A Small Scale CLEW Analysis of the Cape Town Region

Estimating the Effects of Climate Change on the Water Provision

Lydia Petschelt

Master of Science Thesis KTH School of Industrial Engineering and Management Energy Technology EGI-2013-063MSC Division of Energy System Analysis SE - 100 44 Stockholm A Small Scale CLEW Analysis of the Cape Town Region I

Master of Science Thesis EGI-2013-063MSC

A Small Scale CLEW Analysis of the Cape Town Region

Estimating the Effects of Climate Change on the Water Provision

Lydia Petschelt

Approved Examiner Supervisor 2013/09/24 Mark Howells Sebastian Hermann

Abstract

The knowledge of the influences climate change can have on a regional scale is still very limited. Generally it is known that the climate, land use, energy and water resources are intertwined. The CLEW strategy focuses on an approach to quantify these interrelations. In South Africa, experiencing a fast development, water resources are vital for a continuous prosperous growth. Through a methodological approach the local impacts of climate change on water supply and demand for the City of Cape Town are analysed. The focus lies on the Theewaterskloof Dam in the Riviersonderend catchment. For this study, the future climate data are generated in MarkSim for different SRES scenarios. Using the Water Evaluation And Planning system simulation software WEAP the catchment of interest is modelled to estimate future variation in water availability. For all scenarios the findings are consistent with prior studies forecasting an increase in the annual mean temperatures and a decrease in the annual precipitation. The reduction in annual precipitation consequently leads to a decreased water availability in the Riviersonderend catchment. Despite of the fact that the water resources are likely to diminish, the fixed annual water demand supplied by the Theewaterskloof reservoir is expected to be covered in the future without limitations.

Keywords: City of Cape Town, CLEW, climate change, local scale, precipitation, water availability, WCWSS A Small Scale CLEW Analysis of the Cape Town Region II

Table of Contents

A Index of Figures ...... III B Index of Tables ...... V C Nomenclature ...... VI 1 Introduction ...... 1 1.1 Objectives ...... 1 2 Background ...... 2 2.1 The City of Cape Town ...... 2 2.1.1 Historical Demand ...... 3 2.1.2 Future Demand ...... 5 2.2 Historical Supply ...... 6 2.3 Future Supply ...... 8 2.4 Historical Climate ...... 10 2.5 Future Climate ...... 12 3 Methodology ...... 13 3.1 WEAP ...... 13 3.1.1 Sub-Catchment Data ...... 14 3.1.2 River Data ...... 16 3.1.3 Reservoir Data ...... 16 3.1.4 Demand CCT Data ...... 16 3.1.5 Demand Berg WMA and Others Data ...... 17 3.1.6 Gauge Data ...... 17 3.2 MarkSim ...... 17 3.2.1 Future Emission Scenarios ...... 17 3.2.2 Application ...... 18 3.3 Limitations ...... 19 4 Results and Discussion ...... 21 4.1 Future Climate ...... 21 4.1.1 Adapted Future Climate ...... 22 4.2 Water Supply and Demand ...... 26 4.2.1 Sensitivity Analysis of the CCT Demand ...... 31 5 Conclusion ...... 35 D References ...... VII E Appendix ...... IX

A Index of Figures III

A Index of Figures

Figure 2.1: Mismatch between Supply and Demand in the CCT 2003 (H6R001) ...... 2 Figure 2.2: Western Cape Water Supply System (CCT, 2012) ...... 3 Figure 2.3: Historical Population Trend in Greater Cape Town 1970-2001 (South Africa. DWAF, 2007b) ...... 4 Figure 2.4: Historical Annual Water Demand for the CCT 1971-2004 (Ogutu, 2007) ...... 4 Figure 2.5: Distribution of Urban Water Demand ...... 5 Figure 2.6: Historical Population Trend and Projections for Cape Town 1996-2031 (CCT, 2012c) ...... 5 Figure 2.7: Historical Annual Water Demand and Future Projections (South Africa. DWAF, 2007b) ...... 6 Figure 2.8: Storage within the WCWSS (South Africa. DWAF, 2009d) ...... 7 Figure 2.9: Riviersonderend Catchment with its Gauges (South Africa. DWAF, 2009c) ...... 8 Figure 2.10: Reconciliation of Supply and Requirements for the 2011 Reference Scenario ...... 9 Figure 2.11: Precipitation at the TWK Dam and Temperature of the CCT Region for the Year 2003 ...... 10 Figure 2.12: Historical Precipitation at the Theewaterskloof Dam for the Period 1970 to 2004 ...... 11 Figure 2.13: Historical Temperature Measurements for the Period 1970 to 2004 ...... 11 Figure 3.1: Flowchart of Methodological Approach ...... 13 Figure 3.2: WEAP Model of Riviersonderend Catchment ...... 14 Figure 3.3: Weather Stations near the City of Cape Town ...... 15 Figure 3.4: Precipitation Runs at Sonderend Mountain Location for Scenario A2 from MarkSim ...... 20 Figure 4.1: Monthly Temperature Trend for Sub-Catchment TWK ...... 21 Figure 4.2: Annual Precipitation Trend for Riviersonderend Catchment ...... 22 A Index of Figures IV

Figure 4.3: Monthly Temperature Trend with Adapted Data for Sub- Catchment TWK ...... 23 Figure 4.4: Monthly Averaged Temperatures for the Period 2005-2050 ...... 24 Figure 4.5: Annual Precipitation Trend with Adapted Data for Riviersonderend Catchment ...... 24 Figure 4.6: Annual Precipitation Trend for the Period 2005-2050 for Riviersonderend Catchment ...... 25 Figure 4.7: Monthly Averaged Precipitation for the Period 2005-2050 for Riviersonderend Catchment ...... 25 Figure 4.8: Annual Runoff Flow for Riviersonderend Catchment ...... 26 Figure 4.9: Annual Runoff Flow for the Period 2005-2050 for Riviersonderend Catchment ...... 27 Figure 4.10: Comparison of Storage Volume of the TWK Dam for the Period 1970-2004 ...... 27 Figure 4.11: Monthly Storage Volume of the TWK Dam ...... 28 Figure 4.12: Monthly Averaged Storage Volume for the Period 2005-2050 .. 28 Figure 4.13: TWK Dam’s Inflows and Outflows for Scenario A2 ...... 29 Figure 4.14: Supply-Demand-Coverage for Riviersonderend Catchment ...... 30 Figure 4.15: Percent of Time Exceeded Demand Coverage ...... 31 Figure 4.16: Low and High Growth Rates for the CCT Demand ...... 32 Figure 4.17: Monthly Storage Volume of the TWK Dam for the Low Growth Rate ...... 32 Figure 4.18: Monthly Storage Volume of the TWK Dam for the High Growth Rate ...... 33 Figure 4.19: Supply-Demand-Coverage for Riviersonderend Catchment for Low Growth Rate ...... 33 Figure 4.20: Supply-Demand-Coverage for Riviersonderend Catchment for Low Growth Rate ...... 34

Figure E.1: Annual Precipitation for Each Sub-Catchment ...... X Figure E.2: TWK Dam’s Inflows and Outflows for Scenario B1 ...... XI Figure E.3: TWK Dam’s Inflows and Outflows for Scenario HB ...... XI

B Index of Tables V

B Index of Tables

Table 2.1: Major storage dams of the WCWSS (South Africa. DWAF, 2011a) .. 3 Table 2.2: Cape Town’s allocation form the WCWSS (CCT, 2012) ...... 7 Table 2.3: Intervention Options for the Reference Scenario ...... 9 Table 3.1: Gauged Sub-Catchments in Riviersonderend Catchment ...... 15 Table 3.2: General Circulation Models used in MarkSim ...... 19 Table 4.1: Correcting Factors for MarkSim ...... 22

Table E.1: Weather Generation Locations for MarkSim ...... IX

C Nomenclature VI

C Nomenclature

BWAAS Berg Water Availability Assessment Study CCT City of Cape Town CLEW Climate, Land Use, Energy, Water CMA Catchment Management Area DWAF Department of Water Affairs and Forestry GCM General Circulation Model IPCC Intergovernmental Panel on Climate Change MAP Mean Annual Precipitation SRES Special Report on Emission Scenarios TWK Theewaterskloof WCWSS Western Cape Water Supply System WMA Water Management Area 1 Introduction 1

1 Introduction

Today a vast majority of the scientific community agrees on the impact human activities have on climate change. The following changes over time are not the same all over the world, but depend on local conditions. As global climate models are very complex and require manifold information, their resolution does not yet allow a more locally oriented forecast. Therefore there is only little knowledge of the impact on local climate. The human impact on climate change is affected by the use of the world’s resources of land, energy and water. The use of one of these resources not only affects its own demand, but also that of the others. Moreover the climate itself has an effect on these resources. The CLEW strategy, standing for Climate, Land-use, Energy and Water, is an approach to develop quantified interrelations between these resources. This approach was developed to support future guidance of human activities and decision making, in order to lower their impact on climate change. In the past policies where designed considering only issues of these resources. The unanticipated adverse effects a strictly energy or land or water policy could have on the other resources respectively were often kept unnoticed. The aim of a CLEW analysis therefore is a system approach that considers these interdependencies. In countries with high development like South Africa, the resource water, next to energy, is essential for life and progress. Especially in fast growing urban areas the provision of water is an inevitable issue requiring special attention. As the climate is strongly intertwined with water resources, it is of utmost importance to study the interrelations to forecast possible impacts of climate change on water supply. Obvious examples are drought and heat waves followed by water shortages. This particularly affects the surface and atmospheric water resources, representing just 0,1% of global water (Moore, 1989). This water resource however is the main supplier of the bulk water in the Western Cape (CCT, 2012). The special focus of this study lies in the provision of a methodological approach to assess the local impacts of climate change. The linkage between climate, water supply and demand for the City of Cape Town (CCT) are reviewed. A site-specific investigation on a storage dam, analysing impacts of climate change on water resources, is conducted.

