A GIS Case Study of the Vaal River Master Thesis

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Master Thesis

submitted within the UNIGIS MSc programme at the Centre for GeoInformatics (Z_GIS)

Salzburg University

A GIS Case Study of the Vaal River

Identifying Land Use Change in Flood Prone Areas

by

Jacobus Petrus Hermanus Viljoen

UP40904

A thesis submitted in partial fulfilment of the requirements of the degree of

Master of Science (Geographical Information Science & Systems) – MSc (GISc)

Advisor:

Ann Oliver

Pretoria, March 2015

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Science Pledge

The results in this thesis are based on the author’s own research at the UNIGIS Sub-Saharan

Africa Study Centre, Centre for GeoInformatics (ZGIS) of the Salzburg University.

Signed:

Jacobus Petrus Hermanus Viljoen Pretoria, 2014

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Abstract

The question posed with this research document, namely whether there has been development within certain levels of flow of the Vaal River, has been answered with reference to various aspects of different datasets and different types of data have been analysed.

Several land cover datasets covering about 20 years have been sourced to determine where the development or land cover change has occurred over time. Overlaying and analysing the flow line data at several key flows have identified areas of concern should the Vaal River attain these flows. Making use of vector data, such as Eskom’s Spot Building Count, the same patterns have been identified that corroborates the findings of the raster data.

Additionally, slope analysis was done on land adjacent to the Vaal River that indicates that the slope is favourable for property development. Also, property that does not comply with the National Building Regulations Act, according to which the 62m and 32m buffer zones are indicated as areas with severe building restrictions, have been identified. Using a property valuations roll an average price of land per m² has been determined.

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Acknowledgements

The author would like to thank the following persons for assistance with this research project.

 Stuart Martin at GeoTerraImage for the SRTM data and many hours of sitting through my thought processes and analysis results.

 Dr. Piet Wessels at the Department of Water Affairs, as well as Pieter Rademeyer and Danie Van der Spuy at the Department of Water Affairs Flood Centre for the bulk of the data used in this study (including aerial photography, flow line data, cadastre, and DEM data).

 Tshisikhawe Mphaphuli at ESKOM for providing the Spot Building Count data.

 Especially Ann Olivier from UNIGIS for all the help, bouncing ideas around and never-ending support.

Finally, I thank my wife for her enduring patience and love.

Llewelyn, Amelie – I dedicate this to you.

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List of Figures

Figure 1: Comparison of hail event (“Recent hail losses from a reinsurance perspective”, Pieter Visser) 22 Figure 2: GIS – Insurance market claims 28 Nov 2013 (“Recent hail losses from a reinsurance

perspective”, Pieter Visser) ... 23

Figure 3: GIS – Insurance claims distribution of the 3 events (“Recent hail losses from a reinsurance perspective”, Pieter Visser) ... 23

Figure 4: Exposure changes (“Recent hail losses from a reinsurance perspective”, Pieter Visser) ... 24

Figure 5: Locality of Study Area in South Africa ... 29

Figure 6: Study Area with SRTM DEM ... 29

Figure 7: Land use derived from CD:NGI datasets ... 30

Figure 8: Locality of the Vaal Barrage and the Vredefort Dome ... 31

Figure 9: Shows the difference between the new WorldDEM and the existing SRTM DEM (http://www.astrium-geo.com/worlddem/) ... 35

Figure 10: Flow lines and Vaal River centreline overlaid on cadaster of the town of Parys... 37

Figure 11: Cadastral data of a developed within the study area with Vaal River centreline overlaid ... 38

Figure 12 Attributes for SPOT Building Count 2012 ... 39

Figure 13: Portion of cadaster of the town of Parys showing the SBC in green and Town locator in black ... 39

Figure 14: SPOT 5 Satellite imagery with a gauging station and Vaal River centreline overlaid ... 40

Figure 15: Showing cadaster within a flow line area ... 41

Figure 16: Land Cover 1994 - Parys, FS ... 42

Figure 17: Land Cover 2000 - Parys, FS ... 43

Figure 18: Land Cover 2006 – Parys, FS ... 45

Figure 19: Land Cover 2009 – Parys, FS ... 46

Figure 20: Zonal Majority for 2500m3/s flow line 2000 - 2006 ... 50

Figure 21: Zonal Majority for 5000m3/w flow line 2000 - 2006 ... 51

Figure 22: Zonal Majortiy for 10 000m3/s flow line 2000 - 2006 ... 52

Figure 23: Zonal Majority for 16 000m3/s flow line 2000 - 2006 ... 53

Figure 26: Zonal Majority for 10 000m3/w flowl ine 2006 - 2009 ... 56

Figure 27: Zonal Majority for 16 000m3/s flow lne 2006 - 2009 ... 57

Figure 28: SPOT BUILDING COUNT with 2011 aerial photography at 1: 45 000 scale ... 58

Figure 29: SPOT BUILDING COUNT with 2011 aerial photography at 1: 6 000 scale ... 58

Figure 30: SBC 2012 clipped to 2500m3/s flow line ... 59

Figure 31: SBC 2012 clipped to 5000m3/s flow line ... 60

Figure 32: SBC 2012 clipped to 10 000m3/s flow line ... 61

Figure 33: SBC 2012 clipped to 16 000m3/s flow line ... 62

Figure 34 Fill ... 63

Figure 35 Drop raster ... 64

Figure 36 Flow Direction ... 65

Figure 37 Flow Accumulation ... 65

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Figure 38 Values for each direction from the centre of a cell ... 66

Figure 39 Stream to feature ... 66

Figure 40 Stream to feature Select ... 67

Figure 41 Single output ... 68

Figure 42 Raster to point ... 69

Figure 43: Single output 2 ... 70

Figure 44 Spline showing elevation classes ... 71

Figure 45 Spline with flowline ... 72

Figure 46: 2500m3/s 30m SRTM DEM ... 73

Figure 50 Results of the 2 500m3/s flow line elevation profile ... 75

Figure 53: Results of the 16 000m3/s flow line elevation profile ... 77

Figure 54: 30m SRTM DEM Slope ... 78

Figure 57: 90m SRTM DEM and 30m SRTM DEM Slopes ... 81

Figure 58: Results of 16 000m3/s Zonal Majority analysis with elevation ... 83

Figure 59: A 3D view of the Zonal Majority analysis and elevation for the 16 000m3/s flow line ... 84

Figure 60: Development within restricted areas ... 85

Figure 61: Similarity between 500m3/s flowline and 62m building restriction ... 86

