00TO TOWARDS DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR WATER RESOURCE DEVELOPMENT IN SEMI-ARID MICRO-CATCHMENTS ..vary

The objective of (he paper is to bring together, within a single holistic framework, the principle findings of the research into the physical and human aspects a study in Romwe micro-catchment in southern Zimbabwe. The framework is developed using Bayesian Belief Networks (BBNs) to identify and model the central aspects of both physical and human environment (at both the micro and macro scale), and their impacts on each other. In particular I look at the likely impact of effective catchment management (here taken to be physical and technical interventions) on the probability of livelihoods improving. BBNs offer an exciting new way of bringing together very disparate data sources within useful frameworks, which could then be used not only for decision making about development needs, but also to target better the next generation of research. For groundwater, a sizeable improvement in supply can be achieved by moving from poor to good catchment management under medium rainfall conditions, but not under poor or good rainfall. For improved surface water and improved soil moisture, it is particularly under low rainfall conditions that it is worth moving from poor to good catchment management. It is clear from the results that physical catchment management on its own is incapable of having a major impact on peoples' livelihoods other than within a very narrow range of parameters. The suggestion is that outside this range, the decision as to whether or not to lake a physical catchment management approach has a negligible impact on water resources and even less on livelihood's. In relative terms a number of factors both extraneous: (wider economy, underlying geology), and internal (community cohesion, skills levels) to the community rank equally or more highly in terms of general impact on well being. Introduction The Romwe catchment is a small headwater (4.5km) catchment of the Runde river, located about 80km south of Masvingo, provincial capital of Masvingo district. The catchment community of approximately 250 people are mainly engaged in dryland agriculture, although with a rapidly increasing number also undertaking small scale irrigation using groundwater (Moriarty and Lovell, 1997). The catchment has an extensive network of instruments modelling physical hydrology, and the community of the catchment and surrounding area have taken part in extensive participatory surveying to investigate the role of groundwater in their livelihoods (Moriarty and Lovell, 1999). The objective of the paper is to bring together, within a single holistic framework, the principle findings of the research into the physical and human aspects of the study. The framework is used to integrate quantitative findings regarding a physical investigation of the catchment water resources and more qualitative information about their role within the livelihoods of the catchment community. It presents these findings in a reasonably objective and transparent manner, and allows them to be analysed to draw conclusions about the opportunities and constraints for further development of catchment water resources. In 2 At the time of carrying out this work the author worked for the UK Institute of Hydrology, Wallingford, Oxon OX 10 8BB, currently he is a project officer at the IRC International Water and Sanitation Centre, PO Box 2869, 2601 CW, Delft, The Netherlands 2 Towards development of a decision support system addition it is used to assess the merits and demerits of adopting a micro-catchment oriented management approach to natural resource management. The framework is developed using Bayesian Belief Networks (BBNs) to identify and model the central aspects of both physical and human environment (at both the micro and macro scale), and their impacts on and, linkages too, each other. It identifies the conditions under which increased water resource use is most likely to occur, and the subset of these conditions within which micro-catchment management might be an appropriate management paradigm. By taking this approach the framework becomes the core of a decision support system that, by encapsulating the expert knowledge derived form this study, could be used to make management decisions other similar settings. In this paper the term catchment management is used to describe the physical and technical aspects of management, not the institutional ones. Background to decision support systems What is a Decision Support System? At its most basic level, a decision support system (DSS) is a coherent framework that helps people to make decisions about a system in which they are interested. In this work the term is used to encompass computer based systems that provide a framework into which an actor may enter information and out of which will come advice or guidance on what decision to take to achieve some pre-defined goal. While 'expert systems' have been around since the early 1960s, there has been a shift in emphasis from systems that sought to model 'the best expertise in the world' and hence replace the 'expert' or 'manager' to systems that seek to model the domain or system, and to support the 'expert' or 'manager' in making decisions about it (Jensen, 1996). For this study a decision support system is taken to be a framework of rules and relationships that represent a model of a 'problem domain, and which when presented with specific information about the problem domain will provide answers based on clear and transparent rules. The term 'problem domain' has been coined to describe the variables, linkages, and interactions within a system that have an important impact on a problem, question or hypothesis about some other part of the system. When dealing holistically with many aspects of a system the definition of boundaries is crucial and clearly defining the extent and boundaries of the problem domain is essential to satisfactory development of the DSS. As important as defining the problem domain to be dealt with by the DSS, is clearly identifying the end user and ensuring that the output is tailored to their needs and is relevant to the task to which they wish to put it (Fritsh, 1999). Important aspects of a decision support system A DSS should provide a clear and coherent framework so that all stakeholders with interests in a given problem domain may clearly visualise and agree upon the important relations between different sections of the domain. In particular it should make explicit the more important underlying assumptions and tradeoffs implicit in making choices between different actions. Patrick Barre Moriarty 3 The following key points should be central to any DSS that seeks to serve as a basis for participatory and consensual decision making • Ensures all necessary/available information collected helps identify alternative sources • Clearly identifies assumptions that are being made, and explicitly defines uncertainty " Clearly identifies 'prejudices' of those making the decision • Ensures that all things are 'properly' (consensualy) weighted for making a final decision • Ensures that all important cause-efifect relationships are identified, and their implications considered • Allows for new decisions to be made in the light of new information and attitudes (Cain et al, 2000) • Encourages appropriate stakeholder participation in decision making rejects decisions where insufficient consensus exists Bayesian Belief Networks a new framework for decision support The tool used to develop the DSS framework is a Bayesian Belief Network (BBN), a powerful system for the analysis of causal relationships under uncertainty. BBNs were chosen for this work because they explicitly acknowledge that they model beliefs in this case my beliefs (knowledge) about the systems / have been researching for the last 3 years rather than some external and 'objectively valid' reality. The networks therefore serve to bring together, in a transparent and testable manner, the findings of the work and the hypotheses developed from the findings. A key strength of BBNs is their ability to integrate real (measured) and 'knowledge based' information and the associated uncertainty related to the information. In modelling complex systems with limited data availability the ability to expressly acknowledge uncertainty, and quantify its effects is crucial. BBNs can learn. That is, in they can change the underlying relations between nodes on the basis of new data. To do this a BBN is linked to a knowledge base (data base) into which key data from monitoring and evaluation is entered. As more data is entered the probabilistic relationships between nodes will become increasingly sure (as long as the underlying logic of the connections is correct) (Cain et al, 2000). This is a particularly important feature where an adaptive management approach is to be taken to a problem and it is implicit within the management approach that change can and should be made in the light of experience. 4 Towards development of a decision support system A final important advantage of using BBNs is the ability to use the structure of nodes and links to move away from classic modelling concepts such as 'variables', 'sources', and 'sinks'. A node may represent a variable in the classic modelling sense, however it may equally well represent a concept nodes may thus be labelled in terms that come close to representing natural language (Cain et al, 2000); a node may represent 'community ability to work together', which can have 'good' or 'bad' states. It is important that concepts are clearly defined, and where possible open to some kind of more objective appraisal. For instance a node representing 'community ability to work together' may incorporate more concrete concepts such as 'number of successful community projects undertaken in the past'. Despite the need for clear definitions and the potential for confusion where these do not exist, the ability to represent nebulous but clearly important concepts is crucial t