Transactions on Ecology and the Environment vol 16, 1997 WIT Press, ISSN

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1 GIS, DSS and integrated scenario modelling frameworks for exploring alternative futures K.W.J. Malafant and D.P. Fordham Bureau of Resource Sciences, Department of Primary Industries and Energy, Canberra, Australia Abstract Geographic Information Systems (GIS), Decision Support Systems (DSS) and Integrated Scenario Modelling Frameworks provide a set of valuable tools for resource managers and policy makers, allowing them to make more informed decisions. In the past a non-integrated approach has been adopted in applying techniques such as statistical and mathematical analysis, modelling systems, GIS, DSS and visualisation for decision making. However, there is a need to integrate these techniques to convey complex model and analysis information as well as complex spatial and temporal information. Integrated scenario modelling toolkits allow the integration of techniques, using both qualitative and quantitative methods. These tools are becoming increasingly valuable for exploring the behaviour of large ecological, environmental and management systems around the world Ȧ modelling framework is being developed for identifying the likely impact of policy and program options at regional and Basin levels on the biophysical resource base, agricultural productivity and socio-economic structure of irrigated regions of the Southern Murray-Darling Basin in Australia, over the next 20 years. The framework links four main components: biophysical, production, enterprise and regional/national flow-on effects. Testing the framework across a range of price, cost and yield scenarios, enables comparisons of the impacts and sensitivities of changes on business return and level of equity on the farm and the flow-on effects to the community. The exploration of alternative futures can be achieved by comparing decision scenarios generated by the framework. Decision scenarios allow the policymaker to anticipate and understand risk and to discover new options for action. Well documented scenarios support informed debate of the policy decisions built into the scenarios and the resolution of their tensions. The relative merits of alternative futures and policies can be discussed as can the relative merits of reaching the same end point by different means. This paper discusses the concepts which facilitate an appropriate framework for achieving the integration of these technologies under a modular computing structure.

2 670 Ecosystems and Sustainable Development Introduction GIS, DSS and integrated scenario modelling frameworks and the software they use are becoming increasingly valuable tools, allowing land managers and policy makers to integrate and evaluate resource information and make more informed decisions. Land evaluation systems in Australia have been criticised for their lack of flexibility and have generally considered only physical empirical models [1]. This is in stark contrast to the socioeconomic arena where the application of "alternative futures" or scenario modelling is seen as an integral aspect of analysis [2]. The design approach to modelling by Gault et. al. [2] features the tight integration of user, models and simulation framework for exploring the future. Integrated scenario modelling frameworks allow the future to be explored by examining the driving processes, their interrelationships and inconsistencies or tensions, rather than attempting to forecast the future by extrapolating data measured from past activities. The integration of the user with the framework provides interaction, intervention and interpretation. Decision scenarios allow the policy or decision maker to anticipate and understand risk and to discover new options for action. Well documented scenarios provide a way of exploring alternative futures, and can support informed debate of the policy decisions built into the scenarios and the resolution of their tensions. The relative merits of alternative futures and policies can be discussed as can the relative merits of reaching the same end point by different means. This paper discusses some of the concepts and applications of scenario modelling frameworks for integrating environmental, resource, economic and social models to allow decision makers to explore alternative futures. Decision Making Tools The tools that are used to provide information for decision making can be classified into four broad categories [3]: 1. Support Tools include Geographic Information Systems (GIS), Databases, Image Processing systems, visualisation and mathematical and/or statistical analysis tools. 2. Models, methods or concepts present either a concept, method or algorithm for the production of one, or a small set, of analyses. They form the basic building blocks or modules of more complicated analyses and modelling frameworks.

