflectance in each wavelength) and determining classes with

similar signatures and relating those to actual land use/land cover information classes.

Change detection is applied on a pixel by pixel basis, using the spectral signatures to

determine whether a pixel has changed and if so, from which class it has changed to

(Jensen, 1996).

Water-quality

Site Selection

Seven sites were selected along the length of the Okavango River within the Namibia

border, in order to sample for the water-quality analysis (figure 1.3; table 3.1). Suitable


18/74

18

site locations were selected with the aim of having several sites spread along the

Okavango River. The selection criteria were the presence of population centers and

accessibility. The precise sampling site locations changed during the May sampling

period, as the availability of dug out canoes (mokoros) were limited because many had

been confiscated during civil unrest to prevent illegal movement across the river. The

pressure on natural resources (figure 1.10) was used to identify potential water samplingsites along the Namibian section of the Okavango River, with various levels of pressure.

The actual water sampling site locations were determined by the presence of mokoros,

and whether they could be revisited in subsequent sampling periods. From West to East,

in a downstream direction the sample sites are Nkurenkuru, Mupini, Rundu (Nkwasi),

Mupapama, Katere and Ngepi. The main population centers are located at Nkurenkuru,

Rundu and Mupapama (figure 1.6).

Table 3.1: Name, Number and Location of the seven sites used to measure water-quality.

Latitude LongitudeSite Name Site

(Decimal Degrees)

Nkurenkuru 1 17.62092 S 18.61635 E

Mupini 2 17.86290 S 19.62142 E

Nkwasi 3 17.86628 S 19.90678 E

Mupapama 4 17.87833 S 20.29298 E

Katere 5 18.03508 S 20.79983 E

Mukwe 6 18.04998 S 21.43903 E

Ngepi 7 18.11612 S 21.67118 E

The sample site that was supposed to be located just downstream from Rundu (the

largest population concentration in the region) ended up being further downstream than

was ideal, as there were no mokoros available. We also met with either chiefs orheadmen of the region to seek their permission and approval for collecting water samples.

Collaboration with the Namibian Defense Force (NDF) and Police Force and the Angolan

Police (NPLA) was also required due to previous instability of the area. In some cases (at

Mupapama, site 4) we were accompanied by several members from each division to

protect and oversee the operation. When canoes were unavailable motorized boats were

used. This happened at Nkurenkuru (NPLA), Mupapama (NDF) and Nkwasi (owned by

the lodge). The boats were kept in idle so the motor would not affect the water-quality

results from pollutants leaking into the water, or from the motors and stirring up

sediments.

Sampling

Samples were collected during three time periods in 2002 (27 th -31st May, 20th-24th

July and 27th-31st December). The first two were during the dry season and the third

sample was at the beginning of the wet season, before the water had reached flood stage.

The rivers maximum flow is usually reached in April (el Obeid and Mendelsohn, 2001).

The river flow was lowest in July and highest in December, although the depth of the

river did not vary more than half a meter. Sample site locations were determined using a

Direction

of water

flow

West

East


19/74

19

Garmin etrex GPS unit, accurate to within 30 meters. Each parameter was measured once

at each site during the three sampling periods. Therefore there are three replicates per

site, measured over the entire course of the study (May, July and December).

Samples and measurements were taken in the main flow of the river, using a mokoro.

Grab water samples were collected 3-5 cm below the surface in accordance with EPAstandards using 1 liter polyethylene bottles. To ensure that the water collected was not

contaminated by outside sources the bottle and lid were rinsed three times immediately

prior to collecting the water sample. The water samples were refrigerated at the Namibia

Nature Foundation (NNF) office in Rundu, until they were taken to Analytical

Laboratory Services in Windhoek. During transportation, when a fridge was not

available, the samples were kept in a cool box. Field parameters (pH, conductivity,

temperature and dissolved oxygen) were also measured 3-5 cm below the water surface.

Since the canoes are close to the surface of the water, measurements and samples were

obtained just next to the canoe, while it was being paddled in the main stream of the

river. The samples represent the quality of the water sub-surface in the fastest flowing

section of the river, where the nutrient concentrations and conductivity are lowest.


20/74

20

Figure 3.10: Collecting water samples at A: Mupini (site 2) in December and B: Mukwe

(site 6) in May.

pH

The pH was measured using a Hach EC20 Portable pH/ISE meter model 50075 and

calibrated according to the manufacturers specifications with buffers of pH 7 and 4. In

calibration mode, the probe was placed in the pH 7 buffer and left until it had calibrated,

it was then rinsed with de-ionized water and the process repeated for the pH 4 buffer.

The temperature was recorded. The pH is accurate to 0.001 and the temperature isaccurate to 0.01 oC.

Conductivity

Conductivity was measured using a Hach CO150 Conductivity Meter Model 50150,

calibrated using 1413/cm ES and 495 ES/cm standards, according to the instructions in

the manufacturers manuals. This involved placing the meter in the solution until the

correct conductivity was measured. A high and low conductivity standard was used to

A

B


21/74

21

test the accuracy. The standards were both much higher than the expected conductivity of

the river, with values less than 50 ES/cm. The accuracy of the meter calibration was

checked in the laboratory against low conductivity standards and was found to be correct.

Conductivity measurements are accurate to 0.01 ES/cm. The temperature was also

recorded.

Dissolved Oxygen (DO)

The Corning Deluxe Field Analysis System was used to measure DO, and calibrated

(in part) according to the instructions in the manufacturers manual, in the % O2 mode. A

zero per cent oxygen standard was used to obtain the 0 % value. The 100 % value was

obtained by blowing air through a straw into a cup of water sealed with cling film and

contained within a ziplock bag. The meter was held approximately 2 mm above the

surface of the water where the air was saturated. This is a modified version of the

standard method and was improvised in the field, due to lack of instruments that blow air

into the water and a magnetic stirrer (which were not readily available at the campsite)

(Roeis, R, pers comm., 2002). The DO measurements were recorded in mg/l, and were

accurate to 0.001 mg/l. The temperature was also recorded.