1.1 Objectives

The aim of this thesis project is to investigate the interrelation of CLEW in the south- western region of South Africa, focusing on the City of Cape Town. The climate dependency of the water supply and availability of the area is to be analysed by focusing on potential impacts of climate change on reservoirs and dams in the target region. First a review of study objects will be conducted to determine the best suited site to be investigated. A dam and reservoir having a significant contribution to the water supply of CCT will be selected. In a second step a data collection and evaluation is to be performed. Herby the focus lies on historical and future demand data and historical supply data. Using the Water Evaluation And Planning system simulation software (WEAP) a link between this collection of data and historical climate data of precipitation and temperature is modelled. This step uses the determination of the historical trends as a baseline for future forecasts. Next future climate scenarios will be projected for the target region using the climate downscaling tool MarkSim. Future projections on water supply as well as likely changes in the availability are to be presented in the fourth and final step. 2 Background 2

2 Background

2.1 The City of Cape Town

The City of Cape Town is located in the Western Cape Province of South Africa. Cape Town’s topography is mainly characterized by flat plains, known as Cape Flats, as well as hills and mountains. The rivers and the water storage capacity of CCT itself are comparatively small. Only 13% of the supplied water comes from sources within municipal boundaries (CCT, 2012). Therefore most of the water supply has to be delivered from outside of the catchment management area (CMA) of CCT. Additionally to the Sonderend and Palmiet rivers, the Berg River and its tributaries are the main suppliers. The annual precipitation in the City of Cape Town is averaged to 515 mm per year, its mean temperature amounts to 16,7 °C. In the CMA of CCT the precipitation mainly takes place during winter months. In summer, when the water demand is the highest, the lowest runoff is available (cf. Figure 2.1). This mismatch requires a bulk water supply system to ensure the water provision to CCT during the normally dry summers by means of stored water from the winter precipitation period. (CCT, 2008)

Figure 2.1: Mismatch between Supply and Demand in the CCT 2003 (H6R001)

The City of Cape Town is supplied with water by the Western Cape Water Supply System (WCWSS). There are four water management areas (WMA) contributing to this system. These are Breede, Gouritz, Olifants/Doorn and Berg WMA. The water of the first three WMAs is mainly used for agricultural irrigation purposes, the water form the Berg WMA for both the urban and agricultural sector. As the ground water supply in the area is very scarce, surface water sources cover 98,5 % of the water in the WCWSS (CCT, 2012). A network of six major dams interlinked by tunnels and pipelines try to minimize spillage (cf. Figure 2.2). Notice that the major dams are situated to the east of the City of Cape Town in the Cape Fold Mountains (CSIR, 2010). 9.1.1. Water Source

9.1.1.1. Situation assessment Cape Town and its surrounds are situated in a winter rainfall area. Winter rainfall runoff therefore must be stored in raw water storage reservoirs for use throughout the year, especially for use in the hot dry summer months when the demand for water is at a peak.

The City obtains most of its raw water from surface water sources. Approximately 98.5% of the water allocated to the City is obtained from surface water resources, with the remainder obtained from groundwater resources.

The Western Cape Water Supply System (WCWSS), a network of dams and conveyance pipelines, supplies water to Cape Town, neighboring towns and urban areas and agriculture. The various components of the WCWSS are owned and operated by the City, the

2Department Background of Water Affairs and Eskom. The WCWSS is shown in Figure 1. 3

Figure 2.2: Western Cape Water Supply System (CCT, 2012) TheFigure dams 1: Western are owned Cape by Water the CCT Supply and System the Department (WCWSS) of Water Affairs and Forestry (DWAF). A list of the dams, their capacity and the owners can be found in Table 2.1 below. In total the WCWSS provides 905,017 Mm3 of storage capacity. The City of Cape Town allocates 72 % of the annual yield of 556 Mm3 of the WCWSS (CCT,

2012). The remainder is used for agricultural purposes and other urban areas.

Table 2.1: Major storage dams of the WCWSS (South Africa. DWAF, 2011a)

Major Dams Capacity Owner [Mm3] [-] Theewaterskloof 480,4 DWAF 2.149

Voëlvlei 168,0 DWAF Berg River Dam 130,0 DWAF Wemmershoek 58,6 CCT Steenbras Lower 36,2 CCT Steenbras Upper 31,8 CCT

2.1.1 Historical Demand A study on future water requirements by the DWAF uncovered a strong correlation between population growth, economic growth and water demand for the City of Cape Town (South Africa. DWAF, 2007b). The population has been growing continuously since 1970, more intensely in recent years. This trend is visible in Figure 2.3. In 1970 1,9 million people lived in the greater Cape Town area. In 2001 this number has more than doubled to 4,5 million inhabitants. 2 Background 4

Figure 2.3: Historical Population Trend in Greater Cape Town 1970-2001 (South Africa. DWAF, 2007b)

The water demand of the City of Cape Town has also increased steadily since the 1970’s. While in the year 1971 the City’s demand was 89 Mm3, in the year 2000 it rose to 321 Mm3. This corresponds to an increase of more than 3,5 times during that time period of thirty years. The development of the historical water demand can also be seen in Figure 2.4.

Figure 2.4: Historical Annual Water Demand for the CCT 1971-2004 (Ogutu, 2007)

In the past, the water supply did not always cover the required urban demand. To eliminate these shortages, regulations had to be introduced to reduce the demand. To decide on constraints the WCWSS is assessed at the end of each year. As an example, from the year 2000 to 2001 a policy was implemented requiring reduction of demand of 10 %. This reduction of water demand as consequence of the restriction is also visible in Figure 2.4. From 2004 to 2005 an even greater reduction of 20 % was necessary. (South Africa. DWAF, 2007b) When analysing the sectoral distribution of the urban water demand for the City of Cape Town, it becomes apparent that the residential sector is the driving force with a 2 Background 5

demand of 60 to 70 %. Compared to that the commercial and industrial sectors both only have a share of 15 to 18 % of the demand. This distribution is displayed in Figure 2.5. (Ahjum, 2012) 3 DRIVERS OF URBAN GROWTH 3.1.1 Urbanisation Population growth Cape Town is experiencing rapid urbanisation as a result of both 3.1 Key drivers of urban growth in Cape Town natural growth and in-migration. The city’s population expanded by 36,4% between 1999 and 2007,1 and growth in 2010 was As a fast-growing metropolitan area in South Africa, Cape Town estimated at 3% per annum.2 Similar to other metropolitan cities is faced with a number of developmental challenges and trends, in South Africa, it is expected that urbanisation will remain an which inform the way the city grows and functions. These important trend for a number of years. The city’s population is challenges and trends can be best understood by examining expected to continue to grow significantly each year, both from the key drivers of future growth and development in the city – natural growth (although at a slower rate, with fertility levels urbanisation and economic growth – as well as the influences declining) as well as from in-migration. The largest unknown and constraints imposed by the natural environment. This section variable in future growth projections is the nature and extent will examine these key drivers and constraints, the main trends of in-migration, both internal and transnational. The estimated underpinning each of them, and their implications for spatial population for Cape Town in 2010 is 3,7 million;3 this could forward planning. It should be noted that a shift in any of these increase to close to five million people by 2030. Figure 3.1 implies a different future growth scenario. Therefore, the section illustrates different population growth scenarios as projected by concludes with various future growth scenarios based on changes Figure 2.5: Distribution of Urban Water Demand the ‘Dorrington reports’.4 in the key drivers behind growth, as well as their implications for spatial planning. Urbanisation is a positive global phenomenon that allows for the development of productive, urban-based, modern economies, and 2.1.2 Future Demand is associated with sustained improvements in standards of living. However, it also brings challenges such as congestion, crime, As established above, the historical developmentinformality andindicates inadequate livingtowards conditions. a Itcorrelation is thus important 1 between City of Cape Town (2011)the Overview increase of Demographic andof Socio-economic urban Characteristics water of demandthat the and negative the aspects population of urbanisation and are managed economic while the growth.Cape Town, Strategic With Development continuous Information and GIS Department. urbanization takingbenefits place of urbanin the living City(including of greater Cape economic, Town educational, and 2 Ibid. health, social and cultural opportunities) are maximised and 3 expected Ibid. to intensify in the upcoming years, this correlation is the basis for many made accessible to all communities. If planned for and managed, 4 future Dorrington, Rwater (2005) Projection demand of the Population projection of the City of Cape Towns 2001–2021for the and City of Cape Town. Dorrington, R (2000) Projection of the Population of the Cape Metropolitan Area 1996–2031. urbanisation can contribute towards the building of an 5 In City Figureof Cape Town (2011) 2. Overview6 the of Demographic historical and Socio-economic population Characteristics of developmenteconomically, environmentallyis presented and socially by thesustainable red city. line. DCapeifferent Town, Strategic forecast Development Informationing andscenarios GIS Department. by Dorrington from his studies from 1999 and 2005 are also presented (Dorrington, 2005). While the forecast made in 1999 did not depict the actual population development over the recent years, the trends estimated in 2005 picture the actual development much closer. It can be deduced that the high

trend 5forecast 400 from 2005 presents the most accurate predication of future population development. 5 200 5 000 4 800

4 600 4 400

4 200 4 000 3 800

3 600

3 400

3 200 POPULATION (thousands) POPULATION 3 000

2 800 2 600

2 400

1996 2000 2004 2008 2012 2016 2020 2024 2028 2032

Dorrington 1999 - high Dorrington 1999 - medium Dorrington 2005 - high Dorrington 2005 - medium Population

Figure 2.6: Historical Population Trend and Projections for Cape Town 1996-2031 (CCT, 2012c) Figure 3.1: Cape Town population trends and projections: 1996–20315

