Catchment phosphorous run-off network, Morsa Norway

By: Barton, Bechmannet al (2008)

Bayesian network methodology is used in the catchment of Storefjorden, South Eastern Norway, to integrate models of phosphorus (P) abatement costs and effects, as well as models of lake P and eutrophication dynamics. The Bayesian network integrated model was used to explore and evaluate the probable (and improbable) outcomes and uncertainties of (i) the eutrophication problem and (ii) the cost-effectiveness analysis of the corresponding abatement measures. In addition, factors which affect the reliability of transferring cost-effectiveness data for nutrient abatement measures between river basins were detected with a view to informing Norwegian implementation of the EU Water Framework Directive, and the relative uncertainty of model components within the Bayesian influence network was evaluated, with an aim to uncovering "information gaps" in abatement planning, and as a tool for prioritising future eutrophication research.

The interface below demonstrates the CATCHMENT RUN-OFF SUB-NETWORK documented in Barton et al.(2008). The interface lets the user evaluate the effect of different combinations of “mitigation decision variables”, monitoring the effect on Particulate Phosphorous and Dissolved Phosphorous run-off, as well as reading the effect on total Phosphorous loading to the lake. “Descriptions of mitigation” lets the user modify the implementation scenario evaluated in the original study. Based largely on expert judgement, the network has not been calibrated against historical management and is FOR DEMONSTRATION PURPOSES ONLY.

Mitigation Decision Variables and Description of Mitigation

Artificial Wetland

Tillage & Crop

P Application Change

Catchement Through Artificial Wetlands

Tillage Type

P Application New (KG P(ha))


The probability of a loading to lake greater than 20 (1000kg P/ha) is: producing the following traffic light signal shown on the right above.

The traffic light provides a warning signal for passing a pollution threshold which can be defined by decision-makers.

Notice that the probabilities computed above are computed under the information entered above. For instance, if no information is entered, then the probabilities computed are the expected value conditional on no information.


Barton, D. N., Bechmann, M., Eggestad, H.O., Moe, J., Saloranta, T., Kuikka, S. and Haygarth, P.(2008), EutroBayes - Integration of nutrient loading and lake eutrophication models in cost-effectiveness analysis if abatement measures. Norwegian Institute for Water Research, Project number 26014, pages 82