3 Ecosystems and Sustainable Development Management and/or Decision Support Systems (DSS) integrate a mix of models and concepts, generally to solve a specific set of requirements or tasks. These may consist of a mix of the support tools and modelling category and may form part of a framework system, or may be developed in isolation. 4. Integration framework systems have few inherent models, but facilitate the integration of various support tools, models or decision support systems into a single coherent framework. They provide the "glue" to form complex modelling systems from many diverse sources. Scenario modelling frameworks are but one example of an integration framework system. Scenario Modelling Frameworks The scenario modelling framework that we propose encompasses the simple view of DSS outlined by Sprague [4] consisting of a database, some models and an interface to allow the user to interact, while tightly integrating the user into the system. This tight integration of user, models and simulation framework in exploring the future is termed the "design approach" to modelling [2]. With the design approach the user provides novelty and change to the system by the specification of control variables over the simulation time frame and aims to "explore the future rather than predicting it" [2]. The approach is not oriented towards global optimisation or equilibrium conditions and so provides the user with control over the simulation framework. The lack of global optimisation or equilibrium constraints can lead to scenarios which "... produce physically inconsistent or socially unacceptable futures" [2]. These inconsistencies are called tensions by Gault et. al. [2]. The design approach enables the user to resolve these tensions by exploring alternative scenarios or the user may exercise choice and accept scenarios where tensions still exist. An important feature of the modelling approach adopted by these framework systems is workshopping or stakeholder analysis. This involves specialists/stakeholders/clients from a variety of disciplines coming together to develop the framework components and linkages of the system. Development of the framework by a group of people with experience in the individual components of the system and with knowledge of local conditions ensures that the best available knowledge is exploited and that outcomes are realistic and feasible [5]. The development of these frameworks must be seen as an iterative task, with not only the design, but also the construction and component validation changing as the user explores the system behaviour. A framework can be used to integrate the best and most appropriate of existing models and data for a project. Not only are sources and forms of spatial and temporal data highlighted, but also where data and methods are

4 672 Ecosystems and Sustainable Development lacking. The framework design should be flexible; as new research and/or models become available, they can be integrated into the framework. The aim of the framework approach is to determine the best options for integrating environmental, resource, ecological sustainable management, heritage, economic and social analyses for use in planning, conservation and industry development. Specific objectives of the framework concept are to develop an information system which: integrates the outcomes of any analyses being developed by different technical agencies or user groups; is portable to enhance consistency in analyses; permits greater transparency and, hence, adds credibility; is flexible to changes in approach which may be adopted by different user groups; is capable of comparing real world spatial, environmental, resource, economic and social implications of different resource use options; and includes a user friendly interface for the interactive examination of and experimentation with a range of scenarios. Application Software The Whatlf? object-oriented, scenario modelling package [6] developed by ROBBERT Associates, Ottawa, follows the design approach outlined by Gault et al [5]. Whatlf?, provides a structured set of tools for groups of experts to interact, express their ideas and apply concepts to achieve resolutions in the debate of economically and ecologically sustainable resource issues. Whatlf? allows the future to be explored by examining the driving processes in the system and their interrelationships, rather than trying to forecast the future by extrapolating data measured from past activities. The Whatlf? tools consist of three main components: TOOL (Tool On Object Language), an interactive coding language for manipulating data objects; SAMM (Scenario And Model Manager), allows for the management of the framework and the creation of scenarios which are composed of sets of variable instances or objects', and Documenter, a text and graphics system for preparing structural and relationship diagrams and for preparing framework documentation. The system's object-based construction allows an application developed for a small area or limited issue to be expanded as part of a larger system, building on what has already been assembled.

5 Ecosystems and Sustainable Development 673 Application In Southern Australia, there are major concerns for the long term economic viability of some irrigated farms, particularly small farms and those engaged in traditional grazing and mixed cropping activities. Increasing costs associated with environmental restitution and water market reforms will further reduce the viability of farms with consequent impact upon local and regional communities and the environment. A number of urgent policy decisions must be made by resource managers and regional communities to provide a framework for the future shape of sustainable irrigated agriculture. Soundly based information on the implications of these decisions is fundamental to the development of successful programs and policies. The major constraint to regional and central policymaking is the lack of information describing biophysical futures for irrigation districts, though there is a wealth of information describing current biophysical and socio-economic conditions of the irrigated regions. To significantly improve the application of such information an accessible, user-friendly methodology is being developed that will provide the scope to consider trends, address 'whatif questions and, through a gaming approach, explore opportunities for raising the region's economic and environmental performance. The modelling framework being developed (Figure 1), using the Whatif? package, links four main components: biophysical, production, enterprise and regional/national flow-on effects [7]. This draws on the concepts of a prototype framework outlined by Veitch, Fordham and Malafant [8] which demonstrated the feasibility of the design approach to environmental modelling. The biophysical component consists of two hydrological modules based on SWAGSIM [9] and MIDASS [10], which study surface and subsurface water and salt movement in detail to estimate waterlogging, soil salinity and salt export from irrigation catchments. SWAGSIM (Salt, Water And Groundwater SIMulation) consists of a soil water and groundwater simulation model for evaluating watertable fluctuations in irrigation areas at the regional scale. The model accounts for water balance processes above the soil surface, within the unsaturated zone and in the saturated zone. The model provides information on: regional watertable fluctuations for locating recharge or discharge zones and for calculating rates of recharge and discharge; rootzone saturation, which is used to estimate waterlogging; the effects of management decisions such as installation of drains (tile or mole drains) or the effects of groundwater pumping on the watertable level. Using the information on watertable level, a second hydrological component based on MIDASS (Model for Irrigation District Accessions, Streamflows and Saltload), has been developed to evaluate soil salinity and