Nitrogen and Phosphorus

Water samples were analyzed by Analytical Laboratory Services in Windhoek for

nitrogen (total, oxidized, and reduced) and total phosphorus. The oxidized nitrogen is

nitrite and nitrate, while reduced is the Kjeldahl nitrogen (organic and ammonium). The

nitrogen concentrations are accurate to 0.01 mg/l and the phosphorus concentration in

accurate to 0.001 mg/l. Nitrogen and phosphorus concentrations were analyzed using

the methods described in the American Public Health Association guidelines (APHA, et

al., 1995). Table 3.2 shows the preparation and analysis methods used as per the APHA,

et al., (1995). For example, total phosphorus was determined colorimetrically afterreleasing the orthophosphate through persulphate digestion.

Table 3.2: Methods for nitrogen and phosphorus analysis used by Analytical Laboratory

Services described in APHA (1995).

Constituent tested Sample Preparation Test Method

Persulphate digestion 4500-P B. 5.Total reactive phosphorous

Colorimetric 4500-P C.

Total nitrogen Oxidation-colorimetric 4500-N D.

Nitrate (NO3-)

Cadmium reduction-

colorimetric4500-NO3 E.

Nitrite (NO2-) Colorimetric 4500-NO2 B.

Kjeldahl nitrogen (NH4+ + Norg) Calculated: total N minus oxidized NStatistical Analysis

Statistics to determine whether there was a significant spatial or temporal pattern in

the water-quality data were calculated using S-Plus 6. A link was created between the

Microsoft Excel spreadsheets and a S-Plus dataset to perform the statistical analysis. A

one way Analysis of Variance (ANOVA) was conducted on the means of each month,

using the water-quality parameter as the dependent variable and the month as the

independent variable. A linear regression was calculated using the three replications at

each site (one from May, July and December), to determine if there was a correlation


22/74

22

between the distance from the Namibian/Angolan border (independent variable) and

water-quality parameter value (dependent variable).

Image classification and processing was conducted using Research Systems, Inc.

ENVI 3.5 and PCI Geomatica 8.2 software. All of the software and hardware required toprocess the images was made available through the Center for Advanced Spatial

Technology (CAST), at the University of Arkansas, Fayetteville. The images were

initially georeferenced, classified. Subsequently, change detection analysis was run in

ENVI.

Data Acquisition

The imagery used for land use and land cover analysis of the Okavango River basin

was Landsat Thematic Mapper (TM) and Mulitspectral Scanner (MSS), which was

provided by the Namibian Department of Water Affairs, Ministry of Agriculture, Water

and Rural Development. There are 18 available scenes from 1993 (Landsat 5 TM bands

2, 3, 4), 12 from 1973 (Landsat 5 MSS bands 4, 5, 6, 7) a 1997 image from Menongue,Angola (Landsat TM, bands 1, 2, 3, 4, 5, 6, 7) and a 1984 image from Rundu, Namibia

(Landsat MSS bands 4, 5, 6, 7). Landsat TM and MSS do have consistent resolution.

Landsat TM has 30 m spatial resolution, and has three visible bands, three infrared bands

and one thermal band, while Landsat MSS has 79 m spatial resolution and has three

visble and one infrared band.

These satellite images were analyzed for land cover change along the Okavango

River in Namibia and Angola. Three scenes each from 1993 and 1973/5 were used to

assess land use and land cover change over a 20 year period. The 1973 images had a

higher level of processing than the 1993 images, and had a projection of UTM zone 34S,

datum WGS 84.

Table 3.3: Satellite Images to be used for land cover change analysis (west to east). The

scenes will be referred to using their scene number (10, 11 & 12).

Scene

:Scene name Satellite

Imagery

Date

Anniversary

Window

Sun

Azimuth

(degrees)

Sun

Elevation

(degrees)

Landsat 1 08/27/73 55.59 44.2510 Nkurenkuru

Landsat 5 10/13/9347 days

78.13 54.91

Landsat 1 08/26/73 55.27 43.9811 Rundu

Landsat 5 07/18/9339 days

46.67 35.52Landsat 2 08/24/75 41.37 56.73

12Cuito

Confluence Landsat 5 08/12/9312 days

52.22 39.53

Preprocessing

The aim of all preprocessing is to make the images appear as though they were

obtained from the same sensor (Hall, et al., 1991) and to enable the most accurate

comparisons, so that when comparing change over time, you are actually comparing

precisely the same area (pixel). The images should also have the same temporal, spatial,


23/74

23

spectral and radiometric resolutions (Mouat, et al., 1993). Usually for change comparison

the images would have to be adjusted for sun elevation and angle, which affects the

spectral signature of the images. Change detection analysis was conducted post

classification. This removes the need for absolute accuracy in resolution and mainly

depends on the accuracy of the classification. Spatial accuracy is still essential for the

comparison, so the images must be georeferenced.

The bands in the 1993 Landsat TM scenes were in individual files, so the first band

was initially imported into PCI Geomatica 8.2 Focus as pix file. Two image channels

were added and the remaining two bands from the scene were imported into the new

channels. These were exported in ENVI header file format and opened in ENVI 3.5 for

georeferencing and classification. The 1993 images were georeferenced to the 1973/5

images, as the 1973/5 images had a higher processing level (level 9 compared to level 5).

