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Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
Integration of different metrics to a whole water body classification
for all water bodies in WEL catchment
Jannicke Moe (NIVA)
deWELopment project meeting
22.06.2010, Oslo
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
Outline
• What does the WFD require?
• The one-out all-out principle
• The role of uncertainty
• Alternatives to OOAO
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
Integration process for elementsrecommended by the CIS ECOSTAT WG (modified from EC, 2005)
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
This presentation: focus on BQEs
Assessment for whole waterbody
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
Macro
invert
eb
rate
sP
hyto
ben
thos
Hydrology
Acidification
Organic
Example: With data from one waterbody, we have calculated metrics and assigned ecological status class for:-12 metrics
- 4 pressures- 2 BQEs
How can we combine information from the 12 metrics?
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
The ”One-Out All-Out” principle
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
Macro
invert
eb
rate
sP
hyto
ben
thos
Hydrology
Acidification
Organic
Combining metrics and BQEs
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
The ”One-Out All-Out” principle
• The EC recommends to apply the “One-Out All-Out” (OOAO) principle for aggregation at QE level: – when all BQE are classified, the ecological status is initially
determined by BQE with the lowest class– if a supportive QE indicates a worse situation than the BQEs,
the ecological status is downgraded to the supporting QE’s class.
• From deWELopment project description:“The applicability of the one-out-all-out principle recommended in the WFD Classification guidance (2005), is questioned by many scientists, and alternative methods for holistic assessment may be needed to obtain acceptable confidence and precision in the classification result.“
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
Limitations of the OOAO principle
• OOAO always selects the “worst case” – but the worst case may sometimes have been assigned too low class by error– E.g. “p-value <0.05” we accect 5% error rate
• More uncertainty in metrics: higher risk that some measured metrics value is too low (and
others too high)– OOAO can result in too low class
• Higher number of metrics and/or BQEs used in classification: higher risk that some measured metrics value is too low (and
others too high) OOAO can result in too low class
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
Calculation of uncertainty- Uncertainty within station:
- StDev of samples
- Uncertainty within BQE:- pooled stdev of stations
sqrt(avg(stdev^2))
- Uncertainty within WB:- StDev of selected BQE?
0.45 (?)
Example:Randomised EQRs for 3 samples x 3 stations x 3 BQEs
• Lowest EQR• Highest uncert.• Determines class
• Highest EQR• Lowest uncert.• Ignored
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
0.45 (?)
Handling of uncertainty- Aggregation of StDevs
(or other uncertainty measures) can be complicated
Probabilistic approach :- Calculate the probability of
each status class
- Aggregate probability distributions to WB level- Bayesian Network
- Weight-of-Evidence appr.
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
From the deWELopment project description:
• We will test different methods of combination of these single metric results to obtain a total result at the whole element level, taking into account the uncertainty in the different single metrics – one-out-all-out principle– simple averaging– weighted averaging– multimetric approach
• Additional suggestions:– probabilistic approach – Weight-of-Evidence framework
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
”Weight-of-Evidence approach”
• Combines information from multiple lines of evidence to reach a conclusion about an environmental system or stressor (Chapman et al. 2002, Burton et al. 2002).
• Combines analysis of field data (to determine patterns) with experimental hypothesis testing (to determine mechanisms) to make prediction of the future effects and provide appropriate management recommendations (Lowell, 2000)
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
MODELKEY: WoE-based WFD classification tool
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
MODELKEY example: WoE vs. OOAO
OOAO rule:WoE alternative:
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
MODELKEY example: multiple pressures per BQE
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
Macro
invert
eb
rate
sP
hyto
ben
thos
Hydrology
Acidification
Organic
Example: Combining metrics and BQEs
OOAO rule:
each 25%
0%
0%
100%
WoE alternative:
each 25%
20%
20%
60%
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
Recommendations for deWELopment
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
Assessment of within-BQE uncertainties
• Follow recommendations from WISER WP6.1 Uncertainty Workshop (September 2010)
• Communicate with WISER WP6.1 in advance
Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund
Integrating BQE values and uncertainties
In addition to OOAO:
• Try a simplified weighting approach based on the MODELKEY principles and tools
• Decide on weighting of different metrics and BQEs:– Use expert judgement
• More sensitive element / metric higher weighting
– Use uncertainty estimates from deWELopment data• higher uncertainty reduced weighting
• Compare outcome of OOAO vs. WoE approaches on classification of deWELopment waterbodies