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List of Tables

Table 1 Recurrence intervals and probabilities of occurrences ... 15

Table 2: Characteristics of ASTER GDEM and SRTM-X DEM (Czubski et al. 2013) ... 34

Table 3 1994 reclassified ... 43

Table 7: Properties values ... 47

Table 8: Zonal Majority Table for 2500m3/s flow line 2000 – 2006 ... 49

Table 10: Zonal Majority Table for 10 000m3/s flow line 2000 – 2006 ... 51

Table 11: Zonal Majority Table for 16 000m3/s flow line 2000 – 2006 ... 52

Table 13: Zonal Majority table for 5000m3/s flow line 2006 – 2009 ... 54

Table 14: Zonal Majority table for 10 000m3/s flow line 2006 -2009 ... 55

Table 15: Zonal Majority table for 16 000m3/s flow line 2006 – 2009 ... 56

Table 16: Total number of points the SBC dataset broken into years ... 62

Table 17: Elevation comparison for ArcGIS, Idrisi and Global Mapper ... 79

Table 18: Matrix with Z-value ... 82

List of Equations

Equation 1: Matrix with Z-value ... 81

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List of Abbreviations

 Cumec

 SRTM - Shuttle Radar Topography Mission

 ASTER

 DEM - Digital Elevation Model

 FAHP

 NOAA-AVHRR

 ETM+

 LANDSAT

 HEC-RAS

 ESRI

 GIS

 DWA

 ESKOM

 NGA

 NASA

 GCP

 SBC

 SPOT5

 m³/s

 kml

 GIS

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1. Introduction 1.1 Motivation

Ever since the dawn of civilisation, people settled on river banks. Almost all the great cities in the world, Rome on the Tiber River, London on the Thames River, Paris on the Seine River, Vienna on the Danube River, New York on the Hudson River, Delhi on the Ganges River, and Shanghai on the Yangtze River bear testament to this fact.

As stated by Jefferson (2010), most flood fatalities in Africa are not of climatic origin, but due to changes in population trends. Singh (2012) quoted Margareta Wahlström of UNISDR (UN Office for Disaster Risk Reduction):

"As the urban sprawl of rapid urbanization expands outwards and upwards, it provides ready opportunities for hazards such as floods, storms and earthquakes to wreak havoc. Half the world's population now lives in urban areas, and that figure is estimated to rise 70% by 2050. That's a lot of vulnerable and exposed people given that urban floods will represent the lion's share of total flood impact because of infrastructure, institutions and processes that are not yet up to the task ahead."

Damage caused by flood events on the Vaal River between the Vaal Dam and Bloemhof Dam are getting more severe every time a flood occurs. This is due to the fact that property development is happening at a quicker pace than is planned for. River frontage on the Vaal River is considered prime development land, but it falls within a flood risk area. Unfortunately, and this includes informal settlements and agricultural activities, flooding of the Vaal River is not a question of “if”, but a matter of when.

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In 2011 when the Vaal River was in full flood, the amount of water released from the Vaal Dam, as precautionary measure to preserve the dam wall, was in the region of 2 400m³/s, similar to the floods in 1996. However, the number of property damaged was significantly higher than the 1996 flood (Beeld 2011).

Alexander (Alexander 2006) states that the Vaal River has a varied flow, but a significant 21-year cycle impacting the peak flow of the river. He also notes that upstream utilisation, such as water abstraction, have an overall decreasing effect on annual river flows.

After the floods that South Africa experienced in 2011, Dr Piet Wessels, specialist engineer at Hydrological Services and Flood management expert at the Department of Water Affairs, stated that municipalities are to blame for the damage caused by the flood as they have recklessly allowed development to take place on river banks and next to dams. He further stated that the flood event experienced in 1996 had the same amount of water discharge from the Vaal Dam as in 2011, but the magnitude of the damage experienced in 2011 was far greater due to property development below the 1:100 year flood line (Beeld 2011).

Flood events along any water course are inevitable. It is very difficult to predict flood events. Because the catchment areas usually cover large areas, there are many factors that have pronounced effects on the magnitude and the frequency of flood events.

As human population expands and develops, there is an increase in flood risk due to the change in land use along water courses. Increased development such as buildings and pavements prevents water from being absorbed back into the ground. As impervious surfaces (surface that is covered by non-natural objects) increase, the ground is able to absorb less and less water and more water runs off and accumulates in non-natural areas. This can cause flooding as the water does not have natural areas to infiltrate into.

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In his paper, Effects of Urban Development on Floods, Konrad (2013) stated that urban development directly influence the peak discharge and frequency of floods. He also remarked that in Salt Creek, Illinois, large floods have increased by 100 percent, and smaller floods increased 200 percent for the latter part of the 20^th century. This is due to urban development. In his conclusion, he writes that stream flow information provides a scientific foundation for flood planning and management in urban areas.

1.2 Task / Problem Description

How to determine development within flowline boundaries along the Vaal River between the Vaal dam and Bloemhof dam? The main aim of this study is to use existing data sets to (determine whether there has been development) indicate the growth of development that occurred over a 15 to 20 year period that are within areas indicated as vulnerable should the Vaal River flow exceed certain flow levels.

To summarise, the study will focus in analysing the different types of land use and land cover that fall within areas covered by the flow lines for the first decade of the 21^st century. The analysis will then be used to determine and identify trends from the various land use/land cover data sets that might have influence within the study area.

1.3 Expected Results

This study will ascertain whether there has been significant property development as well as agricultural activities within documented flood flow lines on banks of the Vaal River. The analysis should provide insight into the development within the study area.

1.4 Issues that will not be discussed here

Determination of the 1:100 year flood line, flow calculations, catchment and drainage determination, hydrology and property value estimation fall outside of the scope of this study. Similarly, in-depth study

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of the motivation for locating to a flood risk area will not be considered. There are, however, other factors that will be investigated as part of the analysis of the spatial data.

These include datasets supplied the Department of Water Affairs, DEM data, ESKOM Spot Building Count data and land use data.

1.5 Intended Audience

The intended audience for this study is persons involved with the management of water resources, the handling of construction applications, the insurance industry and also the agricultural sector.

1.6 Thesis Structure

This thesis is in the form of a case study, as all the datasets deal with a certain area at a certain time.

Relevant literature of the topic is discussed, where after the approach taken is documented. The next part of the case study deals with the project and the project description. The results and results analysis precede a summary of all analysis and the conclusion derived.

Finally, a discussion section leads to the final chapter concerning future work.