Georeferencing is achieved by selecting common registration points (ground control

points - GCP) between the two images. These were identified by river confluences and

road intersections. River confluences were the main identifier as there is relatively little

development away from the river. Figure 3.2 shows the location of GCPs for the scenethat is west of the confluence of the Okavango and Cuito Rivers, and includes Rundu.

Figure 3.2: Location of common Ground Control Points for Scene 11: Rundu; 1993 (left)

was warped to the 1973 (right) image.

After the ground control points had been selected the 1993 scene was warped to the

1973 scenes projection in ENVI. This 1973 image was resampled using rotation, scaling

and translation and the nearest neighbor method (choices in the ENVI GCP selection

module). This resampling option adjusts the image to the preferred projection and assignseach pixel the digital numbers (the reflectance values) based on its nearest neighbor and

does not change the original data so that little information is lost (Jensen, 1996). Root

Mean Square (RMS) is the statistical error method for measuring residual error. GCPs

can be accepted or rejected, according to their contribution to the RMS (Jensen, 1996). In

the example shown in figure 3.2 there were 93 GCPs, with an RMS of 1.49. Any pixels

that had a disproportionally high RMS were rejected. Figure 3.3 shows the two images


24/74

24

after the 1973 image had been georeferenced and illustrates that the scenes taken from the

different years do not occupy the same geographical extent.

Figure 3.3: The original 1973 image (left) and the newly georeferenced 1993 (right)

image for scene 11.

During the warping required for georeferencing, the spatial resolution was modified for

both the 1993 TM and 1973/5 images. The 1973/5 spatial resolution was artificially

improved from 79 meters to 57 meters, while the 1993 TM resolution was degraded from

30 meters to 57 meters. The pixels have an area of 3249 m2. The digital numbers for the

new pixels were interpolated from the original data using the nearest neighbor method.

The change in pixel dimensions and the new digital numbers introduces some error into

the classification, especially along class boundaries.

Classification

Only three bands were available in the 1993 images (Table 3.4). A composite of these

three bands was used to compare the changes over time. Near infrared is highly indicative

of vegetation health, as water stressed vegetation reflects less near infrared than healthy,

moist vegetation. (Jensen, 1996).

Table 3.4: Bands available for each sensor and their corresponding wavelengths and

electromagnetic (EM) region.

1993 TM Bands Wavelength EM Region4 0.76-0.90 Near Infrared

3 0.63-0.69 Red

2 0.52-0.60 Green

1973/5 MSS Bands Wavelength EM Region

7 0.8-1.1 Short Wave Infrared

6 0.7-0.8 Near Infrared

5 0.6-0.7 Red

4 0.5-0.6 Green


25/74

25

As there was a seasonal difference in the time that images were taken (table 3.3) a

normalized difference vegetation index (NDVI) was run on all the images. NDVI is the

ratio between the red band (R) and the near infrared band (NIR) (equation 3.1). The ratio

is applied to each pixel. The NDVI was used to augment the interpretation of both the

unsupervised and supervised classifications.NDVI = NIR R (Equation 3.1)

NIR + R

The images were classified in two ways unsupervised and supervised. Unsupervised

classification identifies clustering of digital numbers within the three bands and assigns

them to a class. The number of classes and the minimum number of pixels assigned to a

class can be controlled by the user. These spectral classes were then analyzed to

determine if they correspond to any information classes. Spectral classes refer to a cluster

of digital numbers and information classes refer to the spectral signature of objects on the

ground (Richards and Kelly, 1984). For example water has a low reflectance at most

bandwidths, while healthy vegetation has a strong reflection in near infrared and lowreflectance in blue and red wavelengths. The information classes that should be

identifiable from the images used in this study are water, bare ground, healthy vegetation

and dry vegetation.

A K-means unsupervised classification was run in ENVI, with three iterations

(number of times the computer processed the data putting it into different classes) and a

maximum of five classes. This method allocates each pixel to a class by assigning it to

the one which minimizes the distance between pixel value and the class mean. In the first

iteration the classes are assigned randomly; but with every consecutive iteration, the class

boundaries become more appropriate to the pixel value distribution. Three iterations were

chosen as a compromise between increasing accuracy and computer processing time.

Since there was no ground truth data available the number of classes was limited to five

so that relatively broad spectral classes would be identified (Jensen, 1996). By observing

the classified image and determining how the pixels were related and correlated to the

original image the five classes were assigned information classes (Table 3.5). Examining

the distribution of the classes in spectral space using 2-dimensional scatter plots also

assisted in identifying the information classes. Originally a maximum of ten spectral

classes had been chosen, but I was unable to correlate the resulting classification to

information classes, as I did not have any ground truth data (and the time lost from

computer processing did not add enough useful information).


26/74

26

Table 3.5: The relationship between spectral and information classes through K-means

unsupervised classification.

Spectral Class

Number

Spectral Class

ColorInformation Class

Unclassified Grey Unidentifiable

1 Blue No reflectance (very dark water)2 Bright Green Water or very damp vegetation/ground

3 Dark Green Healthy vegetation, (crops and natural)

4 Tan Unhealthy/Partially Cleared vegetation

5 White Bare ground (incl. roads and settlements)

Supervised classification uses training sites that are defined by the user, by

interpreting the image to define areas that are identifiable as information classes. These

are input into the software and the remaining pixels are assigned to the class that they are

closest to. Minimum distance is a common algorithm that assigns the pixel to a class

based on the minimum distance to a class mean. The number of passes through thedataset affected class boundary and pixel assignment (Jensen, 1996). Training sites

(called Regions of Interest - ROI, in ENVI) were identified by examining the spectral

classes that the K-means unsupervised classification had identified, and observing the

position of pixels in spectral space. Only four ROI classes were chosen, because there

was no land cover data available that could be used to accurately identify more classes.

