The National Water Act (NWA) was revised in 1998 in response to political change in South Africa and a realisation for the need for sustainable, equitable and efficient use of water in a water scarce country. With the establishment of the new NWA (1998) came the delineation of South Africa into 19 Water Management Areas (WMAs) which are to be governed by Catchment Management Agencies (CMAs). The Strategic Environmental Assessment (SEA) is expected to provide both information and tools to support decision making of CMAs.

Development opportunities within the WMA need to be considered against other viable options for water use in that area. These different scenarios need to be modelled to simulate their potential impacts in the catchment thereby allowing a decision maker to review the implications of different scenarios. To allow comparative evaluation of alternatives requires a wide range of inputs for a range of scales to facilitate a selection of feasible scenarios to be assessed. These inputs will be required to be stored in a database, which is then linked to various models to ultimately form a Decision Support System (DSS).

The DSS in the context of this document is defined as a set of tools that will enable CMAs to develop their Catchment Management Strategies (CMSs) and assess individual license applications. The DSS is required to incorporate hydrological, ecological, biophysical, social and economic elements of information for processing by decision makers. To access this information requires the design of a Scenario Generator (SG), which will facilitate comparative analyses of the hydrological, economic and social implications of changes in land use or engineering structures.

The first step in the decision making process is the acquisition of information. There are two key components in the information acquisition phase, namely the problem identification and the problem structuring phase which is required to help the decision maker in formulating a decision.

To order to identify which problems will need to be addressed by a CMA information will need to be collected on demographics, issues of public concern and environmental issues in the area for example. This will assist in defining the problem framework. The CMA is required to establish the principles for allocating water to both existing and prospective water users within their respective WMA. To do this the CMA must take into account all matters relevant to the use, development, conservation, management and control of water resources. The CMSs developed by CMAs must be in line with the broader strategies established by the National Water Resource Strategy (NWRS). The CMAs are therefore responsible for both longer term planning for the WMAs and the short term processing of individual water licensing applications.

The information required for the DSS can be divided into three categories: data requirements, modelling requirements and stakeholder participation. Each of these information requirements was investigated. It was identified that the central focus of any CMA is the water allocation plan, which requires the identification of the allocatable quantity of water in the catchment, the projected water demands and the developmental constraints.

To establish the water allocation plan information is required on the current water use. The reserve is perhaps the most fundamental aspect of allocatable water determination. The reserve is defined as the quantity and quality of water required for basic human needs, as well as the quantity and quality of water required to sustain the aquatic ecosystem. While the basic human need reserve remains relatively constant, the environmental reserve fluctuates on a daily basis in some cases on a sub daily basis. In order to implement the reserve at a catchment and sub-catchment level the CMA will need water quantity and quality information on at least a daily basis from both its modelling and monitoring systems.

When assessing individual license applications the CMA needs to establish whether the application is in compliance with the CMS and the NWRS, whether it fulfils the equity, efficiency and sustainability criteria set out by the NWA (1998) and whether the impact of this activity is acceptable in terms of other users and the environment.

To address these and other issues modelling is required to provide social, economic, hydrological and environmental information to estimate what the potential impact of the activity under investigation or for which a license in being applied. In the NWA (1998) there is a strong emphasis on stakeholder participation which places new demands on the models and tools which are used in the decision making process. Information generated from models now needs to gain the trust of the stakeholder community in order to be accepted in decision making. To achieve this the information generated from the model or models must be credible, trusted and promote shared understanding. The processes which yield this type of information therefore need to be replicable and consistent, offering regular, affordable and meaningful communication among stakeholders and their representatives.

To set up an allocation plan several different levels of information are required. While the majority of the information required is in the form of raw data, such as landuse, water use, demographic information and biodiversity data, some information needs to be generated with the use of models, particularly in the case where planning projections are needed to expand beyond the catchments or WMAs current status.

To determine current land and water use requires physical process based water quantity and quality models operating at daily time steps, and operational models operating at daily to weekly time steps

The modelling requirements associated with determining water demand projections include Geographic Information Systems, simple mathematical algorithms that use indicators to assess future water demand projections, processed based hydrological models to assess the impacts of different scenarios and operational system hydrological modelling.

