Identifying and testing adaptive management options to increase river catchment system resilience using a Bayesian Network model

Abstract The cumulative impacts of future climatic and socioeconomic change threaten the ability of freshwater catchments to provide essential ecosystem services. Stakeholders who manage freshwaters require decision-support tools that increase their understanding of catchment system resilience and support the appraisal of adaptive management options to inform decision-making. Our research aims to test the ability of a Bayesian Network model to identify adaptive management scenarios and test their effectiveness across future pathways to 2050. Using the predominantly arable river Eden catchment (320 km 2 ) in eastern Scotland as a case study, we invited stakeholders from multiple sectors to participate in a series of workshops aimed at addressing water quality issues and achieving good ecological status in the catchment both now and in the future. Our participatory methods helped stakeholders overcome multiple layers of complexity and uncertainty associated with future-focused water management. Outputs of a Bayesian Network model simulated both current and future catchment resilience to inform the identification of six management scenarios. The effectiveness of each management scenario was tested using the Bayesian Network model. Two adaptive management scenarios increased catchment resilience and helped achieve good ecological status; a ‘Best Available Technology’ scenario, including aerobic granular sludge treatment, and a management scenario focused on ‘Resource Centre’, including phosphorus recovery from wastewater treatment works and constructed lagoons for crop irrigation. Stakeholders were interested in a 'Nature Based' management scenario including options such as wetland wastewater treatment methods and rural sustainable drainage systems, which improved water quality in the catchment, but had lower certainty in achieving desired outcome. Findings led to a recognition that innovative and collaborative action was required to improve current and future freshwater conditions.

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Publication Details

Journal
Discover Geoscience
Published
2024-09-30
DOI
https://doi.org/10.1007/s44288-024-00066-6
Citations
3
Primary Topic
Hydrology and Watershed Management Studies
Type
article
Field-Weighted Citation Impact
0.60

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article

Identifying and testing adaptive management options to increase river catchment system resilience using a Bayesian Network model

Marc J. Metzger, Miriam Glendell, Jim Pritchard, Katie M. Edwards et al.
3 citations
Discover Geoscience
Hydrology and Watershed Management Studies
0.60
article

Identifying and testing adaptive management options to increase river catchment system resilience using a Bayesian Network model

Marc J. Metzger, Miriam Glendell, Jim Pritchard, Katie M. Edwards, Nicola Melville, Rachel Helliwell, Kerr Adams, C. J. A. Macleod
article en
3 citations

Abstract

Abstract The cumulative impacts of future climatic and socioeconomic change threaten the ability of freshwater catchments to provide essential ecosystem services. Stakeholders who manage freshwaters require decision-support tools that increase their understanding of catchment system resilience and support the appraisal of adaptive management options to inform decision-making. Our research aims to test the ability of a Bayesian Network model to identify adaptive management scenarios and test their effectiveness across future pathways to 2050. Using the predominantly arable river Eden catchment (320 km 2 ) in eastern Scotland as a case study, we invited stakeholders from multiple sectors to participate in a series of workshops aimed at addressing water quality issues and achieving good ecological status in the catchment both now and in the future. Our participatory methods helped stakeholders overcome multiple layers of complexity and uncertainty associated with future-focused water management. Outputs of a Bayesian Network model simulated both current and future catchment resilience to inform the identification of six management scenarios. The effectiveness of each management scenario was tested using the Bayesian Network model. Two adaptive management scenarios increased catchment resilience and helped achieve good ecological status; a ‘Best Available Technology’ scenario, including aerobic granular sludge treatment, and a management scenario focused on ‘Resource Centre’, including phosphorus recovery from wastewater treatment works and constructed lagoons for crop irrigation. Stakeholders were interested in a 'Nature Based' management scenario including options such as wetland wastewater treatment methods and rural sustainable drainage systems, which improved water quality in the catchment, but had lower certainty in achieving desired outcome. Findings led to a recognition that innovative and collaborative action was required to improve current and future freshwater conditions.

Discover GeoscienceVol. 2(1)
James Hutton Institute (GB), Scottish Environment Protection Agency (GB), Institute of Geography (KZ), Scottish Water (United Kingdom) (GB)
Scottish Funding Council
Openalex Percentile: Top 33%
Hydrology and Watershed Management Studies
0.60
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