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2. Literature

2.1 Overview and review of the literature relevant to the thesis topic

A lot of research has been done using remote sensing techniques to demarcate flood prone areas. There are several aspects that influence how these areas are identified and classified. A significant amount of research has been done on the subcontinent of India and its neighbours with regards to flood plain management, flood hazard zonation studies, flood risk and the application of GIS technology, including remote sensing, to determine and manage these areas.

2.2 Statements – why a certain literature source is relevant to the thesis

As there are many different specialist disciplines involved in this study, doing a thorough investigation into the relevant literature is an immense undertaking and almost study in itself.

For the literature review section, broad aspects that have an influence on the study are considered.

2.2.1 Definitions of Floods, Hazards and Disasters

Flood hazard can be defined as a combination of the frequency of flood occurrence, the potential number of affected people, the availability of present infrastructure for evacuation and the vulnerability of the community to a post-flood epidemic (Sanyal 2004).

Hazard refers to the likelihood and magnitude of a disaster occurrence (Sanyal 2004).

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Vulnerability refers to the damage likely to be incurred in a hazardous area should a disaster strike (Sanyal 2004).

According to the USGS, a flood line can be defined as an indication of the expected water level along specific river reach, for a specific annual flood frequency, or the risk of occurrence (also expressed as return period). For example, a 1 in 100 years flood line corresponds to a flood magnitude (peak) with a 1 in 100 year return period, which indicates an annual risk (probability) of 1% that such a flood peak will occur or be exceeded. Similarly a 1 in 20 years flood event indicates an annual risk of 5%. It needs to be noted that these peaks and lines are not static and change over time due to development in the catchment area.

Recurrence intervals and probabilities of occurrences Recurrence

interval, in years

Probability of occurrence in any given year

Percent chance of occurrence in any given year

100 1 in 100 1

50 1 in 50 2

25 1 in 25 4

10 1 in 10 10

5 1 in 5 20

2 1 in 2 50

Table 1 Recurrence intervals and probabilities of occurrences

Article 144 of the National Water Act No 36 of 1998 states that “for the purposes of ensuring that all persons who might be affected have access to information regarding potential flood hazards, no person may establish a township unless the layout plan shows, in a form acceptable to the local authority concerned, lines indicating the maximum level likely to be reached by floodwaters on average once in every 100 years”.

Alexander notes that the Vaal River has a very high confidence in its periodicity of flood cycles and drought events. This confidence is based on historic data that affirms Alexander’s 21-year flood model (Alexander 2006).

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2.2.2 Building regulations

The National Building Regulations and Building Standards Act No. 103 of 1977 dictate that development within the 1:50-year flood line area should require safety considerations and understanding of the underlying natural stream flow process. This is also stated in the Town Planning and Townships Ordinance Regulation 44(3). It emphasises buffering flood areas up to 32 meters from the centre of a stream in cases where the 1:50-year floodline is less than 62 meters wide in total. (CSIR 2005; Van Bladeren et al. 2007; Ngie 2012). Adherence can be clearly observed in formal communities, but in less formalised areas such as informal settlements, and in some cases in the agricultural sector, these stipulations are generally ignored.

It is of extreme importance to identify risks and vulnerabilities pertaining to the planning of

infrastructure development and land use determination to analyse the situation whenever development close to a water source is considered. However, lack of proper risk analysis and implementation of risk analysis assessment exposes these communities to many natural disasters, especially floods.

2.2.3 Flood Vulnerability

Flood vulnerability can be defined as the susceptibility to degradation or damage from adverse factors or influences (Barroca et al. 2005). They also state that both the public and private sectors are

increasingly concerned by flooding disasters and increasing damages, both in humanitarian and economic terms.

In the same article, they observe that in France there is a trend to urbanise and that the increasing level of urbanisation is directly leading to more flooding and increasing loss of life.

In the paper, HAZUS-MH Flood Loss Estimation Methodology I: Overview and Flood Hazard

Characterization(Scawthorn et al 2006), which is comprised of two parts, discusses in the first part a

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Flood Information Tool which allows for quick analysis of various stream discharge data as well as topographic mapping in order to determine flood frequencies over entire flood plains.

The second part of the paper deals with damage and loss estimation through the use of the Flood Model. Through the use of model, economic losses and shelter needs can be determined. Also,

according to the authors some additional analyses that can be performed using the Flood Model include flood warning, structural elevation and flood mapping studies.

Another approach is documented in the paper Remote sensing and GIS-based flood vulnerability assessment of human settlements: a case study of Gangetic West Bengal, India (Sanyal J et al. 2005). It involves identifying rural settlements that are likely to be affected should a flood event reach a certain magnitude. There are two factors that influence the vulnerability of these settlements. These are a) the presence of deep flood water in and surrounding the settlement; and b) the proximity to topographical features that can serve as safe areas during extreme flood events. Key to this process is the use of satellite imagery acquired during the flood event in order to identify non-flooded areas as well as a digital elevation dataset, such as the ASTER DEM, to indicate elevation of areas above the flooded areas.

Another aspect of flood vulnerability is the loss of habitat during a flood event. This is important because most flood events occur along freshwater courses. The loss of wetlands in particular is

worrisome when looking at freshwater turtles in the United States of America (Burke et al. 2002). In the paper Terrestrial Buffer Zones and Wetland Conservation: A Case Study of Freshwater Turtles in a Carolina Bay (Burke et al. 2002) GIS analysis was done to test protection measures in place to monitor the habitat that the freshwater turtles need to complete their life cycles. Buffer zones were identified that are of extreme importance to the freshwater turtles. In effect it highlights the importance of wetland habitat management to secure biodiversity in the affected areas.

Another interesting aspect of flood vulnerability is also considered to be one of the worst natural disasters that can occur, namely flash floods. In the paper, Flash flood risk estimation along the St.

Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery (Youssef et al. 2011), the authors presented a model on the utilization of remote sensing, DEM data, geological, geomorphological and field survey data, in a GIS in order to estimate flash flood risk in

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the southern Sinai in Egypt. The road corridor studied, the Feiran-Katherine road is popular with tourists and has experienced frequent flash flood events in the past, incurring extensive damage.

Youssef used morphometric analyses to estimate flood levels of sub-watersheds within the

affected basin. This is a very interesting approach as the focus of the study is on geomorphological features and investigating active as well as inactive sub-basins, hazard areas were identified and thus appropriate measures could be identified and discussed with regards to future flash flood events.

