AN INTEGRATED APPROACH TO ASSESSING THE MANAGEMENT NEEDS OF THE WATER RESOURCES OF

THE MKOMAZI CATCHMENT

Phase 1: Modelling catchment streamflow generation to assess the impacts of land use change on available water resources

Report to Integrated Water Resources Management System Project Facilitator

and Collaborating Partners

ACRUcons Report June 2000

V Taylor, RE Schulze, MJC Horan, G.W.P. Jewitt, and A Pike

School of Bioresources Engineering and Environmental Hydrology University of Natal, Pietermaritzburg

Private Bag X01, Scottsville 3209, South Africa

Disclaimer While every reasonable effort has been made by the authors to obtain objective and realistic results in this study, neither the authors, the School of Bioresources Engineering and Environmental Hydrology, nor the University of Natal, nor any of their employees, make any warranty, express or implied, or assume any legal liability or responsibility for the accuracy, completeness or usefulness of any information, product or process disclosed by this report.

TABLE OF CONTENTS

1 GENERAL INTRODUCTION 1.1 Background 1.2 Prior Studies and End-User Requirements 1.3 Aims and Objectives 2 THE MKOMAZI CATCHMENT: ISSUES OF CONCERN 2.1 General Issues of Concern 2.2 End-User Requirements 2.3 Agricultural Practices 2.4 Sedimentation 2.5 Inter-basin Transfers 2.6 Environmental Streamflow Requirements 2.7 Summary 3 MODELLING REQUIREMENTS 3.1 Meeting End-user Needs : Modelling Approach 3.2 ACRU Model Requirements 3.3 Subcatchment Delineation 3.3.1 Principal Configuration 3.3.2 Configuration of Subcatchments in terms of Hydrological

Response Units

3.4 Land Use 3.4.1 Land Use Classification 3.4.2 Vegetative Water Use 3.5 Hydrograph and Sediment routing 3.6 Physical Input Variables 3.6.1 Rainfall 3.6.2 Evaporation 3.6.3 Soils 3.6.4 Erosion Response Units 3.7 Water Allocations and Abstractions 3.7.1 Environmental requirements: instream flow requirements 3.7.2 Irrigation and domestic abstractions 3.7.3 Systems Operation: dam operating rules 3.8 Land Use Change, Water Resources Development and Potential

Scenarios

3.8.1 Baseline land cover 3.8.2 Interbasin transfer 3.8.3 Potential Climate Change 4 IMPACTS OF PROPOSED DEVELOPMENT ON WATER RESOURCES

MANAGEMENT

4.1 Verification Studies 4.1.1 Verification of Streamflows at U1H005 4.1.1.1 Accumulated Streamflows 4.1.1.2 Monthly Totals of Daily Streamflows 4.1.1.3 Statistical Analyses of Daily Streamflows 4.1.2 Verification of Streamflows at U1H006

4.1.2.1 Accumulated Streamflows 4.1.2.2 Monthly Totals of Daily Streamflows 4.1.2.3 Statistical Analyses of Daily Streamflows 4.1.3 Verification of Streamflows at U1H003 4.1.3.1 Accumulated Streamflows 4.1.3.2 Monthly Totals of Daily Streamflows 4.1.3.3 Statistical Analyses of Daily Streamflows 4.2 Statement of confidence level 4.3 Assessment of the Hydrological Dynamics of the Mkomazi Catchment 4.3 Assessment of generation of streamflows under present land use 4.4 Impacts of Proposed Dams on Streamflows 4.5 Impacts of Potential Climate Change on Streamflows 4.5.1 Impacts assuming present land use 4.5.2 Impacts with proposed dams 5 RISK ANALYSES OF THE IMPACTS OF DEVELOPMENT ON WATER

RESOURCES

5.1 Impacts of Present Land Use under Dry Conditions 5.2 Impacts of Present Land Use under Wet Conditions 5.3 Impacts of Present Land Use on Low Flows 5.3.1 Impacts in the median year 5.3.2 Impacts in the driest year in 5 5.3.3 Impacts in the driest year in 10 5.4 Impacts of Proposed Smithfield Dam on low flows 5.4.1 Impacts in the median year 5.4.2 Impacts in the driest year in 5 5.4.3 Impacts in the driest year in 10 6 DISCUSSION AND CONCLUSIONS 7 RECOMMENDATIONS 8 REFERENCES 9 APPENDICES APPENDIX A SUMMARY OF IWRMS RESEARCH PARTNERS AND

COMPONENTS

APPENDIX B MKOMAZI STUDY CATCHMENT APPENDIX C ACRU MODELLING REQUIREMENTS FOR THE MKOMAZI

CATCHMNENT

APPENDIX D VERIFICATION STATISTICS APPENDIX E ACRU GENERATED STREAMFLOWS AT POINTS OF

INTEREST

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1. GENERAL INTRODUCTION 1.1 Background The factors affecting the availability of water resources in South Africa are numerous and diverse. Water supply is constrained, not only by the temporal and spatial distribution of rainfall and high evaporation rates, but also by increasing competition for the limited resources from expanding industrial, agricultural, commercial and domestic water use sectors. This competition for water is exacerbated by population growth and mobility, by deteriorating water quality as a consequence of intensification of water use and from environmental water requirements. Constraints imposed by the shortcomings of inadequate databases together with, frequently, limited time permissible for the determination of the availability of water resources, have resulted in water managers requiring tools which assist in their decision making processes. Decisions made to ensure an equitable water allocation must now also meet the provisions of the new National Water Act of South Africa (Act 36 of 1998) which appraises the environmental reserve as having priority over any other water user, other than that for basic human needs. Water resource managers therefore need support from reliable systems that integrate a variety of tested techniques, in order to make sound decisions. In 1997 the European Union (EU) initiated an Integrated Water Resources Management System (IWRMS) project (INCO-DC, 1996) to prepare a decision support system for application in semi-arid regions which would assist in the identification and resolution of a number of topical issues faced by water resource managers. The final product of the project is envisaged as being an “innovative computer based toolset designed as an assembly of tested, validated and well documented procedures comprising techniques of database management, remote sensing, aerial photograph interpretation, GIS analyses, process modelling, Graphical User Interface (GUI) and GIS-based decision support” (INCO-DC, 1996). The EU IWRMS project (the Project) is collaborative, assimilating the findings from a diversity of techniques and applied by a number of international specialists. Integrating the particular strengths of such diverse expertise is anticipated to complement the interpretation of the techniques available to assess the fundamental components of: • catchment hydrological dynamics • sediment transport • vegetative water use • land use classification and • water resource demand. Ultimately, the Project’s objective is the formulation of a system that addresses and meets end-user needs in the assessment of catchment development issues. The particular research interests of the specialists participating in the Project are provided in the Appendix A. Three study catchments within Southern Africa have been identified by the Project; the Mkomazi in KwaZulu-Natal, South Africa; the Mbuluzi in Swaziland and the Mupfure in Zimbabwe. Each of the three catchments is characterised to a certain extent by differences in land use, level of development, management and water allocation issues. However, the approach of the Project is the determination of a generic methodology to assess not only the availability of water resources but also the impacts of potential development within semi-arid regions. This report focuses on the procedures adopted

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towards the integration of a hydrological assessment of the Mkomazi catchment using the ACRU agrohydrological model (Schulze, 1995) to simulate the catchment hydrological dynamics. 1.2 Prior Studies and End-User Requirements The Department of Water Affairs and Forestry (DWAF) has conducted a pre-feasibility study (1997 / 1998) for proposed water resources developments in the Mkomazi Catchment for inter-basin transfer to the Mgeni System. The rationale for the study was that the water resources of the Mgeni River System, which is the main source of water for the Durban / Pietermaritzburg metropolitan area, are already fully utilised. Augmentation to the Mgeni System from the Mooi River is already taking place, with further schemes in an advanced stage of planning. In earlier studies, the Mkomazi River was identified as being the most feasible after the Mooi River to augment the Mgeni System. The Mkomazi-Mgeni Transfer Scheme (MMTS) is intended to store excess water in the upper Mkomazi Catchment in one, and possibly two, impoundment dams for transfer via pipeline to the lower Mgeni System. Because it has historically been DWAF policy that the needs of a donor catchment should be met before consideration can be given to transferring water to other catchments, a reconnaissance level basin study was conducted to determine the present and future water demands within the Mkomazi catchment (DWAF, 1998a). High, middle and low scenarios were assessed. However, the study was carried out at quaternary subcatchment level with monthly time step modelling procedures. Umgeni Water, the principal beneficiary of the Mkomazi component of the EU IWRMS Project, has indicated that any modelling of the hydrology of the catchment would better suit their planning needs if the catchment was discretised at a sub-quaternary level. This provision would facilitate the determination of the impacts of proposed development on the generation of streamflows at potentially critical sub-quaternary regions. Furthermore, the potential to evaluate the streamflow regime at a daily, rather monthly, level would enhance any management decisions required to address those daily streamflow characteristics considered critical for ecological purposes as well as the sustainable development of the Mkomazi catchment. 1.3 Aims and Objectives of this Study In the context of the EU IWRMS Project, the aim of this study by the School of Bioresources Engineering and Environmental Hydrology (BEEH), is to apply the most appropriate data available to the ACRU model in order to simulate and evaluate the hydrological dynamics of the Mkomazi catchment. The primary objective of this report is to describe the approach adopted to model the impacts of development on the hydrological catchment dynamics of the Mkomazi catchment at a sub-quaternary scale and on a daily time step. The simulation of daily flows is considered fundamental to adequately assess those streamflow characteristics associated with sediment yield, routing of the river hydrogragh and Instream Flow Requirements. The simulated daily streamflows can subsequently be used to generate a daily time series of flows, which occurs within the natural flow range. Such a time series is critical for the effective operation of dam release rules designed to meet the needs of all catchment stakeholders. A further important objective relates to the evaluation of the techniques applied in the data acquisition and preparation for the model menu input. The most recognised

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approach to achieve this is to conduct verification studies of the simulated streamflows with reliably gauged flows. A further approach would be to conduct sensitivity studies in which different techniques and levels of data acquisition and preparation were compared and contrasted. This will only be possible with the arrival of the data accrued by the other EU Partners and will constitute a Phase 2 Report on this Project. It is anticipated that the assimilation of hydrological criteria into an appropriately configured daily model will result in an “Installed Modelling System” for the Mkomazi. The benefits of such a system is that analyses of development scenarios require relatively little effort and can be expected to produce reliable answers. In order to achieve the above objectives, the Mkomazi catchment streamflows were simulated under the following land cover conditions and development scenarios: (a) “pristine” land cover conditions, defined for the purposes of this report as Acocks’

Veld Types (Acocks, 1988), (b) present land use conditions, defined in accordance with Thompson’s 1996

classification and interpretation of the LANDSAT TM 1996 image, (c) present land use, but including potential impoundments, in accordance with

DWAF’s MMTS, (d) present land use, but with the climate perturbed in accordance with the climate

change scenarios for a doubling of CO2 conditions as generated by the Hadley Centre 1998 CGM (excluding sulphate forcing) and

(e) with flow routing to assess the influence of routing the hydrograph down the Mkomazi River and its tributaries.

The following chapters of this Report describe the Mkomazi catchment and focus on the current issues of concern and development (Chapter 2) and present the approach adopted in modelling the catchment hydrological dynamics to reflect those issues (Chapter 3). Chapter 4 presents the results of the study and Chapter 5 attempts to address the issues of water resource management required for sustainable development of the Mkomazi catchment.

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2 THE MKOMAZI CATCHMENT: Description and issues of concern The Mkomazi Catchment, as defined for this report, comprises the 12 DWAF Quaternary Catchments (QCs) numbered UI0A to U10M and covers an area of 4383 km2. The catchment is situated around 29° 17’ 24’’ E and 29° 35’ 24’’ S, stretches 170 km from 3300m altitude in the northwest to sea level in the southeast and has a Mean Annual Precipitation (MAP) range of 1283 to 752mm (Appendix B, Table B1). However, the MAP is higher in the upper Mkomazi Catchment (950 – 1283 mm) and consequently most of the catchment runoff is generated in the upstream parts of the Mkomazi (DWAF, 1998a). The Mkomazi Catchment is characterised by steep gradients of altitude and rainfall, highly variable land uses as well as highly variable intra- and inter-seasonal streamflows. The annual water yield of the Mkomazi system under present land use conditions and consumption rates has been estimated to be 905 million m3 (DWAF, 1998a). Despite the variability of the streamflows, the Mkomazi River flows throughout the year. The potential to store the high flows of the river system, together with the proximity of the catchment to the denser distribution of population in the Mgeni system, provides the impetus for impoundment. 2.1 General Issues of Concern Presently the catchment supports commercial afforestation, extensive agriculture (principally livestock grazing and sugarcane), intensive agriculture (citrus and vegetables), subsistence agriculture as well as tourism and leisure activities. With the exception of the coastal town of Umkomaas, the Mkomazi Catchment contains no major towns or industry. The distribution of rural population ranges from moderate to sparse, with greater concentration in the lower catchment. Use of available water resources is therefore considered by DWAF (1998a) to be conservative. Currently, there are no major reservoirs within the catchment, however six potential impoundment sites have been identified by DWAF. Of these six sites, four have been identified as potential development sites for the supply of piped water to rural communities. Two sites on the Mkomazi river have been identified as potential major impoundment sites for the transfer of water from the Mkomazi Catchment to augment the Mgeni, which supplies the heavily populated and industrialised Durban / Pietermaritzburg region. These developments will impact significantly on the natural streamflow regime of the Mkomazi. Therefore, there are potentially conflicting water issues within the catchment. Potential conflicts of water use, demand and allocation are perceived to emanate from competition arising from: • environmental streamflow and estuarine flow requirements • commercial agriculture practices (e.g. streamflow reduction from forestry, land

management practices and irrigation) • urban and peri-urban water demands • rural subsistence-based communities water needs and • inter-basin water transfers • infrastructural development and • the impact of sediment transport on the life expectancy of any proposed dams. These issues have varying impacts on the availability of water resources under present water use as well as potential scenarios of demand and supply. There is a defined need for useful guidance which allows water planners and managers faced with the interconnectedness of these issues to assess the impacts of potential development on available water resources.

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2.2 End-user Requirements The principal beneficiary of the Mkomazi has, from the outset of this Project, been perceived to be Umgeni Water, the parastatal organisation that supplies potable water to the larger proportion of the population of KwaZulu-Natal. However, the National Water Act of South Africa (Act 36 of 1998) makes provision for the nation’s catchments to eventually be managed and controlled by Catchment Management Agencies (CMAs), which will be mandated to represent the interests of all the catchment stakeholders. In practice it is more probable that the present governing water bodies will be the principal stakeholders. However, there is provision for the needs of other interested and affected parties to also be addressed. In the case of the Mkomazi this will include, inter alia, the Environmental Task Group, Foresters Association, Irrigation Boards and KwaZulu-Natal Nature Conservation Services, all of whom have needs which differ from those of the bulk water supplier Umgeni Water. The National Water Act also addresses the protection of all water resources as well as their development, management and control. To achieve this, the Act provides for that part of the water resource known as the Reserve (Chapter 3; Part 1; Sect. 2.3.3 of the Act) which is, in principle, a health reserve and refers to both the quantity and quality of the water in the resource and varies according to the management class or desired future state of the resource. The Reserve is defined as comprising two parts; the basic human needs reserve and the ecological reserve. The basic human needs reserve provides for the fundamental water requirements to sustain the lives of those individuals served by the water resource. This includes drinking water and washing water for food preparation and for personal hygiene. The basic minimum water allocation for individuals has been assessed as being 25 l per person per day, to be provided within 200 m of the dwelling. However, this quantity may be increased in the future to 50 l per person per day and could possibly also include an amount reserved for subsistence farming (Roberts, 1998). The ecological, or environmental, reserve is the water required to protect the aquatic ecosystems of the water resource. Therefore, the ecological reserve provides for that quantity of the water resource required to keep the interacting biological and physical river processes functioning. In practice this will equate to the minimum amount of water required to keep rivers in a reasonable state of health and, as such, will have no bearing on the “historical” river flow (Roberts, 1998). Notwithstanding these provisions, it was projected in 1996 that the Mgeni System water demand would increase from just over 250 million m3 per annum to between 375 and 500 million m3 in 2010 depending on the demographic scenario used (DWAF, 1998a). Beyond 2010, the projected water demand continues to rise exponentially, with expectations that the volume required in 2025 is likely to be between 650 and 900 million m3 (DWAF, 1998a). The principal reason for the increased demand can be attributed to population growth and industrial development in the Durban Metropolitan area. Additionally, Umgeni Water has initiated water supply schemes to deliver piped water to meet the demands of rural communities within the Mkomazi. For these reasons Umgeni Water, in association with DWAF, conducted a pre-feasibility study for the transfer of available water resources from the Mkomazi Catchment to augment the Mgeni Catchment water resources (DWAF, 1998a). Two major potential impoundment sites, viz. Impendle and Smithfield (680 million m3 and 170 million m3 capacity respectively) on the Mkomazi river were initially identified (cf Figure 1). However, after reconsideration of the factors affecting projected water demand, it was provisionally decided that only one dam site (Smithfield) should be

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developed, with the option of augmenting that development with water from another dam at the second site (Impendle) if the expected demand was exceeded. Therefore, the principal needs of Umgeni Water include the assessment of water yield of the Mkomazi catchment under: • various land use conditions, including the impacts of sedimentation as a result of

overgrazing in the upper catchment and exacerbated by the region’s geology, on the life expectancy of any proposed dams

• different water allocation rules, including setting aside that water required to meet the environmental requirements as prescribed by the National Water Act and

• projected abstraction and consumption rates. The redistribution of water resources from the Mkomazi Catchment to the Mgeni Catchment will require complex design and release priority rules. This is especially the case if the scheme to augment the proposed Smithfield dam with water released from the proposed Impendle Dam, via the river, is implemented. Furthermore, it is a legal requirement that the impoundment operating rules of the transfer scheme meet any design releases in periods of hydrological drought (Hughes and Ziervogel, 1998). Figure1 Mkomazi Catchment: Site Features

2.3 Agricultural Practices The Mkomazi Catchment is relatively undeveloped and neither agriculture nor forestry is a major land use. Furthermore, catchment topography, climate and economics constrain the diversity of agricultural practices. Water resources are not a restraint on agricultural practices, per se, as the catchment receives relatively high rainfall by southern African conditions. However, there are substantial, and potentially conflicting, differences between the types of farming practised throughout the Mkomazi Catchment. The MAP of the upper catchment ranges from 1000 to 1300 mm, (Figure 2). This provides conditions that can be considered conducive to those farming practices, which

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do not require supplementary water. This factor, together with steep gradients (Figure 3) which exacerbates the difficulties of leading water from the river valley, has contributed to the major land uses in the upper catchment being those of ranching, especially in those areas formerly part of the KwaZulu homeland, and forestry. The main problems relating to ranching are those associated with overgrazing, which contributes significantly to disturbed soil conditions, soil transport and ultimately sedimentation of river channels and impoundments. Exotic, commercial tree plantations are the focus of a whole gamut of water related issues, ranging from streamflow reduction through interception and transpiration losses, particularly in times of low flows, to water quality problems including excess acidity of headwaters and exacerbation of sedimentation at harvesting. The economic and socio-political structure of the upper Mkomazi also excludes any crop husbandry other than low input commodities. As a result yields per hectare are low. The area generally has poor infrastructure, with rural populations having severely limited access to amenities and agricultural technologies. The economic structure of the Mkomazi Catchment and access to water becomes stronger downstream. Because of relatively high rainfall (950 – 1000 mm), commercial forestry is a significant land use in the middle Mkomazi, particularly around Richmond. The more intensive cultivation of sugarcane and horticulture are also practised in this region, despite growing conditions being marginal as a result of the incidence of frost days. The lower Mkomazi Catchment is more amenable to intensive crop farming. The economic driving forces of the lower part of the catchment are much greater than in the upper catchment and consequently there is greater concentration of farm dams together with high input crops producing high yields per hectare. Paradoxically, these practices are most prevalent in that part of the Mkomazi Catchment that receives least rainfall (less than 850 mm, Figure 2). Farmers in this region have compensated for this factor by storing excess streamflows in numerous farm dams. However, the consequence of this is that river flows in the affected catchments have all but ceased and there is no significant river flow in the river channels downstream of the town of Ixopo. The socio-economic development of the Mkomazi catchment is heavily reliant on agriculture. Therefore, it makes economic, as well as hydrological, sense to utilise the catchment hydrological dynamics to utmost potential. Determination of the different impacts of different agricultural practices on quality and availability of water resources is consequently of primary concern to catchment stakeholders. 2.4 Sedimentation The major water quality concern for the Mkomazi River is that of sedimentation. This is particularly prominent in those areas impacted by overgrazing, poor tillage practice and land cover clearing activities of rural subsistence populations. Together with the inherent geology of the region, these factors have resulted in soil transport culminating in river turbidity in high flows and sediment deposits in low flows. Hydro-geological processes are crucial to sustaining habitat diversity in so far as suspended materials may provide a food supply for some river biota and sediments provide refugia for different stages of aquatic life cycles. However, aquatic ecosystems depend on the processes that would occur within the variability range of the natural flow regime. Excessive sediment deposition leads to encroachment of alien riparian and aquatic species in low flows, and modification of channel geomorphology in high flows (DWAF, 1998b). The potential for soil to be transported into impoundments can have serious consequences for sediment accumulation behind impoundment walls, thereby increasing

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the risk of dam failure by either breaching or hindering the release of flood overflows. Equally serious are the severe impacts of sedimentation the lifespan of any potential impoundment, and as such is an important consideration in the proposal of any expensive potential water resources planning project. Umgeni Water places high importance to these threats and monitors for suspended solids at three sites in the upper Mkomazi, one site in the mid Mkomazi and at two sites in the lower reaches of the river (Figure 1). The contributing upstream areas of these sites are provided in Appendix B, Table B2. The determination of the susceptibility of the catchment to different hydrological processes and land management processes is, therefore, crucial to the formulation of a management strategy for the catchment. Figure 2 Mkomazi Catchment: Mean Annual Precipitation

Figure 3 Mkomazi Catchment: Subcatchment Mean Altitude

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2.5 Inter-basin Transfers At present there are no major impoundments on the Mkomazi River. However the catchment has a historical mean yield of 905 million m3 per annum (DWAF, 1998b) and there are plans to utilise this water resource. At the initiation of the project (September 1997) there were six dam sites proposed for the Mkomazi Catchment. Of these, three sites (Gomane, Nzinga, Bulwer) in the upper catchment are planned to alleviate rural water supply shortages and one site (Ngwadini) in the lower catchment has been identified as an off-channel storage dam for the surrounding agricultural community. The remaining two sites (Smithfield and Impendle, Figure 1) are the focus of proposed inter-basin transfer. It is anticipated that the abstraction of water from the Mkomazi Catchment to augment the Mgeni supply system will impact on those downstream abiotic characteristics of the Mkomazi River (hydrology, geomorphology, chemistry, temperature) as well as on the responses of the ecosystem components (fish, riparian vegetation and aquatic invertebrates). However, the main consideration of this report is assessment of the changes in streamflow generation on the downstream environment, as a result of impoundment of storage water by the proposed Smithfield and Impendle Dams, with respective upstream contributing areas of 2054 km2 and 1424 km2 (Appendix B, Table B3). 2.6 Environmental Streamflow Requirements The DWAF reconnaissance level basin study to determine the present and future water demands within the Mkomazi Catchment (DWAF, 1998a) has identified that environmental requirements, in the form of Instream Flow Requirements (IFRs) are a dominant water resource consideration, requiring approximately 30% of the Mean Annual Runoff. The four IFR sites identified by DWAF as representative reaches are shown in Figure 1. The locations of all the IFR sites are downstream of either or both of the proposed Smithfield and Impendle dam sites (Figure 1). A principal issue of concern relating to impacts of the MMTS on the catchment hydrological dynamics is that the provision of the ecological component of the Reserve will have a significant impact on the yield and operating rules of both proposed dams. The respective upstream contributing areas for the four IFR sites are provided in Appendix B, Table B4. The general consensus regarding the Mkomazi estuarine flow requirement (EFR) is that if the IFRs are satisfied, then the EFR will also be satisfied. 2.7 Summary The Mkomazi Catchment is characterised by diverse land-uses as well as environmental and developmental issues, all of which are significant, albeit to different extents, to the various stakeholders within the catchment. The assessment of the potential impacts of any proposed development on the Mkomazi streamflows is therefore critical to the sustainable management of water resources within the catchment. The approach used to simulate the potential impacts of proposed developments on the streamflows of the Mkomazi is described in Chapter 3.