CTSDF STATUTORY REPORT 2012 18 2 Background 6

Figure 2.7 shows the historical water demand and the future projections based on Dorrington’s different population forecasts as well as projected economic developments. While the projections from baseline year 2003 are very optimistic, not taking in consideration possible restrictions, the projections from 2006 indicate a more realistic approach since the restrictions 2004 to 2005 are considered. The low trend water demand (low economic, low population) results in a growth rate of 1,43% per year starting from the base year 2006. As the population seems to be following Determinationthe of Future high Water tr endRequirements scenario (cf. Figure 2.6), it bases the assumption, that the water15 demand for the CCT will most likely follow the high trend line (high economic, high population) from the base year 2006. For this scenario the resulting average growth Town. It was therefore considered appropriate to use 2003 as the base year for the high water rate of water requirements per year from 2006 until 2030 is estimated to 3,09%. requirement scenario and 2006 for the low water requirement scenario. (South Africa. DWAF, 2007b)

800 Actual 2003 baseline (low eco, low population) 700 2003 baseline (high eco, high population) 2006 baseline (low eco, low population) 2006 baseline (high eco, high population)

600

500

400

300

200 annum) m^3Demand(million per Water Annual 100

0

0 2 8 6 78 06 08 996 998 026 1972 1974 1976 19 198 198 1984 1986 198 1990 1992 1994 1 1 2000 2002 2004 20 20 2010 2012 2014 201 2018 2020 2022 2024 2 2028 2030 Figure 23.4.7: Historical Sensitivity Annual Waterof the Demandforecast and to theFuture choice Projections of base (South year Africa. DWAF, 2007b)

Even when considering the lowest estimation scenario (low population growth, slow economic growth) the water Trendsdemand in base for and the seasonal CCT demands is expected to exceed the available supply by 2020 (South Africa. DWAF, 2007c). With the very likely higher water 350000 0.5 requirement growth rates, a guarantied water supply will be given for an even shorter 45Mcm/a reduction - about 13% of 0.45 period300000. Additionally, studies foresee that the consequencesdomestic of demand the climate change will 46 Mcm/a stress the availability of water supply even further (Western Cape.reduction DEADP,0.4 2011;

Lumsden250000 et al., 2011). 0.35 Khayalitsha & Ikapa projects (14.5Mm3/a) 0.3 200000

2.2 Historical Supply 0.25 150000 0.2 Demand (Ml) 0.15 The 100000Western Cape Water SupplyUrban growth Systemin drier Northern Suburbs (WCWSS) & 22Mcm/a, the reduction interlinked (50% of total) network of installation of automated irrigation systems reservoirs thatRestrictions provides in early '70s the City of Cape Town with water, consists of six0.1 major

dams50000. It is designed to minimize spillage losses. The main provider to the WCWSSdemand total to seasonal of Ratio 25Mcm/a reduction (54% of total) 0.05

is the Theewaterskloof0 (TWK) dam on the Sonderend river in the Breede WMA.0 As

2 5 7 0 5 0 3 8 1 3 6 7 7 7 78 8 83 8 86 88 9 91 9 96 9 99 0 0 04 0 shown in Figure9 2.8, 9the dam9 provides9 9 53%9 of 9 the 9 bulk storage9 9 9 of 0 the WCWSS.0 0 1971 19 1973 1974 1 1976 19 1 1979 1 1981 1982 1 1984 19 1 1987 1 1989 19 1 1992 1 1994 1995 1 1997 1 1 2000 2 2002 20 2 2005 2 Year

Basedemand Seasonal demand Total demand (from Prev Nov) Ratio of Seasonal to base

Figure 3.5 Trends in seasonal demand

It must be noted that predicting future water requirements from 1999/2000 is complicated by the fact that water restrictions were imposed in 2000/2001 and then again in 2003/2004. In parallel to this, the City continued to implement water demand management initiatives. Future water requirements should be monitored and the base year for projections revised when better data is available and the imposition of water restrictions lifted.

WCWSS Reconciliation Strategy Study June 2007

2 Background 7

Figure 2.8: Storage within the WCWSS (South Africa. DWAF, 2009d)

The TWK dam is the most significant contributor not only to the WCWSS itself, but also when considering the surface water supply for the CCT. As can be seen in Table 2.2, the TWK dam provides 29,6% of the total allocation of the CCT. These facts qualify the Theewaterskloof dam as the subject for further investigations in this study.

Table 2.2: Cape Town’s allocation form the WCWSS (CCT, 2012)

Water Supply Share of Total [Mm3/a] [%] Theewaterskloof 118,0 29,6 Völvlei 70,4 17,7 Palmiet 22,5 5,6 Berg River 81,0 20,3 Wemmershoek 54,0 13,5 Steenbras 40,0 10,0 Others 12,8 3,3

The Theewaterskloof Dam is situated in the Riviersonderend catchment and surrounded by mountains to the north, west and south-west. The catchment area comprises 509 km2. The water supply to the TWK dam is given through the Sonderend river, flowing in south easterly direction, Du Toits river, flowing from the north, as well as Elandspad and Waterkloof rivers, flowing from the east (South Africa. DWAF, 2009c). The storage capacity of the TWK dam is 480,406 Mm3. The gauge located TWK sub-catchment near the TWK dam is H6R001. There are three more gauged sub-catchments in the area, namely H6H008 at the Nuweberg Forest, H6H007 at the Du Toits River and H6R002 at the Elandskloof dam (South Africa. DWAF, 2007d). The Riviersonderend catchment and the gauges can be seen in Figure 2.9.

PERIPHERAL RIVERS CATCHMENT HYDROLOGY 7

2 Background 8

Figure 2.9: Riviersonderend Catchment with its Gauges (South Africa. DWAF, 2009c)

In the high lying regions, at the border of the catchment, mainly natural vegetation can be found. Towards the centre of the catchment the topography flattens. In those lower areas, agricultural cultivation is predominant and mostly fruit farming can be found. The water for irrigation is mostly taken from several farm dams, but a small share is extracted directly from the rivers (South Africa. DWAF, 2009b). The catchment is situated in a winter rainfall region. The amount of precipitation in the Riviersonderend Catchment varies between 600 mm in the flatter regions up to 2300 mm per year in the mountains (South Africa. DWAF, 2009c). The TWK dam is connected to the Berg River and its tributaries in the neighbouring Berg WMA with a tunnel system through the Franschhoek Mountains. The Berg catchment doesn’t have sufficient storage capacity for the surplus of water during the winter, so it is channelled through the tunnel to the TWK dam. It will be released back in the summer with additional water from the Breede WMA, when the water demand

Figureexceeds 2.4: Catchment the supply calibration of the Berg gauges catchment. in the Riv Thisiersonderend export out of the TWK dam sums up 3 to 161 Mm per year. The surplus water in the summer from the Berg River into the TWK dam amounts to 25 Mm3 per year. (South Africa. DWAF, 2009a)

The total 1:50 yield of the TWK dam amounts to 241,2 Mm3 per year. This includes not only the water supply to the Berg WMA and the CCT, but also other minor water users. An agreement between the DWAF and the CCT grants the CCT a fixed lawful 3 water use allocation of 90 Mm per year for urban usage as well as a temporaryMAY 2009 irrigation surplus of 28 Mm3 per year. (South Africa. DWAF, 2007a)

2.3 Future Supply

As described in the previous section for the past, the future supply to the CCT will have to be met by the WCWSS. In 2007 a reconciliation strategy study by the DWAF was adopted to reconcile the future water demand and help decision makers on planning. It is expected that under the forecasted growth of water demand the existing WCWSS will suffice until 2014. When employing water conservation 2 Background 9 measures the water supply shortage can be delayed until 2019. To be able to continue the provision of water to the CCT, different intervention option are under investigation and feasibility studies are being conducted. The supply side interventions that could be implemented are an augmentation of surface water schemes, development of groundwater, desalination of seawater and re-use of water. In Figure 2.10 those interventions can be seen for the 2011 reference scenario, based on lowest cost per volume of water produced.

Figure 2.10: Reconciliation of Supply and Requirements for the 2011 Reference Scenario

Figure 2.10 depicts one of the possible path the future development of the WCWSS could take. The single intervention options are listed in Table 2.3. (South Africa. DWAF, 2011b)

Table 2.3: Intervention Options for the Reference Scenario

No Intervention Year of First Water Yield [Mm3/a] 1 Voëlvlei Phase 1 2019 35 2 Lourens 2021 19 3 Cape Flats Aquifer 2022 18 4 DWAF: ASR: West Coast 2023 14 5 TMG Scheme 1 2024 20 6 Raise Lower Steenbras 2025 25 7 Re-use Generic 1 2026 40 8 Re-use Generic 2 2028 40 9 Desalination 2030 80

In addition to the growing water demand, causing the need of new water sources, the possible impact of climate change on water availability needs to be taken into account. This study aims to model the climate impact on the water availability in the reservoir of the TWK dam. 2 Background 10

2.4 Historical Climate

To evaluate the consequences of climate change on the area of interest further down this study, the focus of the historical climate data lies especially in analysing the precipitation and temperature developments over the last years. The correlation of the temperature variation and precipitation distribution over one year is shown in Figure 2.11, exemplarily for the year 2003. It is apparent that the precipitation is lowest when the mean temperatures are the highest during the summer months. In contrast, during the winter months, when the temperatures are much lower the precipitation increases significantly. The figure therefore clearly shows the dynamics of a winter rainfall region. This correlation is important to understand the mismatch of water supply and demand for the City of Cape Town (cf. Figure 2.1). While the water demand is highest during the hotter period, there is not always enough water that can be supplied through precipitation to cover this demand. For this reason the water supply system, which is partially analysed in this study, is of essential importance.

