Post on 12-Apr-2017
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
18.10.2016J Moe, RM Couture, AL Solheim 2
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
18.10.2016J Moe, RM Couture, AL Solheim 7
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)
18.10.2016J Moe, RM Couture, AL Solheim 8
• 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
18.10.2016J Moe, RM Couture, AL Solheim 9
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
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)
-
• 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
18.10.2016J Moe, RM Couture, AL Solheim 20