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Participatory Development of a Bayesian Network Model for Catchment-based Water Resource Management

  • Year: 2010
  • Author: Chan, T.; Ross H.; Hoverman S.; Powell, B.
  • Journal Name: Water Resources Research
  • Journal Number: 46
  • Country: Solomon Islands

A participatory approach was used to develop a Bayesian network model for assisting integration of water resource management in the Kongulai catchment in the Solomon Islands. This catchment provides 40–60% of the water for Honiara, the capital, and management is complex, with sparse data and many competing uses including drinking, domestic, agricultural, and industrial uses for both water and land in the catchment, as well as a range of threats from pollution, increasing population, changing land use, and variable hydrogeology. There are socioeconomic considerations including customary landownership and overlapping institutional responsibilities. A participatory process involving representative stakeholders of three main groups, the customary landowners, the government, and nongovernmental organizations, was central to analyzing the system and building trust in the model development process and model outcomes, and additionally facilitated relationship building between the different groups affecting, and affected by, the catchment. A conceptual model of the Kongulai system with respect to water was developed with all stakeholders. Further elicitation of quantitative aspects took place with a subset of water management professionals for development into a working Bayesian network model. Stakeholder representatives were then presented with the model, some analysis, and scenarios for discussion and feedback. The model provided a number of recommendations that support local management decision making, which were accepted by the wider stakeholder group. The process demonstrates the worth of a well-designed participatory approach to enhance stakeholder contributions and confirms the appropriateness of Bayesian networks for use in developing country contexts where capacity and data may be scarce.

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