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 socio-economic 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 Eden catchment (320 km2), in eastern Scotland as a case study, we invited stakeholders from multiple sectors to participate in a series of workshops aimed at addressing water resource 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 five management scenarios. The effectiveness of each management scenario was tested using the Bayesian Network model. Three adaptive management scenarios increased catchment resilience and helped achieve good status; a ‘Nature-Based’ management scenario including options such as wetland wastewater treatment methods and rural sustainable drainage systems, a ‘Best Available Technology’ scenario, including aerobic granular sludge treatment, and a management scenario focused on ‘Resource Centres’, including phosphorus recovery from wastewater treatment works and constructed lagoons for crop irrigation. Findings led to a recognition that innovative and collaborative action was required to improve the current and future freshwater conditions.