Another method that can be of use, semi-quantitative model and fuzzy analytical hierarchy (FAHP) weighting approach as described in the paper, A GIS-Based Spatial Multi-Criteria Approach for Flood Risk Assessment in the Dongting Lake Region, Hunan, Central China (Wang et al. 2011). By employing spatial multi-criteria techniques a GIS database was created to evaluate flood hazard and flood vulnerability and the resulting flood risk index was used to assign weights to 5 identified categories namely very low, low, medium high and very high. This method showed the concentration of the high and very high risk areas.

2.2.4 Methodologies used around the world

Certain parts of the world are more at risk, and experiences more flood events and disasters than others. The Indian subcontinent which includes Pakistan, Bangladesh, Sri Lanka amongst others, and Asia, which includes Vietnam, Myanmar, China, and surrounding countries such as Indonesia, Malaysia, the Philippines, experience dramatic floods annually during the monsoon season. Because of the frequency and magnitude of these flood events, identifying flood hazards and flood management are of extreme importance.

Hydrodynamic modelling as a tool to determine flood hazard and risk assessment in Bangladesh (Tingsanchali et al. 2005) is an excellent method to employ, but is very labour intensive and time consuming. This type of modelling requires model calibration and verification, specialist knowledge of hydrological systems and access to archived hydro data.

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NOAA-AVHRR (National Océanographie and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVFIRR) imagery) images are remote sensing data sourced from satellite borne sensors (Islam et al. 2000). Multiple NOAA-VHRR images, in this case 4, are used to estimate flooded areas. Three images are from different phases of the flood event, and one image is from the dry season.

Because a flood is a wave, inundation of areas occur at different times during the flood event, this means that the date and time of data collection of recurrent satellite imagery are very important with this method (Islam et al. 2000). Other data sets that are needed with this type of analysis include, but are not limited to boundary data such as administrative areas, physiographic, geological, elevation and drainage network data (Islam et al. 2000).

A similar methodology was employed to determine flood risk areas in Iran (Safaripour et al. 2012).

Instead of making use of NOAA-AVHRR imagery, Landsat ETM+ imagery and digital elevation model data available for the Golestan Province was used. By overlaying and adding weights to layers, a flood hazard layer containing intensity was created. With the use of a two-dimensional matrix, a final flood risk map was produced. Five layers was produced namely a floodplain layer, position of vulnerable villages and cities, flood damages, rate of flood hazard and flood classification. Factors that were identified as having an impact in these layers were flood frequency, loss of life and property, vulnerable populations, and population density. What makes this study very interesting is that a watershed basin was analysed and divided into 6 sub-watershed basins in order to achieve the results obtained in the study.

HEC-RAS is another popular method used in flood plain mapping. The HEC-RAS hydraulic model enables 2D and 3D flood plain mapping and analysis in ESRI’s ArcGIS. HEC-RAS is a software package that models the hydraulics of water flow through natural rivers and channels. The program is one-dimensional implying that there is no direct modelling of the hydraulic effect of cross section shape changes, bends and other 2D and 3D aspects of the water flow. Designed by the Army Corps of Engineers of the United Stated Department of Defence, it has found wide spread use in the public sector since its release. The software was originally designed to assist civil and hydraulic engineers in flood plain determination.

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In their paper, Floodplain mapping using HEC-RAS and GIS in semi-arid regions of Iran (Salajegheh et al.

2009), and three steps were identified in the floodplain mapping process:

 Stream flow (flow lines) associated with the 100-year flood level

 100-year flood line elevation profiles, and

 Flooded areas.

Because HEC-RAS is a one-dimensional modelling system, projection issues arise when data is moved from HEC-RAS to ArcGIS. Also, because HEC-RAS is a one-dimensional system (the only reference used is a measuring station in the river) and ArcGIS uses a coordinate system that is based on locations in the real world with x, y and z (elevation) coordinates.

HEC-RAS uses cross-sections to determine depth and flow of the river, and more applicable in hydraulic studies.

Recently, a study was conducted in the Lamprey River Watershed in New Hampshire, United States of America, assessing the risk of flood events up to 2100 resulting from changes in climate and land use (Wake et al. 2013). They estimated changes in run-off resulting from changes in land use and climate.

For the land use component, land use change from 1960 to 2010 was analysed and growth patterns assessed. These patterns were adjusted to include population growth as well as changes in residential property and commercial/industrial property. Developments likes these cover natural ground with an artificial layer usually concrete, also called an impervious surface, which prevents water to be absorbed back in to ground. The areas of impervious surface increases the ground is able to absorb less and less water and more water runs and accumulate in non-natural areas. This is a leading cause of flooding as water cannot drain naturally back into the earth.

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2.2.5 GIS and the Insurance Industry

The use of geographic information systems in the insurance industry has added flexibility, accuracy, minimalizing of cost and the ability to integrate spatial and non-spatial data to the decision making process and also to the determining of premiums payable to insure risks.

GIS has become an essential business tool in the insurance industry (Nagesh Kumar, 2003; Thomas, 2000). The goal of all insurance companies is minimalize risk exposure by ensuring that the risk is spread amongst wide distribution of policyholders.

In order to incorporate GIS into the pricing operations, a model must be built split into two parts, namely hazards and vulnerability. Hazards deal with the intensity, occurrence and location of hazards whether natural or man-made, whereas vulnerability has to do with quantity of claims par hazard.

(Nagesh Kumar, 2003; Thomas, 2000). Hazards are modelled using external data and vulnerability by assessing internal a history of claims for a certain hazard.

By combining vulnerability with hazard probability of a certain area, insurers can derive a loss curve to estimate total losses experienced for a specific disaster. Using this determined loss calculation, premium pricing can be accurately calculated and adjusted accordingly. The accuracy of this process is subject to the quality of the data used (Nagesh Kumar, 2003; Thomas, 2000).

In his presentation, “Recent hail losses from a reinsurance perspective”, Pieter Visser , an analyst at AON Benfield, showed the benefits derived from using geospatial data calculating the expected losses

incurred during hail storm events experienced between October 2012 and November 2013.

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Figure 1: Comparison of hail event (“Recent hail losses from a reinsurance perspective”, Pieter Visser)

In figure 1 Visser describes some characteristics pertaining to two of the 3 events, showing information such as date, location, hail stone size and information about the storm tracks. Figure 2 shows areas where claims have been received for one of the storm events, also showing the location of the 4 storm tracks.

Figure 3 shows the combined claim distribution area for the three events, and as can be seen in figure 3, there are areas that appear to be affected quite heavily in each of the storm events.

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Figure 2: GIS – Insurance market claims 28 Nov 2013 (“Recent hail losses from a reinsurance perspective”, Pieter Visser)

Figure 3: GIS – Insurance claims distribution of the 3 events (“Recent hail losses from a reinsurance perspective”, Pieter Visser)

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Land cover and land use changes also affects where and how the risk is spread for insurance companies.