Table 3.6 shows the classes that were used to conduct supervised classifications in ENVI

and the characteristics used to select them. Two-dimensional scatter plots of where the

classes position in spectral space were used as visual indicators of how unique the classes

were. If there was significant overlap, the regions of interests were redefined to minimize

the overlap, until the ROIs were suitably defined and spectrally distinct.

Table 3.6: The Regions of Interest (ROI) used for supervised classification in ENVI

Spectral

Class

Class

ColorInformation Class

Spectral & NDVI

Characteristics

1 Blue WaterNo/very low reflectance

(specular), low NDVI value.

2 GreenHealthy vegetation,

(crops and natural)

Higher IR than Red, higher

reflectance values than class 3.

Higher NDVI value than 3.

3 TanDry/Partially Cleared

vegetation

Higher (or similar) red than IR.

Higher NDVI value than 4.

4 WhiteBare ground (incl.

roads and settlements)

High reflectance in all bands.

Low NDVI values.

Pixels were assigned to a class using Maximum Likelihood classification. This is a

hard classifier that assigns a pixel to the class with the highest probability through


27/74

27

statistical analysis of spectral curves. Probability thresholds can be implemented so that

only pixels with a high probability are within a class. The higher the probability threshold

the more pixels will remain unclassified. This method accepts that pixels are not always

one unique class, but may contain more than one class within the 3249 m2 pixel area.

Every pixel has a probability of being in each class (so if there are four classes, then there

are four probabilities), and the pixel is assigned to the class with the highest probability(unless a probability threshold is set). Maximum likelihood has some soft classifier

characteristics (in that it recognized that a pixel may not spectrally pure). A probability

threshold of 0.9 was used in the classification.

Change detection analysis was conducted post-classification. As each scene did not

have the same spatial extent in 1973 as it did in 1993 a region of interest was created that

enclosed the common area from the two images in each scene. This was achieved by

geographically linking the two images and drawing the boundary to the common area.

This common area ROI was used to subset the scenes to create 2 images per scene that

had the same spatial extent. The subset images were then used to determine change

between 1973 and 1993. A confusion matrix was calculated using the 1973 subset imageas the ground truth and the 1993 subset image as the input classification in ENVI 3.5. The

confusion matrix is usually used to test a classification with respect to ground truth data

(either an image or from ROIs) and ENVI outputs the matrix in both percent and pixel

form. In this case the matrix was interpreted for change from 1973 to 1993. The principal

diagonal represents pixels that did not change, while the columns give quantified

information on from (class in 1973/5) the pixels changed to (class in 1993). The pixel

confusion matrix was converted to m2, by multiplying the number of pixels by the area of

each pixel (3249 m2) and then dividing by 1,000,000 which converts the area to km2.

The accuracy of the change detection analysis relies on the accuracy of the

classifications, whose comparability are affected by the factors that caused spectral

reflectance differences between the two images. Change detection assumes constant

temporal (within year), spatial, spectral and radiometric resolution, so that environmental

consideration (phonological stage and atmospheric conditions) are as similar as possible.

The resolutions of the Landsat satellite data that were analyzed were not constant,

although conducting change analysis post-classification minimizes the error associated

with this (Singh, 1988).

Chapter 4:Results

Water-quality

During the period from May to December 2002 the water-quality of the Okavango

River showed some marked temporal and spatial patterns. However the trends were not

consistent between the water-quality parameters, sites and sampling periods.


28/74

28

A summary of the average water-quality parameters measured in each sampling

period are presented in Table 4.1. The pH does not vary much between the sampling

periods. The conductivity is low and there appears to be some temporal pattern. The

dissolved oxygen seems to show some temporal trend, with the highest values in May.

The phosphorus and total nitrogen concentrations in December are double their

concentrations in May and July, although the oxidized nitrogen concentration is greatestin May. In December the reduced nitrogen is triple the concentration measured in May.

Table 4.1: Summary of water-quality results from May to December. Each month is the

mean of seven sites.

May S.E.* July S.E.* December S.E.*

pH 6.8 0.11 6.8 0.10 7.0 0.12

Conductivity (ES/cm) 34.5 0.57 25.3 5.05 41.0 3.29Dissolved Oxygen (mg/l) 7.0 0.74 5.8 0.64 6.4 0.35

Total Phosphorus (mg/l) 0.1 0.01 0.1 0.01 0.2 0.04

Reduced Nitrogen (mg/l)0.9 0.15 0.3 0.08 2.8 0.16

Oxidized Nitrogen (mg/l) 0.5 0.08 0.1 0.0 0.1 0.0

Total Nitrogen (mg/l) 1.4 0.17 0.5 0.06 2.9 0.16

* Standard Error is calculated using: )1(.. nsES

Standard deviation (s) is calculated using

1

2

n

xxs

pH

The pH at the sampling sites along the Okavango River shows spatial and temporal

changes, although they are not consistent trends (Figure 4.1). Over the study period the

pH ranged from 6.5 to 7.6 (Table 4.2). Only five values were greater than pH 7.0, and

only one of these (site 3, December) was greater than pH 7.5. Nkurenkuru (site 1) has thehighest mean pH and Katere (site 5) has the lowest pH (in May). The pH at site 3 in

December (7.58) appears to be anomalously high compared to the other pH values.

