Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

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.

Socio-Economic and Psychological Determinants for Household Water Treatment Practices in Indigenous–Rural Indonesia

“…In this study, a three-level hierarchical model depicting how SECs influence the use of HWT via psychological factors was created (Figure 2). BBN has been widely used in water-related studies (Phan et al, 2016(Phan et al, , 2019, e.g., for water management and water quality, but has been rarely used in a water-related behavioral study. To the best of our knowledge, only one study has utilized BBN modeling in the behavioral context (Daniel et al, 2019).…”

Identifying Ecosystem Key Factors to Support Sustainable Water Management

“…In this respect, Torres et al [61] stresses the importance of conceptualization of nexus modelling and the inclusion of the ecosystem and related ecosystem services as a central element, as well as the need to restrict the number of elements included in the model structure. Bayesian Networks are decision support tools that are widely used for analyzing impacts on aquatic systems and scenario-analyses in water resources management, although this requires the identification of key system variables and understanding of their mutual relationships [62]. An example of groundwater overexploitation for crop production and the economic consequences thereof is provided by Mirzaei et al [63].…”

The Missing Layers: Integrating Sociocultural Values Into Marine Spatial Planning

“…For that, a comprehensive approach that considers the complex and fine-scale interactions between people and their environment may be necessary (Dalton et al, 2010). Examples include the use of Bayesian network analyses to include stakeholder preferences into the MSP decision-making process (Coccoli et al, 2018;Laurila-Pant et al, 2019;Phan et al, 2019;Alexander et al, 2021). These techniques may help find an optimal compromise in the presence of potentially conflicting objectives (Laurila-Pant et al, 2019).…”