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Bayesian belief modeling of climate change impacts for informing regional adaptation options
Journal article   Peer reviewed

Bayesian belief modeling of climate change impacts for informing regional adaptation options

R. Richards, M. Sanó, A. Roiko, R.W. Carter, M. Bussey, J. Matthews and T.F. Smith
Environmental modelling & software, Vol.44, pp.113-121
01/06/2013
DOI: https://doi.org/10.1016/j.envsoft.2012.07.008

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Abstract

Adaptation Bayesian Belief Networks Climate change Group-model building Stakeholder beliefs
A sequential approach to combining two established modeling techniques (systems thinking and Bayesian Belief Networks; BBNs) was developed and applied to climate change adaptation research within the South East Queensland Climate Adaptation Research Initiative (SEQ-CARI). Six participatory workshops involving 66 stakeholders based within SEQ produced six system conceptualizations and 22 alpha-level BBNs. The outcomes of the initial systems modeling exercise successfully allowed the selection of critical determinants of key response variables for in depth analysis within more homogeneous, sector-based groups of participants. Using two cases, this article focuses on the processes and methodological issues relating to the use of the BBN modeling technique when the data are based on expert opinion. The study expected to find both generic and specific determinants of adaptive capacity based on the perceptions of the stakeholders involved. While generic determinants were found (e.g. funding and awareness levels), sensitivity analysis identified the importance of pragmatic, context-based determinants, which also had methodological implications. The article raises questions about the most appropriate scale at which the methodology applied can be used to identify useful generic determinants of adaptive capacity when, at the scale used, the most useful determinants were sector-specific. Comparisons between individual BBN conditional probabilities identified diverging and converging beliefs, and that the sensitivity of response variables to direct descendant nodes was not always perceived consistently. It was often the accompanying narrative that provided important contextual information that explained observed differences, highlighting the benefits of using critical narrative with modeling tools.

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