Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund Integration of different metrics to a whole water body classification for all

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


What does the WFD require?


Integration process for elementsrecommended by the CIS ECOSTAT WG (modified from EC, 2005)


This presentation: focus on BQEs

Assessment for whole waterbody


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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?


The ”One-Out All-Out” principle


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Acidification

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Combining metrics and BQEs


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.“


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


The role of uncertainty


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


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.


Alternatives to OOAO


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


”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)


MODELKEY: WoE-based WFD classification tool


MODELKEY example: WoE vs. OOAO

OOAO rule:WoE alternative:


MODELKEY example: multiple pressures per BQE


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Example: Combining metrics and BQEs

OOAO rule:

each 25%

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0%

100%

WoE alternative:

each 25%

20%

20%

60%


Recommendations for deWELopment


Assessment of within-BQE uncertainties

• Follow recommendations from WISER WP6.1 Uncertainty Workshop (September 2010)

• Communicate with WISER WP6.1 in advance


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


Discussion!

Documents

Polsko-Norweski Fundusz Badań Naukowych / Polish-Norwegian Research Fund Integration of different metrics to a whole water body classification for all