Figure 2.11: Precipitation at the TWK Dam and Temperature of the CCT Region for the Year 2003

The Riviersonderend catchment is located in a winter rainfall region. The annual precipitation at this location varies between 2300 mm in the mountain to 600 mm in the flat land. As being typically for a region where the precipitation takes place during the winter months, the evaporation rates are rather high in the summer months with 230 to 250 mm per month compared to an evaporation rate of 40 to 50 mm per month during the winter months. (South Africa. DWAF, 2009c) The historical precipitation, measured at the gauge station of the TWK dam, for the period from 1970 to 2004 can be seen in Figure 2.12. Hardly any visible variations can be found over the last 30 years. A decrease of less than 1‰ can be stated. 2 Background 11

Figure 2.12: Historical Precipitation at the Theewaterskloof Dam for the Period 1970 to 2004

In Figure 2.13 the historical average daily temperatures of different weather stations in the location of interest are shown (South African Weather Service, 2013). The temperature measurements of all stations follow the same trend and do not deviate more than 6% from the mean value. The trend line in Figure 2.13 indicates an increase of the mean temperatures over the last 30 years. In this period the annual average temperatures rose by 3 °C.

Figure 2.13: Historical Temperature Measurements for the Period 1970 to 2004

2 Background 12

2.5 Future Climate

One goal of this study is to investigate the future climate and to analyse the impact of climate change on its development. In Section 3.2 of this report the forecast tool to project different future climate scenarios will be introduced. As already demonstrated in the historical review of the climate data, a tendency of increased temperatures in the Western Cape region can be observed. The temperature increase of 3°C over the period from 1970 to 2004 is a strong indicator that there will be a rising impact caused by climate change. Other studies confirm this trend and foresee additional impacts due to climate change for Cape Town and the Western Cape. On a global level the IPCC reports that the precipitation cycles will also alter due to climate change. This represents a challenge for the supply of fresh water resources (Bates et al., 2008). On a local level a study additionally points out that the climate change will impact the water availability in south-west region of South Africa (Schulze, 2011). In the Water Service Development Plan for CCT the most likely impacts of the climate change for CCT are listed as an increase in the annual mean temperature and a decrease of precipitation (CCT, 2012). This is especially the case in the winter season wherefore the stored water resources in the region will be diminished. The increased impact of climate change will possibly lead to more frequent and intensive extreme weather occurrences. 3 Methodology 13

3 Methodology

The methodology applied in this study can be divided into the categories of data collection, data generation, data processing and data evaluation. A flowchart demonstrating the steps of this process is given in Figure 3.1. The data collection process is conducted through a thorough literature review focussing on the required parameters needed to model the water system of the Riviersonderend catchment. This includes providing information on the historical climate, supply side and demand side as well as future demand requirements. The future supply side will be provided through future climate files. The latter will be generated in the data generation process. Here the stochastic generator of daily weather data MarkSim will be used. In the next step all information on water demand, water supply and climate will be handled in the data processing step. With the help of the Water Evaluation And Planning system simulation software (WEAP) the water system of the Riviersonderend catchment can be modelled. As a final step the results form the WEAP model can be evaluated. Hereby special focus will lie on the development of the climate, the water availability in the catchment and the coverage of the water demand of the City of Cape Town. In the next sections the simulation software WEAP and the weather generator MarkSim will be described and their application explained.

Figure 3.1: Flowchart of Methodological Approach

3.1 WEAP

To model the Riviersonderend catchment, situated in the Breede WMA, the Water Evaluation And Planning system simulation software (WEAP) will be used. This simulation software was developed by the Stockholm Environment Institute to support experienced water resource planners. The distribution of limited water 3 Methodology 14 resources between agriculture, urban demand and nature is a challenging task of water management. WEAP includes different aspects and needs of the above- mentioned groups to support a suitable and sustainable water resource planning. The simulation software enables one to recreate a local water system based on geographical, climatological as well as water availability and consumption data. Different scenarios can be developed to foresee the changes in water availability. For this study a representative model of the Riviersonderend catchment will be built to estimate the inflow to the TWK dam. This inflow represents the available supply and it can be evaluated if the lawful fixed water allocation to the CCT of 90 Mm3 per year can also be covered in the future. While historical data are available for the period 1970-2004, future projections will reach 2050. As the first step the catchment area of the Riviersonderend catchment is defined as shown in Figure 2.9. Afterwards the catchment and its major components are assembled as given in Figure 3.2. For this study the components are the river Sonderend, receiving its head flow from the sub-catchment H6H008 Sonderend, three other sub-catchments (H6H007 Du Toits, H6R002 Elands, H6R001 TWK), the reservoir of the TWK dam, the demand CCT, the demand Berg WMA and Others, as well as the gauge H6R001 comparing the naturalized flow to the simulated stream flow results from WEAP. Third, historical data for the period 1970 to 2004 are integrated according to the requirements of the components. As a fourth step different climate scenarios generated in MarkSim are created for the target region and the produced data sets are also integrated into WEAP. The data input and its integration into the model are described in more detail in the following sections.

Figure 3.2: WEAP Model of Riviersonderend Catchment 3.1.1 Sub-Catchment Data For the gauged sub-catchments H6H008 Sonderend, H6H007 Du Toits, H6R002 Elands and H6R001 TWK information on the area, latitude, precipitation and temperature have to be integrated. Additionally information on the evaporation and the crop coefficient Kc have to be provided. The area, latitude and mean annual precipitation (MAP) are given in The Assessment of Water Availability in the Berg Catchment Report (BWAAS) and are summarized in Table 3.1 (South Africa. DWAF, 2009c). An exception is the latitude for 3 Methodology 15 the gauge station H6R001 Theewaterskloof taken from MarkSim™ DSSAT weather file generator (International Livestock Research Institute, 2010-2011).

Table 3.1: Gauged Sub-Catchments in Riviersonderend Catchment

Gauge Station Area Latitude MAP [km2] [°] [mm] H6H008 Sonderend 39,06 -34,062222 2320 H6H007 Du Toits 46,02 -33,938611 1455 H6R002 Elands 49,90 -33,964722 1042 H6R001 Theewaterskloof 374,20 -34,078056 1099

The monthly precipitation data are also taken from The Assessment of Water Availability in the Berg Catchment Report (South Africa. DWAF, 2009c). As in the report the figures are presented in percentage of mean annual precipitation (%MAP), for WEAP these need to be converted into mm per month with the given MAP values of each gauge. The South African Weather Service has six weather stations with available historical temperature data in the proximities of the area of interest. These are Malmesbury, Worcester, Paarl, Molento Reservoir, Strand and Cape Point and their location can be found in Figure 3.3. As there are no exact data available for the gauge stations in the Riviersonderend catchment, the average of the regional data will be considered in this case. An analysis of all data sets from the different weather stations indicates that the temperatures follow the same trend without much variation (less than 7%). This supports the decision to take the averaged regional values for the sub- catchments being analysed. Therefore daily averages between maximum and minimum temperatures of the six weather stations are included into WEAP.

Figure 3.3: Weather Stations near the City of Cape Town 3 Methodology 16

The chosen method for determining the internal sub-catchment water demand is the rainfall runoff method (soil moisture model). This model is taken, because no evapotranspiration data was made available for the sub-catchments under investigation. Given the latitude of the site, WEAP is able to determine the evapotranspiration with the help of the specified precipitation and temperature data.

The crop coefficient Kc takes into account certain properties of the plants on the surface and is used to predict the evapotranspiration of the vegetation. Such properties include the plant type, plant variety and the stage of development of the plant. Also the resistance to transpiration, crop height, roughness, reflection, ground cover and rooting characteristics count as properties and will be reflected in the Kc value (Allen et al., 1998). Since no Kc values could be found for the sub-catchment, they are dealt within the key-assumptions section and are not directly integrated into the sub-catchments. Three exemplary Kc values, forest (conifer tree) (Kc=1), vineyard (Kc=0,7) and fruit tree (apples, cherries or pears) (Kc=1,2), are created in the key- assumptions (Allen et al., 1998). It is now possible to access the desired value over the branches in the input field of the Kc value for each catchment. This way allows more flexibility to add or change the Kc values. It is thereby possible to analyse the influence of different types of cultivation on the results. 3.1.2 River Data The Sonderend River flows in a south-easterly direction. It has its source in the Hottentots Holland mountain range. In the model the head flow of the river is therefore given through the inflow from sub-catchment H6H008 Sonderend presented in Section 3.1.1. 3.1.3 Reservoir Data For the reservoir information on the storage capacity, reservoir elevation, net evaporation and the surface area are needed. The storage capacity of the Theewaterskloof dam is 480,406 Mm3 (South Africa. DWAF, 2011a). The initial storage volume for the simulation amounts to 358,83 Mm3. This is the end month volume for December 1969, with the simulation starting in the year 1970. To create the volume-elevation curve, the monthly historical observed volume and the reservoir elevation are taken from the BWAAS Report No. 8 on System Analysis Status Report (South Africa. DWAF, 2008). The data for the net evaporation from the reservoir are also taken from the BWAAS Report No. 8 on System Analysis Status Report (South Africa. DWAF, 2008). As the data are given in m3 per second, for WEAP they needed to be converted into mm per month by aid of the reservoir surface area of 50,59 km2 (South Africa. DWAF, 2011a). In order to achieve more realistic results the future net evaporation is integrated into WEAP by means of an average of the historical net evaporation from 1970 until 2004. 3.1.4 Demand CCT Data As input parameters for the demand CCT for WEAP the annual water use rate and the monthly variation are required. The historical water use rate of the CCT from the TWK dam is given through the lawful fixed water allocation of 90 Mm3 per year (South Africa. DWAF, 2007a). This value will also be considered as fixed for the future, as it is unlikely that the dam can suddenly provide a different amount of water to the CCT. The growing total demand of the CCT will need to be met by other suppliers and sources as presented in Section 2.3. The monthly variation of the fixed annual water use rate is deduced from the monthly water demand of the CCT. The historical water demand of CCT is provided in the 3 Methodology 17 thesis work from Ogutu at Tshwane University of Technology (Ogutu, 2007). In order to achieve more realistic results the future monthly variation is integrated by means of an average of the historical monthly variations from 1971 until 2004. 3.1.5 Demand Berg WMA and Others Data Apart from the CCT demand the Berg WMA Demand is the second main water user of TWK dam. Additionally, the other allocations of the TWK dam to minor water users are included in the model. For the integration of the Berg WMA and Others demand data, the annual water use rate and its monthly variation are required. The tunnel system connecting the Berg River and other smaller water users to the TWK dam is an important component of the interlinked WCWSS. For this reason it is schematically included in the WEAP model. In the Reconciliation Strategy Study by the DWAF, data on the water allocations of the TWK dam are available (South Africa. DWAF, 2007a). As described in Chapter 2.2 the annual water allocation sums up to 241,2 Mm3 per year, of which 151,2 Mm3 per year are supplied to the Berg WMA and the other minor water users. As there was no monthly variation made available for the Berg WMA and Others demand, the monthly variation of the annual water use rate of the CCT demand is taken and integrated into WEAP. This is done in order to obtain more realistic results. 3.1.6 Gauge Data In general gauges are useful to built more detailed simulations. In this study, the gauge just upstream of the TWK reservoir provides the basis to verify the reliability of the WEAP model against real data. At the gauge the cumulative naturalised flows of the sub-catchment H6H008 Sonderend, H6H007 Du Toits, H6R002 Elands and H6R001 TWK dam are to be found. These cumulative flow values can be found in the BWAAS (South Africa. DWAF, 2009c).