Figure 4 shows the change between 2004 and 2014 in an area west of Johannesburg, where significant development occurred.

Figure 4: Exposure changes (“Recent hail losses from a reinsurance perspective”, Pieter Visser)

Through correspondence with Visser, it became apparent that widespread use of GIS in the insurance industry in South Africa is very poor. Data quality and geocoding is a major problem, because the systems in place are not geared towards spatial data and the pipeline of data that comes from the brokers is not very good.

He also says that the insurers are not receptive to the use of GIS in the insurance industry; however there are a few actuaries that have shown interest in using GIS. Unfortunately there seem to be a trend throughout the insurance industry that the use of GIS in risk assessment and risk management is unnecessary expenditure and a waste of resources.

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2.2.6 Summary

There are many different methodologies employed all over the world to study flood and the effects thereof. Also, from the research conducted, there is no standard methodology defined as yet to apply to the methodology to be employed in this study.

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3. Approach

3.1 Theoretical Foundation

The theory is that just as everything has a spatial location, specified as an x and y coordinate, just so does it have an elevation value, or z-value. It is this elevation value that will determine the amount of risk that the property is exposed to.

The level of the Vaal River can be equated to an elevation level. As the water rises due to a flood event, so does the elevation along with the “horizontal footprint” of the water level.

This study will document change between the various years of land use in both urban and non-areas areas

3.2 Methods Applied

Using flow data compiled by the Department of Water Affairs, analysis will be done firstly to get elevation values for all flow data. This is accomplished by converting the line feature type to raster and

“intersecting” the converted flow line with a DEM that was generated for the study area. As most features that are portrayed in DEM datasets are static as well as the flow lines, represented in cubic meters of water per second released from the upstream dam or reservoir, elevation values can be derived for all the different flow scenarios. This being said, the elevation of the flow lines is of extreme importance.

Just as elevation information is relevant to the flow data, just so when considering the land cover data. If a certain land cover classification fall within a certain distance or elevation from the river centreline, there will be likelihood for suffering damage from flooding.

Land use data, aerial photography, satellite imagery, population data, cadastral data and hydro-data will be used to identify and detect changes, whether positive or negative, along the river banks of the Vaal River. Land Use data will be used where available in order to incorporate municipal building regulations for areas that fall within the study area.

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Flood modelling data received from DWA will be used to map flood prone areas, also referred to as hotspots. These flood prone areas will serve as a base layer with the results of other analyses overlaid to identify property under the flood line and property developments that are at risk to floods. The

identified areas will be weighted in order to identify areas critically exposed to flood risk and will be indicated on maps of the study area.

The above methodology will be applied to both urban areas as well as areas of agriculture. The reason for looking at both urban and non-urban areas is that even though the impact will be similar, the effect will be absorbed differently.

3.3 Tools

Arcgis 10

ESRI’s ArcGIS software probably is the industry standard with regards to GIS software. It allows for the capturing, analysis, mapping and dissemination of spatial data.

Global Mapper 15

Global Mapper by Blue Marble Geographics is a GIS software package that allows for quick distance and area calculations, raster blending, spectral analysis and elevation analysis, image rectification, contour generation, 3D analysis, 3D visualization, projection and transforming data, mapping.

3.4 Test Area

The Vaal Orange River System drains half the surface of South Africa. The Orange River, with its two major dams, Gariep and Vanderkloof, originates in Lesotho and flows about 2 000km further into the Atlantic Ocean at Alexander Bay. The Vaal River originates in the Ermelo district and flows together with the Wilge River into the Vaal Dam. From there through the Rand Water Board Barrage down to

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Bloemhof Dam and from there to the confluence with the Orange River just downstream from the town of Douglas (figure 5).

These 4 major dams provide the water needed for the generation of about 80% of South Africa’s gross national product. It is therefore crucial to ensure the optimal operation of these dams, especially during flood conditions.

The Vaal and Bloemhof Dams are effective by optimal use of crest gates. The higher the flow the less influence these dams have on the river flows in the river system and therefore also reducing human influence on the flow.

Land cover refers to the physical surface of the earth, including various combinations of vegetation types, soils, exposed rocks and water bodies as well as elements with a human influence such as agriculture and built environments. On the other hand, Land Use means the purpose to which the land cover is committed, in other words activities on land which are directly related or influenced by the land.

Some land uses, such as agriculture, have a characteristic land cover pattern. These usually appear in land cover classifications. Other land uses, such as business or commercial, are not readily discriminated by a characteristic land cover pattern. For example, where the land cover is “Urban / built-up land:

commercial”, land use may be business related (e.g. shops) or offices (Fourie et al. 2009)

Land Cover is a useful indicator of development that occurred over a period of time over a large area.

The area identified for this study is the Vaal River between the Vaal Dam and Bloemhof Dam. The study area is bounded by 13 municipalities that fall in the Gauteng, Free State and North West Provinces. The river area earmarked for this study is approximately 420km long. It varies in height above mean sea level from 1155m to 1911m (figure 6).

According to the Department of Water Affairs, the average flow of the Vaal River is between 15 and 25 cubic meters/second.

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Figure 5: Locality of Study Area in South Africa

Figure 6: Study Area with SRTM DEM

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Major towns that fall within the study are in Gauteng province is Vanderbijlpark and Vereeniging, in the Free State province is Parys and Sasolburg, and in the North West province it is the town of Orkney.

The Vaal Barrage on the Vaal River near Vanderbijlpark, on the borders of the Gauteng and Free State provinces, also dams up the Vaal River, which keeps the water level above the Barrage fairly constant.

The Vaal Barrage is a dam in itself and was originally the area in which most of Gauteng’s water was stored prior to the construction of the Vaal Dam wall in 1938. It is also often referred to South Africa’s

“inland Riviera” with its abundance of waterfront properties and it is also valued as prime real estate in both Gauteng and Free State provinces.

Figure 7: Land use derived from CD:NGI datasets

As the Vaal River flows west past the town of Orkney, the area becomes more rural with more agriculture than urban areas present in the study area (figure 7). The land cover shown is from 2006.

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The Vredefort Dome, one of the World Heritage Sites in South Africa also forms part of the study area.

Figure 8 shows the Vredefort Dome, as well as the location of the Vaal Barrage.