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3. MODELLING REQUIREMENTS 3.1 Meeting End-user needs : Modelling Approach The EU IWRMS Project has benefited from three integrative workshops, the first of which, held in Pietermaritzburg in September 1997, was designed to set the scope of the Project and to establish the requirements of the associated beneficiaries. Because the pertinence of any approach designed to resolve water resource development issues must address those concerns identified by the end-user, the Mkomazi component of the Project was formulated to meet the needs of Umgeni Water. Attention to the end-user needs also featured prominently in the second workshop (Jena, Germany, July 1999), which was designed to allow discussion of the Project progress. In the time from the conception of the project, South African water law has undergone considerable change, with emphasis now focussing on sustainable and equitable allocation of the nation’s scarce water resources. The new National Water Act of South Africa (Act 36 of 1998) provides for CMAs to represent the interests of all catchment stakeholders, including the incumbent water boards. Notwithstanding this provision, the Act appraises the environmental reserve as having priority over any other water user other than that for basic human needs. The focus of the third workshop (Pretoria, March 2000) was the “end-user” and the potential of the EU IWRMS Project to provide a useful product. Clearly, the product should be tailored to meet not only the water resources planning needs of the end-user but should also be transparent in its formulation and structure. The purpose of this Section is to provide the procedure adopted to ensure that the hydrological modelling of the Mkomazi catchment would not only represent end-user requirements, but would also assimilate the best available data and techniques required to do so. Hydrological model criteria and parameters, as well as associated values are given, complete with descriptions of their sources and relevance. Whilst it can be viewed that the end-user of any water related project is, ultimately, the person turning the tap to draw water, the meeting was concerned with those directly involved in the daily management of bulk water supplies. To this extent, the end-user for the Mkomazi component of the overall EU IWRMS Project remains Umgeni Water. Whilst the Umgeni Water Corporate Planning Section have already installed models to determine their water resource planning requirements, a number of their staff are familiar with the applicability, attributes, shortcomings and operation of the ACRU model. It is therefore, considered that the inclusion of this section is paramount to the credibility of the EU IWRMS Project and acceptance of its results. 3.2 ACRU Model Requirements The catchment hydrological dynamics are being modelled with the ACRU agrohydrological model (Schulze, 1995) to assess the impacts of proposed developments on the availability of water resources. ACRU is a daily time step, physical-conceptual model revolving around multi-layer soil water budgeting. It is a multi-purpose model (Figure 4) with options to output, inter alia, daily values of streamflow, peak discharges, recharge to groundwater, reservoir status, irrigation water supply and demand as well as seasonal crop yields. The model is structured (Figure 5) to be hydrologically sensitive to catchment land uses and changes thereof, including the impacts of proposed developments such as large dams on catchment streamflow and sediment generation, as well as to changes in climate induced by the greenhouse effect.

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Figure 4 ACRU : Concepts of the modelling system (Schulze, 1995) The ACRU model requires input of known, or measurable, factors relating to the catchment including information on: • climate (daily rainfall; temperature; potential evaporation) • soils (horizon depths; soil water retention and drainage characteristics, and tillage

impacts) • baseline land uses ( either for pristine or current conditions; types; seasonal above

and below ground water use related characteristics) • exotic tree species planted (species distributions; extent and levels of site

preparation) • wetlands (capacities; surface areas; releases; abstractions) • impoundements (full supply capacity; surface area at full supply capacity; spillway

characteristics; surface area to volume relationships) • other land use practices or water abstractions, if relevant (e.g. irrigation demand and

supply; domestic or livestock abstractions; amounts; sources of water; seasonality). This information is transformed in the model by considering: • the climate, soil, vegetative, hydrological and socio-economic subsystems • how they interact with one another • what thresholds are required for responses to take place • how the various responses lag at different rates and • whether there are feedforwards and feedbacks which allow the system to respond in a

positive or reverse direction. The model then produces output of the unmeasured variables to be assessed, e.g. • streamflows under different catchment conditions (from different parts of the

catchment; including stormflow and baseflow on a daily basis) and low flows • sediment yield (on an event-by-event basis)

LOCATIONAL CATCHMENT CLIMATIC HYDROLOGICAL LAND CHANGE AGRONOMIC

IRRIGATION DEMANDLAND USERESERVOIRSOILS

SOIL WATER BUDGETING/TOTAL EVAPORATION

MODELLING

POINT or LUMPED

or DISTRIBUTED MODESor G.I.S. LINKED

DYNAMIC TIMEor

ANNUAL CYCLIC CHANGE

INPUTS

MODEL

OPERATIONAL

MODES

SIMULATIONOPTIONS /

COMBINATIONS

OUT-

PUT

DailyMonthlyAnnualRisk Analyses

SPECIFIC

COMPONENTSOBJECTIVES /

RUNOFF

COMPONENTS

RESERVOIR

STATUS YIELD

IRRIGATION

DEMAND

IRRIGATION

SUPPLY

LAND USE

IMPACTS

CROP

YIELD

StormflowBaseflowPeak DischargeHydrograph :- generation- routingEV analyses

Outflows:- overflow- normal flow- seepage- abstractionsInterbasin

transfers

Sediment- generationReservoir- siltation

Crop DemandApplication:- on demand- fixed cycle

- deficit

From :- reservoir- river- river and

Return flows

reservoir

Gradual changeAbrupt changeTotal

Tillage

Wetlandspractices

MaizeWinter WheatSugarcane

- dryland- irrigated- profit / loss

SEDIMENT CLIMATE

CHANGE

Off-channelstorage

- fixed amount

) CO) T ) E) P

2

oooo

oooo

ooo

oooo

oooo

oooo

oooo

oooo

- off channelstorage

evaporation Primaryproductivity

ACRU MODEL

IRRIGATION SUPPLY

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• risk analyses in the form of month-by-month and annual statistical frequency analyses, including flows under median conditions and for the driest flow in, e.g. 5 or 10 years; flow variability; low flow analyses design; peak discharges.

INTERMEDIATE STORE

GROUNDWATER STORE

SPECIFIC YIELD

HYDRAULIC GRADIENT

- - - - - - - - -CAPILLARY FRINGE

HYDRAULIC CONDUCTIVITY

SURFACE LAYER

SUBSEQUENTSOILHORIZONS

INTERCEPTIONCANOPY

PRECIPITATION(RAINFALL ; IRRIGATION)

INTERCEPTION

BASEFLOW

RUNOFF

QUICKFLOW

WILTING POINT . . . . . . . . . .

STRESS THRESHOLD . . . . . .

DRAINED UPPER LIMIT . . . . . .

SATURATION . . . . . . . . . . . . . . . . .

Figure 5 ACRU model structure (Schulze, 1995) 3.3 Subcatchment Delineation 3.3.1 Principal Configuration Initially the Mkomazi catchment was configured to represent 21 major subcatchments, based essentially on a division of the 12 DWAF Quaternary Catchments within the catchment. However, this delineation was done prior to the identification of the locations of the Instream Flow Requirement (IFR) sites (at which an environmental water reserve has to be maintained) and prior to all the proposed dam / abstraction scheme information becoming available. Furthermore, Umgeni Water requested that the final configuration would better suit their long-term planning needs if certain areas were isolated as hydrological entities, and thus delimited as additional subcatchments. A revised configuration was therefore agreed upon, resulting in the original DWAF subcatchments being discretised into 52 major interlinked subcatchments. The main objectives of the delineation were to represent the different land use and management practices as discrete units as well as considering proposed developmental concerns within the catchment. Table C1, Appendix C indicates the criteria used for discretising the individual subcatchments. Figure 6 illustrates the discretisation graphically. However, the final configuration specifically includes: • 2 major proposed dam sites on the Mkomazi river (Impendle and Smithfield) • 4 rural supply abstraction developments • 4 Instream Flow Requirement (IFR) sites to assess the environmental water

requirements, all on the Mkomazi river

13

• Umgeni Water’s water quality sampling sites and gauging stations • the waste water treatment works at Ixopo • 3 sediment test sites identified by Universita di Frienze Florence, Italy (see Appendix

A) • the subdivision of the Drakensberg region because of the steep rainfall and

consequent runoff gradients found there and, • distinction of the different land uses which impact on hydrological responses. The locations of specific sites of interest are indicated on Figure 1.

Figure 6 Criteria for Mkomazi subcatchment discretisation

3.3.2 Configuration of Subcatchments in terms of Hydrological Response Units The School of Bioresources Engineering and Environmental Hydrology, University of Natal, Pietermaritzburg (see Appendix A) has gained considerable experience in a number of impacts-of-development projects over recent years (e.g. Kienzle et al., 1995). This has resulted in a tested subcatchment configuration (Figure 7) which represents the hydrological responses of not only a number of different land uses and different land management practices, but also takes cognisance of DWAF Quaternary Catchments. Nine different land use categories have been identified in the Mkomazi in accordance with their anticipated hydrological responses. The categorisation is shown in Table C2 Appendix C. The land use categorisation shown in Figure 7 for 2 subcatchments, has been applied to each of the 52 subcatchments, giving 468 linked hydrological units. The benefit of this configuration is that the areal extent of each category of land use and parameters associated with different management practices can be altered accordingly within the configuration, thereby allowing minimal change in input data when scenario studies are assessed. A further advantage of isolating the different land uses is that the hydrological responses of each unit are modelled explicitly. The routing of simulated streamflows resulting from the individual categories is also indicated in Figure 7. In particular, the cell discretisation and sub-cell routing configuration used in the Mkomazi catchment is indicated in Figure 8. The Mkomazi subcatchment numbered configuration is shown in Figure 9.

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3.4 Land Use 3.4.1 Land Use Classification The LANDSDAT TM 1996 coverage provided by the Council for Scientific and Industrial Research, CSIR (see Appendix A) has been used in a base present land use input for the ACRU menu. The present land use, in accordance with the LANDSDAT coverage for the Mkomazi catchment, is shown in Figure 10. Nineteen distinct land uses were identified for the Mkomazi catchment from the LANDSAT coverage using the CSIR land use classification (Thompson, 1996). Because it was considered undesirable and impractical to allocate a separate land use based sub-subcatchment to each land use identified in each of the 52 subcatchments, it was decided that the different land uses could be more than adequately represented by categorisation in accordance with their anticipated hydrological response. This resulted in the nine-fold ACRU categorisation of land uses shown in Appendix C Table C2. Each of the nineteen different land uses (Appendix C, Table C2), identified from the LANDSAT coverage using the CSIR land use classification (Thompson, 1996), was ascribed to one of the nine appropriate ACRU land use categories. Ongoing research at the Friederich-Schiller-Universität in Jena, Germany using interpretation of aerial photography and satellite data suggests that while fewer land uses have been identified, it has been possible to distinguish three different levels of land use classification, based on: • a similar categorisation to that formulated by BEEH for the ACRU model, though

regrettably not identical to it, • a secondary level identifying genera of a particular flora (e.g. distinguishing in

commercial forests between eucalypts, pinus, and acaccia genera), and • a tertiary level, where at a further resolution, individual species may be identified. It is anticipated that the process techniques of interpretation of the land use classification research conducted by the Friederich-Schiller-Universität in Jena could enhance any further modelling application. However, this will be addressed in a Phase 2 Report. Figure 7 Schematic representation of the Mkomazi subcatchment configuration

Land Use Categories 1; 10 Forest 2; 11 Plantation 3; 12 Valley Bushveld 4; 13 Dryland

Agriculture 5; 14 Urban 6; 15 Grassland 7; 16 Wetland 8; 17 River Channel 9; 18 Dams &

Irrigation Routing of Streamflows SC Subcatchment

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The Mkomazi subcatchment distribution of present land cover and land use identified by the 1996 LANDSAT TM Image is shown in Appendix C, Table C3.

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Physiography

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Figure 8 Schematic representation of the Mkomazi subcatchment Delineation and

cell configuration

Proposed Dam SitesIFR SitesUmgeni WaterSampling PointsGauging StationsWater TreatmentWorksLanduse ImpactsSediment Test Sites Physiography

ACRU Subcatchments

SCHOOL OF BIORESOURCES ENGINEERINGAND ENVIRONMENTAL HYDROLOGYUNIVERSITY OF NATALPIETERMARITZBURG

29E30' 29E45' 30E00' 30E15' 30E30' 30E45'

29E30' 29E45' 30E00' 30E15' 30E30' 30E45'

29E45'

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Location Map

KwaZulu-Natal

Mkomazi Catchment : Criteria for Subcatchment DelineationLegend

Figure 9 Mkomazi Catchment: Subcatchment delineation and cell configuration

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Figure 10 Mkomazi land (from CSIR, Using LANDSAT TM, 1996) 3.4.2 Vegetative Water Use The total evaporation (formerly termed actual evapotranspiration) rates of each of the nine land use categories within each subcatchment described are being investigated by the Institut Nationale de Recherche en Informatique et Automatique (INRIA) in France (see Appendix A) using techniques of remote sensing. Variations in Land Surface Temperatures (LSTs) are being researched and the link between land cover and erosivity is being evaluated. The determination of the water lost by soil and vegetation under present land use will be useful in identifying ways of improving water use management. INRIA have assessed the total evaporation rates within the Mkomazi catchment using these techniques for the period August 1997 to March 1999. However, until the information derived by these techniques has been verified against observed data for the given period, the vegetative water use by each land use within the nine land categories is assessed using the water use coefficient values given in Smithers and Schulze (1995) and subsequently revised by BEEH (e.g. Summerton, 1996; Schulze et. al. 1996, Schulze et.al. 1999). The hydrologically relevant vegetative characteristic of each land use include: • an interception loss value, which can change from month to month during a plant’s

annual growth cycle, to account for the estimated interception of rainfall by the plant’s canopy on a rainday,

• a monthly consumptive water use (or “crop”) coefficient (converted internally in the model to daily values by Fourier Analyses), which reflects the ratio of water use by vegetation under conditions of freely available soil water to the evaporation from a reference potential evaporation (e.g. A-pan or equivalent), and

• the fraction of plant roots that are active in extracting soil moisture from the respective soil horizon in a given month, this fraction being linked to root growth patterns during a year and periods of senescence brought on, for example, by a lack of soil moisture or by frost.

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A further variable which can change seasonally is the coefficient of initial abstraction (cIa), where, in stormflow generation, the cIa accounts for depression storage and initial infiltration before stormflow commences. In the ACRU model this coefficient takes cognisance also of surface roughness (e.g. after ploughing), rainfall intensity patterns and litter characteristics. Higher values of cIa under forests for example, reflect enhanced infiltration while lower values on grassveld in summer months are the result of higher rainfall intensities (and consequent lower initial infiltrations) experienced during the thunderstorm season. The variables representing the consumptive characteristics of the 19 land uses identified by the CSIR LANDSAT TM 1996 image, together with those of riparian wattle (Schulze et al., 1996) which was assumed to be the prevalent riparian species, were area-weighted within the respective 9 land use categories described in Section 3.4.1. The month-by-month input variables for the land use categories used in the Mkomazi study are provided in Table C4 of Appendix C. 3.5 Hydrograph and Sediment Routing Extensive field work has been undertaken by BEEH at 56 sites (see Figure 1) to collect the data required to model the routing of channel flows down the Mkomazi and its tributaries. The subcatchments have been assessed for channel dimensions of: • channel width (top and bottom) • depth • floodplain slopes, • river reach slope and • channel shape and roughness.

Each of the 56 sites have also been photographed and it is intended that representative images of river reaches be incorporated in an IWRMS Integrated Catchment Information Systems (ICIS) site for the Mkomazi catchment. Additionally, an extensive channel dimension data base, including stream reaches, for the Mkomazi catchment configuration has been completed and the associated values have been input to a base ACRU hydrograph routing menu. The menu has been tested, but as of the time of writing (June 2000) results have not yet been analysed. 3.6 Physical Input Variables The assessment of the impacts of development on water resources using a physical-conceptual model such as ACRU, also requires careful consideration of those physical input parameters relating to climate, soils and sediment transport. It is particularly pertinent to apply the best available rainfall data, since this is considered to be the driving force of such models. Because different characteristics of rainfall and distribution patterns affect the susceptibility of soils to erode and mobilise, these features are considered to be a crucial component of the Project research. 3.6.1 Rainfall The Mean Annual Precipitation (MAP) of the Mkomazi catchment ranges from 1283mm in the northwest to 1053mm in the southeast. However, Figure 2 indicates the variation in the Catchment MAP and clearly shows the driest region as running from Ixopo through the lower Mkomazi valley to yyyy.

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“Rainfall is the fundamental driving force and pulsar input behind most hydrological processes” (Schulze, Dent, Lynch, Schäfer, Kienzle and Seed, 1995). Hydrological responses, in nature and also in a daily model such as ACRU, are highly sensitive to rainfall input, with an error in rainfall estimation often doubling (or more) of the error in runoff estimation (Schulze, 1995; Sculze and Perks, 2000). A major effort was therefore expended in obtaining subatchment rainfall values which could be considered to be as realistic as possible, both spatially and temporally. Daily rainfall data for 92 rainfall stations, in and immediately adjacent to the Mkomazi Catchment, were selected for a first assessment of their pertinence in driving the daily rainfall for the catchment. After infilling missing daily data at each of the rainfall stations’ data using an inverse distance weighting program (Meier, 1997), and for a time period extending from 1945 to 1996, the rainfall stations were screened for appropriateness as driver rainfall stations. This was achieved by applying using the CALC_CORPPT program developed by BEEH to assist in the selection of the best available rainfall station data. The final selection resulted in the 26 driver rainfall stations shown in Figure11 being used in the model configuration. Each rainfall station has monthly adjustment factors factors input to the ACRU menu to provide a more representative subcatchment areal rainfall. The details of the selected rainfall stations are provided in Table B1, Appendix B, and the monthly adjustment factors associated with the rainfall stations for each of the 52 subcatchments is indicated in Table C5, Appendix C

Figure 11 Mkomazi Catchment: Selected “driver” rainfall stations 3.6.2 Evaporation Monthly A-pan equivalent reference evaporation values (Er) were derived on a regional basis by multiple regression analysis from factors such as maximum temperature, day length, distance from sea and altitude (Schulze, 1997). These 1’ x 1’ gridded values are area-weighted per subcatchment and then converted, internally within ACRU, by a Fourier Analysis to daily values. The derived daily values of Er are then adjusted down or up within the ACRU model on a day-by-day basis, according to whether or not a threshold rainfall was exceeded on that day.

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GIS coverages are available for the reference potential evaporation (monthly A-pan evaporations), as well as for monthly means of daily maximum and minimum temperatures for the 52 subcatchments of the Mkomazi catchment. The variability of these climatic features is indicated in Figures 12, 13 and 14, which show the absolute change in these characteristics between the months of January and July for A-pan, maximum and minimum temperatures respectively.

Figure 12 Mkomazi Catchment: Variability in reference potential evaporation

Figure 13 Mkomazi Catchment: Variability in monthly means of daily maximum temperatures

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Figure 14 Mkomazi Catchment: Variability in monthly means of daily minimum

temperatures 3.6.3 Soils Soils play a crucial role in catchments’ hydrological responses by: • facilitating the infiltration of precipitation, and thereby largely controlling stormflow

generation, • acting as a store of water which makes soil water available to plants, • redistributing water, both within the soil profile and out of it, • controlling evaporation and transpiration processes as well as drainage rates below

the root zone which eventually contributes into the groundwater zone which feeds baseflow.

