Bayesian networks for assessment of WFD compliance based ... · predictions • Assessment of the Bayesian Network modelling approach Jannicke Moe 30.05.2013 4 . Outline of presentation

Bayesian networks for assessment of WFD compliance based on lake phytoplankton

Jannicke Moe (NIVA) David Barton (NINA) Koji Tominaga (UiO / NIVA) EUTROPIA conference 30.-31.05.2013

30.05.2013 Jannicke Moe 1

Aim of WFD: "good ecological status" • Ecological status of lakes should be assessed

by biological quality elements (BQEs) • ...and secondarily by supporting quality elements:

Total P, Total N, Secchi depth, pH, etc.

• Status of BQEs should be measured by species composition as well of abundance


How will management actions affect status of different quality elements?


• A Bayesian network (BN) approach in 3 parts

SWAT model: management scenarios

MyLake model: lake chemistry monitoring data:

phytoplankton etc. WFD classification system for Norway

Outline of presentation • WFD ecological classification system for

Norwegian lakes

• A BN model for ecological status of lake Vansjø

• Effects of SWAT management scenarios on ecological status

• Impacts of uncertainty in SWAT/MyLake predictions

• Assessment of the Bayesian Network modelling approach



Norwegian lakes






Classification: information sources


National classifiation guidance, 2009 (in revision) (http://www.vannportalen.no)

Intercalibrated classification system for phytoplankton, 2011 (https://circabc.europa.eu)

Phytoplankton: combination of four metrics into one status


Biovolume

Chlorophyll a

Phytoplankton EQRn

EQRn

average Biomass EQRn

EQRn

PTI EQRn

Cyanomax EQRn*

average

Index for species composition (PTI)

Bloom index: Cyanobacteria yearly maximum biomass

* Cyanomax is used only if EQRn is worse than average EQRn for PTI and Biomass

EQRn = Ecological Quality Ratio, normalised

Supporting physico-chemical elements • Combining different physico-chemical metrics

(Total P, Secchi depth etc.): averaging

• Combining physico-chemical elements with biological quality elements: Phys-chem. can reduce ecological status from High to Good or from Good to Moderate.


Ecological status of Lake Vansjø


Sigrid Haande, Anne Lyche Solheim, Jannicke Moe, Roar Brænden, 2011


Norwegian lakes






Structure of BN model: "nodes" with discrete states


Nodes of Phytoplankton module: states = 2-3 intervals

Nodes of Status module: states = 3 categories: - High/Good - Moderate - Poor/Bad

Nodes of SWAT module: states = scenarios

Nodes of MyLake module: states = 3-8 intervals

Parametrisation of BN: prior probability distributions


"Child nodes": prior probability distributions calculated from parent nodes by Conditional Probability Tables (CPT)

States

Probabilities

"Parent nodes": all scenarios are given the same prior probability

Running the BN model • Use new "evidence" (data, other info) to update

probabilities calculate posterior probability distributions for all affected nodes

• "Evidence" comes from management scenarios

via the SWAT & MyLake model simulations • 15 x management scenarios • 60 x parameter combinations for MyLake • 19 years (1992-2010) • 30 weeks (Jul-Oct)

• Examples shown for month = July


Norwegian lakes






Effects of different abatement actions

• Individual actions give weak effect on status

• More actions combined give stronger effect on status


High/GoodModeratePoor/Bad

BL = baseline WL = wetlands constructed VB = vegetation buffer zone PS = point source reduction GN = grass crop rotation, no structural abatement actions GA = grass crop rotation, all structural abatement actions

structural abatement actions

Effects of GA scenario on status of different quality elements


Positive effect on TP, but reduced by weak effect on Secchi


Positive effect on Chl-a and Cyano reflecting effect on TP

Phytoplankton status determined by Chla, when Chla is worse than Cyano

Lake status dominated by Phytoplankton, whch is usually worse than supporting phys.-chem.

Effects of management on lake status in different years


Bad year

Lake status not improved by Phys.-chem.

Good year

Average year


Lake status not improved by Phytoplankton

Effects of temperature on phytoplankton status


Higher summer temperature gives worse status

High temperature counteracts positive effect of abatement

Phytoplankton status determined by Chla - but what about user suitability?



Norwegian lakes






Comparison of MyLake predictions with monitoring data


(Raoul-Marie Couture, NIVA)

Comparison of MyLake predictions with monitoring data

• Weekly values 1992-2010; Jul-Oct • MyLake: Baseline scenario,

60 parameter combinations • Monitoring data:

www.aquamonitor.no • >200 values from Vanemfjorden, 0-4 m

• Good match: Chla, Temperature • Less good match: Total P, Secchi depth


How does uncertainty in MyLake predictions affect status assessment?


Effects of management "covered" but uncertainty

Predicted Secchi better than observed

Predicted Cyanobacteria slightly worse than observed



Norwegian lakes






Is the BN model a useful tool for modelling effects of abatement actions on ecological status of lakes?

Yes, • for researchers - in communication with

managers • as a supplement to dynamic process-based

models • for aggregating model outcome and combining

with WFD classification systems • for running scenarios - forwards and backwards • for investigating effects of uncertainty


Acknowledgements • Alexander Engebretsen (UMB): SWAT info • Anne Lyche Solheim (NIVA): WFD classification system • Sigrid Haande (NIVA): Vansjø monitoring data • Rolf Vogt (UiO), Eirik Romstad (UMB), Jostein Starrfelt

(NIVA): discussions • Peter Friis Hansen (DNV): technical help with BN macro • EU FP7 project REFRESH (http://www.refresh.ucl.ac.uk/) Thanks for your attention!


Documents

Bayesian networks for assessment of WFD compliance based ... · predictions • Assessment of the Bayesian Network modelling approach Jannicke Moe 30.05.2013 4 . Outline of presentation