Bayesian networks (BNs) are widely implemented as graphical decision support tools which use probability inferences to generate “what if?” and “which is best?” analyses of potential management options for water resource management, under climate change and socio-economic stressors. This paper presents a systematic quantitative literature review of applications of BNs for decision support in water resource management. The review quantifies to what extent different types of data (quantitative and/or qualitative) are used, to what extent optimization-based and/or scenario-based approaches are adopted for decision support, and to what extent different categories of adaptation measures are evaluated. Most reviewed publications applied scenario-based approaches (68%) to evaluate the performance of management measures, whilst relatively few studies (18%) applied optimization-based approaches to optimize management measures. Institutional and social measures (62%) were mostly applied to the management of water-related concerns, followed by technological and engineered measures (47%), and ecosystem-based measures (37%). There was no significant difference in the use of quantitative and/or qualitative data across different decision support approaches (p = 0.54), or in the evaluation of different categories of management measures (p = 0.25). However, there was significant dependence (p = 0.076) between the types of management measure(s) evaluated, and the decision support approaches used for that evaluation. The potential and limitations of BN applications as decision support systems are discussed along with solutions and recommendations, thereby further facilitating the application of this promising decision support tool for future research priorities and challenges surrounding uncertain and complex water resource systems driven by multiple interactions amongst climatic and non-climatic changes.
Water resources management faces many challenges in coastal areas of developing countries, where climate change coupled with high rates of population growth and urbanization have the potential to cause severe water scarcity. Of particular concern, are sea level rise and altered precipitation regimes that will influence spatial and temporal patterns of river discharge, water levels and saltwater penetration in estuaries. A sound understanding of factors affecting the vulnerability of coastal freshwater systems is therefore needed to mitigate the potential impacts of climatic and non-climatic changes. In this study, a system dynamics modeling approach was employed to explore the vulnerability of the coastal freshwater system in Da Do Basin, Vietnam to projected sea level rise, upstream flow decline and socioeconomic development. This system includes the Da Do River and irrigation channels that receive freshwater through sluice gates from the Van Uc and Lach Tray rivers. The model was developed as a learning tool for decision-makers to improve their understanding of the spatial and temporal dynamic behaviors of the system and to inform adaptation decision-making by allowing exploration of plausible future scenarios. The model was developed, calibrated and validated using both historical data and expert knowledge elucidated via stakeholder consultation. Model results indicate that under current conditions,
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