To date in DWAF a multi model approach has arisen to address the multitude of problems that arise in catchments with each model designed to accomplish a certain task. These models are then fed into each other using a series linking approach. While the multileveled, multidisciplinary, multi-model modelling approach does offer many advantages in choosing the level of detail which modelling can follow there has been some concern levelled at this particular approach in the international community. The concern is related to the linkage of different models without complete understanding of the linkages themselves. Added to this concern is the detail required in the implementation of the NWA (1998). Monthly modelling approach adopted by the Pitman – WRYM combinations may not offer the solutions required. While finer scale modelling may be too complicated for many of the tasks required, the upward aggregation of variables from, say, daily to monthly, is a more accurate technique than that of disaggregating monthly to daily flows where many inaccuracies can be introduced.

Calibration models are becoming less attractive for water resources assessment at the level of detail required by the NWA (1998) as they tend to follow a black box approach. The result is a loss of credibility with stakeholders. Calibration models and statistical methods are, in general, situation specific and the results are non transferable to other areas or novel situations. This means that the testing of different scenarios and extending derived estimates at ungauged sites can result in large inaccuracies with the use of calibration and statistical methods. Whereas physically based process models have more complicated algorithms and are generally more time consuming to set up, the inputs and outputs are generally easier to understand as they represent real world quantities.

From the review of available water quantity models that might be applicable for the requirements of the WMA DSS it was concluded that the new ACRU model complied with many of the criteria. It is a physically based, daily time-step model which is particularly suited to land use impact studies. The daily resolution allows for IFRs which are assessed on a daily basis and for the potential assessment of water quality issues, which can fluctuate on a daily and even sub-daily time step. The new operational hydrological components currently being developed in the model allow for the assessment of different water use and water supply impacts. The model should be able to test the water availability yielded from the system as a whole as well as the water availability for individual users.

The DSS will initially be used to address water quantity issues, however, in the future it should ideally be able to address both water quantity and water quality issues. From a review of water quality modelling and monitoring in South Africa it was found that although there is a lack of water quality data in many parts of the country this problem could be addressed by modelling. The complexity of the modelling method used can be altered depending on the status (i.e. stressed or unstressed) of the catchment concerned. Nevertheless, there are not many water quality models available and most are data intensive and complex to set up. Hopefully more monitoring sites will increase the water quality database in South Africa and advances in current research will allow more complex models to be established. The issue of water quality modelling clearly needs to be addressed.

Although there will be specific problems which require specialised data inputs to the DSS there is general information which will be required for all WMAs. This includes invariant (e.g. climate, catchment attributes), variant (e.g. land use), ecological, environmental and economic data. Data is also required on historical, present and projected water demand distribution in the catchments.

A system of housing the data applicable to a WMA is required and some consensus is needed on what form this will take. The database system will need to store geo-reference data, attribute data and time series data. It was also identified that there is a need to store both observed and simulated data in a consistent and easy to use format. Thus, the nature of the database access will be largely controlled by what the analysis and display software requires to be able to operate efficiently and in a user friendly way.

Examples of some database management systems that have been developed along these lines include HYMAS, ICIS, IMPAQ, BASINS and NWBM. From the analysis of various database management system it was concluded that there are two options, either to

• conform to the standards of the USGS – the disadvantage being that South Africa’s data requirements are different to the USA which therefore means data transformation; or
• produce new data storage standards which will suit South African requirements and can build on existing standards – however, to set up such a system would take much time and money.

As neither of these options is considered particularly satisfactory the recommended method of addressing this issue is to choose a database structure which has been developed locally. A system which follows the standards and protocols of DWAF is necessary and one that uses the most recent technology to avoid the system becoming quickly outdated and therefore redundant.

It is clear from the discussions with DWAF and other organisations that the database issue needs to be given a lot more thought. It is, however, clear that ArcView should be used for the spatial data storage component and then be linked to a relational database. At this stage several, options have been suggested in terms of which database to use and link to the ArcView system. The most promising link looks to be that which has been adopted by the Regis system which links an Oracle database to the ArcView GIS. A relational database can reduce the redundancy found in flat format databases and allows for easier querying of data in the database.

The main objective of developing a hydrologically focussed Scenario Generator (SG) is to have a tool that can easily be used to generate water related scenarios, which broadly include scenarios influencing the demand for water and/or changes to the supply of water. The reason for developing this tool is relatively straightforward. The SEA is tasked to assess the water use of current and potential water use and supply conditions. The value of the scenario generator is to assist in the generation of accurate, meaningful water use and supply scenarios.

It is recommended that the SG should be ArcView based. ArcView has the potential to allow easy-to-understand and realistic scenarios to be generated in a transparent manner. The technical challenge is to seamlessly integrate the ArcView SG with the ACRU hydrological model and a carefully designed database. The seamless integration of the SG to ACRU may require that when certain water demand and supply scenarios are invoked, the user of the scenario generator is prompted for information that may be required by the ACRU hydrological model.