3.2 MarkSim

To model the future climate for the Riviersonderend catchment the weather generator MarkSim provided by the International Livestock Research Institute is used. The tool is available on the International Center for Tropical Agriculture website (International Livestock Research Institute, 2010-2011). It is designed to model local daily weather data based on downscaled global climate model outputs for agricultural modelling applications. MarkSim is a third order Markov weather generator using a combination of different downscaling methods. Downscaling implies considering esoteric results generated by a General Circulation Model (GCM) in relation to existing locations somewhere in the world (Jones & Thornton, 2013). The GCMs were not developed to model the weather itself, but to deliver an average temperature of a certain cell resolution in the atmosphere. In these models the precipitation estimations can be determined with the help of a latent heat balance. For weather estimations of local climate, information on the topography, storms, fronts, local and orogenic effects are necessary. Having assembled climate anomalies from historical local climate records, the GCMs can be downscaled to a higher, site-specific resolution. MarkSim uses a combination of stochastic and hierarchical downscaling as well as climate typing methods to produce daily data for temperatures and precipitation. (Jones & Thornton, 2013) 3.2.1 Future Emission Scenarios The data sets used from the General Circulation Models include different scenarios developed in the Special Report on Emission Scenarios (SRES) published by the Intergovernmental Panel on Climate Change (IPCC). There are four qualitative 3 Methodology 18 scenario families, namely A1, A2, B1 and B2, which include key indicators for the upcoming development, emphasizing on different driving forces. The focus of each scenario differs, resulting in a higher economic focus for scenario families A1 and A2 and a more environmental focus for the families B1 and B2. The scenario families also describe different international integration of the world. On the one hand scenario families A1 and B1 have a homogenous view on the development of the world, i.e. assuming that globalisation will be of key importance. On the other hand scenario families A2 and B2 describe a more heterogeneous world in which regionalisation is most likely to be found. MarkSim includes the scenarios A1B, A2 and B1. The scenario A1 specifically is characterized by a fast economic growth as well as a fast development and an introduction of efficient and new technology. In consequence this leads to a global integration due to a decline in regional differences. One of the scenario groups of A1 is a balanced importance across all energy technologies (A1B). The foreseen increase in the global mean temperature for this scenario lies between 1,4 and 6,4 °C. The scenario A2 emphasizes on regionally orientated economic development. It describes a development with a focus on preservation of local identities and self-reliance. The predicted global mean temperature increases by 2,0 to 5,4 °C. Scenario B1 instead will have a focus on global environmental sustainability. It describes a world dominated by a service and information economy that reduces the intensity of material use and introduces clean and resource-efficient technologies. Additionally to those sustainable measurements there will be no climate initiatives, resulting in a rise of the global mean temperature of 1,1 to 2,9 °C. (IPCC Working Group III, 2000) 3.2.2 Application As a first step the locations of the gauge stations need to be selected. It has been found that a selected location has a large impact on the generated outcomes of the data files. This phenomenon arises especially when looking at larger areas containing both mountainous and flat land regions. In these cases, the amount of precipitation and the temperatures can vary significantly. In general it can be said that the amount of precipitation is higher and the temperatures are lower in the mountains compared to lower laying regions. Therefore, additionally to the sub- catchments’ gauge stations Sonderend, Du Toits and Elands a second location in the mountains of the sub-catchments is chosen. This is done to provide more realistic data representing the entire sub-catchment. As the sub-catchment Theewaterskloof is significantly larger compared to the other sub-catchments, three additional locations to the gauge location are chosen. The coordinates for latitude and longitude selected for the calculations as well as the corresponding altitude can be found in Table E.1 in the Appendix (South Africa. DWAF, 2009c; International Livestock Research Institute, 2010-2011). Next, the desired General Circulation Model has to be chosen. The web-based tool includes six different GCMs as well as an average climatology of the same six CGMs. The developing institution and their corresponding GCMs can be found in Table 3.2. 3 Methodology 19

Table 3.2: General Circulation Models used in MarkSim

Model Name (Date) Institution Country BCCR_BCM2.0 (2005) Bjerknes Centre for Climate Research Norway (University of Bergen) CNRM-CM3 (2004) Centre National de Recherches France Météorologiques CSIRO-Mk3.5 (2005) Commonwealth Scientific and Industrial Australia Research Organisation ECHam5 (2005) Max Planck Institute for Meteorology Germany

INM-CM3_0 (2004) Institute for Numerical Mathematics Russia

MIROC3.2 (medres) (2004) Center for Climate System Research Japan (University of Tokyo), National Institute for Environmental Studies and Frontier Research Center for Global Change

In this study the average of the above-mentioned six models is considered to generate the weather data. This allows the prediction to become more reliable, but being limited to those six models (International Livestock Research Institute, 2010-2011). As a third option it is possible to choose between the different SRES scenarios A1B (medium emission scenario), A2 and B1. Here the scenarios A2, as high emission scenario, and B1, as low emission scenario, will be taken into consideration and integrated into WEAP for further investigation. The daily weather data are generated for an averaged ten year time period extending 5 years to either side of the selected simulation year. As this report intends to investigate the occurrences of climate change until the year 2050, the time slices 2010, 2020, 2030, 2040 and 2050 are taken into account. In order to increase the reliability of the climate files five random replications for each respective location, scenario and selected year are produced. This results in a total of 300 climate files containing daily precipitation as well as minimum and maximum temperatures. The average temperature is determined by the arithmetic mean of maximum and minimum temperatures. The random replications are averaged for each location, scenario and year. The integration of the generated weather data into WEAP is done in the same way as for the historical climate data considering the three different scenarios (cf. Section 3.1.1). For a better comparison, an additional baseline scenario is added into WEAP using monthly averaged values of the historical data. As described in Section 2.4, the average precipitation over the period from 1970 until 2004 was almost constant. For this reason the monthly averages of the entire period will be used for the base scenario to achieve the most realistic results. The temperatures on the other hand, showed a clear tendency of increase within this period. Due to this fact, only the monthly averages of the last five years are chosen from the historical data to built the baseline scenario.

3.3 Limitations

When reviewing the limitations of the Riviersonderend catchment model it is possible to distinguish between limitations imposed by WEAP and MarkSim. 3 Methodology 20

In general the precision of the WEAP model is dependent on the amount of available information and their detailedness. In the case of this study two main limitations can be highlighted. As described in Section 3.1.1, the temperature measurements are not available at the actual gauge stations, but only at selected weather stations in the region of interest. The average temperature of six weather stations is taken for the analysis, resulting in a maximum deviation of less than 7% from the average. The available data for the WCWSS did not yet include the newly built Berg River Dam. This could lead to significant changes in the Berg WMA and Others’ demand. When considering the limitations of MarkSim, especially the uncertainties with downscaling need to be highlighted. First of all the GCMs themselves involve a lot of uncertainty, especially regarding precipitation forecasts. Additionally, the understanding of local influences from climate change is still limited. Especially the unclear development of the near future (3 to 20 years from now) poses a significant uncertainty on the future climate change. (Jones & Thornton, 2013) The analysis of the randomized replications of the climate files generated in MarkSim depicted significant variations in the amount of precipitation per year (cf. Figure 3.4). For this reason the five runs are averaged in order to account for these discrepancies.

Figure 3.4: Precipitation Runs at Sonderend Mountain Location for Scenario A2 from MarkSim

4 Results and Discussion 21

4 Results and Discussion

In the following sections the generated future climate data and the simulation results from the water supply provided by WEAP will be presented. The main target is to investigate the potential impacts of climate change on the reservoir. Hereby a special focus will lie on the projected development of precipitation and temperatures and how they will influence the future water availability in the Riviersonderend catchment.

4.1 Future Climate

In this chapter the future climate data, generated in MarkSim, will be presented. Both the data for temperatures and precipitation will be analysed for different future scenarios. In Figure 4.1 the average monthly temperatures for the sub-catchment H6R001 Theewaterskloof for the time period between 1970 and 2050 are given. From 2005 onwards, the Historical Baseline, the generated scenarios A2 and B1 are to be distinguished. The historical development has been described already in Section 2.4 reaching a mean annual temperature of 18 °C in the year 2004. For the year 2005, the mean annual temperatures provided by MarkSim start at only 15,4 °C. By the end of the simulation period in 2050 temperatures between 16 °C for scenario B1 and 16,5 °C for scenario A2 are reached.