6 674 Ecosystems and Sustainable Development regional salt loads from irrigation areas. Salt movement between the rootzone, subsoil and groundwater zone is determined using a simple mass balance. Biophysical Model L J Waterlogging *"" Production Model Yield Enterprise Model Groundwater Conditions Soil Characteristics Climate Landuse 'Crop Type Enterprise Characteristics - Physical - Financial Regional/National Flow on Effects Figure 1. The modelling framework diagram showing the four main components, linkages, feedback, outputs and data requirements. Linked to the biophysical component is a production component. This component determines the total yield of crop produced taking into account salinity and waterlogging losses, soil type and achievable crop production. The effects of salinisation on crop yield are evaluated using the method described by Maas and Hoffman [11], modified to reflect knowledge of the local conditions. For the initial study area, which is predominantly pasture, waterlogging-pasture yield loss functions are based on the work by Maher, Greenslade and Noble [12]. Little work has been carried out investigating the joint effects of salinity and waterlogging. For a recent review on the topic see Tregaskis and Prathapar [13]. For the purposes of this study, the salinity and waterlogging effects are assumed to be additive. Linked to the production component is an enterprise or economic component. This component identifies the financial performance of farms in the region, the economic efficiency of use of land and water resources and the impact of management skills on financial performance using the Farm Management Analysis Models described by Young and Dowell [14]. Detailed physical, financial and productivity survey data enable the calculation of the enterprise gross margin, which can be compared on a unit area basis, per megalitre of plant water use, per kilogram of butterfat or milk protein or per

7 Ecosystems and Sustainable Development 675 tonne of produce. These measures provide an estimate of the ability of farmers to efficiently utilise productive resources. The model enables an assessment of the long term financial viability by estimating the farm business return (profit); any surplus to business return enables a farmer to undertake further capital investment. Business return taken in conjunction with liquidity measures and the farmer's equity gives an indication of financial risk faced by the farmer. Testing the model across a range of price, cost and yield scenarios, based on salinity and waterlogging impacts, enables comparisons of the impacts and sensitivities of changes on business return and level of equity in the farm. The long term decision making process can be enhanced by comparing the capital and operating costs of a range of land and water management practices against the benefits from implementing each of these strategies. This can be presented as a benefit/cost analysis using a range of discount rates over the simulation timeframe. The benefit/cost ratio and net present value estimates provide an economic measure of the options. The flow-on effects of irrigated agriculture to the rest of the economy are those generated by the use of purchased inputs, labour, capital and research in irrigated agriculture and the marketing and handling of the production. These effects can be estimated using Input-Output (I/O) analysis, a standard technique for assessing the economic impact for policy evaluation or economic planning purposes of an industry in an economy [15]. Activities relating to later stage manufacturing, most wholesale activities and all retail activities are excluded to limit complexity. Inter-relationships between main sectors in the regional economy can be identified. The application framework is being used for identifying the likely impact of policy and program options at regional and Basin levels on the biophysical resource base, agricultural productivity and socio-economic structure of irrigated regions of the Southern Murray-Darling Basin in Australia, over the next 20 years. Discussion The design approach to modelling emerged from work on the Canadian Socio- Economic Resource Framework (SERF) models. This modelling framework placed emphasis on physical stocks and flows and on exploring alternative futures. SERF evolved at Statistics Canada and was a computer simulation consisting of 44 separate models which represented the socio-economic system. There were no imposed equilibriums in the system; equilibrium only being possible by the interaction of the user and the framework [16]. Similarly the design approach adopted by the Whatlf? framework does not inherently implement equilibrium or optimisation strategies, rather optimisation is just one of the possible scenarios. This allows the user to develop a suite of scenarios, which can be used to explore the relative merits of alternative management decisions.

