FI: Ansa Pilke and Liisa Lepisto, Finnish Environment InstituteNO: Dag Rosland, Norwegian National Pollution Control Authority Anne Lyche Solheim, Norwegian Institute for Water ResearchSE: Mikaela Gonzci, Swedish EPA and Eva Willen, SLU UK: Geoff Phillips and Sian Davies, Environmental Agency for

England and WalesIE: Deirdre Thierney, and Wayne Trodd, Irish EPA

Lakes Northern GIGPhytoplankton (chla) / Eutrophication

Types and participationType Type description Countries

participating,

LN1 Lowland, mod. alk., clear, shallow NO, UK, IE

LN2a Lowland, low alk, clear, shallow NO, SE, FI, UK, IE

LN2b Lowland, low alk, clear, deep NO, UK

LN3a Lowland, low alk. mesohumic (30-90 mg Pt/L), shallow NO, SE, FI, UK, IE

LN5a Boreal, low alk., clear, shallow (may also include high latitude lakes)

NO, SE,

LN6a Boreal, low alk., mesohumic, shallow(may also include high latitude lakes)

NO, SE,

LN8a Lowland, mod alk., mesohumic, shallow NO, SE (?), FI, UK, IE

IC approach

• NGIG has used option 2 (common metric) combined with 3 (national systems), so hybrid approach

Data

• Both common GIG datasets (REBECCA) and separate MS data sets were used– Reference lake dataset (already in MS6)– Dataset of chla from all lakes (type-divided)– Dataset with phytoplankton indicator metrics

(basis for dose-response curves)• All datasets will be put out on Circa before

the deadline for revision of MS6 report (early autumn 2006)

National classification methodscountries, method, metrics, status Country Status

Finland Chla and other metrics are under development

Sweden Chla, Vol, % Cyano, % Chryso, Div: First draft available, but will be revised according to IC results

Norway Chla, Vol, %Cyano, %Chryso: First draft available, but will be revised according to IC results

UK Chla and other metrics are under development (almost ready?)

IE Chla and other metrics are under development, will be adjusted according to IC results

Setting of Reference conditions Common approach for setting of reference conditions: • Using existing sites, supplemented with paleodata and models!• Reference criteria for selection of ref. sites:

• <10% agriculture (most countries), • No major point sources (most countries)• Some additional pressure criteria (some countries) • Ecological criteria (low chla or biovol, low TP), (some countries)• Paleodata validation of existing ref-sites (some countries)• Expert judgement (most countries)

• Reference lake dataset: (REBECCA dataset: next slide)• Procedure:

– Ref. values: Type-specific median of ref.lake chla distribution– Small range of ref.values agreed, based on intra-type differences within

NGIG (humic gradient and climatic gradients from west to east: eastern part of NGIG has drier climate and more humic matter, which gives higher ref. chla)

Reference conditions (chla in g/L)

Type Type descr. N Mean Min Max

LN1 Mod Alk, shallow, clear, lowland

21 3 2.5 3.5

LN2a Low Alk, shallow, clear, lowland

59 2 1.5 2.5

LN2b Low Alk, deep, clear, lowland 64 2 1.5 2.5

LN3a Low Alk, shallow, humic, lowland

47 3 2.5 3.5

LN5a Low Alk, shallow, clear, mid-altitude

35 1.5 - -

LN6a Low Alk, shallow, humic, mid-altitude

7 2 - -

LN8a Mod Alk, shallow, humic, lowland

8 4 3.5 5

Setting of Boundaries - Procedure H/G boundary: • Statistical distribution approach (REBECCA data + other national datasets)

• 90th %ile of ref.lakes for clearwater lakes• 75th %ile of ref.lakes for humic lakes (due to some ref.sites with rather high chla values)

• Non-linear dose-response curves of phytoplankton indicators (REBECCA data)• mostly < 20% change in indicator proportions of total biomass

G/M boundary:• Statistical distribution of chla (REBECCA data + other national datasets):

difference between H/G boundary and the worst value was equally distributed for the other class boundaries using log scale intervals

• Non-linear dose-response curves of phytoplankton indicators (REBECCA data), using breakpoints and/or crossing points between the different indicators (see next slide)

For both boundaries:• Small range allowed to account for intra-type differences due to climate and

humic matter• Final adjustment of boundary values (mean, min and max) to give the same

EQRs across the range within the type, and also the same EQRs for all types (ensure same ambition level across the range, and user-friendly, simple classification systems)

Non-linear dose-response curves used for boundary setting: ref., early warning, impact ind.

