Bayesian network models in MARS:

Case study Lake VansjøTask 7.3: Combining abiotic and biotic models for river

basin management planning

Jannicke Moe, Raoul Couture, Anne Lyche Solheim (NIVA)

MARS WP7 meeting 18.10.2016, Den Helder (Netherlands)

18.10.2016J Moe, RM Couture, AL Solheim 1

Progress since the Oslo meeting

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More details: http://www.slideshare.net/JannickeMoe/mars-wp7-bnvansjojmo20151113

Lake Vansjø – basic info• Vansjø-basin Vanemfjorden• Catchment dominated by forest

and agriculture• Long history of eutrophication• Extreme rain events • Moderate ecological status due

to eutrophication• Phytoplankton (dominated by

Cyanobacteria), macrophytes, total P

18.10.2016J Moe, RM Couture, AL Solheim 3

Haande, Lyche Solheim, Moe & Brænden 2011. NIVA report

The MARS conceptual model

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The MARS conceptual model: example

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Mapping the BN for Vansjø to the MARS conceptual model (DPSIR)

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DRIVER

DRIVER

PRESSURE (nutrient

loads etc.)

STATE: ABIOTICINDICATORS

STATE: ABIOTIC INDICATORS

STATE: BIOTIC INDICATORS

STATE:

BIOTIC IND.

RESPONSE

STATE:WFD STATUS

• What about IMPACT - functions and services?

Other BNs for Vansjø include IMPACT

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Barton et al. 2016. Eutropia – integrated valuation of lake eutrophication abatement decisions using a Bayesian belief network. In: Z.Neal (ed.). Handbook of Applied Systems Science. Routledge.

• IMPACT nodes can be linked to STATE nodes• Suitability for fishing • Suitability for bathing

IMPACT

IMPACTSTATES

A BN for multiple stressors in lake Vansjø

Moe, Haande & Couture. Ecological Modelling (2016)

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• Aim: predict effects of scenarios on ecological status• 4 modules: different sources of information

Module 1: Scenarios (from REFRESH)

•Climate scenarios:• Reference• «Hadley»: higher temperature, more precipitation

•Management scenarios:• Reference• Best: less TP (~Consensus world)• Worst: more TP (~Techno or Fragmented world)

•Will re-do using MARS scenarios for climate and land-use

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Module 2: Output from process-based models

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• Process-based models: • Persist (hydrology)• INCA-P (catchment) • MyLake (lake) input to BN

• 60 realisations of the model (parameter combinations) give rise to probability distributions in the BN model

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Module 3: Monitoring data - cyanobacteria

• Multiple regressions:Identify significant predictor variables structure of nodes and arrows in BN model

• Regression tree analysis:Identify breakpoints in predictor variables discretisation (setting intervals) of nodes in BN

Empirical relationships between abiotic and biotic variables quantified by data analysis (WP4)

What are inside the arrows? - conditional probability tables (CPT)

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CPT for Cyano•Based on 90 observations

CPT for Status Phytoplankton•Based on knowledge (combination rules)

States

Probabilities

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Module 4: Ecological status• Status for different quality elements are combined in

CPTs according to the national classification system

• E.g. status of phytoplankton:• If status of cyanobacteria < chl-a,

the combined status is averaged• If status of cyanobacteria > chl-a,

cyanobacteria are not considered

Results of model for Scenario: reference

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Probability of Poor-Bad status equal for Cyanobacteria and Chl-a (~45%)

Results of model for Scenario: best management, future climate

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Probability of Poor-Bad status higher for Cyano (40%) than for Chl-a (36%)

Results - all scenarios

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0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Secchi depth

Pro

babi

lity

(%)

(a)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Total P(b)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Phys.-chem.(c)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Chla

Pro

babi

lity

(%)

(d)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Cyanobacteria(e)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Phytoplankton(f)

Poor-BadModerateHigh-Good

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Lake

Pro

babi

lity

(%)

(g)

0

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40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Secchi depth

Pro

babi

lity

(%)

(a)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Total P(b)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Phys.-chem.(c)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Chla

Pro

babi

lity

(%)

(d)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Cyanobacteria(e)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Phytoplankton(f)

Poor-BadModerateHigh-Good

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Lake

Pro

babi

lity

(%)

(g)

-

• Chl-a: Climate change impact is negative, but small compared to land use impact

• Cyanobacteria: responses to scenarios are similar to chl-a, but...

• Including cyanobacteria reduces the probability of good ecological status for phytoplankton

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Secchi depth

Pro

babi

lity

(%)

(a)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Total P(b)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Phys.-chem.(c)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Chla

Pro

babi

lity

(%)

(d)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Cyanobacteria(e)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Phytoplankton(f)

Poor-BadModerateHigh-Good

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Lake

Pro

babi

lity

(%)

(g)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Secchi depth

Pro

babi

lity

(%)

(a)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Total P(b)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Phys.-chem.(c)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Chla

Pro

babi

lity

(%)

(d)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Cyanobacteria(e)

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Phytoplankton(f)

Poor-BadModerateHigh-Good

0

20

40

60

80

100

Ref Had Ref Had Ref HadClimate scenario

Worst Ref BestManagement scenario

Lake

Pro

babi

lity

(%)

(g)

Problems encountered1) How to link predicted and observed values

(Total P (pred.) is now the "cause" of Total P (obs.))- No better solution found

2) How to handle the poor match between predicted and observed values (especially Total P)- Improvement needed in the process-based model

Problems encountered3) How to deal with missing or few values for CPTs

(columns with all zeros)- Will try combination with expert judgement

4) How to make better use of additional information(data on cyanobacteria from 400 other Norwegian

lakes)

- Will try built-in method for updating CPT with new data

5) Model validation: more objective methods should be tried

Vansjø + 400 lakes

How our BN can be of use for water management in locally and elsewhere• as a bridge between the coarse MARS conceptual

model and the detailed process-based models• aggregating input and output of process-based models• linking abiotic and biotic components• including biotic components where data are sparse but

knowledge is available• for quickly re-running scenarios

• a kind of model emulator• forwards and backwards

• for incorporating and visualising uncertainty• for communication with stakeholders: model

structure, scenarios, results and uncertainties18.10.2016J Moe, RM Couture, AL Solheim 19

Next steps for Lake Vansjø BN

February - April 2017• Apply MARS future scenarios - aggregate the

outcome of WP4• Improve the CPT for cyanobacteria

• Expert judgement; update with large-scale dataset• Add colour (organic C) as abiotic state variable,

with potential negative impact on cyanobacteria • from empirical analysis in WP4

• Try PTI (Phytoplankton Trophic Index) as additional biotic state variable

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