Figure 8: Locality of the Vaal Barrage and the Vredefort Dome

3.5 Test Datasets

The main datasets that will be used for this study are flow lines supplied by the Department of Water Affairs, Spot Building Count from Eskom, DEM data from the Chief Directorate: National Geospatial Information (CD:NGI), ASTER DEM data and Land Cover data from the early 1990’s to 2011 sourced from the North West, Free State and Gauteng provinces.

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32 3.5.1 Vector Datasets

The datasets from the Department of Water Affairs comprise flow line data for the Vaal River and gauging station data at 4 locations throughout the study area. These datasets are of great historic importance as some of the records date back to 1938. These are however not hundred percent

complete and some have gaps that covers months and in some cases years. That being said, there still is plenty of data relevant to this study as the records are very complete from the late 1980’s up to as recently as this writing.

3.5.2 Raster Datasets

The DEM data used as mentioned above, the SRTM 90m DEM, SRTM 30m DEM and the ASTER 30m DEM, proves invaluable because making use of the DEM data, elevations can attributed to the other datasets and that will enable analysis using elevation as indicator of the elevation of growth in relation to certain geographical features.

Satellite imagery and aerial photography also forms part of raster data.

Cadastral data, Census data and aerial photography and/or satellite imagery in conjunction with the Land Use/Land Cover data will/can be used as “snapshots” of the situation on the ground at the time of the generation of the dataset.

3.5.3 ASTER DEM vs SRTM DEM

The Shuttle Radar Topography Mission (SRTM and the ongoing Advanced Space borne Thermal Emission and Reflection radiometer (ASTER) are the DEM data sets available for this study.

3.5.3.1 SRTM

The SRTM DEM is a product of the National Geospatial-intelligence Agency (NGA) and National

Aeronautics and Space Administration (NASA). The mission was launched in 2000 with 1arcsec (+/- 30m)

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available for the United States of America and is reduced to 3 arcsec (+/- 90m) globally. (Colosimo et al.

2009). The SRTM dataset is generated by recording radar wavelengths as clouds do not have an effect on radar recorded data (Forkuor et al.2012).

Recently, the NGA and NASA released 1 arcsec (+/- 30m) DEM for Africa. This updated DEM dataset will be used in this study.

3.5.3.2 ASTER

The ASTER Digital Elevation Model (DEM) product is generated using bands 3N (nadir-viewing) and 3B (backward-viewing) of an ASTER Level-1A image acquired by the Visible and Near Infrared (VNIR) sensor.

The VNIR subsystem includes two independent telescope assemblies that facilitate the generation of stereoscopic data. The DSM generation (on request) is based on an automated stereo-correlation method that generates a relative DEM without any ground control points (GCPs).

The ASTER DEM is a single-band product with 30-meters horizontal postings. It was made available for public use in 2009 (Colosimo et al. 2009).

3.5.3.3 Accuracy

In general terms, both the SRTM and ASTER DEMs meet the predefined specifications for vertical accuracy of 16m and 20m for the SRTM and ASTER DEMs respectively. The flatter an area is, the higher the vertical accuracy is for both the DEMs. When comparing the SRTM DEM to the ASTER DEM in RMSE (Root Mean Square Error) terms, the ASTER DEM underestimates elevation, even if filled, whereas the SRTM overestimates the elevation (Forkuor et al.2012), due to the fact that the SRTM records the reflective surface (Forkuor et al.2012).

The same study (Forkuor et al.2012), found that the SRTM DEM is more accurate than the ASTER DEM in terms of relative accuracy.

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Without the use of ground control points, only relative accuracies can be achieved with both the SRTM and ASTER DEMs.

3.5.3.4 Comparison between SRTM and ASTER

ASTER GDEM v2 SRTM

Data Supplier METI/NASA NASA/NGA/DLR/ASO

Version 2 1

Period of Collection 2000-2010 11 days in 2000

Acquisition techniques Stereo pairs, visible and near infrared

Radar interferometry

Main distortion factor clouds Radar shadow and echo

Datum (horizontal) WGS1984 WGS1984

Datum (vertical) EGM96 geoid WGS1984 ellipsoid

Horizontal resolution 1 arc second 1 arc second

Horizontal accuracy ±30m (abs.) 95% CE ±20m (abs.) 90% CE Vertical accuracy ±20m (abs.) 95% LE ±16m (abs.) 90% LE

Data format GeoTIFF, 16-bit signed integer DTED-2, 16-bit signed integer

Table 2: Characteristics of ASTER GDEM and SRTM-X DEM (Czubski et al. 2013)

3.5.3.5 New DEM data

Astrium have recently announced a new product, Elevation12 – The WorldDEM (http://www.astrium- geo.com/worlddem/). It is a product of the TanDEM-x Mission (TerraSAR-x addon for DEM) which is a joint operation between Airbus DS and the German Aerospace Centre (DLR).

This new DEM has a resolution of 12m and 2m relative accuracy and 4m absolute accuracy. According to Astrium this dataset will cover the globe from pole to pole and will be a standardized DEM for the whole of the planet, meaning that in contrast to the 90m SRTM DEM and the 30m ASTERDEM where certain areas have a higher resolution and accuracy, the coverage of the dataset will have the same resolution and accuracy for any location on earth. Figure 9 shows the difference between the new 12m DEM and the 30m DEM.

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Figure 9: Shows the difference between the new WorldDEM and the existing SRTM DEM (http://www.astrium- geo.com/worlddem/)

However, since Astrium has only recently started the process of acquiring the data, only some areas of North America and Europe is currently commercially available.

Should the WorldDEM acquisition process be completed, and freely available, it will be an invaluable global dataset that can assist planning for as well as rescue operations during natural disasters when accurate DEM data is needed.

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4. Project

4.1 Data Preparation

The data used for the project has been described in the previous section, Test Datasets. In this section, insight will be given into the preparation of the various datasets.

4.1.1 River centreline and flow lines

The centreline of the Vaal River is represented as a line feature.

The flow data received from the Department of Water Affairs was in polyline shapefiles. In order to make use of the data, it must be converted to polygons. The reason for this is that areas are needed for each of the different flow lines provided.

For this study, each line was assessed and where there were gaps between line segments, lines were added in order to be able to “close” the lines. These “closed” lines were then converted to polygons using ArcGIS 10 (figure 10).

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Figure 10: Flow lines and Vaal River centreline overlaid on cadaster of the town of Parys

4.1.2 Cadastral data

There are several polygon datasets used for this study. They are the mentioned lines converted to polygons, cadastral data, 1:50 000 reference grid data, and a polygon dataset indicating the location of dams within the study area.