Table 4.2: Table of pH values, means and standard errors of sites 1-7, in May, July and

December, 2002

Site Name Site May July December Site Mean S.E.*

Nkurenkuru 1 7.38 7.40 7.16 7.31 0.08

Mupini 2 6.57 6.89 6.80 6.75 0.09

Nkwasi 3 6.72 6.79 7.58 7.03 0.27

Mupapama 4 6.95 6.80 6.79 6.85 0.05

Katere 5 6.50 6.53 6.98 6.67 0.15

Mukwe 6 7.03 6.80 6.60 6.81 0.12

Ngepi 7 6.75 6.67 6.88 6.77 0.06

Month Mean 6.84 6.84 6.97

S.E.* 0.11 0.10 0.12

A slight spatial trend can be observed in July (figure 4.1). The pH decreases from

sites 1 through 5, and then increases at site 6 before decreasing slightly at site 7. There is


29/74

29

not a consistent trend in May and December. The pH in May has greater variation

between the sites. It decreases by about 0.8 between sites 1 and 2, increases by about 0.4

to site 4, decreases similarly to site 5, increases (by about a half) at site 6 and site 7 it had

decreased again by 0.28. The December pH also has spatial variability, although it does

not replicate the trend observed in May, as the pH increases and decreases between

consecutive sites.

6.40

6.60

6.80

7.00

7.20

7.40

7.60

7.80

0 100 200 300 400

River Distance (km)

p

H

May

July

December

1 2 3 4 5 6 7

Site Number

Figure 4.1: The pH of the water from sites 1-7, in May, July and December 2002. River

Distance is measured from the origin of the Okavango River as the Namibian border with

Angola.

Temperature

The temperature of the water of the Okavango River showed a strong temporal trend,

with the lowest values in July and highest in December (figure 4.2). This corresponds to

the seasonal air temperature variation. During May and July the water temperature did

not vary more than 1.1 oC. December was the only period that had a distinct spatial

pattern. The temperature rose 2.2 oC between sites 1 and 2, and then steadily decreased by

7.3 oC to site 5. Between site 5 and 7 there was an increase of 5 oC.


30/74

30

Table 4.3: Table of temperature values (oC), means and standard errors of sites 1-7, in

May, July and December, 2002.


Nkurenkuru 1 20.7 17 27.6 21.77 3.11

Mupini 2 19.9 17.9 29.8 22.53 3.68

Nkwasi 3 20.2 17 29.5 22.23 3.75Mupapama 4 20.2 17.5 26.7 21.47 2.73

Katere 5 20.9 17 22.5 20.13 1.63

Mukwe 6 20.5 18 27.4 21.97 2.81

Ngepi 7 20.2 16.9 28.9 22.00 3.58

Month Mean 20.4 17.3 27.5

S.E.* 0.13 0.18 0.94

10

15

20

25

30

35

0 100 200 300 400

River Distance (km)

Temperature

0C

May

July

December

1 2 3 4 5 6 7

Site Number

Figure 4.2: The temperature measured with the Hach EC20 Portable pH/ISE meter model50075, in May July and December 2003 from sites 1 to 7. River Distance is measured

from the origin of the Okavango River as the Namibian border with Angola.


31/74

31

There seems to be some relationship between water temperature and the time of data

collection (figure 4.3). The variation in time was greater than the temperature variation

between sites.

Figure 4.3: The relationship between water temperature and time of day sample was

collected.

Electrical Conductivity

The electrical conductivity of the Okavango River ranges from 24.5 ES/cm to 47.2

ES/cm (both in December, at sites 7 and 2, respectively). May has the most consistent

values with the lowest value at site 3 (32.3 ES/cm) and the highest at site 5 (36.3 ES/cm)

(table 4.4). The greatest spatial variation occurs in July ( 5.05 ES/cm). December

generally has the highest values except for site 7, where December is the lowest value

(figure 4.4).

Table 4.4: Conductivity (ES/cm) of the Okavango River in May, July and December

2002. No data is represented with x


Nkurenkuru 1 32.8 37.5 46.7 39.00 4.08

Mupini 2 35.4 x 47.2 29.51 12.27

Nkwasi 3 35.5 x 43.3 28.45 11.18

Mupapama 4 35.1 35.8 46.4 39.10 3.66

Katere 5 36.3 32.3 45.6 38.07 3.94

Mukwe 6 32.3 29.8 33.6 31.90 1.12


32/74

32

Ngepi 7 33.9 29.1 24.5 29.17 2.71

Month Mean 34.5 25.3 41.0

S.E.* 0.57 5.05 3.29

0

5

10

15

20

25

30

35

40

45

50

0 100 200 300 400

River Distance (km)

Conductivity(E

S/cm)

May

July

December

1 2 3 4 5 6 7

Site Number

Figure 4.4: Conductivity (ES/cm) of the Okavango River in May, July and December

2002. River Distance is measured from the origin of the Okavango River as the Namibian

border with Angola.

Dissolved Oxygen (DO)

The DO concentrations vary markedly over space and time, with no apparentlyconsistent trend in either case. In May there is an increase from sites 1 to 3 by 2.9 mg/l,

followed by a drop of 7.6 mg/l at site 4. It then increased steadily from site 4 to site 7

(figure 4.5).

Table 4.5: Dissolved Oxygen (mg/l) of Okavango River in May, July and December

2002.

Site Name Site May July Dec Site Mean S.E.*

Nkurenkuru 1 7.3 7.4 7.5 7.4 0.06


33/74

33

Mupini 2 7.7 8.6 7.8 8.0 0.28

Nkwasi 3 10.2 5.4 6.5 7.4 1.45

Mupapama 4 3.9 5.6 6.2 5.2 0.69

Katere 5 5.6 3.3 5.8 4.9 0.80

Mukwe 6 6.7 5.2 5.5 5.8 0.46

Ngepi 7 7.5 5.3 5.6 6.1 0.69Month Mean 7.0 5.8 6.4

S.E.* 0.74 0.64 0.35

There is an overall decrease in DO in July, with the exception of site 2 (where there

was a slightly higher concentration) and site 5 (which had the lowest concentration

measured at 3.3 mg/l). In December the DO increased at site 2, and then steadily

decreased by 2.2 mg/l to site 7. In July and December site 2 had the highest DO. May had

the greatest range of dissolved oxygen concentrations of the three sampling periods.

