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