Reviewing Bayesian Networks potentials for climate change impacts assessment and management: A multi-risk perspective

Area of Science:

• Environmental Science
• Climate Change Research
• Risk Assessment

Background:

• Climate change impacts necessitate a multi-risk perspective, considering interconnected stressors on natural and human systems.
• Bayesian Networks (BNs) are established integrated modeling tools for complex, uncertain domains.
• The application of BNs in climate change assessment remains an underexplored area.

Purpose of the Study:

• To review existing applications of Bayesian Networks in environmental management.
• To discuss the potential and limitations of using BNs for climate change risk assessment.
• To identify areas for improvement to enhance BN utility in climate change impact assessment and management.

Main Methods:

• Literature review of Bayesian Network applications in environmental management.
• Analysis of BNs' capabilities in handling multiple stressors, uncertainty, and scenario analysis.
• Identification of limitations concerning temporal-spatial dynamics and quantitative validation.

Main Results:

• Bayesian Networks offer significant potential for climate change risk assessment by integrating multiple stressors and endpoints.
• BNs provide flexibility in managing uncertainty inherent in climate projections and facilitate scenario analysis.
• Key limitations include challenges in representing temporal and spatial dynamics and the need for robust quantitative validation.

Conclusions:

• Bayesian Networks present a promising framework for advancing climate change risk assessment and management.
• Overcoming identified limitations is crucial for the wider adoption and effectiveness of BNs in this field.
• Further research and methodological development are recommended to fully leverage BNs for climate change impact studies.