Using Bayesian belief networks to evaluate uncertainty and thresholds in a DPSI framework EUTROPIA Kick‐off David N. Barton NINA/NIVA DPSIR framework Drivers Costs of P‐abatement measures Pressures Damage function States Impact Incentives Field level effects of P‐abatement measures Catchment hydrological P‐loading to lakes Response Lake water quality (P, ChlA, Secchi, cyanobacteria) Willingness to pay for water quality improvements (Secchi, colour, ecological status) BMW EUTROBAYES AQUAMONEY EXIOPOL NIVA MODEL SIP Avrennings‐nettverk Innsjø‐nettverk Decreasing incremental impact of as we move down the DPSI model chain Baseline scenario Decreasing incremental impact of as we move down the DPSI model chain Scenario with measures Lake water quality (P, ChlA, Secchi, cyanobacteria) Tot‐P – ChlA dose response is a ”weak link” in impact assessment Secchi‐depth 0 0 Tot‐P concentration ChlA ‐ concentration Tot‐P loading Tot‐P concentration 0 ChlA ‐ concentration No clear dose‐response relationship in summer average ChlA – concentrations predicted by the MyLake model Discretization of probability distributions is an additional source of uncertainty, but ”an information cost” of integrating the interfaces between models in the damage function chain Willingness to pay for water quality improvements (Sight depth, water colour, ecological status) Precision of water quality descriptions is lower than water quality model predictions Water user suitability thresholds for water quality are heterogenous Different user groups have different suitability threholds which vary by location and often don’t coincide with official standards. NOK/year per household WTP is a strongly non‐linear function of quality and distance to the water body Distance decay of implicit WTP for different improvements in water quality status Kilometers between Vansjø and household Morsa policy relevance Use the Bayesian belief network to • do “what‐if” and “if‐what” analysis of environmental targets and abatement practices (scenarios) • document the need for a derogation from the aim of “good ecological status” in Vestre Vansjø • provide framework for comparing cost‐ effectiveness of abatement measures versus economic incentives (?) EUTROPIA challenges Data challenges: • Costs : improving the description of costs of abatement practices across types of farm (small, contract). Include costs of economic incentives. • Space: effects of combinations of abatement measures under principle combinations of soil, erosion risk and farm type conditions across catchment • Time: develop separate networks to account for annual/summer average conditions and daily climate and bloom episodes. Methodological challenges: • Behaviour: can we model implementation effectiveness of economic incentives ? • Integration: reducing the discretization errors of linking models • Network calibration: ? • Validation: of scenarios with Morsa Project stakeholders