Umgeni Water made a detailed coverage of Soil Land Types for the Mkomazi Catchment, compiled by the Institute for Soil, Climate and Water (ISCW), available to the Project. For each of the 177 Soil Land Types (as shown in Table C6, Appendix C), a vast amount of information on percentages of soil series per terrain unit, soils depths, texture properties and drainage limiting properties was provided by the ISCW. This Land Type information was translated to those hydrological soil characteristics for a two-horizon soil profile as required by ACRU using the School’s AUTOSOILS program, developed by Pike and Schulze (1995) from information contained in Schulze (1995). AUTOSOILS output includes the thicknesses of the topsoil and subsoil horizons, values of the soil water content at permanent wilting point, drained upper limit (i.e. field capacity) and saturation (porosity) for both soil layers and saturated drainage redistribution rates. Values of the above variables were determined for each soil series making up a Land Type and then area-weighted according to the proportions of Land Types found in a subcatchment. An average dominant texture class of sandy clay loam was assumed for all subcatchments. The final subcatchment values of the variables used in simulations identified in Section 1.2 of this Report are summarised in Table C7, Appendix C. The table also contains runoff related values, derived from Land Type information, of two further variables, viz. fractions of (i) adjunct impervious areas within a subcatchment, constituting the areas around channel zones assumed to be permanently wet and from

21

which direct overland flow is hypothesised to occur after a rain and (ii) disjunct impervious areas such as rock outcrops, from which rainfall running off, infiltrates into surrounding areas and influences their water budgets. In accordance with recommendations made by Summerton (1996), the thickness of the subsoil horizons is increased by a 0.25 m, to account for the trees' deeper rooting patterns, in those subcatchment areas where the land use classification is represented by either the forest or plantation categories (Figure 7). With assistance from the Dipartimento di Scienza del Suolo e Nutrizione della Pianta Universita di Frienze, Florence in Italy (see Appendix A), sediment routing is to be incorporated to the ACRU simulations to determine the sediment yield of those critical regions upstream of the two major proposed dam sites. The determination of the rate of sediment transport down the river channels of the Mkomazi could provide water managers with essential information on which to base certain water use licence approvals, required under the National Water Act (1998). 3.6.4 Erosion Response Units The approach adopted by the Project in the determination of soil erosivisity and sediment transport rates is to delineate Erosion Response Units (ERUs) and sites of potential land degradation based on a classification of erosion type features. The delineation of the ERUs, by Universita di Frienze Florence in Italy, was based on analyses and interpretation of stereo-aerial photographs and orthophotos. Different approaches to the modelling of erosion using ERUs have been reviewed and the Sidorchuk Gully Erosion Model (Sidorchuk, 1996) has been selected as appropriate. While this model has not been used extensively in southern Africa, it has been found to be robust in the simulation of gully erosion in semi-arid areas in Australia. It is anticipated that the details of the findings of Universita di Frienze, Frienze, Italy will be incorporated in a subsequent report. 3.7 Water Allocations and Abstractions To effectively assess the impacts of any water resource development on the generation of streamflows, it is necessary to simulate the conditions which satisfy the Reserve (National Water Act, 1998) as well as any direct anthropogenic streamflow abstractions performed within the Catchment. For the purpose of this study these were viewed as comprising two basic water allocations, viz: • environmental requirements and • irrigation and domestic abstractions 3.7.1 Environmental requirements: instream flow requirements The determination, and fulfilment, of the ecological reserve for the Mkomazi Catchment is a major issue of concern. Much preparatory work has already been conducted to determine the Instream Flow Requirements (IFRs) for the Mkomazi River (IWR Environmental, 1998). The output of this work is a set of tables in which flow values for each month of the year are specified at certain points of interest known as “IFR Sites”. The translation of these tables into information, which can be used as input to hydrological models, has been the focus of recent work by Hughes et. al., (1994) and Hughes and Ziervogel, (1998). With the use of two models developed by these authors,

22

the IFR tables are translated, firstly into a time-series of “IFR Demands” and then into time-series of outflow from a reservoir. However, the BEEH has not as yet applied the Mkomazi ACRU simulated streamflows to estimate the total release required by each of the proposed Dams, in order to satisfy the IFR requirement for the nearest downstream IFR site. However, it is anticipated that this will form part of a Phase 2 Report. 3.7.2 Irrigation and domestic abstractions There is a paucity of information available to accurately perform the necessary ACRU irrigation modules for the Mkomazi catchment. For this reason, certain field observed experience-based assumptions were made to simulate effective irrigation abstraction and these are provided in Appendix C, Section 2. A similar situation prevails for the simulation of domestic abstractions. Consequently this factor was omitted from the base menu simulations. However, the GeoData Institute, of the University of Southampton in the UK is conducting extensive field research on primary water use within the Mkomazi catchment. It is anticipated that their research findings will be incorporated in a subsequent report. 3.7.3 Systems Operation: dam operating rules The level of dam operating rules, required for the operation of the ACRU model was, at the time of writing, unavailable for the proposed Smithfield Dam. However, certain assumptions were incorporated to the model simulations and these are provided in Appendix C, Section 3. Several of the values derived from these assumptions are provided in Table C8, Appendix C. 3.8 Land Use Change, Water Resources Development and Potential Scenarios 3.8.1 Baseline land cover It is necessary to have a baseline land cover against which to compare the hydrological responses of different land uses. This is especially pertinent to the assessment of the impacts of potential land use change and scenarios on water resources. In this Study, the land use classification used to represent natural land cover conditions was Acocks’ Veld Types (Acocks, 1988), in accordance with the recent research by Schulze (2000). Figure 15 shows the Acocks Veld Type distribution of the Mkomazi catchment. The Mkomazi Veld Types and areal extents applied in baseline simulations are provided in Appendix C, Table C9. The Mkomazi Veld Types and areal extents applied in the grassland category of all other land use simulations are provided in Appendix C, Table C10. The water use coefficients applied were taken from Schulze et. al. (1999) and Schulze (2000) and are provided in Appendix C, Table C3.

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Figure 15 Mkomazi Catchment: Acocks’ Veld Types 3.8.2 Inter-basin Transfer The first phase of the MMTS will involve the building the proposed Smithfield Dam on the Mkomazi River. DWAF plan that initially 5.6 m3.s-1 will be a transferred (with peak transfer capacity of 7.0 m3.s-1) from the proposed Smithfield Dam via a pumpstation-shaft-tunnel to an existing dam near Baynesfield in the Mgeni Catchment. The operation of the inter-basin transfer representing Smithfield Scheme Phase1 of the MMTS was input to the ACRU reservoir routine in accordance with the considerations outlined in Appendix C, Section 3 and the input values given in Appendix C, Table C8. The input values of the dam storage capacity at full supply level (FSL), 137 x 106 m3, and surface area, 583 ha, at FSL are those given in the DWAF MMTS Pre-Feasibility Study (DWAF, 1998b). The transfer capacity rate was input to the ACRU Phase 1 menu as a daily draft out of the configured Mkomazi system and is also provided in Appendix C, Table C8. 3.8.3 Potential Climate Change Predictions of the impacts of changes in atmospheric concentrations of CO2 are generally made using the output from large scale General Circulation Models (GCMs) run for an effective doubling of CO2 from 280 to 560 ppmv. The GCM output of temperature and precipitation is transposed for application as monthly adjustments to daily or monthly baseline climate conditions, assuming no change in climatic variability or in daily persistences of wet and dry and sequences between the baseline and future climates (Schulze and Perks, 1999). The Hadley transient GCM, which includes a coupled ocean model and runs at a spatial resolution of 2.50O latitude and 3.75O longitude, was developed by the UK Meteorological Office and the Hadley Centre. In this Study, scenarios of potential climate change were carried out by applying conversions of the Hadley 1998 GCM (Version2) simulations. GCM grid point output for monthly values of precipitation, maximum and minimum temperature was provided to the BEEH by the University of Cape Town, South Africa.

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These values were downscaled for South Africa to a 1/4o x 1/4o resolution grid using procedures described in Perks et. al. (2000). The relevant values for each of the 52 subcatchments were extracted from this 1/4o surface for this climatic change impact assessment. In this Study, the simulation output applied includes greenhouse gas forcing (excluding sulphates) through a 1% per year increase in atmospheric CO2 concentrations, with the future 2x CO2 scenarios being estimated to represent the period 2030 – 2059. For each subcatchment of the Mkomazi the following changes were made to the present land use menu: • the monthly A-pan equivalent reference potential evaporation values were increased

to account for the change in atmospheric demand, this being assessed at a 3% increase per OC increase of maximum temperature (Schulze and Kunz, 1993)

• the option for soil water evaporation and plant transpiration to be computed separately was switched on, to call the ACRU routine which can simultaneously account for enhanced soil water evaporation rates as well as transpiration loss feedbacks under a 2x CO2 scenarios

• the CORPPT values, were adjusted for each month as a result of the GCM generated changes in precipitation, as downscaled to the Mkomazi

• the enhanced CO2 -induced stomatal conductance routine in ACRU was invoked for

both C3 and C4 plants, depending on whether the dominant land use in a subcatchment was C3 or C4.

The values used for the adjustment of monthly A-pan values and for the month to month adjustment of daily precipitation values in the ACRU menus for the climate change impact assessment for the Mkomazi study area are provided in Appendix C, Tables C11 and C12 respectively. The Mkomazi land use applied to the ACRU climate change menu was the same as that applied to the ACRU present land use menu. Furthermore, a present land use simulation was run with soil water evaporation and plant transpiration computed separately in order to have a baseline by which to compare any hydrological impacts of climate change.

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4. IMPACTS OF PROPOSED DEVELOPMENT ON MKOMAZI WATER RESOURCES

As a physical-conceptual model, physically realistic and observationally derived variables on climate, soils, vegetation, catchment characteristic, irrigation and dams are input in ACRU. Structurally and conceptually ACRU has, therefore, been designed specifically to simulate predictive scenarios based, inter alia, on land use change. ACRU is not a parameter-fitting model, which is calibrated until simulated values mimic observed values. It is a deterministic model structured to give realistic answers for the right hydrological reasons. It is, however, essential to have the assurance that the modelled streamflow values reflect observed values. For this reason verification studies were performed to compare the ACRU simulated streamflows with the observed streamflow records at the subcatchments representing the DWAF gauging stations U1H005 (Camden), U1H006 (Delos Estate) and U1H003 (Umkomazi Drift), all on the Mkomazi River (see Figure 1). The ACRU model was run with an initial menu comprising the present land use of the Mkomazi Catchment using the land use identified from the 1996 LANDSAT TM image and those input factors identified in Chapter 3. While this initial run cascaded the streamflows as indicated in Figure 8, flow routing was not performed. The difference in Median Annual Runoff (MED) at the Mkomazi estuary mouth, applying the present land use with ACRU (1040 million m3) and the Mean Annual Runoff (MAR) assessed by BKS (959 million m3) using calibrated monthly time step Pitman model simulation and with some present day water-use (DWAF, 1998b) is 8%. This comparison is only partially valid in that different scales apply in terms of both the modelling time step (daily vs. monthly) as well as the duration for which the streamflows were assessed (ACRU: 1945 to 1996; Pitman: 1925 to 1995). Moreover the comparison is made between values of MED and MAR. However, it does confirm that the initial uncalibrated ACRU simulated streamflows for the entire catchment are within the predicted magnitude. 4.1 Verification Studies Statistical verification results for the Mkomazi with the present land use input menu (PLM) for the streamflows simulated at the three DWAF gauging stations are provided in Appendix D, Table D1. The verification results are provided graphically for U1H005, U1H006 and U1H003 in Figures 16, 17 and 18 respectively.

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Figure 17 Comparisonsof (i) accumulated streamflows and (ii) monthly totals of daily streamflows at Goodenough (U1H006)

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Figure 18 Comparisons of (i) accumulated streamflows and (ii) monthly totals of daily streamflows at Delos Estate (U1H003)

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4.1.1 Verification of streamflows at Camden (U1H005) The difference between the median annual ACRU simulated streamflows (630 million m3) and the observed streamflows (594 million m3) at Camden (U1H005) when using the present land use ACRU menu is only 6%. This result is considered excellent for an initial uncalibrated simulation of the upper Mkomazi catchment streamflows. However, it is necessary to analyse the streamflows at a finer resolution to be confident of the model results. The automation of gauging records at U1H005 only commenced in 1960 and it was therefore considered appropriate to restrict the analysis of the verification of ACRU simulated streamflows to the period from 1960 to 1996. 4.1.1.1 Accumulated Streamflows The accumulated monthly streamflows, over the 37-year period from 1960 to 1996 are shown in Figure 16 (i). Comparing the accumulated simulated values with the observed values indicates that, for the given time period, ACRU is over-simulating the Mkomazi streamflows with the present land use input values. Over the time period there is a 13.5% difference in accumulated streamflows. As previously stated, these are the results from an initial simulation and it is necessary to identify the reason(s) for the discrepancy. However, a possible explanation for the divergence could be attributable to changes in land use over the time period. 4.1.1.2 Monthly Totals of Daily Streamflows The differences in streamflows described in Section 4.1.1.1 can be investigated by comparing the individual monthly totals of daily simulated streamflows with the observed streamflows for the same time period, as shown in Figure 16 (ii). In this study, zero values were input where there were periods of missing observed data. While it is necessary to bear this in mind, it does appear that with the present menu, ACRU is over-simulating baseflows, the most notable instances of which are during the years 1964, 1973, 1982, 1986, 1988, 1992 and 1994. However, these were drought years in which the El Niño-Southern Oscillation signal was very strong and a possible explanation for the differences could be attributed to dam or river abstractions not accounted for in the initial menu preparation. Figure 16 (ii) indicates a substantial divergence between the simulated and observed values for October 1987 when the observed monthly value greatly exceeds the ACRU simulated monthly value. Analyses of the daily rainfall input files shows the flood event which occurred at that time was not identified by the ACRU simulations because at daily rainfall stations the extreme rainfall event of late September 1987 was recorded as discrete events over a period of four days (26, 27, 28 and 29 September 1987). In reality the rainfall occurred as a continuous single event. The time series of simulated streamflows is reproduced for shorter time spans in Figure 19 (i), (ii), (iii) and (iv) for enhanced illustration of the features that require further consideration. Overall, the simulated streamflows follow the observed streamflows very well. Nonetheless, the present land use ACRU menu is under-simulating peak flows as well as over-simulating baseflows. This can be seen for 1961, 1967, 1978, 1979, 1984, and 1988. However, these were La Niña (i.e. wet) years and it is hypothesised that the soil characteristics in the present land use menu do not generate realistic antecedent soil moisture conditions for continuous rain events. This indicates that the soils data input of the present land use menu requires some refinement. It is anticipated that this will be one focus of a further Report.

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Verification of ACRU simulated streamflows at Camden

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It does appear that over the 37 years there is an increasing divergence in the correlation between the simulated and the observed of the upper Mkomazi streamflows. This could be attributable to a change in land use over the time period or to problems with the recording equipment. The upper Mkomazi is relatively unimpacted by anthropogenic use and change, and it is postulated that the apparent oversimulation shown in Figure 19 (iv) is as a result of gauging problems. However, Figure 19 (i), (ii) and (iii) show that overall seasonal magnitudes and trends are simulated well and the simulations for the period 1960 to 1969, c.f. Figure 19 (i), are particularly good, reflecting a close correlation to the observed streamflows. Some of the features identified by the verification study still need further investigation and these include phasing of the rainfall data to ensure that records reflect the same time and day as the runoff records, e.g. January to February 1976, as well as routing flows down the catchment. It is clear that there have been gauging problems at certain times in the history of the DWAF records. There are several lengthy periods of no recorded data in the record. Furthermore, when the gauging mechanism was overtopped by flood events, e.g. January / February 1976 and September 1987, and a no data collection was recorded, subsequent recordings appear to be anomalous. Thus, in the statistical analysis of the monthly values of streamflows, values were adjusted so that where there was doubt as to the reliability of the recorded data, both the respective simulated and the observed values were zeroed to account for this discrepancy. 4.1.1.3 Statistical Analyses of Monthly Totals of Daily Streamflows The statistical analysis was performed for individual month-by-month simulated vs observed values for median flows. The following are some statistical results from a first run blind simulation, i.e. with no alterations to force the model to simulate better. Table

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D2 of Appendix D provides a more comprehensive list of the conservation and regression statistics. Results are for monthly totals of daily values. • Difference between coefficients of variation of observed and simulated flows: 27.8% • Difference between the skewness coefficients of observed and simulated flows:

20.6% • Correlation coefficient: 0.89 • Coefficient of agreement, observed vs simulated flows: 0.944 • Slope of a scatter plot of observed vs estimated flows: 0.739 The verification study of streamflows at Camden instilled confidence that the configuration and ACRU present land use menu for the upper 1742 km2 of the Mkomazi system could be expected to give realistic results for scenarios which assess the impacts of potential catchment development. 4.1.2 Verification of streamflows at Goodenough (U1H006) At Goodenough (U1H006), the difference between the median annual ACRU simulated streamflows (1035 million m3) and the observed streamflows (747 million m3) when using the present land use ACRU menu is 39% for the initial run. However, this result is only superficially disappointing when one considers the unreliability of the DWAF recording gauge. This gauge has a low discharge table limit and therefore, unreliable high flow measurements (DWAF, 1989b). Furthermore, other sources (DWAF, 1989a) estimate the present MAR at U1H006 to be 956 million m3 (BKS) and 973 million m3 (Surface Water Resources of South Africa, 1990) for the period 1920 to 1995. Again, this confirms that the initial ACRU simulated streamflows at Goodenough are within the predicted magnitude. Notwithstanding the anomalies of the recording gauge at U1H006, it was considered appropriate to analyse the streamflows in a similar procedure to that performed for the streamflows at U1H005. The automation of gauging records at U1H006 commenced in 1962 and it was therefore considered pertinent to restrict the analysis of the verification of ACRU simulated streamflows to the period from 1962 to 1996. 4.1.2.1 Accumulated Streamflows The accumulated monthly streamflows, over the 35-year period from 1962 to 1996 are shown in Figure 17 (i). As expected, the accumulated ACRU values for the downstream Mkomazi streamflows resulting from the present land use menu are greatly oversimulated when compared with the observed values. Over the time period there is a 47 % difference in accumulated streamflows. The land use in the downstream Mkomazi has experienced more anthropogenic changes than the upper catchment. However, in light of the knowledge that the gauging station is unreliable this component of the verification study is somewhat irrelevant. 4.1.2.2 Monthly Totals of Daily Streamflows Figure 17 (ii) shows the differences in the individual monthly totals of daily, simulated streamflows with the observed streamflows for the same time period. The principal of applying zero values where there were periods of missing observed data, described above, was repeated. In spite of the gauging problems at U1H006, ACRU simulated streamflows do follow the same trends of magnitude and seasonality as the observed

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streamflows. However, it is apparent that even when ignoring the affects of the low discharge table, the present land use ACRU menu results in the over-simulation of base flows. Comparing Figure 16 (ii) with Figure 17 (ii) shows that there are similarities in the hydrological regimes of the two sites. This concurs with the study performed by Smakhtin and Hughes for the DWAF Mkomazi IFR Study in 1998 (DWAF, 1998b) in which it was also noted that gauge U1H006 also shows a slight increase in low flows, particularly in dry months of the year, relative to U1H005. Smakhtin and Hughes suggest that this might be as a result of a slightly more baseflow driven regime in the lower reaches of the Mkomazi river (DWAF, 1998b). The relative increase in low flows can be seen in Figure 17 (ii), but is further investigated in Figure 20, which shows the time series of simulated streamflows reproduced in shorter time spans.

Verification of ACRU simulated streamflows at Goodenough

Figure 20 Comparisons of monthly streamflows for the periods (i) November 1962 - December 1969, (ii) January 1970 - December 1979, (iii) January1980 - December 1989 and (iv) January 1990 - September 1996

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The most notable instances of over-simulation of baseflows in the lower Mkomazi catchment are during the years 1964, 1965, 1972, 1973, 1974, 1976, 1981, 1982 1985, 1986, 1991and 1994. The occurrence of this feature increases with time, so it may well be that the present land use of the lower Mkomazi has changed more substantially than in the upper part of the catchment. This suggests that the influence of dam and / or river abstractions in the most downstream reaches of the Mkomazi are much larger than captured by the present land use ACRU menu. In contrast to the verification study of streamflows at Camden, the present land use ACRU menu appears to be over-simulating peak flows, e.g. 1979, 1984, 1987 and 1992. This feature was investigated by examinination of the ACRU daily output file of streamflows at Goodenough. It transpires that during and subsequent to extreme rainfall events, the gauging weir experiences over topping, with observed daily streamflows, when recorded, consistently being measured as between 4.00 and 4.50 mm.

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There is however, very good correlation of streamflows for the periods 1968 to 1970, (c.f. Figure 20 (i) and (ii)), 1977 to 1980, (c.f. Figure 20 (ii) and (iii)) and 1989 to 1990 (cf Figure 20 (iii) and (iv)), when the problem of overtopping does not arise. As in the verification analyses of the upper Mkomazi, some of the hydrograph features identified by the simulations still need further investigation. This includes the phasing of the rainfall data to ensure that records reflect the same time and day as the runoff records, e.g. March 1973. 4.1.2.3 Statistical Analyses of Monthly Totals of Daily Streamflows A statistical analysis was performed for individual month-by-month simulated vs observed values for median flows. Some of the statistical results are included below. Table D3 of Appendix D provides a more comprehensive list of the conservation and regression statistics. Results are for monthly totals of daily values. • Difference between coefficients of variation of observed and simulated flows: 23.60% • Difference between the skewness coefficients of observed and simulated flows:

22.62% • Correlation coefficient: 0.88 • Coefficient of agreement, observed vs simulated flows: 0.981 • Slope of a scatter plot of observed vs estimated flows: 0.934 Clearly the gauging problems at U1H006 have resulted in unreliable recorded data of high flows in the lower reaches of the Mkomazi. However, it is considered that the present land use ACRU menu is quite acceptable for the application of scenarios studies to assess the impacts of potential Mkomazi catchment development. 4.1.3 Verification of Streamflows at Delos Estate (U1H003) At Delos Estate (U1H003), only slightly downstream of Goodenough, there is a difference of 25% (vs 47%) between the median annual ACRU simulated streamflows (1052 million m3) and the observed streamflows (1417 million m3) when using the present land use ACRU menu is. However, the recording of streamflow data at Delos Estate ceased in 1969. The reason for this is unclear, but it can be assumed that the records were recorded manually. A check of the daily streamflows in the ACRU files does reveal that there were only two periods when the streamflows were not recorded, i.e. for a few weeks in September 1957 and from March to May in 1959. This, therefore, represents a relatively complete, though short, time series of historical flows. Unfortunately the era of the record has doubtful implications for the assessment of any present land use ACRU simulations. Nonetheless, the historical data available for the period 1957 to 1969 were included in the preparation of the ACRU present land use menu so that some evaluation of the simulated streamflows could be ascertained.

4.1.3.1 Accumulated Streamflows The accumulated monthly streamflows, over the 13 year period from 1957 to 1969 are shown in Figure 18 (i). The accumulated ACRU values for the most downstream reaches of the Mkomazi, resulting from the present land use menu, are considerably under simulated when compared with the observed values. Over the time period there is a 27%

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difference in accumulated streamflows. It is postulated that the land use in the downstream Mkomazi has undergone substantial anthropogenic change since the late 1950s and 1960s and that comparison of accumulated streamflows in this instance is unpractical. 4.1.3.2 Monthly Totals of Daily Streamflows Nevertheless, Figure 18 (ii) shows that, once again, the ACRU simulated streamflows follow the seasonality of the observed streamflows. The magnitude of the base flows is remarkably closely correlated in some years i.e 1958, 1960, 1961, 1962, 1963 and 1968. However, it is also clear that under more natural land use conditions the hydrological regime of the lower Mkomazi included a greater magnitude of both high and flushing flows. Furthermore, the verification study of streamflows at Delos Estate also confirms that there are problems with the observed streamflow record at Goodenough. For example, whilst Delos Estate gauging weir is only slightly downstream of Goodenough, Figure 18 (ii) indicates that at the beginning of 1963 the streamflows at Delos Estate were 140 mm whereas Figure 18 (ii) indicates that for the same period in time the streamflows at Goodenough were 109 mm. 4.1.3.3 Statistical Analyses of Monthly Totals of Daily Streamflows A statistical analysis was performed for individual month-by-month simulated vs observed values for median flows. Some of the statistical results are included below. Table D4 of Appendix D provides a more comprehensive list of the conservation and regression statistics. Results are for monthly totals of daily values. • Difference between coefficients of variation of observed and simulated flows: 29.92% • Difference between the skewness coefficients of observed and simulated flows:

25.75% • Correlation coefficient: 0.86 • Coefficient of agreement, observed vs simulated flows: 0.441 • Slope of a scatter plot of observed vs estimated flows: 0.923 Clearly the gauging problems at U1H006 have resulted in unreliable recorded data in high flows in the lower reaches of the Mkomazi. However, it is considered that the present land use ACRU menu is quite acceptable for the application of scenarios studies to assess the impacts of potential Mkomazi catchment development. 4.2 Statement of Level of Confidence ACRU simulates streamflow volumes similar to those of the DWAF studies (DWAF, 1998a) and the verification studies of the ACRU present land use menu give encouraging results. The Mkomazi catchment is, relatively, unimpacted anthropogenically and this is reflected in the results for the upper catchment. More information is required with respect to the farm dam and river abstractions and at present the irrigation scheduling is unknown. While this is a concern, irrigation is not a major land use even in the lower Mkomazi catchment. In view of these considerations the BEEH express high confidence in the results of the generation of realistic streamflows at the points of interest specified by Umgeni Water.