It is suggested that the SG be developed with the following capabilities:

Potential opportunities with respect to the SG include that the SG has the potential to facilitate the generation of feasible, transparent scenarios; and the SG may continue to be developed to include increased functionality, such as real time systems for risk evaluation; application with forecasting applications; and the SG could be developed to include planning functionality.

It is critical to refine the information generated using the DSS into quantities that can easily be used and interpreted by model users and decision makers. It is thus important to, in consultation with decision makers, identify the critical information requirements necessary for them to make certain sets of decisions. Visualisation of information can take the form of graphical output at specific points of interest within the catchment, spatial output that shows descriptions of various critical indicators in a GIS format giving an idea of the spatial distribution in a particular area; and specific indicator output at critical points in the catchment.

In order for the decision maker to make a decision it may be necessary to have an objective criterion of ranking and scoring the different indicators in order to transform them into a specific solution that can then be used to compare different scenarios analysed. Once this type of scoring has been collated it is possible for the decision maker to then analyse and weigh up the different options that have been produced and understand the trade offs that result from the different options.

The DSS has three main components which are a database component, a processing component (models) and a visualisation component. In terms of the database component it appears that ArcView is introducing a relational database component, however, these facilities are not available at present. The database design will therefore take on the form of a coupled system, which will link a relational database to the ArcView GIS. The database design will follow the standards set by the integrator, making sure that the database structure and architecture conform to the DWAF standards regardless of the database chosen to store the data. In such a case the system can be relatively easily translated if the data needs to be stored in another database at a later stage.

Hydrological, economic, social and ecological modelling will be required to be performed using the DSS in the processing phase. Hydrological modelling will be carried out using primarily the new ACRU model, however, the DSS will have the facility to introduce the options of other models through the addition of a relevant transformation routine. The economic modelling is necessary to facilitate decision makers to assess both the feasibility of growing crops in certain areas and the economic viability of these crops in the selected areas. To carry out economic modelling will require a multidisciplinary team, including GIS, economic, hydrological and agronomic expertise. The initial DSS will be set up to account for a broad range of crops with only a few different genus included for the larger species. The database can be modified when more information is required.

The social modelling will use layers of information which are given certain weightings in a similar way to the economic modelling component. Ecological variables could be determined in much the same way as those in the economic and social modelling components. This component will require extensive GIS modelling where layers of information, such as biodiversity and conservation areas, could be draped together with different weights to determine certain ecological indices.

The visualisation component of the DSS consists of two main aspects. The initial part is the translation of variables produced by simulation scenarios and base data into indicators that could be used by the decision maker and the second aspect is the display system used to display the different indicators in a suitable format that can be used by the decision maker. Once a generic set of indicators has been defined through consultation with the CMAs the system could be put into place to generate the indicators from the database and visually display them. It is recommended that the ArcView GIS package is used for visualisation purposes in conjunction with the Visual Basic programming language.

Summary of recommendations

• Develop a database that conforms with the DWAF standards that links spatial, attribute and time series data by coupling a GIS with a relational database. The ArcView/Oracle link is suggested as the most promising option at this stage. The Regis system used by the Geohydrology section of DWAF will be investigated to see if it can perform this task.
• The ACRU hydrological model that should be used to perform the hydrological modelling in the processing component of the DSS. The model is a physically based conceptual model that allows for the analysis of different scenarios and can be used with reasonable confidence in ungauged areas. The new developments currently being added to the model will allow it to analyse flow networks with realistic simulations of the operational hydrology. Water quality routines are also been added which will increase the flexibility of the model. The model is also locally developed and able to handle local conditions and data with the minimum of data manipulation needed.
• The system should not be restricted to the use of one particular model. Although the ACRU model has been suggested as the model of preference the system should be able accommodate the use of other models reasonably easily.
• The ArcView GIS should be used as it is the most widely used and accepted GIS available both locally and internationally. Its full range of manipulation abilities is required in the manipulation and display of information in the DSS.
• Economic, social and environmental modules will use both simplified weighting and regression techniques coupled with the ArcView GIS to manipulate and display the outputs.
• The system will need to be seamlessly integrated to simplify the user requirements, making the system more user friendly and hence easier to use. The visual basic programming language could be used for this purpose.
• The information produced by the model needs to be credible, and the assumptions made explicit to enhance stakeholder buy in and understanding.