Figure 4.1: Monthly Temperature Trend for Sub-Catchment TWK

The trend of increasing temperatures in the generated climate files consequently is consistent with the historical trend. However, a drastic drop of 2,6 °C is noticeable when comparing the Historical Baseline with the generated files from MarkSim. A similar occurrence is observable for the generated annual precipitation data depicted in Figure 4.2. While the Historical Baseline delivers an average precipitation of around 630 Mm3 per year, the two generated scenarios A2 and B1 result in much lower amounts of annual precipitation, around 413 and 412 Mm3 respectively. 4 Results and Discussion 22

Figure 4.2: Annual Precipitation Trend for Riviersonderend Catchment

As described above for the temperatures in Figure 4.1 and for the precipitation in Figure 4.2, the generated data for scenarios A2 and B1 do not coincide with the historical values. From the year 2004, where the historical climate data end, to the year 2005, where the generated data start, a drastic drop in both temperatures and precipitation for the scenarios A2 and B1 occurs. Even though changes of the trend are to be expected in the future, this drop indicates that there is a discrepancy between the real historical values and those simulated in MarkSim. A reason for this mismatch could be that some local geographical conditions could not be modelled. As compensation the introduction of a correcting factor for the generated climate data in MarkSim is needed. This will be described in the following section more closely. 4.1.1 Adapted Future Climate As explained above, the generated data from MarkSim need to be corrected so they match with the historical trend. For this adjustment the generated files are compared to the historical data in order to assess a constant correcting factor for each sub- catchment. The corresponding time periods for the comparison are the same as for the Historical Baseline (HB) scenario described in 3.2.2. For the precipitation correction factor therefore all the historical data are taken for comparison, while for the temperature correction factor only the values of the last five years are considered. Next, the average values for those time periods are compared with the average of the first time period (2010) of the MarkSim data to determine the correcting factor. The calculated correction factors can be found in Table 4.1. With the help of this approach the trends of the generated data are preserved.

Table 4.1: Correcting Factors for MarkSim

Catchment Temperature Precipitation H6H008 Sonderend 1,32 1,34 H6H007 Du Toits 1,27 1,76 H6R002 Elands 1,28 2,46 H6R001 Theewaterskloof 1,31 1,41

Including these factors into the WEAP model for each corresponding catchment, the adjusted generated temperature and precipitation trends can be seen in Figure 4.3 and Figure 4.5. 4 Results and Discussion 23

In Figure 4.3 the historical and adapted future averaged monthly temperatures of the sub-catchment H6R001 Theewaterskloof are exemplarily shown. It can be noticed that the mean temperature is increasing for both scenarios A2 and B1 until the year 2050. In the period from 2005 until 2050 the annual average temperature rose by 1,8 °C for scenario A2 and by 1,5 °C for scenario B1. The different changes for the scenarios A2 and B1 are consistent with the expected trends. The higher increase in temperatures is observed for the high emission scenario A2. For scenario B1, the lower temperature increase coincides with this low emission scenario. When compared to the historical data, the generated future data show a more gently increase of the annual average temperature. This trend does not coincide perfectly with the expected outcome of a more rapid increase in mean temperatures due to climate change over the upcoming years. Possibly, the reliability of the generated data by MarkSim has to be questioned and could be seen as a cause for this inaccurate trend. On the other hand, the span between the temperatures has increased over the last years, starting in the 90’s, showing as well a continuous trend for the future. This is consistent with the trend of having more extreme variations in seasonal weather conditions enforced by climate change.

Figure 4.3: Monthly Temperature Trend with Adapted Data for Sub-Catchment TWK

To highlight the above-described impacts of climate change, Figure 4.4 depicts the monthly temperatures over the year, averaged for the time period between 2005 and 2050. Compared to the HB scenario, the temperatures for scenarios A2 and B1 are more extreme. On the one hand, in the summer month January the temperatures for scenario A2 and B1 are roughly 2,5 °C higher compared to scenario HB. On the other hand, in the winter month June the temperatures for scenario A2 and B1 are about 1°C lower compared to scenario HB. 4 Results and Discussion 24

Figure 4.4: Monthly Averaged Temperatures for the Period 2005-2050

The historical and adapted future precipitation can be seen in Figure 4.5. The figure depicts the sum of the precipitation of all sub-catchments combined for the three different simulated scenarios. As described in Section 2.4, the historical annual average precipitation did not change mentionable over the period from 1970 to 2004.

Figure 4.5: Annual Precipitation Trend with Adapted Data for Riviersonderend Catchment

In the period of the generated future data, slightly visible variations in precipitation can be observed. In order to stress these changes, Figure 4.6 focuses only on the time period between the year 2005 and 2050. Now, a decrease of almost 3% compared to the Historical Baseline scenario can be observed. More specifically, a decrease in precipitation of 20,26 Mm3 for scenario A2 and of 17,34 Mm3 for scenario B1 is visible. These variations are consistent due to the fact that a higher reduction in precipitation is expected for the high emission scenario A2 and a lower reduction for the low emission scenario B1. 4 Results and Discussion 25

Figure 4.6: Annual Precipitation Trend for the Period 2005-2050 for Riviersonderend Catchment

As described in Chapter 2.4 the annual precipitation lies between 305 Mm3 for the low laying regions and 1323 Mm3 for mountainous areas in the Riviersonderend catchment. Considering the sum of all sub-catchments, the amounts of annual precipitation vary between 610,72 Mm3 per year for scenario A2 and 629,63 Mm3 per year for the HB scenario (cf. Figure 4.6) and are therefore in the above-mentioned range. The detailed amounts of annual precipitation simulated for each sub- catchment and scenario are depicted in Figure E.1 in the appendix. The Riviersonderend catchment is located in a winter rainfall region. This development is also visible in the generated climate data from MarkSim. In Figure 4.7 the monthly precipitation over the year is averaged for the time period between 2005 and 2050.

Figure 4.7: Monthly Averaged Precipitation for the Period 2005-2050 for Riviersonderend Catchment

For all scenarios the largest part of the annual precipitation takes place during late fall and winter (May until August). It becomes apparent that there is a change in the trend. Compared to the Historical Baseline scenario having most precipitation in June and July, the precipitation peak for scenarios A2 and B1 can be found in May. Additionally, summer and autumn will be a little less dry for these two scenarios. In June and July, the precipitation is foreseen to have a strong decrease for both scenarios A2 and B1, before it levels out again with the historical data in August. It can be pointed out, that the extremes in scenario B1 are reduced compared to 4 Results and Discussion 26 scenario A2. This reduction is consistent with the scientific knowledge of increasing extreme weather conditions when having higher emissions. As outlined in Section 2.5 studies foresee that the annual mean temperature is likely to increase and the precipitation likely to decrease in the target region (CCT, 2012). As seen above, both the original and adapted climate files generated in the local downscaling tool MarkSim confirm these trends.

4.2 Water Supply and Demand

In this chapter, the water supply and demand simulated in WEAP will be presented. On the water supply side the focus will lie on the runoff in the Riviersonderend catchment and the storage volume of the TWK reservoir. The water demand side will focus on the demand coverage. Additionally, inflow and outflow characteristics of the supply and demand side will be investigated. In Figure 4.8 the simulated historical and future annual runoff flow of the entire Riviersonderend catchment is depicted. For the future runoff flow between three different simulated scenarios can be distinguished. The runoff flow follows the same pattern as the precipitation characteristic of the catchment (cf. Figure 4.5). Comparing these two figures it can be seen, that between 50 and 65% of the precipitation is transferred to runoff flows. On average 56% of the precipitation is available to be used as water supply. Here, as well as for the precipitation, it can be observed that the runoff decreases slightly in the future for both scenarios A2 and B1.

Figure 4.8: Annual Runoff Flow for Riviersonderend Catchment

Here again, in order to stress the future changes in the different scenarios, Figure 4.9 focuses only on the time period between the year 2005 and 2050. In this period, a decrease in runoff compared to the Historical Baseline scenario of 19,09 Mm3 for scenario A2 and of 17,02 Mm3 for scenario B1 is to be noticed. This corresponds to a reduction of 5,39% for scenario A2 and 4,80% for scenario B1. As described in Section 4.1.1, this reduction in precipitation for both scenarios is consistent with the knowledge about climate change. As indicated before, also the reduction for scenario A2 being larger than the one of scenario B1 coincides with the theory. 4 Results and Discussion 27

Figure 4.9: Annual Runoff Flow for the Period 2005-2050 for Riviersonderend Catchment

The historical storage volume of the TWK reservoir is depicted in Figure 4.10. The figure shows a comparison of the historical storage volume data provided by the DWAF and the simulated data of WEAP for the period of 1970 until 2004. It can be observed that both data sets follow a similar course, with the generated storage volume data sometimes lying a little higher. When the storage capacity of the TWK dam of 480 Mm3 is reached, the surplus water is send downstream.