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4.3 Assessment of the Hydrological Dynamics of the Mkomazi Catchment The following results of the hydrological dynamics of the Mkomazi catchment are available:

- Annual quantities of streamflows at each of the 52 SCs under conditions of: - baseline (Acocks, 1988) land cover - present land use - present land use with development to include the operation of the proposed

Smithfield Dam, Phase 1 - potential climate change conditions for an effective doubling of atmospheric CO2

concentration, but assuming present land use - potential climate change assuming present land use, but including the operation of

the proposed Smithfield Dam, Phase 1. The volumes of streamflow (106m3) generated at each of the 52 SCs for the median year, are provided in Appendix E, Table E1.

- Daily, as well as monthly streamflows at each of the 52 SCs under the above conditions.

- Month-by-month and annual frequency analyses of the above streamflow scenarios at the 5th, 10th, 20th, 33rd, 50th, 67th, 80th, 90th and 95th percentiles of non-exceedence.

4.4 Impacts of Streamflows under Present Land Use Conditions The principles applied to the assessment of the impacts of present land use, using Acocks’ Veld Types as a baseline land cover, were discussed in Section 3.8.1. The procedure for the assessment required two ACRU runs, the first with Acocks Veld Types as the baseline land cover and the second with the present land use and cover. In order to ascertain the relative impacts of present land use on the streamflows of the Mkomazi, the volumes of MED simulated under present land use for each of the 52 SCs was expressed as a percentage of the corresponding volume of MED simulated under the baseline conditions. The percentages of change in MED are shown in Figure 21 (top). It can be seen from Figure 21 (top) that the most impacted areas are those which have experienced greatest anthropogenic changes. The afforested areas around Goodhope (SC 25), Boston (SC 26), Bulwer (SC 22), Donnybrook (SC 29 and 30), Ixopo (SC 33, 40, 43 and 44) are among the most severely impacted SCs with percentage reductions exceeding 30 % and reaching up to 80% (Ixopo, SC 40). A number of these SCs also feature irrigation as a land use. However, it transpires that under present land the most severely impacted SCs are those in which irrigation is the major land use. The agricultural region around Ixopo is clearly the most impacted region in the Mkomazi catchment with SCs 41 and 42 experiencing streamflow reductions of 96% and 97% respectively. Subcatchments where the areal extent of afforestation exceeds the areal extent of irrigation (e.g. SCs 22, 25, 26, 29, 30, 33 and 40, 43 and 44) have a less deleterious impact on streamflows than those where irrigated land use exceeds afforestation (SCs 41 and 42). This concurs with Taylor’s (1997) findings in the Pongola catchment.

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Figure 21 Mkomazi Catchment: Impacts of Present Land Use as a % change in MED from Acocks Veld Types (top), accumulated streamflows (106 m3) (middle) and individual subcatchment streamflows (106 m3) (bottom). The generation of streamflows in the upper Mkomazi catchment shows very little change from baseline conditions. This is not surprising since the major present land cover of this area was identified from the 1996 LANDSAT TM image as unimproved grassland (Figure 10). Also, this area is sparsely populated and consequently there are few anthropogenic effects on streamflow generation. There are, however, some exceptions that require

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explanation. First the agricultural areas around Polela (SC 12 13 and 17), where irrigation and afforestation are practiced, indicate moderate streamflows reductions (16%, 7% and 9% respectively) from baseline conditions. Secondly, as mentioned above, where there is substantial afforestation such as around Bulwer (SCs 20, 21 and 22, c.f. Figure 9), the streamflows are reduced to a greater extent (20%, 27% and 36% respectively). Thirdly, there is greater population density around Impendle (SCs 15 and 16) and the major land use is semi-commercial / subsistence dryland agriculture (SC 15), with indication of degraded unimproved grassland (SC 15). In this instance, the streamflows generated are higher than those under baseline conditions by 11% (SC15) and 0.75% (SC16) respectively. Other areas indicate an increase in streamflow generation under present land use. There is a slight increase (0.20%) in streamflows from SC6 in the High Drackensberg mountain range. However, this is probably due to the assumption built into the present land use menu that there is some alien riparian vegetation which did not feature under baseline conditions. Subcatchments 38 and 39 were specifically delineated to represent the impacts of subsistence agriculture (SC 38) and degraded land (SC 39). The streamflows generated from SC 38 are substantially higher (25%) than under baseline conditions whereas the increase in streamflows from SC39 is 5%. The increase (22%) in SC 46 is the result of an invasion of thicket and bushland (Figure 10) into an area which under baseline conditions was coastal forest and thornveld. The main Mkomazi River is relatively unimpacted. However, the impacts of reduced flows from the Donnybrook and Ixopo area as a result of commercial land use can be observed as a reduction in the Mkomazi River flows downstream from the Hela Hela Valley (SC 35).

4.4.1 Accumulated streamflows in the Mkomazi

The volumes of accumulated streamflows under present land use for a year of median flows are indicated in Figure 21 (middle). As expected the generation of streamflows in the head waters of the Drakensberg Mountains is substantial. Figure 21 (middle) shows that the quantity of water available in the upper Mkomazi increases significantly at the confluence of the three main tributaries of the Nzinga, Mkomazi and Mkomazana at the site of the proposed Impendle Dam (SC14). Further down the Mkomazi catchment the influence of individual tributaries becomes less pronounced with, as expected, little contribution from the Ixopo area. The areas where there is greatest anthropogenic influence result in the least water producing areas. However, the Mkomazi River valley can be clearly seen as the streamflows accumulate downstream.

4.4.2 Individual subcatchment contribution

The individual subcatchment contributions for the median year are also shown in Figure 21 (bottom). The visual impact of a pattern of generated streamflows is less striking in this instance than in the case of accumulated streamflows. However, examination of the catchment configuration supports the findings above. The most significant volumes of streamflows are generated in the upper catchment, whilst the streamflows around the Ixopo area are so heavily impacted that they have all but ceased.

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4.5 Impacts of Proposed Dams on Streamflows As described above there is substantial available water in the upper Mkomazi catchment for allocation to other land uses or to inter-basin transfers. This section focuses on the impacts of Phase 1 of the proposed MMTS in which water from the proposed Smithfield Dam (Phase1) is to be transferred out of the Mkomazi and into the Mgeni system as discussed in Section 3.8.2. Figure 22 (top) illustrates the impacts of transferring 7m3.s-1 out of the Mkomazi catchment from the proposed Smithfield Dam, assuming that with exception of this development, all other land use and cover remains the same as under present land use conditions.

Figure 22 Mkomazi Catchment: Impacts of (top) Phase 1 of Smithfield Dam, (middle)

potential climate change and (bottom) potential climate change, and including the operation of Phase 1 of Smithfield Dam on streamflows

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Clearly the greatest impact on accumulated streamflows is at the site of the proposed transfer where there is expected to be a 32% change in MED from present land use. The impact of the proposed transfer diminishes downstream. However, the Mkomazi River does not recover and median annual streamflows at the estuary are still reduced by 22%. Whilst the main water consumers under present land conditions i.e. irrigated agriculture and afforestation are not directly affected by the proposed Smithfield Dam, there are streamflow reductions in accumulated flows where tributaries from the Donnybrook area, viz. SCs 29 and 30, meet the mainstream Mkomazi. The impacts of afforestation and irrigated land use on streamflows from the Ixopo area do not appear to exacerbate the reduction in mainstream Mkomazi river flows. Nonetheless, it is intended that Phase 2 of this Study will highlight whether their operation, in conjunction with the releases from Smithfield Dam, may need regulation in order to satisfy the requirements of the environmental Reserve. 4.6 Impacts of Potential Climate Change on Streamflows The principles adopted to assess the impacts of potential climate change on the generation of streamflows in the Mkomazi catchment were described in Section 3.8.3. The procedure for the assessment required two ACRU runs, the first with a present land use adapted to compute the soil water evaporation separately from plant transpiration to represent a baseline condition and the second with the potential climate change to precipitation, temperature and evaporation, as described in Section 3.8.3 for a 2 x CO2 scenario. In order to ascertain the relative impacts of climate change on the streamflows of the Mkomazi, the volumes of MED simulated for each of the 52 SCs were expressed as a percentage of the corresponding volume of MED simulated under the present baseline condition. The percentages of change in MED are shown in Figure 22 (middle and bottom). 4.6.1 Impacts assuming present land use practices Figure 22 (middle) indicates that virtually the entire the Mkomazi is likely to be heavily impacted by potential climate change and that the SCs most likely to be affected are those in which there is presently excess water which can be utilised by plants in transpiration. Streamflow reductions in the upper Drakensberg are shown as being as significant as reductions in the lower Mkomazi. Areas supporting commercial afforestation as well as irrigation are amongst those most severely affected (SCs 25 and 40). However, Figure 22 (middle) highlights an apparent anomaly, in that where there is a greater areal extent of irrigated land compared with afforestation, the percentage reduction from present land use is negligible (SCs 41 and 42). This can be attributed to there being no available water in those SCs under present land use conditions and concurs with the findings in Section 4.4. SC 17 in the upper catchment is also anomalous in that this SC experiences the greatest streamflow reductions under potential climate change. Since this SC has not experienced any major anthropogenic change, it is hypothesised that this is attributable to increased soil water evaporation under conditions of low basal cover of the grassland. The percentage change in MED as a result of potential climate change is greater than that experienced by the Smithfield Phase 1 inter-basin transfer.

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4.6.2 Impacts of proposed dams with climate change Figure 22 (bottom) also indicates the impacts of transferring 7m3.s-1 out of the Mkomazi catchment from the proposed Smithfield Dam under the scenario of climate change described in Section 3.8.3. As expected, and concurring with the study of the impacts of climate change on the percentage change in MED under present land use, the greatest impact on accumulated streamflows is at the site of the proposed transfer. However, under conditions of potential climate change there is expected to be a 79% reduction in MED at the site of Smithfield Dam, compared to the 32% reduction without climate change. The impact of the proposed transfer diminishes downstream, however, with the Mkomazi’s streamflows at the estuary being reduced by 45% (compared with 22% without climate change). The impacts of commercial forestry and irrigated land do not appear to compound streamflow reductions under potential climate change, presumably because, as stated in Section 4.6.1, the tributaries from those catchments do not suffer significant streamflow reductions. 4.7 Implications of Development for Water Resource Management

Several conclusions can be drawn from the above studies regarding implications for water resource development. The assessment of streamflows under present land use conditions revealed that there are areas in the Mkomazi where present streamflow generation is greater than under natural conditions. This was highlighted in SCs where there is evidence of urban / built up land, subsistence agriculture and degraded grassland. Furthermore, it appears that the impacts of subsistence agriculture is greater than that of degraded land. This is of significance in the Mkomazi catchment since these activities and their impacts on streamflows are expected to increase. The perpetuation of these activities is likely to lead to increased soil erosion, ultimately posing problems of sedimentation of rivers, farm dams and proposed major impoundements. While the impacts of irrigation are greater than afforestation, the impacts of both these commercial practices on the generation of Mkomazi streamflows are high. Impacts of irrigation on streamflows around the Ixopo area are such that there is no discernible streamflow even in the median year. However, this does not indicate that there is no water, only that the streamflows have been dammed. Phase 1 of the Smithfield inter-basin transfer reduces the Mkomazi catchment MED by just over 22% at the estuary. However, investigation of the simulated daily streamflows is necessary to ascertain whether the instreamflow requirements as provided by the (NWA) can be satisfied. The simulated releases can be use to generate a daily time series of flows which occurs within the natural flow range. Such a time series is critical for the effective operation of dam release rules designed to meet the needs of all catchment stakeholders. While this procedure is beyond the scope of this Report, it is intended that it will be incorporated in a subsequent Report.

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5. RISK ANALYSES OF THE IMPACTS OF DEVELOPMENT ON THE WATER RESOURCES OF THE MKOMAZI

5.1 Assessment of Water Resources under Different Climatic Conditions The assessment of the impacts of proposed development on Mkomazi water resources described in Chapter 4 were carried out on the simulated median annual streamflows from each of the 52 subcatchments. However, water resource managers frequently need to make decisions regarding the impacts of land use change under different climatic conditions, particularly in instances where low flows are already impacted by present land use. This Chapter addresses the more extreme streamflow scenarios or sequences likely to be of interest to water resource managers. First an assessment is made to ascertain which subcatchments are more susceptible than others in dry years and wet years under present land use. This is followed by assessment of the impacts on low flows under (a) present land use and (b) as a result of the proposed Smithfield Dam Phase1. 5.2 Impacts of Present Land Use under Dry Conditions The impacts of present land use on the generation of Mkomazi streamflows in dry hydrological years was assessed by the evaluation of the 20th percentile of exceedence. This represents the driest year in five, i.e. a year in which the annual streamflow depth is exceeded in four years out of five. The percentage change in accumulated streamflows from those in a year with median flows, as a result of the impacts of present land use in the driest year in five, are shown in Figure23.

Figure 23 Mkomazi Catchment: Impacts of dry climatic hydrological conditions

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Figure 23 shows that under dry hydrological conditions all subcatchments experience at least a 20% reduction in streamflows from the median years. The upper Mkomazi, mid Mkomazi valley and lower Mkomazi experience reductions in the range of 20% - 40%, with areas of the upper catchment being as impacted as those in the lower catchment. However, the subcatchments identified as being most impacted in the median year (Section 4.4) are also the most impacted in dry climatic conditions. Figure 23 shows that the SCs where commercial afforestation and irrigation are practiced are likely to have reductions in streamflows exceeding 45% from those in the median year. The reductions for the areas around Donnybrook and Ixopo (SCs 29, 30, 33, 40, 43, and 44) are 45%, 46%, 48%, 83%, 67% and 57% respectively. SCs 41 and 42, where irrigated land cover exceeds that devoted to afforestation, should also be expected to have high percentage reductions. However, investigation of the streamflows reveals that these SCs were already heavily stressed under in the median year. There is thus little change in simulated streamflows under dry climatic conditions, because even in the median year the streamflows are fully utilised. 5.3 Impacts of Present Land Use under Wet Conditions The impacts of present land use on the generation of Mkomazi streamflows in wet years were assessed by the evaluation of the 80th percentile of exceedence. This represents the wettest year in five, i.e. a year in which the annual streamflow depth is exceeded only in one year out of five. The percentage change in accumulated streamflows from the median year, as a result of the impacts of present land use in the driest year in five, are shown in Figure 24.

Figure 24 Mkomazi Catchment: Impacts of wet climatic hydrological conditions Figure 24 indicates that under wet hydrological conditions all subcatchments experience at least a 30% increase in streamflows from those of the median year. This change is fairly uniform throughout the catchment. The exceptions to this are, again, those areas

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where there is most anthropogenic change as a result of afforestation and irrigation and SCs 29, 30, 33, 40, 41, 42, 43 and 44 experience increases in streamflows over those in the median year in excess of 80%. The extent of the percentage increase over the median year in streamflows from these subcatchments is another indication that these are, generally, severely impacted areas. 5.4 Impacts of Present Land Use on Low Flows Water resources managers and consumers repeatedly identify low flows as being the most critical period of the streamflow regime. Frequently conflict arises over water usage at this time and instances of moratoria for new afforestation permits have been enacted on the strength of perceived reductions in low flows attributable to commercial forests (Schulze et. al., 1996). Whilst this report does not set out to assess the water usage of any individual land use, it is considered pertinent that an evaluation of the impacts of both the present land use and Phase 1 of the MMTS be carried out on the low flows of the Mkomazi. It is anticipated that this evaluation will prove beneficial to water resources managers in any decision making. The monthly low flows of the Mkomazi were examined to ascertain which consecutive months experienced the lowest flows. These were identified as June through November. In order to evaluate the impacts of low flows in average years and dry years, flows for these months were cumulated and the total assessed as a percentage of annual flows in the: • year of median flows, • flows in the driest year in 5 (at the 20th percentile) and • flows in the driest year in10 (at the 10th percentile). Whilst in ACRU’s frequency analysis each month is treated as an entity and not as part of a sequence of flows at a specified percentile, this comparison is statistically not ideal, but is nevertheless appropriate. The impacts of present land use on the simulated low flows of the Mkomazi are shown in Figure 25.

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Figure 25 Mkomazi Catchment: Impacts of present land use on simulated low flows

(June – November) as a percentage of annual flows for (top) median flow conditions, (middle) the driest year in 5 and (bottom) the driest year in 10

5.4.1 Impacts in a year of median flows In a year of median flow conditions, accumulated low flows in the Mkomazi range from 5% - 38% of the annual accumulated streamflows. Figure 25 (top) shows that, generally,

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there is little variation between subcatchments, with most of the Mkomazi catchment experiencing low flows of between 16 and 21% of median annual streamflows. Not unexpectedly, the areas that are already over-committed in terms of water allocation are the areas that have least of their annual streamflow regime represented as low flows. The Ixopo SCs 41 and 42 appear to be excluded from this feature, with low flows representing 37% and 50% of the median annual streamflows respectively. However, this indicates that in these SCs, the median annual streamflows that are impacted to a greater extent than the low flows. During periods of low flows in these SCs there is virtually no available water according to simulations, except for the seepage releases from dams. 5.4.2 Impacts in the driest year in five Figure 25 (middle) indicates that the impacts of present land use on the Mkomazi low flows in the driest year in five are more severe than those described in Section 5.4.1 for the median year. Again this impact is fairly uniform throughout the catchment and almost the entire mainstream river has low flows as a percentage of annual flows in the range of 14 to 18%. The areas, which have been described as being already water stressed, show the greatest impacts on low flows with some SCs simulated to having virtually no low flows in the driest year in five (SCs 25, 26, 33 and 43). The appearance that there is an increase in the percentage of low flows in the driest year in five from the median year (from 5% to 32%) for SC 41 can be given the same explanation as that given above for SCs 41 and 42. In these SCs the annual streamflows in the driest year in five are impacted to a greater extent than the low flows. During periods of low flows in these SCs there is virtually no available water, except for the seepage releases from dams. 5.4.3 Impacts in the driest year in ten The impacts of present land use on the Mkomazi low flows are further exacerbated in the driest year in ten. Figure 25 (bottom) shows that the change in impacts on low flows as a percentage of the annual streamflows in the driest year in ten, from the driest year in five, is greatest for the upper Mkomazi. Nonetheless, the impacts on the main Mkomazi River seem to be in the same range as in the driest year in five. 5.5 Impacts of Smithfield Dam on Low Flows Water resource managers require information regarding the impacts of dam operations on downstream flows in order to devise dam operating rules that the meet the needs of downstream users, including the environmental Reserve. The proposed Phase 1 of the MMTS, which will store water in the Smithfield Dam for transfer to the Mgeni catchment, was described in Section 3.8.2. It is anticipated that the transfer will have greatest impact in periods of low flows. The evaluation of the impacts of Phase 1 of the MMTS on low flows in years with median flow conditions and dry years was carried out following the procedure described in Section 5.4. The impacts of Phase 1 of the MMTS on the simulated low flows of the Mkomazi are shown in Figure 26.

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Figure 26 Mkomazi Catchment: Impacts of Smithfield Dam on simulated low flows

(June – November) as a percentage of annual flows for (top) median flow conditions, (middle) the driest year in 5 and (bottom) the driest year in 10

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5.5.1 Impacts in a year of median flows In a year of median flows, accumulated low flows in the Mkomazi range from 5% to14% of the annual accumulated streamflows. Figure 26 (top) shows that the impact of Phase1 of the MMTS on Mkomazi low flows is greatest at the site of the transfer (SC 23). The impact of the transfer diminishes downstream. However, the percentage of low flows available are considerably less than under present land use conditions without the dam in place. This could have critical implications for the efficacy of determining sufficient and timeous dam releases to satisfy the requirements of all downstream users in periods of low flows. 5.5.2 Impacts in the driest year in five Figure 26 (middle) shows that the impact of Phase1 of the MMTS on the percentage of low flows at the site of transfer (SC23) is, is as expected, lower in the driest year in five than in a year of median flows. This is a result of lower generation of streamflows in the Mkomazi in drier years. However, those subcatchments immediately downstream of Smithfield Dam (SCs 24 and 27) experience equivalent impacts on low flows to those in the median year, whereas those SCs further downstream experience greater impacts on low flows than in a year of median flows. Moreover, there is less variability between subcatchments in dry years than in the median year, with most of the downstream Mkomazi catchment experiencing low flows of between 7% and 10% of annual streamflows. 5.5.3 Impacts in the driest year in ten Figure 26 (bottom) shows that the impact of Phase 1 of the MMTS on the low flows of the Mkomazi in the driest year in ten is less than that experienced in the driest year in five for all subcatchments downstream of the Smithfield dam site. The percentages of accumulated low flows range from 9% to 11% of annual streamflows in the driest year in ten. This is, again, as a result of virtually no streamflows being generated in low flow periods under very dry conditions. The portions of streamflows generated in low flow months are only slightly less than in the driest year in five. However, the annual streamflows generated in the driest year in ten are much more reduced. The result is the appearance that low flows in the driest year in ten are less impacted than in the driest year in five when, in fact, it is the total streamflow (i.e. the denominator in the fraction) that is most affected. 5.6 Implications of Low and High Flow Years for Water Resource

Management Assessment of water resources under different climatic conditions reveals that the impacts of land use change in the Mkomazi could have significant influence on the streamflow generation, especially in dry years, where the land use has already been altered from natural conditions. The impacts of present land use on streamflows in the Mkomazi results in low flows for most of the catchment being between only 16% and 21% of median annual streamflows. Present land use conditions in drier years result in increasingly greater impacts. Those subcatchments that are already heavily utilised by afforestation and, particularly by irrigated land use are unlikely to be able to support any further large-scale commercial agricultural development.

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As expected, transferring water from the Mkomazi catchment results in greater reductions of low flows than under present land use with Smithfield Dam in place. Furthermore, the greatest relative influence in the variability of the low flow component of the streamflow regime is indicated as being in a year of median flows. This feature could intensify the difficulties associated with maintaining some semblance of the natural flow regime in the release of environmental flows from the proposed Smithfield Dam.