Generally the up-stream sites (1-3) have higher concentrations of DO.

0

2

4

6

8

10

12

0 50 100 150 200 250 300 350 400 450

River Distance (km)

Dissolve

dOxygen(mg/l)

May

July

Dec

1 2 3 4 5 6 7

Site Number

Figure 4.5: Dissolved Oxygen (mg/l) of Okavango River in May, July and December

2002. River Distance is measured from the origin of the Okavango River as the Namibian

border with Angola.

Nitrogen

Reduced (kjeldahl) nitrogen concentration is responsible for most of the total nitrogen

measured (figure 4.6, tables 4.6 and 4.7). Kjeldahl nitrogen is organic nitrogen and


34/74

34

ammonium (NH4+), while oxidized nitrogen is nitrite (NO2

-) and nitrate (NO3-). In July

and December the oxidized nitrogen concentrations were 0.1 mg/l at all sites, therefore

the total nitrogen is 0.1 mg/l greater than reduced (kjeldahl) nitrogen in the two sampling

periods. May was the only sampling period with measurable nitrate and nitrite, and all

other values were all less than 1.0 mg/l. It also did not follow the spatial trend of the

reduced nitrogen concentrations. The oxidized nitrogen was only greater than the reducedat site 3 and then only by 0.2 mg/l (see the blue circles in figure 4.6).

Table 4.6: Oxidized nitrogen concentrations for May, July and December 2002, with the

month and site means.


Nkurenkuru 1 0.5 0.1 0.1 0.24 0.13

Mupini 2 0.1 0.1 0.1 0.11 0.00

Nkwasi 3 0.8 0.1 0.1 0.34 0.24

Mupapama 4 0.5 0.1 0.1 0.24 0.13

Katere 5 0.4 0.1 0.1 0.21 0.10

Mukwe 6 0.6 0.1 0.1 0.27 0.17

Ngepi 7 0.5 0.1 0.1 0.23 0.13

Mean 0.5 0.1 0.1

S.E.* 0.08 0.00 0.00

0

0.5

1

1.5

2

2.5

3

3.5

4

0 50 100 150 200 250 300 350 400 450

River Distance (km)

Nitrogen(mg/l)

May-Total

July-Total

Dec-Total

May-Ox

July-Ox

Dec-Ox

May-Red

July-Red

Dec-Red

1 2 3 4 5 6 7

Site Number


35/74

35

Figure 4.6: Oxidized (NO2- & NO3

-); reduced (Norg & NH4+) and total nitrogen

concentrations (mg/l) for May, July and December 2002. River Distance is measured

from the origin of the Okavango River as the Namibian border with Angola.

The reduced nitrogen in May and July was also low, with only one value (May, site 6)

having a concentration greater than 1.5 mg/l. The reduced nitrogen concentrations were

greater in May than July. December had the greatest reduced nitrogen concentrations of

the three sampling periods.

Table 4.7: Reduced nitrogen concentrations for May, July and December 2002, with the



Nkurenkuru 1 1.1 0.5 2.8 1.46 0.69

Mupini 2 0.7 0.3 2.6 1.20 0.71

Nkwasi 3 0.6 0.3 2.4 1.10 0.66

Mupapama 4 0.9 0.7 3.7 1.76 0.97

Katere 5 0.6 0.1 2.6 1.10 0.76

Mukwe 6 1.6 0.2 3.1 1.63 0.84

Ngepi 7 0.8 0.5 2.7 1.33 0.69

Mean 0.9 0.4 2.8

S.E.* 0.14 0.08 0.16

The spatial trend in the reduced nitrogen was similar in all three months, with adecrease from site 1 to site 3, an increase to site 4, and a decrease to site 5. In May and

December there was a marked increase between site 5 and 6, followed by a decrease to

site 7. In July there was an increase from site 5 to site 7, although site 6 had the second

lowest reduced nitrogen concentration (0.2 mg/l). In July and December site 4 had the

greatest reduced nitrogen concentration, while in May site 6 had the greatest

concentration.

Phosphorus

The total phosphorus contents are an order of magnitude lower than the total nitrogen;

with the maximum total phosphorus being 0.37 mg/l while the maximum total nitrogen is

3.8 mg/l. The spatial trend and concentrations of total phosphorus are very similarbetween May and July (figure 4.7). Most sites in May and July were below the detection

limit of the method at 0.7 mg/l (APHA, et al., 1995). Only sites 4, 6 and 1 (in July) had

measurable concentrations of phosphorus. These were less than 0.15 mg/l (table 4.8).


36/74

36

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 50 100 150 200 250 300 350 400 450

River Distance (km)

TotalPhosphorus(mg/l)

May

July

Dec

1 2 3 4 5 6 7

Site Number

Figure 4.7: Total phosphorus (mg/l) concentrations of the Okavango River in May, June

and December 2002. River Distance is measured from the origin of the Okavango River

as the Namibian border with Angola.

In December every site had measurable phosphorus, but they did not exceed 0.37

mg/l. The spatial pattern of phosphorus and nitrogen concentrations was similar in May

and July, but not in December. Site 2, 3, 4 and 5 were the only sites with greater than 0.2

mg/l. Sites 1, 6 and 7 had less than 0.15 mg/l (figure 4.5 and table 4.8).

