21 Jul 2022
21 Jul 2022
Status: this preprint is open for discussion.

Developing a Bayesian network model for understanding river catchment resilience under future change scenarios

Kerr J. Adams1,2, Christopher A. J. Macleod2, Marc J. Metzger1, Nicola Melville3, Rachel C. Helliwell2, Jim Pritchard3, and Miriam Glendell2 Kerr J. Adams et al.
  • 1University of Edinburgh, School of Geoscience, Edinburgh, Scotland
  • 2The James Hutton Institute, Craigiebuckler, Aberdeen, Scotland
  • 3Scottish Environment Protection Agency, Strathallan House, Stirling, Scotland

Abstract. The resilience of river catchments and the vital socio-ecological services they provide are threatened by the cumulative impacts of future climatic, land use and socio-economic change. Stakeholders who manage freshwaters require tools for increasing their understanding of catchment system resilience when making strategic decisions. However, unravelling causes, effects and interactions in complex catchment systems is challenging, typically leading to different system components being considered in isolation.

In this research, we tested a five-stage participatory method for developing a BN model to simulate the resilience of the Eden catchment in eastern Scotland to future pressures in a single trans-disciplinary holistic framework. The five-stage participatory method involved co-developing a BN model structure by conceptually mapping the catchment system and identifying plausible climatic and socio-economic future scenarios to measure catchment system resilience. Causal relationships between drivers of future change and catchment system nodes were mapped to create the BN model structure. Appropriate baseline data to define and parameterise nodes that represent the catchment system were identified with stakeholders.

The BN model measured the impact of diverse future change scenarios to a 2050 time-horizon. We applied continuous nodes within the hybrid equation-based BN model to measure the uncertain impacts of both climatic and socio-economic change. The BN model enabled interactions between future change factors and implications for the state of five capitals (natural, social, manufactured, financial and intellectual) in the system to be considered providing stakeholders with a holistic catchment scale approach to measure the resilience of multiple capitals and their associated resources. We created a credible, salient and legitimate BN model tool for understanding the cumulative impacts of both climatic and socio-economic factors on catchment resilience based on stakeholder evaluation. BN model outputs facilitated stakeholder recognition of future risks to their primary sector of interest, alongside their interaction with other sectors and the wider system. Participatory modelling methods improved the structure of the BN through collaborative learning with stakeholders, while providing stakeholders with a strategic systems-thinking approach for considering river basin catchment resilience.

Kerr J. Adams et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-617', Laura Uusitalo, 27 Jul 2022 reply
    • AC1: 'Reply on RC1', Kerr Adams, 09 Sep 2022 reply

Kerr J. Adams et al.


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Short summary
We used participatory methods to create a Bayesian Network (BN) model that 1) mapped complex interactions between human and ecological systems at the catchment scale and 2) measured the impacts of diverse scenarios for future climate, population and land-cover change on the catchment system. Our methods and findings provided stakeholders with a holistic approach for measuring river catchment resilience.