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6. DISCUSSION AND CONCLUSIONS It is very important that not only the end-users, per se, but also their requirements are identified and defined at the outset of any IWRM project. Identifying the issues of concern sets the scope of the project and allows the formulation of an approach to address those issues of concern to the end users. Furthermore, it is considered critical that the major stakeholders of any IWRM project feel that they are an integral part of the scheme, rather than being informed at a later stage. There are many hydrologically relevant parameters that can be determined using remote sensing. Satellite or aerial photography data can be used to supplement the information required to make sound water management decisions and this could be relevant in areas where data density is poor. The identification of additional land uses and associated management criteria, suggests that there is scope for the Mkomazi catchment hydrological dynamics to be simulated at a finer resolution than that previously available. Because the impacts of different land use and different management practices have varying impacts on streamflows, this factor instills greater confidence in the assessment of those water resources available for allocation. Umgeni Water has already estimated water demand in the Mkomazi catchment in preparation for the proposed transfer scheme from the Mkomazi to the Mgeni system. However, it is the intention of the EU Project to integrate the yield from the HRUs described, with those anthropogenic factors affecting water demand and water allocation patterns. This approach will focus on water resource demand at those spatial and temporal scales typical of the “traditional” engineering hydrology approach, with the major difference being that the information will be available at a daily rather than monthly scale. The provision of daily time series of streamflows is considered critical for the planning and design of effective operation of dam release rules designed to meet the needs of all catchment stakeholders. It is intended further to incorporate the impacts of water resource availability in terms of water infrastructure, water law and policy. This will result in Water Resource Response Units (WRRUs), defined by the EU Project as spatial entities that describe the catchments’ water resources balances. Umgeni Water have emphasised that there are few water quality issues of concern to them in the Mkomazi catchment and that sediments and sediment transport are considered the most important (Simpson, 1997, pers.comm.; 1998; 1999, pers. comm.). Many of the difficulties faced in sustaining adequate water quality can be linked to adequate water supply infrastructure and the implementation of water regulatory policies. Because the EU Project focuses on these aspects in the approach of WRRUs, it is anticipated that any attention required in addressing water quality will, to a certain extent, already be installed. Water balance models are routinely applied in the assessment of water resource availability. However, the benefits of the EU Project are seen to be the improvement of present water resources management through meeting the demand for an integrated water allocation decision support system. The present ACRU configuration for the Mkomazi catchment was designed to meet the specific needs of the Umgeni Water. This Report has shown that the configuration can provide reliable support to water resource management and planning under not only present land use conditions, but is useful also for the application of scenarios of further development.

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7. ACKNOWLEDGEMENTS We wish to acknowledge the European Union Commission, Brussels, for funding the overall Integrated Water Research Management Systems Project. This particularly complements the continued research on Integrated Catchment Management and the recently initiated research on Installed Hydrological Modelling Systems supported financially by the Water Research Commission, Pretoria. Research into integrating the modelling of catchment hydrological dynamics into an IWRMS has also been supported by a grant from the Foundation for Research Development. The University of Natal Research Office is also thanked for its support. We also wish to thank the Computing Centre for Water Research for computational expertise, assistance and facilities.

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8. REFERENCES Acocks, J.P.H. (1988). Veld types of Southern Africa. Botanical Research Institute,

Pretoria, RSA, Botanical Survey of South Africa, Memoirs 57: 1-146. DWAF (1998a). Mkomazi IFR Study. Starter document for IFR workshop. Department

of Water Affairs and Forestry, Pretoria, RSA. DWAF (1998b). 856Mkomazi IFR Study. Starter document for IFR workshop.

Department of Water Affairs and Forestry, Pretoria, RSA. Hadley, 1998 GCM Hughes et. al., (1994) Hughes, D.A. and Ziervogel, G. (1998). The inclusion of operating rules in a daily

reservoir simulation model to determine ecological reserve releases for river maintenance. Water SA 24(4): 293-302.

INCO-DC (1996). The development of an innovative computer based “Integrated Water

Resources Management System (IWRMS)” in semiarid catchments for water resources analyses and prognostic planning. European Union Commission, Brussels. Project Proposal 961369.

Keinzle, S.W and Schulze, R.E. 1995. Modelling the impatcs of forest and other lanf use

on streamflow in the Mgeni catchment. Proceedings, Sixth Annual Congress of the SA Institute of Civil Engineers on “Engineering the Environment”. SAICE, Port Elizabeth, RSA. 25pp.

Meier, K. (1997). Development of a spatial data base for hydrological model applications

in South Africa. Unpublished MSc.Eng dissertation. Department of Agricultural Engineering, University of Natal, Pietermaritzburg, RSA.

National Water Act (1998). National Water Act (1998). RSA Government Gazette No.36

of 1998: 26 August 1998, No. 19182. Cape Town, RSA. Perks L.A. (2000) Pike and Schulze (1995) Roberts, P.J.T. (1998). Personal Communication of 30 October 1998, Institute for

Commercial Forestry Research, University of Natal, Pietermaritzburg, RSA. Schulze, R.E. (1995). Hydrology and Agrohydrology : A Text to Accompany the ACRU

3.00 Agrohydrological Modelling System. Water Research Commission, Pretoria, RSA. Report TT69/95.

Schulze, R.E. (1996). Schulze, R.E., Pike A., Lecler, N.L., Esprey, L.J., Howe, B.J. and Zammit, R.M. (1996).

Hydrological impacts of land use practices in the Pongola-Bivane catchment. ACRUcons Report 11, to Forest Industries Association, Pietermaritzburg, RSA, 49 pp.

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Schulze, R.E. (1997). South African Atlas of Agrohydrology and –Climatology. Water Research Commission, Pretoria, RSAtt82/96: 1-273.

Schulze, R.E. (1999). Schulze, R.E. (2000). The Determination of Baseline Variables for the Assessments of

Land Use Impacts on Hydrological Responses in South Africa. Unpublished In-house Paper. School of Bioresources Engineering and Environmental Hydrology, University of Natal, Pietermaritzburg, RSA.

Schulze, Dent, Lynch, Schäfer, Kienzle and Seed, 1995 (Schulze and Kunz, 1993) Schulze, R.E. and Perks, L.A. (2000). Assessment of the impact of climate change on

hydrology and water resources in South Africa. University of Natal, Pietermatizburg, School of Bioresources Engineering and Environmental Hydrology Report to South African Country Studies for Climate Change Programme. pp 100.

Sidorchuk, 1996 Summerton M.J. (1996). Process and modelling studies in forest hydrology.

Unpublished MSc dissertation. Department of Agricultural Engineering, University of Natal, Pietermaritzburg, RSA. 1-147 plus appendices.

Taylor. V. (1997). The application of simulation modelling in hydrological conflict

resolution: Pongola catchment case study. Unpublished MSc dissertation, Department of Agricultural Engineering, University of Natal, Pietermaritzburg, RSA.

Thompson, M. (1996). A standard land-cover classification scheme for remote-sensing applications in South Africa. South African Journal of Science 1996, 92:34 – 42.

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ACRONYMS ACRU Agrohydrological Catchment Research Unit CMA Catchment Management Agency DWAF Department of Water Affairs and Forestry EU European Union GCM General Circulation Model IWRMS Integrated Water Resources Management System IFR Instream Flow Requirements INCO-DC INternational COoperation with Developing Countries MAP Mean Annual Precipitation MAR Mean Annual Runoff MED Median Annual Runoff MMTS Mkomazi-Mgeni Transfer Scheme NWA National Water Act QC Quaternary Catchment SC Subcatchment

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APPENDIX A: MKOMAZI CATCHMENT Table A1: Summary of IWRMS Research Partners and Components

Research Partner Research Component Friederich- Schiller-Universität Jena Institut für Geographie Jena, Germany iwmrs@geogr.uni.jena.de

Classification of topographical analyses Classification of land use Identification of criteria for Water Resource Response Units (WRRUs) Enhancement of ACRU by OMS using WRRUs Management of project database Technique refinement in accordance with prototype IWRMS requirements

Ecole des Mine de Paris France ranchin@cenerg.cma.fr

Classification of informal settlements Technique refinement in accordance with prototype IWRMS requirements

Institut Nationale de Recherche en Informatique et Automatique (INRIA) France isabelle.herlin@inria.fr

Classification of Land surface temperatures (LSTs) and Leaf Area Index (LAIs) Modelling of evapotranspiration using LSTs and LAIs Technique refinement in accordance with prototype IWRMS requirements

Dipartimento di Scienza del Suolo e Nutrizione della Pianta Universita di Frienze Frienze, Italy rodolfi@ges.fi.cnr.it

Classification of erosion type features and land degradation Identification of criteria for Erosion Response Units (ERUs) Modelling of erosion using ERUs Technique refinement in accordance with prototype IWRMS requirements

GeoData Institute University of Southampton, UK M.J.Clark@soton.ac.uk

Identification of current water management strategies Identification of generic water demand map Investigation of community stakeholder participation Dissemination and implementation of IWRMS

School of Bioresources Engineering and Environmental Hydrology University of Natal Pietermaritzburg, South Africa schulze@aqua.ccwr.ac.za

Identification of minimum input data for hydrological modelling Modelling catchment hydrological dynamics, Mkomazi Hydrological impacts of potential development scenarios

Council for Scientific and Industrial Research (CSIR) ENVIRONMENTEK Pretoria, South Africa MThomps@csir.co.za

Classification of Image preparation Identification of water demand assessment Identification of criteria for water deficiency units

University of Zimbabwe Department of Geography Harare, Zimbabwe dtrevera@geog.uz.zw

Improvement to modelling of streamflow simulations, Mupfure Identification of water demand assessment Hydrological impacts of potential development scenarios

University of Swaziland Department of Geography, Environmental Science and Planning Swaziland matondo@science.uniswa.sz

Improvement to modelling of streamflow simulations, Mbuluzi Identification of water demand assessment Hydrological impacts of potential development scenarios

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APPENDIX B MKOMAZI STUDY CATCHMENT Table B1 Mkomazi Subcatchment Information

SC No

QC No

Area (km2)

“Driver” Rainfall Station

“Driver” Rainfall Station Name

Mean Altitude

(m)

Longitude (decimal degrees)

Latitude (decimal degrees)

MAP (mm)

1 U10B 162.91 0237606 W Sani Pass (Pol) 2124 29.33 29.60 1107 2 U10B 63.32 0237606 W Sani Pass (Pol) 1959 29.33 29.60 1095 3 U10B 141.69 0268359 W Cyprus 1533 29.70 29.50 1044 4 U10B 29.22 0238132 W Snowhill 1373 29.60 29.72 962 5 U10A 142.97 0268199 W Highmoor (Bos) 2165 29.62 29.32 1283 6 U10A 57.76 0237731 A Cobham, Himeville 2088 29.42 29.68 1179 7 U10A 208.01 0268359 W Cyprus 1639 29.70 29.50 1095 8 U10D 47.09 0238341 W Paulholme 1410 29.70 29.68 982 9 U10D 189.23 0268359 W Cyprus 1851 29.70 29.50 1040

10 U10D 77.44 0238636 W Impendle (Pol) 1643 29.87 29.60 946 11 U10C 93.12 0237606 W Sani Pass (Pol) 2104 29.33 29.60 1068 12 U10C 32.87 0238132 W Snowhill 1685 29.60 29.72 945 13 U10C 148.15 0238045 W Himeville (Mag) 1568 29.52 29.75 951 14 U10D 29.97 0238341 W Paulholme 1339 29.70 29.68 906 15 U10E 18.87 0238636 W Impendle (Pol) 1680 29.87 29.60 935 16 U10E 70.94 0238636 W Impendle (Pol) 1492 29.87 29.60 965 17 U10E 69.99 0268359 W Cyprus 1678 29.70 29.50 1088 18 U10E 158.55 0238468 W Bulwer (Tnk) 1310 29.77 29.82 1055 19 U10F 77.69 0238636 W Impendle (Pol) 1435 29.87 29.60 997 20 U10F 55.13 0238468 W Bulwer (Tnk) 1723 29.77 29.82 1073 21 U10F 24.12 0238293 W Rockleigh 1554 29.67 29.88 988 22 U10F 9.06 0238468 W Bulwer (Tnk) 1506 29.77 29.82 1089 23 U10F 145.82 0238806 W Emerald Dale 1232 29.95 29.93 931 24 U10F 65.33 0238806 W Emerald Dale 1155 29.95 29.93 897 25 U10G 136.00 0238636 W Impendle (Pol) 1543 29.87 29.60 1003 26 U10G 106.48 0239133 W Vaucluse 1351 30.08 29.72 947 27 U10G 116.18 0239133 W Vaucluse 1172 30.08 29.72 970 28 U10H 137.54 0239133 W Vaucluse 1090 30.08 29.72 980 29 U10H 117.43 0238806 W Emerald Dale 1372 29.95 29.93 947 30 U10H 122.67 0238837 A Emerald Dale 1260 29.97 29.95 860 31 U10H 76.55 0239472 W Richmond (Tnk) 975 30.27 29.87 997 32 U10J 7.28 0239566 A Little Harmony 780 30.32 28.93 876 33 U10J 76.07 0238837 W Emerald Dale 1229 29.97 29.95 853 34 U10J 101.10 0210099 W Ixopo (Pol) 855 30.07 30.15 844 35 U10J 211.44 0239138 W Whitson 866 30.08 29.80 916 36 U10J 11.94 0239359 W Naauwpoort 585 30.20 29.98 802 37 U10J 97.83 0239566 A Little Harmony 761 30.32 29.93 938 38 U10L 23.74 0209795 W Hancock Grange 840 29.95 30.25 778 39 U10L 56.04 0209825 A Grange,

Umzimkulu 725 29.97 30.25 758

40 U10K 48.36 0210136 A Finchley, Ixopo 1161 30.08 30.27 888 41 U10K 29.74 0210099 W Ixopo (Pol) 1091 30.07 30.15 810 42 U10K 30.07 0210099 W Ixopo (Pol) 1026 30.07 30.15 819 43 U10K 143.62 0210099 W Ixopo (Pol) 914 30.07 30.15 767 44 U10K 109.86 0239359 W Naauwpoort 742 30.20 29.98 768

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45 U10L 226.22 0239359 W Naauwpoort 567 30.20 29.98 752 46 U10M 16.76 0211228 S Esperanza 359 30.63 30.30 906 47 U10M 199.77 0210826 W Sawoti 305 30.48 30.27 822 48 U10M 25.70 0211546 S Illovo Mill 132 30.82 30.10 919 49 U10M 26.23 0211407 S Renishaw 144 30.73 30.28 955 50 U10M 0.79 0211437 W Scottburgh (Mun) 63 30.75 30.28 1023 51 U10M 2.30 0211546 S Illovo Mill 95 30.82 30.10 1011 52 U10M 5.72 0211437 W Scottburgh (Mun) 53 30.75 30.28 1053

Table B2 Umgeni Water Quality Sampling Sites: Contributing areas Sampling

Point Locality Longitude

(degrees decimal)

Latitude (degrees decimal)

SSC Number

Upstream area (km2)

144 Impendle 29.74 29.64 13 274.14 145 Impendle 29.74 29.63 8 852.96 146 Impendle 29.78 29.62 10 266.67 147 Josephine’s Bridge 30.77 30.01 36 3339.57 125 Sappi SAICORR 30.77 30.18 49 4373.50

125.1 Sappi SAICORR 30.78 30.19 51 4376.58 Table B 3 Proposed Dams: Contributing areas

Dam Longitude (degrees decimal)

Latitude (degrees decimal)

SC Number Upstream area (km2)

Impendle 29.78 29.65 14 1423.74 Smithfield 29.93 29.77 23 2053.57 Gomane 29.88 29.60 15 18.87 Nzinga Abstraction 29.76 29.58 9 189.23 Ngwadini 30.61 30.14 46 16.76 Bulwer 29.76 29.84 20 55.13 Table B4 Instream Flow Requirement Sites: Contributing areas IFR Site

Locality Longitude (degrees decimal)

Latitude (degrees decimal)

SC No Upstream area (km2)

1 Lundy’s Hill 29.91 29.75 19 1819.43 2 Hela Hela 30.09 29.92 32 2939.01 3 Josephine’s Bridge 30.23 30.02 35 3327.62 4 Mfume 30.67 30.12 47 4321.56

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Table B5 DWAF Gauging Stations: Contributing areas Gauging Station

Locality Longitude (degrees decimal)

Latitude (degrees decimal)

SSC Number

Upstream area (km2)

U1H005 Lundy’s Hill 29.90 29.74 18 1741.74 U1H006 Goodenough Weir 30.70 30.17 48 4347.27 U1H003 Delos Estate 30.77 29.74 50 4374.28

Table B6 Water Treatment Works (WTW): Contributing area WTW Locality

Longitude (degrees decimal)

Latitude (degrees decimal)

SSC Number Upstream area (km2)

Ixopo 30.07 30.15 41 78.11

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APPENDIX C ACRU MODELLING REQUIREMENTS FOR THE MKOMAZI

CATCHMENT SECTION 1 TABLES OF INFORMATION Table C1 Mkomazi Subcatchment Discretisation

SC No QC No Criteria for discretiation 1 U10B Physiography: High Berg 2 U10B Physiography: High Berg 3 U10B Physiography: Mid Berg 4 U10B Sediment Test sites 5 U10A Physiography: High Berg 6 U10A Physiography: High Berg 7 U10A Physiography: Mid Berg 8 U10D Umgeni Water Sampling Point 145 9 U10D Proposed Nzinga Abstraction

10 U10D Umgeni Water Sampling Point 146 11 U10C Physiography: High Berg 12 U10C Rainfall Gauge: Snowhill Ranch 13 U10C Physiography: Mid Berg / Umgeni Water Sampling Point 144 14 U10D Proposed Impendle Dam 15 U10E Proposed Gomane Abstraction 16 U10E Land use: Sediment contribution 17 U10E Land use: Sediment contribution 18 U10E DWAF gauging station (U1H005) / USWP 143: Lundy’s Hill 19 U10F IFR1 20 U10F Proposed Bulwer Dam 21 U10F Land use: Edgehill isolated from former homelands 22 U10F Land use: Bulwer isolated from rural area 23 U10F Proposed Smithfield Dam 24 U10F Land use impacts: relatively unimpacted / some rural settlement 25 U10G Land use: forestry 26 U10G Land use: farms / cultivation 27 U10G Land use impacts: relatively unimpacted 28 U10H Land use: rural settlement 29 U10H Land use: plantation 30 U10H Land use: Donnybrook / farms 31 U10H Land use: plantation 32 U10J IFR2 33 U10J Land use: upstream forestry / downstream semi-urban 34 U10J Land use: isolation of Lufafa 35 U10J IFR3 36 U10J Umgeni Water Sampling Point 147 37 U10J Kwamagoda Tributary 38 U10L Land use: relativley unimpacted 39 U10L Land use: degraded / thicket & bushland 40 U10K Land use: forestry isolated from Ixopo 41 U10K Land use: Ixopo industrial area / Water Treatment Works 42 U10K Land use: thicket / bushland isolated from Ixopo / forestry

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43 U10K Land use impacts: relatively unimpacted / some 44 U10K Land use impacts 45 U10L Land use impacts 46 U10M Proposed Ngwadini Dam 47 U10M IFR4 48 U10M DWAF Gauging station U1H006 49 U10M Umgeni Water Sampling Point 125 50 U10M DWAFGauging station U1H003 51 U10M Umgeni Water Sampling Point 125.1 52 U10M Outlet of catchment

Table C2 Mkomazi Catchment: Land use categorisation ACRU

categorisation LANDSAT TM Classification ACRU

land use code

Forest Forest Forest & Woodland

7010108 2010101

Plantation Forest Plantation 6020203a Valley Bushveld Thicket & Bushland 2040101 Dryland Agriculture Cultivated permanent: commercial sugarcane

Cultivated temporary: commercial dryland Cultivated temporary: semi-commercial / subsistence dryland

3020702 7010206 3040101

Urban Barren rock Degraded: thicket & bushland Degraded unimproved grassland Urban / built-up land: residential Urban / built-up land: residential (small holdings; bushland) Urban / built-up land: transport

7010205b 2040101c 2030102d 9010107 1040201 1010201e

Grassland Shrubland & low fynbos Unimproved grassland

2030324f Acocksg

Wetland Wetland 4040102 Riparian None identified 9010105

9010201 Dams and Irrigation Improved grassland

Waterbodies Cultivated: temporary: commercial irrigated

3021001h 9010201 7770118 3020601 3021101

Notes a The LANDSAT TM image does not distinguish the genera of commercially planted

trees. Therefore, for the purposes of this study it was assumed that all plantations comprised pines of intermediate age and the sites prepared by pitting.

b The values used for barren rock were those identified for bare rock / soil in the

Pongola-Bivane ACRUcons 11 Report (Schulze et al., 1996).

71

c Values, adapted from those applicable to valley bushveld, were ascertained taking account of the hydrological response anticipated from this land cover in poor hydrological condition.

d The values applied were those assessed for veld on poor hydrological condition

(Schulze et al., 1996). e The values applied were those for the pervious portion of industrial land use. f The values applied were, with some relevant alterations, those applicable to

Pietersberg plateau false grassland. g To provide an accurate assessment of the hydrological responses of the land cover

identified as unimproved grassland by the LANDSAT TM imagery, an Acocks veld type coverage was overlayed with the subcatchment boundaries of the digitised Mkomazi catchment. This identified which particular veld type(s) occurred within the subcatchment. Where more than one type occurred, the relevant proportions were calculated and area-weighted to give a representative value for the water use coefficients. The veld types and relevant proportions assigned to each subcatchment are provided in Table C5 of Appendix C.

h From ground truthing, it was considered that “improved grassland” was pasture under

under irrigation. i In the absence of any information to the contrary, irrigated crops in the upper

Mkomazi catchment were assumed to be cabbages planted on 1 March (and harvested 3 months later) and potatoes planted on 1 August (also harvested 3 months later).

i Irrigated crops in the lower Mkomazi catchment were assumed to be citrus.