Table 4.8: Total phosphorus concentrations for May, July and December 2002, with the



Nkurenkuru 1 0.07 0.1 0.15 0.11 0.02

Mupini 2 0.07 0.07 0.37 0.17 0.10

Nkwasi 3 0.07 0.07 0.23 0.12 0.05

Mupapama 4 0.10 0.1 0.22 0.14 0.04

Katere 5 0.07 0.07 0.28 0.14 0.07

Mukwe 6 0.13 0.1 0.12 0.12 0.01

Ngepi 7 0.07 0.07 0.08 0.07 0.00

Month Mean 0.1 0.1 0.2

S.E.* 0.01 0.01 0.04


37/74

37

Previous studies on the water-quality of the Okavango River

Bethune (1991) conducted a survey on the hydrology, physiology, fauna and flora of

the Okavango River in Namibia from 1984 to 1986. This included analyzing the water-

quality of the mainstream and backwaters of the Okavango River. The mainstream resultsfor the conductivity and pH are presented in table 4.9, and the nutrient concentrations are

presented in table 4.10. Only the mean values were available, so no spatial relationships

can be determined from this data. In general, Bethune (1991) found that the waters were

clear, well mixed and oxygenated, and the water temperatures within a degree of the air

temperatures, with diurnal variation. The mean conductivity ranged from 31.8 ES/cm to

43.7 ES/cm (table 4.9). December had the highest conductivity measured in 1984, which

corresponds to the higher conductivity measured in 2002 (tables 4.1; 4.4 & figure 4.4).

There is an increase in mean conductivity in 1984 from March through December. The

pH ranges from 6.79 to 7.19, and has the same temporal trend as the conductivity, in that

it increases from March to December. In 2002, December also had the highest pH.

Table 4.9: The mean conductivity and pH measured in the field for the mainstream sites

from 1984 to 1986. No data collected is symbolized with x.

1984 1986

March June October December June

Conductivity

(ES/cm) 31.8 1.5 36.9 3.92 38.0 8.34 43.7 2.83 33.0 7.35pH 6.79 0.14 6.89 0.14 7.09 0.13 7.19 0.54 x

In March and June of 1984 the concentration of nitrite and nitrate was below the limitof the detection. The oxidized nitrogen from 2002 was the sum of nitrate and nitrate

concentrations. These were also too low to be detected in July and December of 2002 and

the highest concentration was 0.8 mg/l in May, which is double the highest concentration

measured in 1984 through 1986, at 0.43 mg/l in May 1985 (Table 4.10). Kjeldahl

(reduced) nitrogen is ammonia and organic nitrogen. As in 2002 these contribute more to

the total nitrogen than the oxidized nitrogen. In 1984 to 86 the ammonia concentration

ranged from below detection limits to 1.68 mg/l, while the organic nitrogen ranged from

below detection limits to 6.27 mg/l. Organic nitrogen had a much greater range than the

ammonia, although there does not appear to be a relationship between the two. The

highest ammonia and organic nitrogen concentrations were measured in March and June,

1984, at 1.68 mg/l and 6.27, respectively. The dissolved oxygen ranged between 5.3 to

9.4 mg/l, although no temporal data is available.


38/74

38

Table 4.10: The minimum and maximum concentrations of nitrogen and phosphorus

(mg/l) field for the mainstream sites from 1984 to 1986. Concentrations below the

detectable limit of analysis are represented by -. Nitrite: NO2-, Nitrate (NO3

-);

Ammonium (NH4+) Organic Nitrogen (Norg) & Phosphorus (P)1984 1985 1986

Mar Jun Oct Dec May Jun

min max min max min max min max min max min max

NO2- - - - - - 0.25 - 0.02 - 0.002 - 0.17

NO3- - - - - - - - - 0.02 0.43 - 0.10

NH4+ 0.56 1.68 - 1.12 0.24 2.46 - 0.62 0.06 0.50 0.28 0.84

Norg 0.28 0.56 1.23 6.27 * 1.14 0.11 0.34 - 1.57 0.56 1.12

Total

P

0.03 0.10 - 0.10 0.03 0.07 - 0.07 - 0.05 - -

The water-quality was monitored at 9 sites along the Okavango River on six

occasions between 1993 and 1994 and the results were presented in Hays et. al. (2000)

(Appendix A; table A-E). The locations of these sites are illustrated in figure 4.8 along

with the approximate locations of the 2002 sampling sites presented in chapter 4. The pH

varied inconsistently in both spatial and temporal scales. Four pH values were greater

than 8.0, and all of these occurred in autumn and winter of 1994, at Bunya, Rundu and

Mbambi (sites 4, 5 and 7). The two highest pH values measured occurred at Bunya. The

highest pH measured in 2002 was downstream from Rundu (site 3, Nkwasi) and was

measured in December, which was the warmest period (summer). There were also nine

instances where the pH was less than 6.5, four of which occurred in spring 1994, andthree were in autumn 1993. Three sites (1, 3 and 4) had a pH less than 6.5 two out of the

six times measured. There was more variation in the pH in 1993 and 1994 than in either

1984 or 2002.


39/74

39

Figure 4.8: Location of sites in 1993/94 used in Hays et al., (2000), at Kakuru, Matava,

Musese, Bunya, Rundu, Cuito, Mbambi, Popa Falls and Kwetze (black numbers). The

approximate locations of the 2002 sites are labeled in red. Modified from Hays et al.,

(2000).