0.2 0.5 1.0 2.0 5.0 10.0 20.0

0.0

0.2

0.4

0.6

0.8

1.0

L-N2aL-N5

Chl-a

% b

iovo

l.

Ref H/G G/M

Boundaries

LN1 Mod. Alk. Lowland, shallow, clear

mean min max Ref 3 2.5 3.5 HG 6 5 7.0 GM 9 7.5 10.5 EQR HG 0.50 0.50 0.50 EQR GM 0.33 0.33 0.33

N=73• Max values if long

retention time

• Min values if short retention time

Alternative to using a range: Split into subtypes, but then too little data to intercalibrated

Low EQR values because of

• generally very low chla values

• flat response curves for all indicators untill the threshold (previous slide)

Boundaries – clearwater lakesLN2a

Low. Alk. Lowland, shallow, clear

mean min max Ref 2 1.5 2.5 HG 4 3.0 5.0 GM 7 5.0 8.5 EQR HG 0.50 0.50 0.50 EQR GM 0.29 0.30 0.29 LN2b Low. Alk. Lowland, deep, clear mean min max Ref 2 1.5 2.5 HG 4 3.0 5.0 GM 6 4.5 7.5 EQR HG 0.50 0.50 0.50 EQR GM 0.33 0.33 0.33

N=89

N=96

Low alk lakes (LN2) have lower values than mod alk. lakes (LN1)

Deep lakes have lower G/M values than shallow lakes

Boundaries – humic lakesLN3a

Low. Alk. Lowland, shallow, humic (30-90 mg Pt/L)

mean min max Ref 3.0 2.5 3.5 HG 6.0 5.0 7.0 GM 10.0 8.0 12.0 mean mg Pt/L 50-70 30-50 70-90 retention time Long EQR HG 0.50 0.50 0.50 EQR GM 0.30 0.31 0.29

LN8a Mod. Alk. Lowland, shallow, humic (30-90 mg Pt/L)

mean min max Ref 4 3.5 5 HG 8 7 10 GM 12 10.5 15 EQR HG 0.50 0.50 0.50 EQR GM 0.33 0.33 0.33

N=104

N=68

Max values if high humic content

Min values if low humic content

Humic lakes have higher values than clearwater lakesLow alk lakes (LN3) have lower values than mod alk lakes (LN8)

Boundaries – Boreal lakesLN5

Low. Alk. Mid-altitude, shallow, clear

mean min max Ref 1.5 HG 3 GM 4.5 EQR HG 0.50 EQR GM 0.33

LN6a Low. Alk. Mid-altitude, shallow, humic (30-90 mg Pt/L)

mean min max Ref 2 HG 4 GM 6 EQR HG 0.50 EQR GM 0.33

N=49

N=21

• Less need for range since these types are only shared by Norway and Sweden,

• Too little data to assess range

Use of IC results in national typologies/assessment systems

• Different approaches will be used to transform IC results into national systems:– Many national types are similar to IC types. For these

types the IC boundaries will be used (within the range)– For other national types comparability with IC types will

be checked– Ref.values for other national types will be compiled,

using type-specific or site-specific approaches– Using the same EQRs for national types as for IC types

to set boundary values for national types. This will ensure the same ambition level for all types.

Problems, gaps, difficulties encountered in the IC process

• Deviations between IC types and national types• Insufficient data for some types• Different indicators in different countries: i.e.

chrysophytes not relevant in UK• REBECCA dataset dominated by NO, FI: can

response curves be trusted in other countries in NGIG (UK, IE, SE)?

• Too short pressure gradient in national datasets for some countries (FI, SE) cause lack of thresholds in some national dose-response curves. Can REBECCA thresholds be trusted?

Conclusion• Type-specific chla boundaries agreed for all

NGIG types• The approach of using a range of boundary

values, but similar EQRs across the range is considered to be a good approach for assessment of ecological status, because this ensures the same ambition level for all sites, but still allows a site-specific flexibility, and a user-friendly simple assessment system.

• Taxonomic metrics (indicators) will be focused in the continuation: % Cyanobacteria is possible

Future work for other elements

Macrophytes Spring 2007

Benthic invertebrates and acidification

Spring 2007

Other elements and pressures

After 2007