73

Table C3 Mkomazi Subcatchment Distribution (%) of present land cover and land use (after CSIR LANDSAT TM, 1996)

Sub- and Quaternary Catchment Numbers

Total Area (km2)

Forest (km2)

Plantation (km2)

Valley Bushveld

(km2)

Dryland (km2)

Urban (km2)

Grassland (km2)

Wetland (km2)

Channel (km2)

Dams & Irrigation

(km2)

1 U10B 162.91 0.001 0.401 0.866 0.001 0.001 160.538 0.001 1.095 0.001 2 U10B 63.32 0.001 0.806 0.001 0.001 0.001 61.635 0.001 0.867 0.001 3 U10B 141.69 0.001 6.659 4.196 0.636 15.269 113.357 0.001 1.535 0.032 4 U10B 29.22 0.001 1.952 0.833 0.001 17.551 8.156 0.001 0.712 0.001 5 U10A 142.97 0.237 0.001 0.001 0.001 0.001 141.636 0.001 1.081 0.001 6 U10A 57.76 0.825 0.173 0.001 0.001 0.001 55.799 0.001 0.946 0.001 7 U10A 208.01 0.268 2.804 12.608 5.258 11.196 172.560 0.001 2.712 0.597 8 U10D 47.09 0.001 0.001 0.046 0.007 25.206 20.334 0.001 1.484 0.001 9 U10D 189.23 0.001 0.545 5.806 0.649 0.001 179.017 0.946 1.464 0.801

10 U10D 77.44 0.001 4.191 1.879 19.236 4.029 44.327 0.001 1.137 2.641 11 U10C 93.12 0.208 1.569 0.001 0.001 0.001 89.965 0.001 1.367 0.001 12 U10C 32.87 0.140 0.606 0.001 1.084 0.001 29.126 0.001 0.360 1.546 13 U10C 148.15 0.001 12.190 1.323 4.685 0.318 123.587 0.001 2.144 3.898 14 U10D 29.97 0.001 2.026 0.289 6.071 3.478 17.607 0.001 0.487 0.001 15 U10E 18.87 0.001 0.001 0.001 10.052 0.229 8.378 0.001 0.195 0.001 16 U10E 70.94 1.337 0.422 7.124 1.990 8.517 50.614 0.001 0.924 0.001 17 U10E 69.99 1.214 3.213 3.537 0.350 0.351 59.695 0.001 0.618 0.676 18 U10E 158.55 8.802 27.145 7.589 2.276 22.561 86.517 0.001 3.644 0.001 19 U10F 77.69 5.952 12.981 3.241 2.629 0.299 50.856 0.001 1.021 0.712 20 U10F 55.13 0.561 14.615 2.560 0.001 0.001 35.890 0.001 0.604 0.897 21 U10F 24.12 0.001 12.890 1.071 0.617 0.001 9.009 0.001 0.474 0.060 22 U10F 9.06 1.195 3.146 1.065 0.001 0.206 3.182 0.001 0.256 0.001 23 U10F 145.82 5.155 1.517 3.010 29.196 41.336 63.457 0.001 2.085 0.060 24 U10F 65.33 0.001 3.103 3.517 12.166 30.415 15.622 0.001 0.494 0.001 25 U10G 136.00 6.130 27.127 3.031 1.594 0.001 86.816 0.115 1.039 10.143 26 U10G 106.48 0.001 24.295 4.201 3.448 0.001 53.340 0.001 0.812 20.383 27 U10G 116.18 1.678 4.815 16.951 5.594 0.023 86.356 0.001 0.731 0.026

74

28 U10H 137.54 .0534 27.231 22.246 16.187 0.001 70.052 0.001 0.626 0.663 29 U10H 117.43 3.550 40.945 1.569 9.856 5.566 52.263 0.001 1.497 2.189 30 U10H 122.67 0.001 34.809 2.352 2.595 0.405 53.349 0.001 0.771 28.385 31 U10H 76.55 0.001 30.884 19.460 5.182 0.001 19.376 0.001 1.607 0.034 32 U10J 7.28 0.001 1.408 5.034 0.001 0.001 0.344 0.001 0.48 0.001 33 U10J 76.07 0.001 29.901 0.409 2.642 0.001 29.040 0.001 0.693 13.377 34 U10J 101.10 0.001 7.412 23.039 19.832 34.941 14.257 0.001 1.602 0.001 35 U10J 211.44 10.317 76.603 56.221 7.995 0.001 56.700 0.001 3.595 0.001 36 U10J 11.94 0.001 0.110 7.685 0.447 0.001 3.588 0.001 0.101 0.001 37 U10J 97.83 0.001 28.057 19.877 16.911 2.093 28.899 0.001 1.980 0.001 38 U10L 23.74 0.001 0.001 0.107 5.285 10.473 7.582 0.001 0.28 0.001 39 U10L 56.04 0.001 0.920 24.933 0.195 10.296 19.148 0.001 0.533 0.001 40 U10K 48.36 0.001 31.786 0.001 1.304 0.001 9.739 0.001 0.308 5.221 41 U10K 29.74 0.001 12.016 0.001 1.464 1.497 1.493 0.001 0.134 13.137 42 U10K 30.07 0.001 4.653 0.599 1.994 0.001 12.285 0.001 0.242 10.289 43 U10K 143.62 0.001 21.866 62.541 4.032 0.001 40.647 0.001 1.122 13.410 44 U10K 109.86 0.001 8.487 60.751 11.588 2.322 24.741 0.001 0.863 1.108 45 U10L 226.22 0.001 14.511 129.489 30.601 7.458 40.247 0.001 3.520 0.392 46 U10M 16.76 0.001 0.001 15.160 0.988 0.317 0.001 0.001 0.280 0.001 47 U10M 199.77 0.001 0.001 146.414 10.367 35.697 1.758 0.001 5.517 0.001 48 U10M 25.70 0.001 0.001 24.321 0.001 0.327 0.001 0.001 1.040 0.001 49 U10M 26.23 0.001 0.001 21.620 3.194 0.104 0.001 0.001 1.3 0.001 50 U10M 0.79 0.001 0.001 0.392 0.119 0.152 0.001 0.001 0.1065 0.001 51 U10M 2.30 0.001 0.001 0.623 0.391 0.936 0.001 0.001 .0333 0.001 52 U10M 5.72 0.001 0.001 3.544 0.780 0.821 0.426 0.001 0.132 0.001

NB When a land use category was absent in a subcatchment, it was assigned a fictitiously small area of 0.001 km2 for computational

purposes.

75

Table C4 Month-by-month input variables for the land use categories used in the Mkomazi study

ACRU category LANDSAT TM Classification

Variable Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Forest Forest Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.80 2.50 0.80 0.35

0.80 2.50 0.80 0.35

0.80 2.50 0.80 0.35

0.80 2.00 0.90 0.35

0.70 2.00 0.90 0.35

0.70 2.00 0.90 0.35

0.70 2.00 0.90 0.35

0.70 2.00 0.90 0.35

0.75 2.00 0.90 0.35

0.80 2.50 0.80 0.35

0.80 2.50 0.80 0.35

0.80 2.50 0.80 0.35

Forest Forest and Woodland Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.85 3.00 0.70 0.35

0.85 3.00 0.70 0.35

0.85 3.00 0.70 0.35

0.85 3.00 0.70 0.35

0.85 3.00 0.70 0.35

0.85 3.00 0.70 0.35

0.85 3.00 0.70 0.35

0.85 3.00 0.70 0.35

0.85 3.00 0.70 0.35

0.85 3.00 0.70 0.35

0.85 3.00 0.70 0.35

0.85 3.00 0.70 0.35

Plantation: (Pines: intermediate age, pitted)

Forest Plantation Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.85 3.30 0.60 0.35

0.85 3.30 0.60 0.35

0.85 3.30 0.60 0.35

0.85 3.30 0.60 0.35

0.85 3.30 0.60 0.35

0.85 3.30 0.60 0.35

0.85 3.30 0.60 0.35

0.85 3.30 0.60 0.35

0.85 3.30 0.60 0.35

0.85 3.30 0.60 0.35

0.85 3.30 0.60 0.35

0.85 3.30 0.60 0.35

Valley Bushveld Thicket & Bushland Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.65 1.60 0.75 0.20

0.65 1.60 0.75 0.20

0.60 1.60 0.75 0.25

0.55 1.40 0.75 0.30

0.45 1.20 0.75 0.30

0.40 1.10 0.75 0.30

0.35 1.10 0.75 0.30

0.40 1.10 0.75 0.30

0.45 1.20 0.75 0.30

0.55 1.45 0.75 0.25

0.60 1.55 0.75 0.20

0.65 1.60 0.75 0.20

Dryland Cultivated: permanent - commercial sugarcane

Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.79 1.80 0.60 0.20

0.80 1.80 0.60 0.20

0.88 1.80 0.60 0.20

0.95 1.80 0.60 0.20

0.98 1.80 0.60 0.30

0.94 1.80 0.60 0.30

0.89 1.80 0.60 0.30

0.85 1.80 0.60 0.30

0.82 1.80 0.60 0.30

0.81 1.80 0.60 0.30

0.81 1.80 0.60 0.35

0.81 1.80 0.60 0.25

Dryland Cultivated: temporary - commercial dryland

Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.89 1.00 0.79 0.20

1.10 1.50 0.74 0.20

0.96 1.40 0.78 0.20

0.46 1.30 0.91 0.20

0.20 1.20 1.00 0.30

0.20 0.50 1.00 0.30

0.20 0.50 1.00 0.30

0.20 0.50 1.00 0.30

0.20 0.50 1.00 0.30

0.20 0.50 1.00 0.30

0.35 0.50 1.00 0.35

0.60 0.50 0.92 0.25

Dryland Cultivated: temporary - semi-commercial / subsistence dryland

Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.80 1.00 0.74 0.20

0.70 1.00 0.78 0.20

0.30 0.60 0.91 0.20

0.30 0.50 1.00 0.20

0.30 0.50 1.00 0.30

0.30 0.50 1.00 0.30

0.30 0.50 1.00 0.30

0.30 0.50 1.00 0.30

0.30 0.50 1.00 0.30

0.30 0.50 1.00 0.30

0.35 0.50 0.92 0.35

0.60 0.80 0.79 0.25

Urban Barren rock Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.45 0.50 1.00 0.15

0.45 0.50 1.00 0.15

0.45 0.50 1.00 0.15

0.35 0.50 1.00 0.15

0.30 0.50 1.00 0.15

0.20 0.50 1.00 0.15

0.20 0.50 1.00 0.15

0.20 0.50 1.00 0.15

0.30 0.50 1.00 0.15

0.35 0.50 1.00 0.15

0.45 0.50 1.00 0.15

0.45 0.50 1.00 0.15

Urban Degraded: Thicket & Bushland

Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.45 1.40 0.70 0.15

0.45 1.40 0.70 0.15

0.40 1.40 0.70 0.15

0.35 1.20 0.70 0.15

0.25 1.00 0.70 0.15

0.20 0.90 0.70 0.15

0.15 0.90 0.70 0.15

0.20 0.90 0.70 0.15

0.25 1.00 0.70 0.15

0.35 1.25 0.70 0.15

0.40 1.35 0.70 0.15

0.45 1.40 0.70 0.15

76

Urban Degraded: Unimproved Grassland

Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.55 0.80 0.90 0.20

0.55 0.80 0.90 0.20

0.55 0.80 0.90 0.20

0.45 0.80 0.94 0.20

0.20 0.80 0.94 0.30

0.20 0.80 0.94 0.30

0.20 0.80 0.80 0.30

0.20 0.80 0.80 0.30

0.30 0.80 0.80 0.30

0.40 0.80 0.80 0.30

0.50 0.80 0.80 0.20

0.55 0.80 0.80 0.20

Urban Urban / built-up land: residential

Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.80 1.40 0.85 0.20

0.80 1.40 0.85 0.20

0.70 1.30 0.85 0.20

0.60 1.20 0.90 0.30

0.40 1.10 0.95 0.30

0.40 1.00 0.95 0.30

0.40 1.00 0.95 0.30

0.40 1.00 0.95 0.30

0.60 1.00 0.90 0.30

0.70 1.30 0.85 0.30

0.80 1.40 0.85 0.25

0.80 1.40 0.85 0.30

Urban Urban / built-up land: residential (small holdings: bushland)

Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.65 1.20 0.90 0.20

0.65 1.20 0.90 0.20

0.65 1.20 0.90 0.20

0.55 1.20 0.94 0.30

0.30 1.20 0.94 0.30

0.20 1.20 0.94 0.30

0.20 1.20 0.94 0.30

0.20 1.20 0.94 0.30

0.30 1.20 0.92 0.30

0.50 1.20 0.92 0.30

0.55 1.20 0.90 0.20

0.65 1.20 0.90 0.20

Urban Urban / built-up land: transport

Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.7 0 1.40 0.90 0.15

0.70 1.40 0.90 0.15

0.70 1.40 0.90 0.15

0.60 1.40 0.94 0.15

0.40 1.20 0.94 0.15

0.40 1.20 0.94 0.15

0.30 1.20 0.94 0.15

0.30 1.20 0.94 0.15

0.50 1.20 0.92 0.15

0.70 1.40 0.92 0.15

0.70 1.40 0.90 0.15

0.70 1.40 0.90 0.15

Grassland Shrubland & low Fynbos Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.50 0.80 0.80 0.20

0.50 0.80 0.80 0.20

0.45 0.80 0.80 0.20

0.40 0.80 0.90 0.20

0.30 0.75 0.90 0.30

0.20 0.70 1.00 0.30

0.20 0.60

1.00 0.30

0.25 0.70

1.00 0.30

0.30 0.75

0.90 0.30

0.35 0.80 0.90 0.30

0.45 0.80 0.80 0.20

0.50 0.80 0.80 0.20

Grassland (Coastal forest and thornveld)

Unimproved grassland

Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.85 2.00 0.75 0.30

0.85 2.00 0.75 0.30

0.85 2.00 0.75 0.30

0.85 2.00 0.75 0.30

0.75 2.00 0.75 0.30

0.65 2.00 0.75 0.30

0.60 2.00 0.75 0.30

0.65 2.00 0.75 0.30

0.75 2.00 0.75 0.30

0.85 2.00 0.75 0.30

0.85 2.00 0.75 0.30

0.85 2.00 0.75 0.30

Grassland (Highland sourveld and doune sourveld)

Unimproved grassland

Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.60 1.00 0.90 0.15

0.60 1.00 0.90 0.15

0.60 1.00 0.90 0.15

0.45 1.00 0.95 0.20

0.20 1.00 1.00 0.25

0.20 1.00

1.00 0.25

0.20 1.00 1.00 0.25

0.20 1.00

1.00 0.25

0.30 1.00

1.00 0.25

0.50 1.00

0.95 0.20

0.60 1.00 0.90 0.20

0.60 1.00 0.90 0.15

Grassland (Ngongoni veld)

Unimproved grassland

Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.65 1.20 0.90 0.15

0.65 1.20 0.90 0.15

0.65 1.20 0.90 0.20

0.55 1.20 0.94 0.25

0.50 1.20 0.97 0.25

0.30 1.20 1.00 0.25

0.30 1.20 1.00 0.25

0.30 1.20 1.00 0.25

0.45 1.20

0.97 0.25

0.55 1.20 0.94 0.20

0.60 1.20 0.90 0.20

0.65 1.20 0.90 0.15

Grassland (Ngongoni veld of natal mist belt)

Unimproved grassland

Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.63 1.11 0.90 0.15

0.63 1.11 0.90 0.15

0.63 1.11 0.90 0.15

0.50 1.11 0.94 0.20

0.35 1.11 1.00 0.25

0.25 1.11

1.00 0.25

0.25 1.11 1.00 0.25

0.25 1.11

1.00 0.25

0.40 1.11

1.00 0.25

0.53 1.11

0.94 0.20

0.63 1.11 0.90

0.2 0

0.63 1.11 0.90 0.15

Grassland (Southern tall grassland)

Unimproved grassland

Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.55 2.00 0.80 0.20

0.55 2.00 0.80 0.20

0.50 2.00 0.80 0.20

0.45 2.00 0.80 0.20

0.40 2.00

0.90 0.30

0.30 2.00

0.95 0.30

0.30 2.00

0.95 0.30

0.30 2.00 0.95 0.30

0.35 2.00 0.95 0.30

0.45 2.00 0.80 0.30

0.50 2.00 0.80 0.20

0.55 2.00 0.80 0.20

77

Grassland (Themeda-festuca alpine veld)

Unimproved grassland

Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.65 0.80 0.80 0.20

0.55 0.80 0.80 0.20

0.50 0.80 0.80 0.20

0.40 0.80

1.00 0.20

0.30 0.80 1.00 0.30

0.20 0.80

1.00 0.30

0.20 0.80

1.00 0.30

0.20 0.80 1.00 0.30

0.20 0.80 1.00 0.30

0.30 0.80

0.97 0.30

0.50 0.80 0.94 0.20

0.55 0.80 0.90 0.20

Wetland Wetland Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.80 0.60 1.00 0.20

0.80 0.60 1.00 0.20

0.80 0.60 1.00 0.20

0.70 0.60 1.00 0.20

0.60 0.60 1.00 0.30

0.50 0.60 1.00 0.30

0.40 0.60 1.00 0.30

0.40 0.60 1.00 0.30

0.40 0.60 1.00 0.30

0.50 0.60 1.00 0.30

0.60 0.60 1.00 0.20

0.70 0.60 1.00 0.20

Channel Riparian Exotics

Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.78 1.60 0.87 0.23

0.78 1.60 0.87 0.23

0.78 1.60 0.87 0.23

0.72 1.60 0.89 0.23

0.58 1.50 0.91 0.30

0.53 1.40 0.92 0.30

0.55 1.40 0.92 0.30

0.55 1.40 0.92 0.30

0.61 1.50 0.92 0.30

0.71 1.60 0.89 0.30

0.73 1.60 0.87 0.23

0.75 1.60 0.87 0.23

Channel River Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.70 0.00 1.00 0.20

0.70 0.00 1.00 0.20

0.70 0.00 1.00 0.20

0.70 0.00 1.00 0.20

0.70 0.00

1.00 0.30

0.70 0.00

1.00 0.30

0.70 0.00

1.00 0.30

0.70 0.00 1.00 0.30

0.70 0.00 1.00 0.30

0.70 0.00 1.00 0.30

0.70 0.00 1.00 0.20

0.70 0.00 1.00 0.20

Dams & Irrigation

Water Bodies Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.70 0.00 1.00 0.20

0.70 0.00 1.00 0.20

0.70 0.00 1.00 0.20

0.70 0.00 1.00 0.20

0.70 0.00 1.00

0.30

0.70 0.00 1.00

0.30

0.70 0.00 1.00

0.30

0.70 0.00 1.00 0.30

0.70 0.00 1.00 0.30

0.70 0.00 1.00 0.30

0.70 0.00 1.00 0.20

0.70 0.00 1.00 0.20

Dams & Irrigation

Improved Grassland Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.80 1.40 0.80 0.30

0.80 1.40 0.80 0.30

0.80 1.40

0.80 0.30

0.70 1.40

0.90 0.30

0.60 1.20 1.00 0.30

0.5 0 1.00

1.00 0.30

0.50 1.00 1.00 0.30

0.50 1.20 1.00 0.30

0.60 1.30 0.90 0.30

0.70 1.40 0.90 0.30

0.80 1.40 0.80 0.30

0.80 1.40 0.80

0.2 0 Dams & Irrigation (winter cabbages & spring potatoes)

Cultivated: temporary - commercial irrigated

Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.20 1.00 1.00 0.20

0.20 1.00 1.00 0.20

0.50 1.00 1.00 0.20

0.70 1.00 1.00 0.20

0.70 1.00

1.00 0.30

0.40 1.50 1.00 0.30

0.60 1.50 1.00 0.30

0.80 1.50 1.00 0.30

0.20 1.00 1.00 0.30

0.20 1.00 1.00 0.30

0.20 1.00 1.00 0.20

0.20 1.00 1.00 0.20

Dams & Irrigation (citrus)

Cultivated: temporary - commercial irrigated

Water use coefficient Interception loss Roots in topsoil Coefficient of Ia

0.67 1.70 0.40 0.20

0.67 1.70 0.40 0.20

0.67 1.70 0.40

0.20

0.67 1.70

0.40 0.20

0.67 1.70 0.40 0.30

0.67 1.70 0.40 0.30

0.67 1.70 0.40 0.30

0.67 1.70 0.40 0.30

0.67 1.70 0.40 0.30

0.67 1.70 0.40 0.30

0.67 1.70 0.40 0.20

0.6.7 1.70

0.40 0.20

78

Table C5 Month-by-month factors used for adjustment of daily precipitation values in the “present land use” ACRU menus for the Mkomazi study area

Sub- and Quaternary Cat. Nos

Daily Precipitation Adjustment Factors

SC QC Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1 U10B 1.01 0.90 1.11 1.19 1.10 0.79 0.60 0.78 0.84 0.99 0.91 0.91