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Figure 11: Cadastral data of a developed within the study area with Vaal River centreline overlaid

The cadastral data is of extreme importance when assessing the locality of a specific cadastral portion in relation to the river centreline and also once analysed using the raster DEMs of the area, elevation above average flow of the river (figure 11).

4.1.3 Town and Spot Building Count Data

One of the point datasets that are used in this study is a point dataset indicating towns and major settlements.

Another point dataset used is called the SBC (Spot Building Count) for 2007 and an updated dataset from 2012. This data set was generated by putting a point on visible structures using the SPOT5 imagery for the previous year. While the building is captured, a land use classification is attributed to the building as can be seen in figure 12.

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Figure 12 Attributes for SPOT Building Count 2012

Having a vector dataset available that gives an indication of land use enables additional analysis when, such as in this case, the Spot Building Count data is joined cadastral data.

Figure 13: Portion of cadaster of the town of Parys showing the SBC in green and Town locator in black

It is also a useful dataset if an overview of structures types in the study area is needed (figure 13).

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Satellite imagery and aerial photos (figure 14) will be used for mapping and visual verification of what is indicated in cadastral, SBC 2007 and land cover/land use datasets.

Figure 14: SPOT 5 Satellite imagery with a gauging station and Vaal River centreline overlaid

4.1.6 DEM Data

The raster datasets used for this study are the SRTM DEM for the study area (30m), the ASTER DEM (30m). These raster datasets will be the source of the elevation values used throughout the study.

The use of raster datasets is preferable when working with large datasets that contain a lot of detail due to the size of the raster dataset when compared to a vector dataset. That being said, there are factors that determine the size of a raster dataset. The resolution of the dataset and the number of attributes are the biggest factors that determine the size of the dataset. And obviously, the size of the area of interest is the biggest factor that determines the size of the dataset.

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The land cover/land use datasets are also raster datasets. These land cover/land use datasets are for 1994, 2000, 2006 and 2009/2010.

The DEM generated from the SRTM/ASTER DEM data will be used to allocate an elevation value to some of the datasets used in this study. The land cover/land use data will be used to give an elevation value to:

 The Vaal River centreline

 The cadastral data

 The flow line data

These elevation calculations derived from the DEM datasets will be used to ascertain whether the elevation at a certain location for a certain dataset places that specific feature within harm’s way or not (figure 15).

Figure 15: Showing cadaster within a flow line area

4.2 Project Description

Both raster and vector change analysis will be performed within the 2500m3/s, 5000m3/s, 10 000m3/s, and 16000 m3/s flow line polygons. The reason for both vector and raster change analysis is that due to the raster data being different resolutions, the vector data will either backup or disprove the results of the raster based change analysis.`

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Also, property value will be briefly investigated to ascertain whether an average value for affected properties can be determined.

And lastly, using DEM data such as the 30 ASTER DEM, 90m SRTM DEM or 30m SRTM DEM, try to recreate the flow line areas received from the Department of Water Affairs. This is important because should this process prove to be accurate or close enough to the flow lines determined by the

Department of Affairs, it might be used in other areas in South Africa.

4.2.1 Land Cover change

Figure 16: Land Cover 1994 - Parys, FS

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Value Count LAYER LANDCOVER

0 407486 Unimproved grassland Natural Open Space

1 338107 Cultivated: temporary - commercial dryland Cultivated Agriculture

2 27544 Thicket & bushland (etc.) Natural Open Space

3 12008 Mines & quarries Mining

4 15800 Urban / built-up land: residential (small holdings: grassland) Smallholding

5 10238 Waterbodies Water and Wetlands

6 4742 Cultivated: temporary - commercial irrigated Cultivated Agriculture

7 15593 Urban / built-up land: residential Urban

8 4824 Urban / built-up land: industrial / transport Urban

9 1755 Urban / built-up land: commercial Urban

10 906 Improved grassland Natural Open Space

11 3842 Forest plantations Plantation

12 34841 Forest and Woodland Plantation

13 3008 Wetlands Water and Wetlands

Table 3 1994 reclassified

Figure 17: Land Cover 2000 - Parys, FS

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Value Count DESCR LANDCOVER

0 466838 Unimproved (natural) Grassland Natural Open Space

1 3299 Urban / Built-up, (industrial / transport : heavy) Urban

2 62388 Thicket, Bushland, Bush Clumps, High Fynbos Natural Open Space 3 238350 Cultivated, temporary, commercial, dryland Cultivated Agriculture 4 15856 Urban / Built-up (smallholdings, grassland) Smallholding

6 3561 Mines & Quarries (surface-based mining) Mining

7 14316 Cultivated, temporary, commercial, irrigated Cultivated Agriculture

9 10238 Urban / Built-up (residential, formal suburbs) Urban 10 418 Urban / Built-up, (commercial, education, health, IT) Urban 11 630 Urban / Built-up, (commercial, mercantile) Urban

12 1554 Improved Grassland Natural Open Space

13 1124 Mines & Quarries (underground / subsurface mining) Mining 14 4680 Mines & Quarries (mine tailings, waste dumps) Mining

15 349 Forest Plantations (Pine spp) Plantation

16 1602 Urban / Built-up, (industrial / transport : light) Urban 17 3739 Urban / Built-up (residential, formal township) Urban 18 241 Forest Plantations (Other / mixed spp) Plantation 19 1641 Urban / Built-up (residential, informal township) Urban

20 57 Urban / Built-up (residential, hostels) Urban

21 2466 Urban / Built-up (residential) Urban

22 157 Urban / Built-up (residential, mixed) Urban

23 9 Bare Rock and Soil (erosion : dongas / gullies) Natural Open Space

24 805 Forest Plantations (Eucalyptus spp) Plantation

25 95 Degraded Thicket, Bushland, etc. Natural Open Space

26 361 Bare Rock and Soil (natural) Natural Open Space

27 6562 Degraded Unimproved (natural) Grassland Natural Open Space 28 664 Urban / Built-up (residential, informal squatter camp) Urban

29 460 Cultivated, temporary, subsistence, irrigated Cultivated Agriculture 30 92 Cultivated, temporary, subsistence, dryland Cultivated Agriculture Table 4 2000 reclassified

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Figure 18: Land Cover 2006 – Parys, FS