The conductivity values measured along the Okavango River in 1993/4 are three

orders of magnitude greater than those measured in 1984 and 2002. The 1993/4 valuesare close to the conductivity of sea water. The oxygen levels ranged from 2.50 to 10.50

mg/l, with all of the winter 1994 values being below 5 mg/l. The four highest values were

measured in summer and autumn of 1994 at sites 6, 7 and 8 (Cuito, Bunya and Popa

Falls). Site 8 tends to have higher values than the rest of the sites. There was no reduced

(Kjeldahl) nitrogen data available for 1993/94. Most of the oxidized nitrogen (nitrate plus

nitrite) were less than 1 mg/l, although in winter of 1993 and 1993, Cuito (site 6) had

values of 1.1 mg/l. The levels at Rundu, Popa Falls and Kwetze (sites 5, 8 and 9) were

less than or equal to 0.40 mg/l at all sampling times. The majority of the phosphorus

levels were less than 0.1 mg/l, with the exception of Kakuru and Musese in autumn 1993

and spring 1994, and Rundu in winter 1993.

Statistics

A one way Analysis of Variance (ANOVA) was calculated on the means of each

month to determine if they were statistically different. All of the temporal trends of the

water-quality parameters were statistically significant at the 95 % confidence level, with

the exception of pH and dissolved oxygen (table 4.11). Nitrogen and temperature have

the lowest P-values, (P < 0.01) and have highly significant temporal trends. The

temperature was significantly different in each month, with December being the warmest

and July being the coldest (figure 4.2). In the case of oxidized nitrogen (figure 4.6), the

Site 1

Site 2 Site 3

Site 4

Site 6Site 5

Site 7


40/74

40

values in May are higher than the values in July and December. Reduced nitrogen has

higher values in December compared to May and July, and total nitrogen is different in

all three months. The total phosphorus concentrations in December are very significantly

(P 0.01) higher than the concentrations in May and July (figure 4.7). The conductivity

is significantly different in May, July and December, with highest values occurring in

December and the lowest in July.

Table 4.11: Statistical Significance of the temporal trends observed for water-quality in

2002, calculated using one-way ANOVA. NS: not significant; * significant; ** very

significant; *** highly significant.

Water-quality Parameter P-Value Confidence Level Significance

pH 0.660 NS

Conductivity 0.017 95 % *

Dissolved Oxygen 0.412 NSTemperature 5.12 x 10-10 99.9 % ***

Total Nitrogen 1.88 x 10-9 99.9 % ***

Oxidized Nitrogen 9.98 x 10-6 99.9 % ***

Reduced Nitrogen 1.89 x 10-10 99.9 % ***

Total Phosphorus 0.0011 99 % **

A linear regression was calculated on the three replications at each site, to determine

if there was a correlation between the distance from the Namibian/Angolan border and

water-quality parameter value (table 4.12). Only December showed a linear spatial trend

in any of the water-quality parameters (conductivity, dissolved oxygen and totalphosphorus). Both conductivity and dissolved oxygen have linear decreases in December

from site 1 to site 7. Total phosphorus shows an overall decrease, although this is not

consistent from site 1 to 7.


41/74

41

Table 4.12: Statistical Significance of the spatial trends observed or water-quality in

2002, calculated through linear regression. NS: not significant; * significant; ** very

significant; *** highly significant.

May July DecemberWater-quality

Parameter P-Value R 2

P-Value R 2

P-Value R 2

pH 0.390 NS 0.150 0.034 NS 0.625 0.290 NS 0.219

Conductivity 0.894 NS 0.001 0.731 NS 0.026 0.037 * 0.615

Dissolved Oxygen 0.668 NS 0.040 0.116 NS 0.418 0.006 ** 0.808

Temperature 0.994 NS 0.000 0.839 NS 0.009 0.674 NS 0.038

Total Nitrogen 0.619 NS 0.053 0.300 NS 0.010 0.772 NS 0.170

Oxidized Nitrogen 0.716 NS 0.029 0.914 NS 0.003 0.201 NS 0.302

Reduced Nitrogen 0.685 NS 0.035 0.835 NS 0.009 0.769 NS 0.019

Total Phosphorus 0.338 NS 0.183 0.683 NS 0.004 0.019 ** 0.173

Image Classification

False Color Infrared Images

A mosaic of the three images used in this study is provided in figure 4.9. Each image

has a scene number (table 3.3) that is identified in figure 4.9. The 1973/5 images did notcontain the eastern portion of the Okavango River (Scene 12), so the data are limited to

upstream of the confluence between the Cuito and Okavango Rivers.


42/74

42


43/74

43

Normalized Difference Vegetation Index (NDVI)

NDVI is a ratio of the near infrared portion of the electromagnetic spectrum (0.95

Em) and the red portion (0.65 Em) and varies between minus one and one. A pixel with

high reflectance in the near infrared and low in the red wavelengths will have a high

NDVI value. High NDVI values are indicative of healthy vegetation. A color template

(figure 4.10) was imposed on the images to highlight the differences between NDVIvalues. The NDVI images were stretched to the same values (-0.126 to +0.174) for a

more valid comparison (Mouat, et al., 1993).

Figure 4.10: The color template imposed on the Normalized Differential Vegetation

Index images.

The images from 1993 generally have higher NDVI values than 1973/5 (figures 4.11

4.12, & 4.13), even in Scene 10 where the 1993 image was taken 47 days further into the

dry season than the 1973 image (figure 4.11). In Scene 10 there is a fairly distinct

northern trend with an increase in NDVI values, both in 1993 and 1973. The relic sand

dunes (omarumba) can be seen in the southeastern portion of Scene 10, where there is

linear change pattern. In 1973 it varies between blue and green, which corresponds to

NDVI values -0.120 and -0.010 respectively. In 1993 the NDVI values change from -0.07

(dark green) to 0.01 (pale green).

27th

Aug 73

-0.126 0.174


44/74

44

Figure 4.11: Normalized Differential Vegetation Index of 1973 (above) and 1993

(below), S

Documents

Thesis on Okavango Water Quality