2 U10B 0.99 0.90 1.10 1.17 1.06 0.76 0.67 0.79 0.83 0.95 0.90 0.90

3 U10B 0.97 0.98 0.97 0.92 0.94 0.81 1.06 0.84 0.91 0.89 0.96 0.98

4 U10B 1.00 0.98 0.98 0.95 0.91 0.70 0.71 1.06 1.04 1.12 1.09 1.01

5 U10A 0.98 0.97 1.11 0.99 1.19 0.83 1.89 1.32 1.09 0.87 1.03 1.03

6 U10A 1.12 0.97 1.02 1.00 1.04 0.99 0.76 0.86 1.09 1.08 1.03 1.06

7 U10A 1.03 1.02 1.01 0.98 0.98 0.88 0.95 0.91 0.97 0.98 1.01 1.03

8 U10D 1.05 1.07 1.07 1.10 0.98 1.30 1.30 1.30 1.10 1.17 1.18 1.08

9 U10D 0.96 0.96 0.96 0.96 0.94 0.91 0.89 0.95 0.95 0.97 0.97 0.96

10 U10D 1.01 1.01 0.98 0.91 1.08 0.70 0.71 0.85 0.96 1.05 0.92 0.98

11 U10C 0.95 0.89 1.05 1.11 1.01 0.74 0.70 0.82 0.81 0.92 0.88 0.88

12 U10C 0.98 0.98 0.98 0.97 0.93 0.73 0.84 0.99 1.01 1.04 1.05 0.99

13 U10C 0.98 1.02 1.00 1.02 1.12 0.78 0.70 1.40 1.24 1.13 1.02 1.04

14 U10D 0.95 0.97 0.99 1.04 0.92 1.30 1.30 1.30 1.05 1.09 1.11 0.98

15 U10E 0.97 0.96 0.96 0.93 0.99 0.70 1.11 0.98 1.02 1.01 0.95 0.95

16 U10E 1.00 1.00 1.00 0.96 1.05 0.70 1.00 0.92 1.01 1.03 0.97 0.98

17 U10E 0.99 1.00 1.03 1.03 1.04 0.72 1.01 0.86 1.02 0.95 1.00 0.98

18 U10E 0.96 0.94 0.93 0.92 0.92 0.70 0.83 0.90 0.88 0.92 0.89 0.93

19 U10F 1.02 1.03 1.03 1.03 1.14 0.7 1.30 1.02 1.11 1.08 1.02 0.99

20 U10F 0.96 0.96 0.95 0.94 0.93 0.87 0.83 0.88 0.92 0.92 0.94 0.96

21 U10F 0.99 0.98 1.01 1.04 1.01 1.22 1.14 1.08 1.09 1.04 1.10 0.98

22 U10F 0.97 0.96 0.96 0.96 0.94 0.85 0.93 0.95 0.95 0.96 0.96 0.96

23 U10F 1.11 1.05 1.03 1.06 0.96 0.76 0.81 0.94 0.90 0.96 1.02 1.08

24 U10F 1.05 1.01 0.99 1.01 0.92 0.72 0.83 0.93 0.90 0.95 0.99 1.02

25 U10G 0.99 1.01 1.00 0.99 1.21 0.74 1.30 1.06 1.21 1.16 1.02 1.05

26 U10G 0.98 0.85 0.99 0.81 0.71 0.78 0.76 1.06 0.79 0.83 0.92 0.98

27 U10G 0.98 0.89 1.02 0.81 0.73 0.79 0.76 1.03 0.83 0.85 0.93 0.97

79

28 U10H 0.96 0.88 1.04 0.80 0.68 0.93 0.71 1.13 0.88 0.86 0.95 0.97

29 U10H 1.08 1.05 1.05 1.04 0.97 0.81 0.87 0.98 1.02 1.05 1.05 1.08

30 U10H 1.00 0.98 0.98 0.97 0.97 0.76 0.89 0.96 1.03 1.00 0.95 0.98

31 U10H 1.05 1.08 0.95 1.04 1.08 0.65 0.97 0.88 0.93 0.86 0.98 0.95

32 U10J 1.07 1.02 1.01 0.86 0.82 0.70 0.96 0.75 0.81 0.90 0.97 0.98

33 U10J 0.94 0.98 0.92 0.94 1.01 0.76 0.79 0.95 1.01 1.02 0.92 1.00

34 U10J 1.10 1.15 1.08 1.02 0.83 1.06 1.13 1.22 1.09 1.01 1.04 0.92

35 U10J 0.99 0.91 0.98 0.95 1.28 1.30 1.09 1.12 1.11 1.04 0.99 1.09

36 U10J 1.13 1.01 1.03 0.98 1.08 1.30 1.30 1.21 1.12 1.12 1.06 1.06

37 U10J 1.13 1.03 1.10 0.97 0.95 0.83 0.91 0.84 0.93 0.99 1.04 1.01

38 U10L 1.03 1.01 1.01 0.99 0.99 1.30 1.30 1.30 1.11 1.30 1.02 1.03

39 U10L 1.02 0.98 0.97 1.04 1.16 1.30 1.30 1.30 1.16 1.20 0.99 0.99

40 U10K 0.99 0.98 1.07 0.95 1.20 0.70 0.70 0.94 0.80 1.02 0.92 1.05

41 U10K 1.03 1.05 1.02 1.02 0.88 1.00 1.09 1.07 1.05 0.97 1.01 0.94

42 U10K 1.04 1.07 1.02 1.04 0.89 1.02 1.11 1.11 1.09 0.98 1.03 0.94

43 U10K 0.96 0.99 0.95 0.99 0.80 1.26 1.24 1.14 1.06 0.91 0.94 0.86

44 U10K 1.07 0.94 0.95 0.86 1.06 1.30 1.30 1.21 1.05 1.07 1.01 1.03

45 U10L 1.04 0.86 0.88 0.92 1.10 1.30 1.30 1.30 1.15 1.09 0.97 0.94

46 U10M 1.02 1.00 0.99 0.94 0.75 0.89 0.93 1.13 0.89 0.90 0.99 0.80

47 U10M 0.90 0.96 0.90 1.09 1.14 0.89 1.01 0.95 1.05 0.90 0.90 0.89

48 U10M 0.93 1.01 0.85 0.84 1.04 0.97 0.86 1.14 0.98 0.93 1.02 0.90

49 U10M 1.00 1.00 1.01 0.89 0.76 0.71 0.77 0.90 0.87 1.01 0.94 1.08

50 U10M 1.01 0.99 1.01 0.95 0.86 0.81 0.87 0.91 0.93 0.99 0.96 1.05

51 U10M 0.99 1.07 0.95 0.94 1.23 1.12 1.03 1.24 1.10 1.01 1.09 0.97

52 U10M 1.03 1.01 1.04 0.98 0.90 0.89 0.89 0.94 0.95 1.01 0.99 1.06

80

Table C6 Land Types identified in the Mkomazi study area A RED-YELLOW APEDAL, FREELY DRAINED SOILS Aa Humic soils > 40% 14 19 20 Ab Red, dystrophic and / or mesotrophic 137 138 139 140 141 148 149 150 161 162 163 164 165 166 171 172 Ac Red and yellow dystrophic and / or mesotrophic 215 224 227 228 229 234 237 238 239 240 241 242 243 244 245 246 247 248 250 254 255 256 257 258 260 267 295 297 298 302 306 307 309 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 348 349 350 351 352 353 354 355 356 357 358 395 396 Ad Yellow, dystrophic and / or mesotrophic 27 31 B PLINTHIC CATENA: RARE UPLAND DUPLEX AND MARGALITIC SOILS Bb Upland duplex / margalitic soils > 10% 114 118 C PLINTHIC CATENA: COMMON UPLAND DUPLEX AND MARGALITIC SOILS Ca Upland duplex / margalitic > 10% 101 E ONE OR MORE OF VERTIC, MELANIC, OR RED STRUCTURED SOILS Ea Vertic, melanic or red structured > 50% 195 196 203 F GLENROSA AND / OR MISPAH SOILS Fa Lime not encountered regularly 473 474 481 482 483 484 485 486 487 488 524 541 542 543 544 545 546 547 548 550 551 553 554 555 556 557 558 561 585 586 587 591 640 692 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 738 746 879 H GREY REGIC SANDS Hb Deep grey sands > 20% , < 80% 92 I MISCELLANEOUS SOILS Ic Exposed country rock, stones or boulders > 80% 171

Table C7 Mkomazi Catchment: Subcatchment soils related information

Permanent Wilting Point

(m.m-1)

Drained upper limit

(m.m-1)

Porosity (m.m-1)

Saturated Redistribution (fraction.day-1)

SC and QC Numbers

Dominant Soil

Texture Class

Thickness of A

horizon (m)

Thickness of B

horizon (m)

A

B

A

B

A

B

A

B

Adjunct impervious

area (fraction)

Disjunct Impervious

area (fraction)

1 U10B 0.22 0.22 0.138 0.147 0.229 0.244 0.438 0.420 0.34 0.34 0.015 0.198 2 U10B 0.23 0.26 0.135 0.149 0.227 0.246 0.441 0.422 0.37 0.37 0.012 0.128 3 U10B 0.26 0.41 0.143 0.174 0.236 0.266 0.432 0.417 0.39 0.39 0.018 0.076 4 U10B 0.23 0.27 0.146 0.167 0.238 0.259 0.433 0.417 0.35 0.35 0.036 0.107 5 U10A 0.22 0.25 0.142 0.158 0.233 0.253 0.435 0.417 0.34 0.34 0.016 0.214 6 U10A 0.23 0.30 0.144 0.164 0.239 0.263 0.433 0.422 0.37 0.37 0.023 0.175 7 U10A 0.24 0.34 0.141 0.170 0.232 0.259 0.437 0.418 0.37 0.37 0.020 0.104 8 U10D 0.25 0.36 0.152 0.186 0.242 0.275 0.426 0.413 0.36 0.36 0.038 0.086 9 U10D 0.23 0.29 0.147 0.166 0.236 0.254 0.425 0.404 0.34 0.34 0.017 0.126

10 U10D 0.26 0.42 0.139 0.178 0.222 0.255 0.408 0.387 0.37 0.37 0.024 0.080 11 U10C 0.22 0.24 0.138 0.148 0.228 0.242 0.441 0.420 0.34 0.34 0.013 0.166 12 U10C 0.26 0.38 0.137 0.171 0.225 0.256 0.432 0.413 0.39 0.39 0.028 0.045 13 U10C 0.27 0.41 0.138 0.175 0.229 0.261 0.435 0.414 0.40 0.40 0.026 0.070 14 U10D 0.28 0.47 0.144 0.192 0.231 0.270 0.431 0.410 0.40 0.40 0.035 0.048 15 U10E 0.28 0.49 0.151 0.189 0.245 0.282 0.425 0.413 0.38 0.38 0.017 0.138 16 U10E 0.28 0.47 0.145 0.190 0.234 0.272 0.429 0.410 0.40 0.40 0.027 0.104 17 U10E 0.27 0.44 0.156 0.177 0.254 0.282 0.418 0.415 0.38 0.38 0.016 0.064 18 U10E 0.29 0.51 0.158 0.196 0.251 0.290 0.423 0.417 0.37 0.37 0.017 0.073 19 U10F 0.29 0.51 0.148 0.189 0.241 0.279 0.426 0.412 0.38 0.38 0.015 0.068 20 U10F 0.30 0.55 0.162 0.200 0.262 0.310 0.409 0.419 0.38 0.38 0.019 0.044 21 U10F 0.31 0.55 0.163 0.207 0.259 0.309 0.413 0.419 0.38 0.38 0.024 0.032 22 U10F 0.30 0.52 0.165 0.194 0.267 0.309 0.408 0.421 0.37 0.37 0.018 0.067 23 U10F 0.27 0.45 0.173 0.205 0.260 0.294 0.429 0.426 0.34 0.34 0.020 0.068 24 U10F 0.28 0.49 0.181 0.217 0.264 0.298 0.437 0.430 0.33 0.33 0.022 0.043 25 U10G 0.29 0.50 0.143 0.190 0.232 0.271 0.430 0.408 0.40 0.40 0.013 0.092 26 U10G 0.28 0.49 0.146 0.190 0.235 0.273 0.427 0.409 0.39 0.39 0.014 0.041

27 U10G 0.25 0.39 0.159 0.188 0.249 0.278 0.431 0.419 0.33 0.33 0.017 0.032 28 U10H 0.26 0.43 0.151 0.179 0.247 0.282 0.425 0.422 0.39 0.39 0.021 0.048 29 U10H 0.30 0.54 0.166 0.199 0.266 0.311 0.412 0.426 0.38 0.38 0.026 0.030 30 U10H 0.25 0.41 0.163 0.190 0.259 0.292 0.418 0.420 0.34 0.34 0.020 0.036 31 U10H 0.28 0.46 0.152 0.179 0.250 0.284 0.420 0.416 0.38 0.38 0.014 0.036 32 U10J 0.27 0.40 0.157 0.178 0.253 0.282 0.419 0.417 0.35 0.35 0.014 0.045 33 U10J 0.27 0.46 0.144 0.186 0.232 0.270 0.407 0.396 0.35 0.35 0.019 0.035 34 U10J 0.27 0.46 0.145 0.172 0.238 0.268 0.434 0.424 0.41 0.41 0.050 0.039 35 U10J 0.26 0.37 0.149 0.166 0.243 0.266 0.432 0.423 0.35 0.35 0.026 0.030 36 U10J 0.21 0.11 0.144 0.138 0.228 0.231 0.447 0.425 0.27 0.27 0.025 0.047 37 U10J 0.29 0.47 0.158 0.189 0.254 0.293 0.420 0.421 0.35 0.35 0.019 0.028 38 U10L 0.25 0.36 0.167 0.179 0.247 0.258 0.437 0.413 0.28 0.28 0.023 0.000 39 U10L 0.27 0.45 0.141 0.142 0.232 0.239 0.454 0.438 0.41 0.41 0.029 0.014 40 U10K 0.33 0.62 0.159 0.211 0.256 0.309 0.415 0.419 0.40 0.40 0.017 0.015 41 U10K 0.32 0.60 0.165 0.212 0.259 0.307 0.419 0.421 0.39 0.39 0.028 0.017 42 U10K 0.32 0.57 0.223 0.257 0.294 0.327 0.428 0.439 0.30 0.30 0.032 0.038 43 U10K 0.27 0.39 0.177 0.95 0.261 0.281 0.432 0.426 0.30 0.30 0.028 0.024 44 U10K 0.24 0.26 0.152 0.156 0.240 0.250 0.437 0.420 0.29 0.29 0.020 0.026 45 U10L 0.27 0.41 0.133 0.138 0.226 0.237 0.450 0.434 0.41 0.41 0.025 0.033 46 U10M 0.23 0.27 0.131 0.118 0.229 0.224 0.447 0.428 0.40 0.40 0.013 0.077 47 U10M 0.24 0.31 0.136 0.131 0.230 0.232 0.445 0.427 0.39 0.39 0.018 0.108 48 U10M 0.21 0.19 0.128 0.119 0.220 0.216 0.453 0.429 0.35 0.35 0.019 0.010 49 U10M 0.21 0.19 0.128 0.126 0.216 0.219 0.455 0.432 0.33 0.33 0.011 0.005 50 U10M 0.22 0.26 0.134 0.140 0.216 0.225 0.441 0.422 0.35 0.35 0.016 0.000 51 U10M 0.26 0.43 0.122 0.135 0.198 0.215 0.421 0.408 0.40 0.40 0.031 0.000 52 U10M 0.24 0.36 0.133 0.143 0.213 0.226 0.430 0.414 0.41 0.41 0.023 0.001

83

Table C8 Input information on dams in the Mkomazi study area

SC and QC Numbers

Number of

Dams

Capacity - all

Dams (m3)

Surface Area -

all Dams (ha)

Seepage - Each, for all Dams

(m3.day-1)

Normal Flow*

(m3.day-1)

Inter Basin Transfer

(106m3.month-1)

3 U10B 1 55123 3.22 30.20 0 0 7 U10A 1 771139 3.92 38.98 0 0 9 U10D 6 2265793 80.15 1241.53 0 0

10 U10D 2 155673 8.29 85.30 0 0 12 U10C 2 422266 17.97 231.38 0 0 13 U10C 1 48184 2.91 26.40 0 0 17 U10E 6 959639 41.12 525.83 0 0 19 U10F 7 1818672 71.16 996.53 0 0 21 U10F 3 89337 5.96 48.95 0 0

* 23 1 137000000 583 0 91333 18.14 25 U10G 10 2509852 91.86 1375.26 0 0 26 U10G 11 730529 39.47 400.29 0 0 27 U10G 2 42319 2.69 23.19 0 0 28 U10H 3 72371 5.07 39.66 0 0 29 U10H 4 3249871 80.05 1780.75 0 0 30 U10H 19 282110 125.23 1549.65 0 0 31 U10H 1 58283 3.36 31.94 0 0 33 U10J 2 117955 6.75 64.63 0 0 39 U10L 1 4323 0.45 2.39 0 0 40 U10K 4 441646 20.05 242.00 0 0 41 U10K 10 1508845 55.50 826.76 0 0 42 U10K 6 1924595 56.15 1054.57 0 0 43 U10K 11 239749 15.80 131.37 0 0 44 U10K 2 29055 2.11 15.92 0 0

* proposed Smithfield Dam (phase 1)

Table C9 Mkomazi Veld Types and areal extents applied in baseline (Acocks’

Veld Types, Acocks, 1988) simulations

Sub- and Quaternary Cat. Nos

Veld Types and areal extents (km2)

SC QC Coastal forest and Thornveld

Ngongoni veld

Valley Bushveld

Highland Sourveld

and Dohne Sourveld

Ngongoni veld of

Natal Mist Belt

Southern Tall

Grassland

Themeda-Festuca

Alpine Veld

1 U10B 0 0 0 123.06 0 0 39.85 2 U10B 0 0 0 45.20 0 0 18.12 3 U10B 0 0 0 131.32 0 10.36 0 4 U10B 0 0 0 12.76 0 16.47 0 5 U10A 0 0 0 67.21 0 0 75.75 6 U10A 0 0 0 54.10 0 0 3.66 7 U10A 0 0 0 182.88 0 25.13 0

84

8 U10D 0 0 0 20.28 0 26.81 0 9 U10D 0 0 0 164.38 0 24.85 0

10 U10D 0 0 0 63.30 0 14.14 0 11 U10C 0 0 0 63.09 0 0 30.02 12 U10C 0 0 0 32.87 0 0 0 13 U10C 0 0 0 121.30 0 26.85 0 14 U10D 0 0 0 11.86 0 18.11 0 15 U10E 0 0 0 18.87 0 0 0 16 U10E 0 0 0 37.02 0 33.92 0 17 U10E 0 0 0 62.17 0 7.48 0 18 U10E 0 0 0 80.95 5.91 71.68 0 19 U10F 0 0 0 55.42 0 22.27 0 20 U10F 0 0 0 55.13 0 0 0 21 U10F 0 0 0 24.08 0.04 0 0 22 U10F 0 0 0 7.62 1.44 0 0 23 U10F 0 0 0 31.69 44.50 69.64 0 24 U10F 0 0 0.77 0 28.01 36.55 0 25 U10G 0 0 0 128.47 7.52 0 0 26 U10G 0 0 0 50.68 53.06 2.74 0 27 U10G 0 0 9.79 2.78 52.77 50.84 0 28 U10H 0 0 31.93 10.70 64.13 30.80 0 29 U10H 0 0 4.68 42.25 36.00 34.50 0 30 U10H 0 0 2.01 59.80 35.94 24.92 0 31 U10H 0 0 30.28 0 31.33 14.93 0 32 U10J 0 0 4.13 0 0.74 2.41 0 33 U10J 0 0 0 27.09 48.98 0 0 34 U10J 0 47.77 23.53 0.05 29.74 0 0 35 U10J 0 50.04 82.09 18.09 53.27 7.96 0 36 U10J 0 6.45 5.49 0 0 0 0 37 U10J 0 65.45 27.13 0 5.25 0 0 38 U10L 0 23.8 0.46 0 0 0 0 39 U10L 0 44.03 12.00 0 0 0 0 40 U10K 0 0 0 11.97 36.42 0 0 41 U10K 0 0 0 1.46 28.29 0 0 42 U10K 0 0 0.75 0 29.32 0 0 43 U10K 0 16.078 50.18 0 76.66 0 0 44 U10K 0 30.72 68.20 0 10.93 0 0 45 U10L 9.85 117.44 98.93 0 0 0 0 46 U10M 16.00 0 0.76 0 0 0 0 47 U10M 76.01 35.15 88.61 0 0 0 0 48 U10M 25.70 0 0 0 0 0 0 49 U10M 26.23 0 0 0 0 0 0 50 U10M 0.78 0 0 0 0 0 0 51 U10M 2.30 0 0 0 0 0 0 52 U10M 5.72 0 0 0 0 0 0

85

Table C10 Mkomazi Veld Types and the relevant proportions of these Veld types applied in the ACRU Grassland category in all other land use simulations

Sub- and Quaternary Cat. Nos

Veld Types and areal extents (km2)

SC QC Coastal forest and Thornveld

Ngongoni veld

Highland Sourveld and

Dohne Sourveld

Ngongoni veld of Natal

Mist Belt

Southern Tall Grassland

Themeda-Festuca

Alpine Veld

1 U10B 0 0 118.720 0 0 38.448 2 U10B 0 0 43.988 0 0 17.632 3 U10B 0 0 105.050 0 8.290 0 4 U10B 0 0 3.565 0 4.601 0 5 U10A 0 0 63.004 0 0 71.010 6 U10A 0 0 48.166 0 0 3.254 7 U10A 0 0 151.691 0 20.844 0 8 U10D 0 0 8.117 0 10.733 0 9 U10D 0 0 155.473 0 23.503 0

10 U10D 0 0 36.217 0 8.093 0 11 U10C 0 0 59.340 0 0 28.243 12 U10C 0 0 29.111 0 0 0 13 U10C 0 0 101.185 0 22.393 0 14 U10D 0 0 6.971 0 10.647 0 15 U10E 0 0 8.378 0 0 0 16 U10E 0 0 26.416 0 24.210 0 17 U10E 0 0 53.254 0 6.411 0 18 U10E 0 0 44.181 3.228 39.119 0 19 U10F 0 0 36.206 0 14.548 0 20 U10F 0 0 35.893 0 0 0 21 U10F 0 0 8.984 0.016 0 0 22 U10F 0 0 2.687 0.506 0 0 23 U10F 0 0 13.789 19.364 30.304 0 24 U10F 0 0 0 6.783 7.286 0 25 U10G 0 0 81.977 4.801 0 0 26 U10G 0 0 25.379 26.568 1.371 0 27 U10G 0 0 2.252 42.833 41.265 0 28 U10H 0 0 7.094 42.531 20.421 0 29 U10H 0 0 19.550 16.656 15.96 0 30 U10H 0 0 26.427 15.881 11.013 0 31 U10H 0 0 0 13.100 6.242 0 32 U10J 0 0 0 0.080 0.264 0 33 U10J 0 0 10.337 18.690 0 0 34 U10J 0 8.781 0.010 5.467 0 0 35 U10J 0 21.933 7.929 23.349 3.489 0 36 U10J 0 3.598 0 0 0 0 37 U10J 0 26.763 0 2.147 0 0 38 U10L 0 7.582 0 0 0 0 39 U10L 0 18.613 0 0 0 0 40 U10K 0 0 0 2.401 0 7.319 41 U10K 0 0 0.0717 1.392 0 0

86

42 U10K 0 7.295 0 12.240 0 0 43 U10K 0 18.240 0 33.320 0 0 44 U10K 0 37.132 0 6.493 0 0 45 U10L 3.12 0 0 0 0 0 46 U10M 0.001 0.556 0 0 0 0 47 U10M 1.20 0 0 0 0 0 48 U10M 0.001 0 0 0 0 0 49 U10M 0.001 0 0 0 0 0 50 U10M 0.001 0 0 0 0 0 51 U10M 0.001 0 0 0 0 0 52 U10M 0.001 0 0 0 0 0

NB Valley Bushveld was been categorised as a separate ACRU

category because it was anticipated that its hydrological responses would be different from those of the general unimproved grassland classified by the LANDSAT TM imagery.