Figure 4.10: Comparison of Storage Volume of the TWK Dam for the Period 1970-2004

In addition to the historical storage volume, Figure 4.11 also shows the future development of the storage volume for the different scenarios. The simulation shows that from the year 2004 on there is no decrease in annual storage volume anymore. It is constant and only follows seasonal variations. The seasonal oscillation for scenario A2 and scenario B1 are alike for the storage volume, however the HB scenario shows a larger oscillation. To the top the oscillation is limited by the storage capacity. 4 Results and Discussion 28

Figure 4.11: Monthly Storage Volume of the TWK Dam

The seasonal variation of the storage volume for the different scenarios is depicted in Figure 4.12 for the time period 2005 until 2050 in more detail. The values over zero represent an increase in storage volume and the values lower than zero stand for a decrease in storage volume. The curve fits roughly to the trend given through precipitation (cf. Figure 4.7). As typical for this rainfall area the major water storage increase takes place during the winter season. While the storage peak for the HB scenario is in June, for scenario A2 and scenario B1 it is in May. It can be observed that the increase of the two SRES scenarios is much steeper compared to the Historical Baseline. This is consistent with the characteristics of climate change of having stronger extreme weather conditions. After the peak in May the storage volume continuously decreases for scenario A2 and B1. In January, when the precipitation is very low, the largest amount of water is extracted from the stored volume of the TWK dam.

Figure 4.12: Monthly Averaged Storage Volume for the Period 2005-2050

As presented in Section 2.5, a study conducted by the CCT highlights that in the future the stored water resources in the region are expected to diminish (CCT, 2012). The simulated water availability of this region in WEAP confirms this trend. Although the storage volume of the TWK reservoir is expected to stay intact over the upcoming years, the water resources in the region are expected to diminish. This is clearly visible when analysing the decrease in runoff flow in the Riviersonderend catchment for the different scenarios. Even if this decrease may not be significant in the storage 4 Results and Discussion 29 volume of the dam, it can be visualized when examining the downstream flow of the river. A representation of the inflows and outflows of the TWK dam for scenario A2 is given in Figure 4.13. Depicted are the runoff flow represented as inflow from upstream, the outflow to the CCT, the outflow to the Berg WMA and Others, the net evaporation and the outflow to downstream of the TWK dam. The runoff flow develops as described above. It represents the available water of the Riviersonderend catchment, which is not stored. The outflow to the CCT is, due to a fixed demand od 90 Mm3 per year, constant over the simulation years. The same applies for the outflow to the Berg WMA and Others, with a constant allocation of 151,2 Mm3 per year from the TWK dam. The net evaporation is a function of the evaporation from and the precipitation onto the reservoir surface. The net evaporation follows the trend of the storage volume. The less water is stored in the reservoir, the less water evaporates and vice versa. The above discussed decrease in outflow to downstream is also visible in Figure 4.13. When there is a lot of precipitation and the reservoir’s maximum capacity is reached all surplus water will be send downstream the Sonderend river. For the future years, as the runoff decreases, the surplus water decreases. This leads to the noticeable reduction in water resources in the Riviersonderend catchment.

Figure 4.13: TWK Dam’s Inflows and Outflows for Scenario A2

For scenario B1, shown in Figure E.2 in the appendix, hardly any differences to scenario A1 can be identified. Only a little lower reduction in runoff can be found, leading to an increase of the outflow to downstream. For scenario HB, relying only on historical data, no changes are to be seen in future developments (cf. Figure E.3). The annual historical outflow to downstream is given through the cumulative flows from the gauges provided by the DWAF and then subtracting the annual water demand of 241,2 Mm3. When comparing the historical downstream outflow to the simulated outflow of WEAP, it becomes apparent that the simulated flow is too small. The average historical flow to downstream results in 123,04 Mm3 per year. The generated annual outflow to downstream for scenario HB is calculated to 75,7 Mm3, corresponding to an average offset of 38,48%. Even though the runoff flows were calibrated to match the data provided by the DWAF, this mismatch could indicate inaccuracies between the model and the historical data. The trends however are preserved, showing a decrease in outflow to downstream in the upcoming years. 4 Results and Discussion 30

This finding again supports the conclusion that there is a decrease in water availability for the Riviersonderend catchment. The supply-demand coverage curve of the simulated historical and future development for the different scenarios can be seen in Figure 4.14. The supply represents the annual available amount of water in the Riviersonderend catchment, apart from what is stored in the reservoir. It is calculated by subtracting the net annual evaporation from the inflows of the sub-catchments. The demand compiles the CCT demand and the Berg and Others demand. Seasonal variations are neglected in this representation. It can be observed in Figure 4.14 that for the future the available amount of water per year in the catchment can always meet the demand, even though it decreases for both scenarios A2 and B1. In the past, the stored water of the TWK dam was needed occasionally to cover the unmet demand.

Figure 4.14: Supply-Demand-Coverage for Riviersonderend Catchment

Figure 4.15 illustrates the above discussed demand coverage in percent of time exceeded. Here again only the annual available amount of water in the catchment is analysed, not including seasonality and the water stored in the reservoir. The water available on an annual rate in the catchment alone can cover the demand 89% of the time period 1970 until 2050. The remainder is balanced through the storage volume of the TWK dam. 4 Results and Discussion 31

Figure 4.15: Percent of Time Exceeded Demand Coverage

When looking at the total available water in the Riviersonderend catchment, including the storage water of the TWK dam, the annual demand of 241,2Mm3 can be delivered every month for all the years under investigation. The reliability of the supply and the demand side’s coverage is consequently 100% for all scenarios. The water shortages mentioned earlier in Chapter 2.1.1, leading to water restriction for the CCT, were only necessary when looking at the total demand of the CCT and the other supply sites. When reviewing the storage volume for the TKW reservoir (cf. Figure 4.10), a reduction prior to the restriction periods can be observed. For the TWK dam this could have been a consequence of reduced precipitation during that phase. The requested fixed demand could however always be covered by the TWK dam. 4.2.1 Sensitivity Analysis of the CCT Demand In order to determine possible future variations of the demonstrated results alterations of the demand side are taken into consideration. As described in Section 2.1.2 the total demand of the CCT is very likely to increase in the upcoming years. It is therefore probable that the fixed lawful supply from the TWK dam will be adapted in order to at least partially meet the growing demand of the CCT. Assuming different growth rates a sensitivity analysis is conducted to determine the year from when the water availability will most likely not suffice any longer. For the following analysis the allocation from the TWK dam to the CCT is not considered fixed, but the demand growth of the CCT is taken into account. The water requirement growth rates, foreseen by the DWAF and described in Section 2.1.2, are taken as the two extremes. The trends are once more depicted in Figure 4.16. On the one hand there is the low growth rate of 1,43% per year, standing for low economic and low population growth. The high growth rate of 3,09% per year on the other hand is representing a high trend of these two indicators. The water demand of the Berg WMA and Others is considered constant throughout the sensitivity analysis.

4 Results and Discussion 32

Figure 4.16: Low and High Growth Rates for the CCT Demand

When looking at the simulated storage volume of the TWK dam in Figure 4.17, considering the low demand growth rate, it can be seen, that the oscillations of the storage volume increase in comparison to the fixed demand findings (cf. Figure 4.11). Because of the increasing demand more water needs to be taken from the reservoir and vice versa more water is needed to refill the reservoir. This variation consequently reduces the outflow to downstream. For scenario A2 and B1 the storage volume is experiencing a decrease starting from the year 2040 onwards. It means that from this year on not enough annual water is available in the catchment to cover the increasing demand.

Figure 4.17: Monthly Storage Volume of the TWK Dam for the Low Growth Rate

When considering the high demand trend, presented in Figure 4.18, the oscillations of the storage volume of the TWK dam increase even faster. A drastic drop in the storage volume can be observed starting around the year 2025. By 2035, all the stored water of the reservoir is taken in the summer period to cover the fast growing demand of the CCT. The reservoir is entirely empty during these months and not enough water is available in the catchment. During the winter a continuously reduced increase in storage volume can be found. The refilling of the dam however does not exceed a storage volume of 100 Mm3 for scenario A2 and B1. Generally, when considering the high demand growth rate it can be observed that the developments for both SRES scenarios are more drastic than the Historical Baseline scenario. 4 Results and Discussion 33

Figure 4.18: Monthly Storage Volume of the TWK Dam for the High Growth Rate

The supply-demand coverage curve of the varied growing demand for the CCT can be found in Figure 4.19 and Figure 4.20. When looking at the supply-demand coverage in Figure 4.19 for the low growth rate one can see that the increasing CCT demand together with the fixed Berg WMA and Others demand surpasses the available supply by the year 2040 for scenario A2 and B1. For scenario HB the available supply is exceeded only in 2048. It can therefore be concluded that the impact of climate change on the water supply is represented in the reduction of 8 years of water availability when comparing the Historical Baseline and the SRES scenarios.

Figure 4.19: Supply-Demand-Coverage for Riviersonderend Catchment for Low Growth Rate

The supply-demand coverage curve for the high growth rate is depicted in Figure 4.20. Here the rapid increasing water demand of the CCT exceeds the available supply by the year 2023 for scenario A2, by 2024 for scenario B1 and for scenario HB in 2025. The impacts of climate change are not as apparent as it was the case for the low demand growth trend. Due to the rapid growth of the demand the variations between the SRES scenarios and scenario HB are less dominant in the near future. It has to be noticed that the supply appears to be increasing after the demand surpasses the supply from 2025 onwards. The reason for this is that there is less 4 Results and Discussion 34 water in the reservoir and the net evaporation from the reservoir surface is significantly reduced. Consequently the losses are lower and more water is available. This is because the available water supply is calculated by subtracting the net evaporation from the runoff flows. After the year 2035 the supply is represented only by the runoff flow, as there is no net evaporation anymore.