8 676 Ecosystems and Sustainable Development The scenario modelling framework being developed seeks an integrated approach to addressing environmental, economic and social concerns. The component models have been validated against historical patterns adding to the credibility and confidence of individual components. However, once the individual components are integrated into a complex framework we may have little intuitive concept of the overall system behaviour. The user may need to exercise choice in which particular scenarios to accept, since the system behaviour may be counter-intuitive. Although the framework is being developed for three study areas, the design and models are applicable to any irrigation catchment. The major problem will be the data intensiveness of models, which must be reduced to broaden the framework's applicability, portability and usefulness. Simplifying the models and processes will greatly increase the system's usability. The next phase of the development involves exploring system behaviour and developing scenarios to identify the likely impacts of policy and program options on the biophysical resource base, agricultural productivity and socio-economic structure of irrigated regions over the next 20 years. Scenarios to be considered include: implications of the 'do-nothing' scenario; changes in salinity levels; alternative land use and mix of activities; Council of Australian Governments (COAG) water market reforms; the effects of changing input costs and/or commodity prices and the impact of industry development. The framework is being developed for policymakers, regional resource management organisations and community groups to enable a more informed basis for decision-making. It will also be a useful awareness-raising and educational tool that allows difficult concepts and technical information to be presented in an easy to understand form. The system will enable evaluation of scenarios and exploration of innovative approaches to natural resource management. Acknowledgements The authors would like to acknowledge the help of Mr. Jeff Matthews and Mr. Peter Eversons of the Bureau of Resource Sciences in preparing the diagram. The help of Mr. Mike Young in drafting the original description of the Enterprise component is acknowledged. The authors also acknowledge the Murray Darling Basin Commission for funding the project work. References [1] Johnson, A.K.L and Cramb, R.A. Development of a simulation based land evaluation system using crop modelling, expert systems and risk analysis, Soil? Mm., 1991,7(4): [2] Gault, F.D., Hamilton, K.E., Hoffman, R.B. and Mclnnis, B.C. The Design Approach to Socioeconomic Modelling, Futures, Feb., 1987, 3-25.

9 Ecosystems and Sustainable Development 677 [3] Malafant, K.W.J. and Davey, S.M. Review of Information Technologies for Consideration in Comprehensive Resource Assessments of Forests, Report to Commonwealth Integration Technical Working Group on Comprehensive Regional Assessments, Feb., [4] Sprague, R.H. A framework for the Development of Decision Support Systems, Management Information Systems Quarterly, 1980, 4(4), [5] Grayson, R.B., Blake, T. and Doolan, J.M. Application of AEAM to water quality in the Latrobe River Catchment, Proceedings, Hydrology and Water Resources Symposium, Newcastle, Australia, [6JROBBERTS Associates, Whatlf? Administrators Guide, Version 3.0, ROBBERT Associates, Ottawa, Canada, [7] Fordham, D.P. and Malafant, K.W.J. Biophysical, Agricultural Production and Socioeconomic futures in Irrigation Regions: A twenty year profile, Proceedings of Agricultural and Biological Engineering - New horizons, new challenges, Newcastle Upon Tyne, England, September, [8] Veitch, S.M., Fordham, D.P. and Malafant, K.W.J. Whatif? Scenarios to Explore Land Management Options, in Application of Advanced Information Technologies: Effective Management of Resources (ed C.D. Heatwole), Washington, , [9] Prathapar, S.A., Meyer, W.S., Jain, S. and Van der Lelij, A. A Soil Water and Groundwater Simulation Model, CSIRO Division of Water Resources Report No 94/3, Melbourne, Australia, [10] Nathan, R.J. A Lumped Conceptual Model for the Prediction of Regional Salt Loads from Irrigated Catchments, Institute of Engineers Australia National Conference Publication No. 93/14, Newcastle, Australia, , [11] Maas, E.V. and Hoffman, G.J. Crop Salt Tolerance - Current Assessment, Journal of Irrigation and Drainage Div., 1977, Vol. 103, No. IR2, [12] Maher, S.E., Greenslade, R.L. and Noble, C.L. The Effect of Waterlogging on the Performance of Perennial Pasture, in Resource Potential of Shallow Water-tables (ed D.C. Poulton), NRMS project V2137, Murray- Darling Basin Commission, Canberra, Australia, 1995.

10 678 Ecosystems and Sustainable Development [13] Tregaskis, J.L. and Prathapar, S.A. Selected Papers Researching the Effects of Salinity and Waterlogging on Crops within the Basin. CSIRO Technical Memorandum 94/11, [14] Young, M.E.S. and Dowell, H.R. Farm Viability in the Berriquin Irrigation Area. Proceedings of 37th Annual Australian Agricultural Economic Society Conference, Sydney, Australia, Feb., [15] Powell, R.A., Jensen, R.C. and Gibson, A.L. The Economic Impact of Irrigated Agriculture in NSW, NSW Irrigators Council, 163, [16] Hoffman, R.B. Overview of the Socio-Economic Resource Framework, WP , Structural Analysis Division, Statistics Canada, Ottawa, Canada, 1986.