Value Count LAND_USE LANDCOVER

0 426903 VACANT / UNSPECIFIED Natural Open Space

1 7002 RESIDENTIAL Urban

2 4

3 2258 COMMERCIAL / INDUSTRIAL Urban

4 11102 MINING Mining

5 3604 FORESTRY Plantation

6 311783 CULTIVATED LAND Cultivated Agriculture

7 10630 CONSERVATION Natural Open Space

8 43040 Unimproved grassland Natural Open Space

9 28193 Cultivated: temporary - commercial dryland Cultivated Agriculture

10 985 Thicket & bushland (etc.) Natural Open Space

11 856 Mines & quarries Mining

12 14490 Urban / built-up land: residential (small holdings: grassland) Smallholding

14 733 Cultivated: temporary - commercial irrigated Cultivated Agriculture

15 9868 Urban / built-up land: residential Urban

16 2677 Urban / built-up land: industrial / transport Urban

17 1612 Urban / built-up land: commercial Urban

18 420 Improved grassland Natural Open Space

19 236 Forest and Woodland Plantation

20 141 Forest plantations Plantation

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Figure 19: Land Cover 2009 – Parys, FS

Value Count LANDCOVER

0 24796187 Cultivated Agriculture

1 27723595 Natural Open Space

2 907963 Mining

3 2434605 Water and Wetlands

4 2205669 Urban

5 545478 Plantation

6 591705 Smallholding

7 293091 Transport

8 4012 Municipal

Figures 16 to 19 and tables 3 to 6 show the land cover data for 1994, 2000, 200 and 2009 showing the varying resolutions as well as the different classifications systems applied for each dataset of the datasets.

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4.2.2 Property value – value to property affected

Adding a monetary value to a property is a very subjective exercise at best. There are so many factors that influence the value of property that for application in this study, only the property valuation roll for the town of Parys, done in 2008, will be used. An average value per m² for urban property and for agricultural land an average value in hectare will be calculated. These values will be used for all Table 7:

Properties values

Row Labels

Average of REGISTERED EXTENT (m2)

Average of MARKET

VALUE R/m2

AGRICULTURE 200.0388344 R 668,649.13 R 3,342.60

COMMERCIAL 85.6532 R 16,230,000.00 R 189,485.04

GOVERNMENT 117.2649026 R 162,307.69 R 1,384.11

PUBLIC SERVICE

INFRASTRUCTURE 5.158671429 R 43,333.33 R 8,400.10

RESIDENTIAL 2.588441818 R 2,133,272.73 R 824,153.25

UNDEVELOPED LAND 3.487572131 R 595,573.77 R 170,770.31

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Grand Total 185.0102371 R 663,165.25 R 3,584.48

Table 8: Properties values

4.2.3 Using a DEM to determine/confirm flow lines

Knowing where the change occurred is as important as knowing the elevation of the areas of change.

Because the flow lines used in this study are mapped at certain flows, namely 2500m³/s, 5000m³/s, 10 000m³/s and 16 000m³/s, and because the flow lines are polygons, elevation values can be linked to these flow lines.

Using the 30m SRTM DEM and ArcGIS, especially the 3D Analyst and the Spatial Analyst extensions, a model will be created in order to determine if the flow lines used in this study can be recreated.

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4.2.3 Using a DEM to determine the elevation of the areas of change

Using the DEM data as described, the flow lines used in this study were used to clip the DEM data in order to investigate the minimum, maximum and mean elevations for each of the flow lines.

Also, it will be interesting to the slope 16000m3/s, the largest of the flow lines used in the study.

Global Mapper is a very powerful tool for raster analysis. The four raster datasets will be compared to each other, and the 3D capability of Global Mapper will used to overlay various over the previous analysis results (raster and vector). ArcGIS will be used in conjunction with Global Mapper in analysing the DEM.

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5. Results

Using existing data sets analysis was performed on the land cover data, the land use data and the DEM data. The analysis proposes to show that there has been development within areas that are likely to be inundated should the Vaal River attain certain flow levels.

5.1 Land Cover Change Analysis

Using the area in hectare for each flow line, the following tables and maps indicate the extent of the area that changed in each scenario.

5.1.1 Land Cover Analysis 2000 to 2006

The following figures and tables show the results of the land cover change analysis for the period of 2000 to 2006. The hectare (Ha) column was derived by calculating the percentage from the Area of the flow line for each of the land cover classifications.

LANDCOVER ZONE_CODE COUNT AREA MAJORITY % Ha

Natural Open Space 1 11763 0.00810353000 0 48% 8200.01

Urban 2 95 0.00006544550 8 0% 67.07

Cultivated Agriculture 3 546 0.00037613900 6 2% 380.08

Smallholding 4 354 0.00024387100 9 1% 245.93

Water and Wetlands 5 11835 0.00815313000 0 48% 8249.88

Mining 6 78 0.00005373420 8 0% 55.03

Table 9: Zonal Majority Table for 2500m3/s flow line 2000 – 2006

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Figure 20: Zonal Majority for 2500m3/s flow line 2000 - 2006

Table 8 serves as an explanation to the legend of figure 20.

Urban 2 327 0.00022527000 15 1% 240.75

Smallholding 4 677 0.00046638500 9 2% 497.92

Mining 6 224 0.00015431400 8 1% 164.15

Plantation 7 1 0.00000068890 0 0% 0

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Figure 21: Zonal Majority for 5000m3/w flow line 2000 - 2006

Urban 2 891 0.00061381000 15 2% 666.68

Smallholding 4 1233 0.00084941400 12 2% 922.78

Mining 6 773 0.00053252000 4 1% 581.31

Plantation 7 55 0.00003788950 0 0% 40.65

Table 11: Zonal Majority Table for 10 000m3/s flow line 2000 – 2006

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Figure 22: Zonal Majortiy for 10 000m3/s flow line 2000 - 2006

Urban 2 1490 0.00102646000 15 2% 1117.43

Smallholding 4 1567 0.00107951000 12 2% 1176.24

Mining 6 914 0.00062965500 4 1% 686.14

Plantation 7 73 0.00005028970 0 0% 53.91

Table 12: Zonal Majority Table for 16 000m3/s flow line 2000 – 2006

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Figure 23: Zonal Majority for 16 000m3/s flow line 2000 - 2006

5.1.2 Land Cover Analysis 2006 to 2009

Similar to the land cover change analysis for the period 2000 to 20006, the following figures and tables show the results of the land cover change analysis for the period of 2006 to 2009. The hectare (Ha) column was derived by calculating the percentage from the Area of the flow line for each of the land cover classifications.

Urban 2 99 0.00006820110 1 1% 87.71

3 2 0.00000137780 1 0% 1.72

Mining 4 81 0.00005580090 1 0% 72.23

Plantation 5 17 0.00001171130 3 0% 15.48

Smallholding 7 142 0.00009782380 3 1% 125.55