Figure 4.20: Supply-Demand-Coverage for Riviersonderend Catchment for Low Growth Rate

For the simulations there is no minimum storage volume set for the TWK dam. In reality, when taking water from the reservoir, the consideration of the ecosystem of the reservoir and the water requirements of other users along the Riviersonderend downstream of the TWK dam is essential. 5 Conclusion 35

5 Conclusion

The task given in this study was to identify the potential impacts of climate change on the Theewaterskloof dam in the Riviersonderend catchment. By generating climate files for the target region, different future scenarios were obtained. These were integrated in a representative model of the catchment in which the water supply and demand was simulated. The results indicate that on the one hand the temperatures in the Riviersonderend catchment are likely to rise for both investigated SRES scenarios. On the other hand the amount of precipitation is likely to reduce for these scenarios. Both trends are consistent with local studies for this specific region. Due to these changes the runoff flows in the catchments will consequently decrease, leading to a noticeable reduction in water resources in the Riviersonderend catchment. This reduction is only to be seen as a consequence of climate change impacts. The impacts of climate change can also be noticed by yielding in increased extremes in seasonal variations for the simulated future. For all developments analysed, scenario A2 resulted in more extreme changes over the upcoming years for both yearly and seasonal trends. Even if the differences are not very large for the investigated time period, this coincides with the expectations of being the high emission scenario. Apart from these limitations on the water availability however, the fixed demand of the CCT and the Berg WMA and Others can be covered at all times. When considering the findings from the sensitivity analysis of the CCT demand it can be concluded that it is possible to exploit the reservoir a little further in the near future. More water could be allocated to the City of Cape Town in order to meet some of the increasing demand. However the increasing growth rates for water demand of the CCT would lead to diminished water resources in the Riviersonderend catchment between the years 2023 and 2040 depending on the considered scenario and growth rate. If the impact of climate change, this study showed, has the same effect for the entire WCWSS, it will be necessary to increase the effort for an augmentation of the water resources. In any way the supply of water will become more complex, expensive and energy intensive in the future. It has to be noticed, that if the temperatures are likely to increase and the precipitation to decrease in the catchment, the water demand for irrigation will probably increase in the future. This would lead to an increasing demand that needs to be supplied by the TWK dam. In this model this interrelation has not yet been included, but could, when investigated in future studies, lead to a faster decease of available water in the catchment. The results of this study also highlight the CLEW interrelations, especially the investigated relation between climate and water availability. When looking at the water resources and the growing future urban water demand, it is important to consider how this requirement can be met. The CLEW interdependencies show that there is a decrease in available water because of the changing climate. However the depletion of water resources and increase in temperatures could indicate changes in land use and energy as well. For land use, the irrigation demand is likely to increase or alter in type. For energy the integration of new water supply schemes could lead to an increased need for power. This could be further investigated in future studies. MarkSim can be used to generate likely projections of future climate on a local scale based on the SRES scenarios. It is however of utmost importance to relate the generated data with historical data and if needed, adjust the findings with a correction factor. It has to be remembered that the generated future climate only represents one possible development and is therefore only an indication of a probable trend. In this study the reliability of the generated data in MarkSim was only 5 Conclusion 36 investigated for one target region. The reliability of the tool can better be determined by investigating the results for different locations and geographical conditions. The accuracy of the WEAP model is very dependent on the amount of available information and their degree of detailedness. The need for historical data is essential to compare the outputs of the model with real data. If necessary this comparison can function as a calibration of the model and result in more realistic outcomes. For future works it is recommended to further improve the accuracy and the amount of details of the WEAP model. As an example, the exactness of WEAP simulation could be improved by integrating a more complex hydrology, information on supply infrastructure and the interlinked tunnel system. Another interesting option for future works could be to increase the scale of the WEAP model and investigate the entire WCWSS. This would open the possibility to include also the growing total demand of the City of Cape Town and not focus only on the fixed allocations. Thus the influences of possible new future water supply options could also be analysed.

D References VII

D References

Ahjum, F., 2012. Energy for Urban Water Services- A City of Cape Town Case Study. MSc Thesis. Cape Town: University of Cape Town University of Cape Town. Allen, R., Pereira, L., Raes, D. & Smith, M., 1998. Crop Evapotranspiration- Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper , (56). Bates, B., Kundzewicz, Z., Wu, S. & Palutikof, J., 2008. Climate Change and Water. Technical Paper of the Intergovernmental Panel on Climate Change, p.210. CCT, 2008. Water Service Development Plan for City of Cape Town 2008/09- 2012/13 (Status: Draft 2a). Development Plan. Cape Town: City of Cape Town. CCT, 2012c. Cape Town Spatial Development Framework. Statutory Report. Cape Town. CCT, 2012. Water Service Development Plan for City of Cape Town 2012/13- 2016/15 Module 2. Development Plan. Cape Town: City of Cape Town. CSIR, 2010. A CSIR Perspective on Water in South Africa- 2010. South Africa: Council for Scientific and Industrial Research. Dorrington, R., 2005. Projection of the Population of the City of Cape Town 2001- 2021. Cape Town: City of Cape Town. International Livestock Research Institute, 2010-2011. MarkSim(TM) DSAAT weather file generator (International Center for Tropical Agriculture). [Online] Available at: http://gismap.ciat.cgiar.org/MarkSimGCM/ [Accessed June 2013]. IPCC Working Group III, 2000. IPCC Special Report on Emissions Scenarios. Summary for Policymakers. Intergovernmental Panel on Climate Change. Jones, P.G. & Thornton, P.K., 2013. Generating downscaled weather data from a suite of climate models for agricultural modelling applications. Agricultural Systems, (114), pp.1-5. Lumsden, T., Stuart-Hill, S., Schulze, R. & Kunz, R., 2011. Climate Change Impacts and Adaption in the Berg Water Management Area of South Africa. South Africa: University of KwaZulu-Natal. Moore, J., 1989. Balancing the Needs of Water Use. New York, USA: Springer- Verlag. Ogutu, C., 2007. Water Demand Management Options for Cape Metropolitan Area- South Africa. Magister Thesis. Pretoria: Tshwane University of Technology. Schulze, R., 2011. Approaches towards practical adaptive management options for selected water- related sectors in South Africa in a context of climate change. WRC 40-Year Celebration Special Edition 2011 , 37(5), pp.621-46. South Africa. DWAF, 2007a. Western Cape Water Supply System Reconciliation Strategy Study: Reconciliation Strategy. Vol. 1. Pretoria: Department of Water Affairs and Forestry. South Africa. DWAF, 2007b. Western Cape Water Supply System Reconciliation Strategy Study: Determination of Future Water Requirements. Vol. 2. Pretoria: Department of Water Affairs and Forestry. South Africa. DWAF, 2007c. Western Cape Water Supply System Reconciliation Strategy Study: Summary Report. Vol. 7. Pretoria: South Africa. Department of Water Affairs and Forestry. South Africa. DWAF, 2007d. The Assessment of Water Availablitity in the Berg Catchment (WMA 19) by Means of Water Resource Related Models, Report No. 3 D References VIII

The Assessment of Flow Gauging Stations. Pretoria: Department of Water Affairs and Forestry. South Africa. DWAF, 2008. The Assessment of Water Availability in the Berg Catchment (WMA 19) by Means of Water Resource Related Models, Report 8 System Analysis Status Report. Pretoria: South Africa. Department of Water Affairs and Forestry. South Africa. DWAF, 2009a. The Assessment of Water Availablitity in the Berg Catchment (WMA 19) by Means of Water Resource Related Models, Report No. 4 Land Use and Water Requirements. Report 4, Vol. 1: Data in Support of Catchment Modelling. Pretoria: Department of Water Affairs and Forestry. South Africa. DWAF, 2009b. The Assessment of Water Availablitity in the Berg Catchment (WMA 19) by Means of Water Resource Related Models, Report No. 4 Land Use and Water Requirements. Report 4, Vol. 3: Water Use and Water Requirements. Pretoria: Department of Water Affairs and Forestry. South Africa. DWAF, 2009c. The Assessment of Water Availability in the Berg Catchment (WMA 19) by Means of Water Resource Related Models, Report No. 5 Update of Catchment Hydrology. Report 5, Vol. 3: Peripheral River. Pretoria: Department of Water Affairs and Fortestry. South Africa. DWAF, 2009d. Western Cape Water Reconciliation Strategy Study: March Newsletter. Pretoria: South Africa. Department of Water Affairs and Forestry. South Africa. DWAF, 2011a. Dam Safety Office, List of Registered Dams. [Online] Available at: http://www.dwaf.gov.za/DSO/ [Accessed 31 January 2013]. South Africa. DWAF, 2011b. Reconcilation Strategy for the Western Cape Water Supply System- Home Page. [Online] Available at: http://www.dwaf.gov.za/Projects/RS_WC_WSS/ [Accessed 5 July 2013]. South African Weather Service, 2013. South African Weather Service. [Personal Communication] Cape Town: South African Weather Service Available at: http://www.weathersa.co.za/web/index.php/sclimate [Accessed 13 March 2013]. Data extracted from ordered txt data files; Weather elements: precipitation, temperature; Weather stations: Strand, Paarl, Malmesbury, Worcester, Molento Reservoir, Cape Point. Western Cape. DEADP, 2011. Western Cape IWRM Action Plan: Status Quo Report - Climate Change. Final Draft. Cape Town: Western Cape Government.

E Appendix IX

E Appendix

Table E.1: Weather Generation Locations for MarkSim

Locations Latitude Longitude Altitude [°] [°] [m] Sonderend at Gauge -34,062222 19,073056 370 Sonderend in Mountains -34,035900 19.006500 1245 Du Toits at Gauge -33,938611 19,171389 385 Du Toits in Mountains -33,904700 19,191300 1183 Elandskloof Dam -33,964722 19,292222 407 Elandskloof in Mountains -33,919300 19,307100 1032 Theewaterskloof Dam -34,078056 19,2891667 294 TWK in Mountains (north) -33,972100 19,243300 1528 TWK in Mountains (west) -33,998100 19,083800 674 TWK in Mountains (south) -34,111100 19,112700 781

E Appendix X

Figure E.1: Annual Precipitation for Each Sub-Catchment E Appendix XI

Figure E.2: TWK Dam’s Inflows and Outflows for Scenario B1

Figure E.3: TWK Dam’s Inflows and Outflows for Scenario HB