Integrating Bayesian Networks into ecosystem services assessment to support water management at the river basin scale

Abstract Freshwater ecosystems are negatively affected by climate change and human interventions modifying together supply and demand of ecosystem services (ES). Research on ES focused on assessing risks arising from the interaction among both stressors, integrating empirical data with expert knowledge. This work aims at incorporating Bayesian Networks (BN) approaches into ES appraisal, identifying key factors driving changes and trade-offs among ES potential under different scenarios. Applying the designed BN to the Taro River basin (TRB) in Italy, the outcomes showed a limited space to improve ES potential, as well as trade-offs between water yield and nutrient retention services due to changes in precipitation and land use patterns. Moreover, the analysis of key input variables highlighted that precipitation is the main driver affecting provisioning services while land use for the regulating ones. The results imply a low capacity to provide services in the medium term for the TRB where water was exploited for multiple competing objectives. Therefore, “win-win” spatial planning and water management strategies are needed to improve freshwater ES potential. The designed BN model represents a valuable decision support tool to quickly perform ES assessment and to identify the most suitable management plan to maintain benefits from freshwater ecosystems.