Abstract
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.
Highlights
Changes in precipitation, increased temperature and sea-level rise are major drivers of changes in water availability and water demand in these systems [2]. Interactions among these climatic drivers coupled with population growth and urbanization can lead to water scarcity, making it increasingly challenging for managers to satisfy growing water demands from multiple water users [3]
This paper presents the findings of a systematic quantitative literature review conducted to explore the applications of Bayesian networks (BNs) as decision support tools for managing water resource under threats from climate change and socio-economic stressors
The resulting database of academic research papers reporting on BN applications in decision-making supports for water resource management was attributed according to the following fields: (a) The research aims of reviewed studies, (b) the types of data used in BN development for identifying adaptation options, (c) consideration of climate change impacts and socio-economic stressors on water resource management, (d) the basis for informing decision support, and (e) the types of adaptation measures considered
Summary
Changes in precipitation, increased temperature and sea-level rise are major drivers of changes in water availability and water demand in these systems [2]. Where water resources are insufficient to supply socio-economic development and ecosystem functions, competitive behaviour among water use sectors can result [1], with considerable potential for conflict [3]. In such circumstances, it is important to identify appropriate, effective and efficient adaptive responses to alleviate these potential conflicts, and inform judicious trade-offs between competing demands [4]
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