Table C11 Month-by-month factors used for adjustment of A-pan values in

the “climate change” ACRU menus for the Mkomazi study area

Sub- and Quaternary Cat. Nos

Daily Precipitation Adjustment Factors

SC QC Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1 U10B 1.09 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.07 1.06

2 U10B 1.09 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.07 1.06

3 U10B 1.09 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.07 1.06

4 U10B 1.09 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.07 1.06

5 U10A 1.09 1.10 1.07 1.10 1.10 1.07 1.08 1.08 1.08 1.05 1.07 1.06

6 U10A 1.09 1.10 1.07 1.10 1.10 1.07 1.08 1.08 1.08 1.05 1.07 1.06

7 U10A 1.09 1.10 1.07 1.10 1.10 1.07 1.08 1.08 1.08 1.05 1.07 1.06

8 U10D 1.10 1.10 1.07 1.10 1.10 1.07 1.08 1.08 1.08 1.05 1.07 1.06

9 U10D 1.10 1.10 1.07 1.10 1.10 1.07 1.08 1.08 1.08 1.05 1.07 1.06

10 U10D 1.10 1.10 1.07 1.10 1.10 1.07 1.08 1.08 1.08 1.05 1.07 1.06

11 U10C 1.09 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.07 1.06

12 U10C 1.09 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.07 1.06

13 U10C 1.09 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.07 1.06

14 U10D 1.10 1.10 1.07 1.10 1.10 1.07 1.08 1.08 1.08 1.05 1.07 1.06

15 U10E 1.10 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.06 1.06

87

16 U10E 1.10 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.06 1.06

17 U10E 1.10 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.06 1.06

18 U10E 1.10 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.06 1.06

19 U10F 1.10 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.07 1.06

20 U10F 1.10 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.07 1.06

21 U10F 1.10 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.07 1.06

22 U10F 1.10 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.07 1.06

23 U10F 1.10 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.07 1.06

24 U10F 1.10 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.07 1.06

25 U10G 1.10 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.07 1.06

26 U10G 1.10 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.07 1.06

27 U10G 1.10 1.10 1.07 1.10 1.10 1.08 1.08 1.08 1.08 1.05 1.07 1.06

28 U10H 1.10 1.10 1.07 1.10 1.09 1.08 1.08 1.08 1.08 1.05 1.07 1.09

29 U10H 1.10 1.10 1.07 1.10 1.09 1.08 1.08 1.08 1.08 1.05 1.07 1.09

30 U10H 1.10 1.10 1.07 1.10 1.09 1.08 1.08 1.08 1.08 1.05 1.07 1.09

31 U10H 1.10 1.10 1.07 1.10 1.09 1.08 1.08 1.08 1.08 1.05 1.07 1.09

32 U10J 1.09 1.10 1.07 1.10 1.10 1.02 1.08 1.08 1.08 1.05 1.06 1.06

33 U10J 1.09 1.10 1.07 1.10 1.10 1.02 1.08 1.08 1.08 1.05 1.06 1.06

34 U10J 1.09 1.10 1.07 1.10 1.10 1.02 1.08 1.08 1.08 1.05 1.06 1.06

35 U10J 1.09 1.10 1.07 1.10 1.10 1.02 1.08 1.08 1.08 1.05 1.06 1.06

36 U10J 1.09 1.10 1.07 1.10 1.10 1.02 1.08 1.08 1.08 1.05 1.06 1.06

37 U10J 1.09 1.10 1.07 1.10 1.10 1.02 1.08 1.08 1.08 1.05 1.06 1.06

38 U10L 1.09 1.10 1.07 1.10 1.09 1.08 1.08 1.08 1.08 1.05 1.07 1.06

39 U10L 1.09 1.10 1.07 1.10 1.09 1.08 1.08 1.08 1.08 1.05 1.07 1.06

40 U10K 1.09 1.10 1.07 1.09 1.10 1.08 1.08 1.08 1.08 1.08 1.05 1.06

41 U10K 1.09 1.10 1.07 1.09 1.10 1.08 1.08 1.08 1.08 1.08 1.05 1.06

42 U10K 1.09 1.10 1.07 1.09 1.10 1.08 1.08 1.08 1.08 1.08 1.05 1.06

43 U10K 1.09 1.10 1.07 1.09 1.10 1.08 1.08 1.08 1.08 1.08 1.05 1.06

44 U10K 1.09 1.10 1.07 1.09 1.10 1.08 1.08 1.08 1.08 1.08 1.05 1.06

45 U10L 1.09 1.10 1.07 1.10 1.09 1.08 1.08 1.08 1.08 1.05 1.07 1.06

46 U10M 1.08 1.09 1.07 1.09 1.09 1.07 1.08 1.08 1.08 1.05 1.06 1.04

47 U10M 1.08 1.09 1.07 1.09 1.09 1.07 1.08 1.08 1.08 1.05 1.06 1.04

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48 U10M 1.08 1.09 1.07 1.09 1.09 1.07 1.08 1.08 1.08 1.05 1.06 1.04

49 U10M 1.08 1.09 1.07 1.09 1.09 1.07 1.08 1.08 1.08 1.05 1.06 1.04

50 U10M 1.08 1.09 1.07 1.09 1.09 1.07 1.08 1.08 1.08 1.05 1.06 1.04

51 U10M 1.08 1.09 1.07 1.09 1.09 1.07 1.08 1.08 1.08 1.05 1.06 1.04

52 U10M 1.08 1.09 1.07 1.09 1.09 1.07 1.08 1.08 1.08 1.05 1.06 1.04

Table C12 Month-by-month factors used for adjustment of daily

precipitation values in the “climate change” ACRU menus for the Mkomazi study area

Sub- and Quaternary Cat. Nos

Daily Precipitation Adjustment Factors

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 U10B 0.81 0.81 1.00 0.96 0.78 0.95 0.72 1.02 0.94 1.19 0.93 0.75 2 U10B 0.79 0.81 1.00 0.95 0.75 0.91 0.72 1.03 0.93 1.14 0.92 0.74 3 U10B 0.78 0.89 0.88 0.75 0.66 0.97 1.09 1.10 1.02 1.07 0.98 0.80 4 U10B 0.80 0.89 0.89 0.77 0.64 0.84 0.73 1.38 1.16 1.34 1.11 0.83 5 U10A 0.78 0.88 0.99 0.80 0.83 1.00 1.33 1.69 1.21 1.04 1.05 0.84 6 U10A 0.89 0.88 0.91 0.81 0.73 1.19 0.78 1.12 1.21 1.29 1.05 0.86 7 U10A 0.82 0.92 0.90 0.79 0.69 1.18 0.98 1.18 1.07 1.17 1.03 0.84 8 U10D 0.84 0.97 0.96 0.88 0.68 1.56 1.31 16.84 1.22 1.40 1.19 0.86 9 U10D 0.77 0.87 0.86 0.77 0.65 1.09 0.90 1.23 1.05 1.16 0.98 0.77

10 U10D 0.81 0.91 0.88 0.73 0.75 0.84 0.72 1.10 1.06 1.26 0.93 0.78 11 U10C 0.76 0.80 0.94 1.00 0.72 0.89 0.71 1.07 0.90 1.11 0.90 0.72 12 U10C 0.78 0.88 0.88 0.88 0.66 0.88 0.86 1.29 1.13 1.25 1.07 0.81 13 U10C 0.78 0.92 0.89 0.92 0.79 0.94 0.71 1.69 1.38 1.36 1.04 0.85 14 U10D 0.60 0.76 0.85 0.61 0.54 1.09 1.20 2.08 1.27 1.21 1.03 0.64 15 U10E 0.78 0.87 0.87 0.75 0.69 0.85 1.12 1.28 1.13 1.22 0.96 0.76 16 U10E 0.80 0.91 0.91 0.77 0.73 0.85 1.01 1.20 1.11 1.24 0.98 0.79 17 U10E 0.79 0.91 0.93 0.83 0.73 0.87 1.02 1.12 1.13 1.15 1.01 0.79 18 U10E 0.77 0.85 0.84 0.74 0.64 0.85 0.84 1.17 0.97 1.11 0.90 0.75 19 U10F 0.82 0.92 0.92 0.83 0.80 0.84 1.30 1.33 1.22 1.30 1.02 0.80 20 U10F 0.77 0.86 0.85 0.76 0.65 1.05 0.83 1.15 1.01 1.11 0.94 0.77 21 U10F 0.80 0.88 0.91 0.84 0.71 1.47 1.14 1.41 1.20 1.25 0.10 0.79 22 U10F 0.78 0.86 0.86 0.77 0.66 1.02 0.93 1.24 1.05 1.16 0.96 0.77 23 U10F 0.89 0.94 0.92 0.85 0.68 0.92 0.81 1.23 0.99 1.16 1.02 0.87 24 U10F 0.85 0.91 0.89 0.81 0.65 0.87 0.83 1.21 0.99 1.14 0.99 0.82 25 U10G 0.79 0.91 0.90 0.79 0.84 0.89 1.30 1.38 1.33 1.39 1.02 0.84 26 U10G 0.78 0.76 0.89 0.65 0.49 0.94 0.76 1.38 0.87 1.00 0.92 0.78 27 U10G 0.78 0.80 0.92 0.65 0.51 0.95 0.76 1.34 0.91 1.02 0.93 0.77 28 U10H 0.77 0.80 0.93 0.64 0.48 1.12 0.71 1.47 0.97 1.03 0.96 0.79 29 U10H 0.86 0.95 0.94 0.83 0.68 0.97 0.87 1.28 1.13 1.26 1.06 0.87 30 U10H 0.80 0.89 0.88 0.78 0.68 0.91 0.89 1.25 1.14 1.20 0.96 0.79 31 U10H 0.84 0.98 0.85 0.83 0.76 0.78 0.97 1.15 1.03 1.03 0.99 0.77 32 U10J 0.85 0.91 0.90 0.69 0.58 0.83 0.96 0.97 0.89 1.08 0.97 0.78 33 U10J 0.75 0.86 0.82 0.75 0.71 0.91 0.79 1.23 1.11 1.23 0.92 0.80

89

34 U10J 0.88 1.03 0.97 0.81 0.58 1.26 1.13 1.58 1.19 1.21 1.04 0.73 35 U10J 0.79 0.81 0.88 0.76 0.90 1.55 1.09 1.45 1.22 1.25 0.99 0.87 36 U10J 0.90 0.90 0.92 0.78 0.76 1.55 1.30 1.57 1.23 1.35 1.06 0.85 37 U10J 0.90 0.92 0.98 0.77 0.67 0.99 0.91 1.09 1.02 1.19 1.04 0.81 38 U10L 0.82 0.91 0.90 0.80 0.70 1.56 1.30 1.70 1.23 1.56 1.03 0.83 39 U10L 0.81 0.89 0.87 0.84 0.81 1.56 1.30 1.70 1.28 1.44 1.00 0.80 40 U10K 0.80 0.89 0.96 0.77 0.84 0.84 0.70 1.23 0.89 1.24 0.94 0.86 41 U10K 0.83 0.95 0.92 0.82 0.62 1.20 1.09 1.40 1.17 1.18 1.03 0.77 42 U10K 0.84 0.97 0.92 0.84 0.63 1.23 1.11 1.45 1.21 1.19 1.05 0.77 43 U10K 0.77 0.90 0.85 0.80 0.56 1.52 1.24 1.49 1.18 1.11 0.96 0.70 44 U10K 0.86 0.85 0.85 0.69 0.75 1.57 1.30 1.58 1.17 1.30 1.03 0.84 45 U10L 0.83 0.78 0.79 0.74 0.77 1.56 1.30 1.70 1.27 1.31 0.98 0.76 46 U10M 0.82 0.92 0.87 0.77 0.52 1.07 0.93 1.49 1.00 1.10 1.05 0.67 47 U10M 0.73 0.88 0.79 0.89 0.79 1.07 1.01 1.25 1.18 1.10 0.96 0.74 48 U10M 0.75 0.93 0.75 0.69 0.72 1.17 0.86 1.50 1.10 1.14 1.08 0.75 49 U10M 0.81 0.92 0.89 0.73 0.53 0.85 0.77 1.18 0.98 1.24 1.00 0.90 50 U10M 0.81 0.91 0.89 0.78 0.60 0.98 0.87 1.20 1.04 1.21 1.02 0.88 51 U10M 0.80 0.98 0.83 0.77 0.85 1.35 1.03 1.63 1.23 1.24 1.16 0.81 52 U10M 0.83 0.93 0.91 0.80 0.62 1.07 0.89 1.24 1.07 1.24 1.05 0.89

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APPENDIX C SECTION 2 ASSUMPTIONS REGARDING IRRIGATION ABSTRACTIONS • With the exception of those irrigated areas identified by the LANDSAT TM image as

improved grassland, irrigated areas in upper subcatchments, SC Numbers 1-27, were assumed to be under winter wheat and spring cabbages, with typical maximum rooting depths of 0.3 m, whereas irrigated areas in lower subcatchments, SC Numbers 28-52, were assumed to be under citrus, with typical maximum rooting depths of 1.00 m.

• Soil water extraction by winter wheat and spring cabbages was concentrated in the top 0.2 m, whereas for citrus it was in the top 0.6 m

• The crop coefficient of the irrigated crop at which ground cover / shading was at a maximum was at a maximum was 80% of the maximum monthly water use coefficient as shown in Appendix C, Table C3, and recommended as such from previous ACRU verification studies.

• The crop coefficient of the irrigated crop when rooting depth reached a maximum was also 80% of the maximum monthly water use coefficient.

• The irrigation scheduling was for a 15 mm net application in a 5 day cycle. • The irrigation cycle was halted once a threshold daily rainfall of 15 mm was exceeded. • Irrigated soils were assigned the same soil water retention values as their respective

subcatchments’ soils. • Conveyance as well as wind drift and spray evaporation were taken as 10% each. • Where water bodies had been identified by the LANDSAT TM image these were

confirmed as being farm dams from examination of Aerial Photographs (1:30 000) of the Mkomazi catchment. Where farm dams existed, irrigation abstractions were drawn from this source, within each subcatchment. In those subcatchments where there was an absence of farm dams, irrigation abstractions were drawn from river sources. In all instances irrigation return flows were returned to their own subcatchments.

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APPENDIX C SECTION 3 ASSUMPTIONS REGARDING RESERVOIR MODELLING AND

OPERATING RULES • For computational purposes, all dams within a given subcatchment were treated as

one combined dam, located at the respective subcatchment outlet, for evaporation and irrigation purposes.

• All reservoirs (including the proposed Smithfield Dam) were assumed to fully operational, accounting for daily gains from streamflows and rainfall on the water surfaces, and losses through surface water evaporation, irrigation abstractions and other operations, overflow, normal flow releases and seepage.

• The LANDSAT TM, image used to determine land cover in the Mkomazi catchment identified the surface area of the water body during April 1996. As well as being unrepresentative of the full supply capacity criteria required for reservoir modelling in ACRU, no other technical information regarding dam capacity or length of dam wall was available. In such instances of inadequacy of available information, the surface area available is applied in the following relationship to obtain the volume of the reservoir: A = 7.2 ( Sv )0.77

where, A is the surface area of the reservoir and Sv is the volume of the reservoir.

• Area / volume relationships were also unavailable for the Mkomazi reservoirs,

therefore, the default area / volume relationship was applied. This negated the necessity for the knowledge of the length of the reservoir wall.

• It was assumed in the case of farm dams, that no legal flow would be released in practice. However, because of their earth bound construction, seepage volumes where calculated as being that volume of water, that if released, would take 5 years to empty the reservoir. These were calculated using the following relationship: Sp or Qn = Sv / 1500

where, Sp is the volume seeping from the dam,

Qn is the volume of normal flow released from the dam and Sv is the volume of the reservoir at full supply capacity.

• No seepage was assumed from the proposed Smithfield Dam, because it is considered that seepage from dams with impermeable lining and concrete walls is negligible.

• Legal flow abstractions are anticipated from the proposed Impendle and Smithfield Dams. However, in the absence of the necessary daily design abstractions and operating rules for dam releases to meet those flow requirements in terms of the Reserve, these were preliminarily estimated in the same manner as seepage volumes, i.e. at 1/1500 of full supply capacity per day, on two conditions incorporated in the ACRU model. If the total streamflow into the reservoir on a given day was less than the normal flow releases, the releases were reduced to equal those of the total inflows. If the storage volumes were below dead storage level, no normal flow releases were made.

• The dead storage level of the reservoir, i.e. the level below which no further abstractions can take place, nor would normal flow be released, was input as 10% of full supply capacity as suggested by Smithers and Schulze (1995).

• All the reservoirs where set to full capacity at the beginning of the simulation period. Although, this assumption is anomalous, there is insufficient information regarding the

92

operating rules of the different reservoirs in the Mkomazi Catchment. Therefore, it was assumed to commence model runs with all the reservoirs at the same storage capacity value in order to obtain comparative values.

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APPENDIX D: VERIFICATION OF MKOMAZI STREAMFLOWS GENERATED WITH THE ACRU MODEL

Table D1 Verification results using the present land use ACRU input menu for

the streamflows generated from 1945 to 1995 at the DWAF gauging stations

DWAF Gauge

Number

SC No Upstream area (km2)

ACRU Streamflows (106m3)

Observed Streamflows

(106m3)

Difference %

U1H005 18 1741.74 630.86 594.11 6.19 U1H006 48 4347.27 1035.08 747.29 38.51 U1H003 50 4374.28 1038.89 1416.83 26.67

Table D2 Verification statistics of streamflows at Camden (U1H005) Sample size 266

CONSERVATION STATISTICS Sum of observed values 7186.76 Sum of simulated values 8235.6 Mean of observed values 27.018 Mean of simulated values 30.961 % difference between means -14.594 t Statistic for comparing means -1.497 Variance of observed values 1094.581 Variance of simulated values 749.841 % difference between variances 31.495 Standard deviation of observed values 33.084 Standard deviation of simulated values 27.383 % difference between standard deviations 17.232 Standard error of observed means 2.029 Standard error of simulated means 1.679 % difference between standard errors 17.232 Total root mean square error (RMSE) 15.549 Coeff. of variation of observed values (%) 122.454 Coeff. of variation of simulated values (%) 88.445 % difference between coeffs. of variation 27.773 Skewness coefficient of observed values 2.181 Skewness coefficient of simulated values 1.732 % difference between skewness coefficients 20.571 Kurtosis coefficient of observed values 5.669 Kurtosis coefficient of simulated values 4.049 % difference between kurtosis coefficients 28.575 t Statistic for correlation testing 32.17

REGRESSION STATISTICS

94

Correlation coefficient (Pearson's r) 0.893 Slope of the regression line 0.739 Y intercept of the regression line 11 Total sum of squares (SST) 198707.9 Sum of squares due to regression (SSR) 158320 Residual sum of squares (SSE) 40387.85 Computer rounding error (SST - (SSR+SSE)) 0 Coefficient of determination (SSR/SST) 0.797 Coefficient of efficiency 0.676 Coefficient of agreement 0.94 Table D3 Verification statistics of streamflows at Goodenough (U1H006) Sample size 299

CONSERVATION STATISTICS Sum of observed values 3705.420 Sum of simulated values 5398.120 Mean of observed values 12.393 Mean of simulated values 18.045 % difference between means -45.682 t Statistic for comparing means -5.091 Variance of observed values 165.146 Variance of simulated values 204.601 % difference between variances -23.891 Standard deviation of observed values 12.851 Standard deviation of simulated values 14.304 % difference between standard deviations -11.306 Standard error of observed means 0.743 Standard error of simulated means 0.827 % difference between standard errors -11.306 Total root mean square error (RMSE) 8.810 Coeff. of variation of observed values (%) 103.697 Coeff. of variation of simulated values (%) 79.229 % difference between coeffs. of variation 23.596 Skewness coefficient of observed values 1.867 Skewness coefficient of simulated values 1.445 % difference between skewness coefficients 22.615 Kurtosis coefficient of observed values 3.859 Kurtosis coefficient of simulated values 1.912 % difference between kurtosis coefficients 50.447 t Statistic for correlation testing 32.154

REGRESSION STATISTICS Correlation coefficient (Pearson's r) 0.881 Slope of the regression line 0.981 Y intercept of the regression line 5.896 Total sum of squares (SST) 60971.071

95

Sum of squares due to regression (SSR) 47364.523 Residual sum of squares (SSE) 13606.548 Computer rounding error (SST - (SSR+SSE)) 0 Coefficient of determination (SSR/SST) 0.777 Coefficient of efficiency 0.619 Coefficient of agreement 0.934 Table D4 Verification statistics of streamflows at Delos Estate (U1H003) Sample size 141

CONSERVATION STATISTICS Sum of observed values 3888.020 Sum of simulated values 2830.620 Mean of observed values 27.575 Mean of simulated values 20.075 % difference between means 27.196 t Statistic for comparing means 2.491 Variance of observed values 1014.301 Variance of simulated values 264.063 % difference between variances 73.966 Standard deviation of observed values 31.848 Standard deviation of simulated values 16.250 % difference between standard deviations 48.976 Standard error of observed means 2.682 Standard error of simulated means 1.368 % difference between standard errors 48.976 Total root mean square error (RMSE) 20.925 Coeff. of variation of observed values (%) 115.498 Coeff. of variation of simulated values (%) 80.945 % difference between coeffs. of variation 29.916 Skewness coefficient of observed values 1.913 Skewness coefficient of simulated values 1.421 % difference between skewness coefficients 25.750 Kurtosis coefficient of observed values 3.941 Kurtosis coefficient of simulated values 2.184 % difference between kurtosis coefficients 44.570 t Statistic for correlation testing 20.206

REGRESSION STATISTICS Correlation coefficient (Pearson's r) 0.864 Slope of the regression line 0.441 Y intercept of the regression line 7.923 Total sum of squares (SST) 36968.843 Sum of squares due to regression (SSR) 27579.743 Residual sum of squares (SSE) 9389.100 Computer rounding error (SST - (SSR+SSE)) 0

96

Coefficient of determination (SSR/SST) 0.746 Coefficient of efficiency -0.670 Coefficient of agreement 0.923

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APPENDIX E MKOMAZI SIMULATED STREAMFLOWS Table E1 Volumes of accumulated streamflow generated by ACRU at

individual subcatchments, for the median year, under various land use conditions

SC No

Upstream conrtibuting SC Nos

Acocks land use

(106m3)

Present Land use (106m3)

Present Land use with

Smithfield Dam (phase 1)

(106m3)

Potential Climate Change (106m3)

Potential Climate

Change with Smithfield

Dam (phase 1) (106m3)

1 1 83.54 83.26 83.26 41.26 41.26 2 2 28.31 28.03 28.03 11.77 11.77 3 1, 2, 3 141.69 140.18 140.18 59.82 59.82 4 3, 4 150.47 149.56 149.56 63.10 63.10 5 5 97.24 97.20 97.20 52.54 52.54 6 6 31.43 31.50 31.50 15.04 15.04 7 5, 6, 7 187.20 185.07 185.07 84.04 84.04 8 4, 7, 8 361.91 360.12 360.12 167.78 167.78 9 9 60.86 59.32 59.32 21.17 21.17 10 9, 10 84.08 79.97 79.97 29.63 29.63 11 11 42.28 42.86 42.86 19.35 19.35 12 12 9.95 8.32 8.32 2.47 2.47 13 11, 12, 13 99.76 92.60 92.60 41.50 41.50 14 8, 10, 13, 14 556.39 542.30 542.30 232.35 232.35 15 15 6.04 6.68 6.68 2.37 2.37 16 15, 16 28.02 28.22 28.22 10.08 10.08 17 17 19.11 17.30 17.30 3.45 3.45 18 14, 16, 17, 18 653.54 630.86 630.86 253.77 253.77 19 18, 19 665.19 647.35 647.35 263.27 263.27 20 20 15.24 12.16 12.16 4.06 4.06 21 20, 21 21.98 16.12 16.12 5.55 5.55 22 22 2.60 1.65 1.65 0.60 0.60 23 19, 21, 22, 23 735.16 704.15 476 287.08 60.99 24 23, 24 742.67 713.85 495.61 291.56 65.05 25 25 49.56 34.43 34.43 9.45 9.45 26 25, 26 77.40 45.68 45.68 16.05 16.05 27 24, 26, 27 844.60 778.45 575.53 321.59 110.99 28 27, 28 869.52 802.83 606.96 337.61 127.09 29 29 20.63 13.89 13.89 4.42 4.42 30 30 17.98 6.84 6.84 14.28 14.28 31 28, 29, 30, 31 962.20 869.85 642.34 364.12 151.28 32 31, 32 962.82 870.83 642.47 364.44 151.65 33 33 10.33 3.13 3.13 1.56 1.56 34 33, 34 28.26 20.73 20.73 10.29 10.29 35 32, 34, 35 1031.56 923.41 695.47 390.00 178.03 36 35, 36 1032.59 924.39 696.63 391.40 179.00 37 36, 37 1042.21 932.56 705.01 402.17 183.56 38 38 3.44 4.29 4.29 1.78 1.78 39 38, 39 9.28 9.74 9.74 4.63 4.63

98

40 40 5.61 1.06 1.06 0.25 0.25 41 40, 41 9.11 0.37 0.37 0.28 0.28 42 41, 42 13.77 0.39 0.39 0.39 0.39 43 42, 43 31.57 8.11 8.11 4.68 4.68 44 43, 44 46.76 21.81 21.81 12.19 12.19 45 37, 44, 39 1131.24 999.89 770.03 439.20 223.29 46 46 2.39 2.91 2.91 2.43 2.43 47 45, 46, 47 1156.88 1031.12 801.65 464.57 246.76 48 47, 48 1159.85 1035.08 805.55 470.37 249.53 49 48, 49 1161.60 1035.70 808.66 479.33 260.66 50 49, 50 1161.81 1038.89 808.80 479.42 260.71 51 50, 51 1161.98 1039.44 809.67 480.11 261.28 52 51, 52 1162.19 1039.92 810.29 482.05 263.38