Methodology
Methodology for indicator calculation
Methodology for indicator calculation (including description of data used)
The data used in this indicator is part of the WISE - State of the Environment (SoE) data, available in Waterbase - TCM (Transitional, Coastal and Marine) waters. Waterbase is the generic name given to EEA´s database on status, quality and quantity of Europe´s water resources. Waterbase – TCM waters contains data collected both from EEA member countries (i.e. belonging to the EIONET) and from the Regional Seas Conventions through the WISE-SoE TCM data collection process (WISE-SoE was formerly known as Eionet-Water and Eurowaternet). The resulting WISE SoE TCM dataset is therefore made of sub-samples of national data assembled for the purpose of providing comparable indicators of state and impact of transitional, coastal and marine waters () on a Europe-wide scale.
Annual mean summer surface concentrations of Chl-a, and classification of concentration levels
The primary aggregation consists of:
- Identifying stations and assigning them to countries and sea regions (in line with the geographical regions specified in the MSFD)
- Creating statistical estimates for each combination of station and year and deriving the average annual mean summer surface concentration of Chl-a
- Classifying Chl-a concentration levels for each station (i.e. according to low, moderate, and high boundaries)
1. Identifying stations and assigning them to countries and sea regions
All geographical positions defined in the data are assigned to a sea region by coordinates. The used regional and subregional seas of Europe are in line with the geographical regions and sub-regions specified in the Marine Strategy Framework Directive (MSFD) (see below). Other European Seas (Icelandic Sea, The Norwegian Sea, the Barents Sea and the White Sea) are not covered by this indicator due to current lack of data. Also, because of the limited amount of data, only the following (sub)regions are distinguished in the maps: Baltic Sea, Celtic Seas, Greater North Sea, Bay of Biscay and Iberian coast, Mediterranean Sea, Black Sea.
| Regional Sea | Subregional Sea |
| Baltic Sea |
None |
|
North East Atlantic Ocean
|
Greater North Sea
Celtic Seas
Bay of Biscay and the Iberian coast
Macaronesian region
|
| Mediterranean Sea |
Western Mediterranean Sea
Adriatic Sea
Ionian Sea and Central Mediterranean
Aegan - Levantine Sea
|
| Black Sea |
none |
The stations are then further classified as coastal or off-shore (>20 km from coast) by checking them against the coastal contour. Off-shore stations – open seas - are distinguished per sub-regional sea, whereas coastal stations are further attributed to country. These classifications are done in ArcView. Smaller regions within the regional and sub-regional seas described above are used in the aggregation process of different determinants.
EIONET stations
WISE SoE TCM data reported directly from countries are assigned to station identifiers (i.e. EIONET stations) that are listed with coordinates. For these data, which are mostly along the coast of the reporting country, stations are kept as defined.
Regional Seas Conventions data
For the data reported the Regional Sea Conventions (and assembled by ICES), there are no consistent station identifiers available in the reported data, only geographical positions (latitude/longitude). The reported coordinates for what is intended to be the same station may vary between visits, because the exact achieved position is recorded, not the target position. Identifying station on exact position may therefore fragment time series too much.
Furthermore, duplicates between Eionet and RSC data may occur for coastal stations. A visual inspection of coastal data (< 20 km from shoreline) is therefore needed to eliminate these duplicates.
For the open waters (>20 km from shoreline), coordinates are rounded to 2 decimals, and this is used to create stations (i.e. for the purpose of establishing time series) with station names derived from rounded coordinates. As coordinates for the stations are used averages over visits to the station, rather than the rounded coordinates. This ensures that in cases were most observations are in a tight cluster within the rounding area, a position within the cluster is used. The open water observations are not assigned to countries, but listed as belonging to 'Open waters' in the Country column, without reference to country.
For the coastal ICES stations, there may be overlap with Eionet stations, and for the stations close to the coast, rounding coordinates to 2. decimal may be too much (about 500 m to 1 km). However, in this update, the rounding is done also for coastal stations, but the grouping of observations to rounded coordinates is done only within observations from each country separately, and the originator country is listed. Note that these stations are not necessarily close to the coast of the originator country.
2. Annual concentration of Chl-a per station
The statistical aggregation for calculating annual concentrations for Chl-a is done in two- or three-stage query sequences, which include:
- Selecting season (month) and depth
- If needed, building a cross-table with determinants in columns, and water samples in rows, and deriving composite determinants from that.
- Aggregating over depth for each combination of station and date.
- Aggregating over dates within each combination of station and year.
The basic data consists of two tables:
| Measurements values table |
| WaterbaseID (Country and Station) |
| Date (Year, Month and Day) |
| SampleDepth |
| SampleID |
| Determinant with the Determinant code "Chlorophyll" |
| Stations table |
| Unique identifier: data provider, Country and StationID |
| Position |
| Sea region (Atlantic, North Sea, Baltic, Mediterranean and the Black Sea |
The two tables are combined in a query which joins data to stations, linked by WaterbaseID, and including Country Code and Sea Region (used in Selection Criteria below). This query (or a table made from it) is used in the Aggregate queries.
| Description of specific aggregation query sequences | Chlorophyll |
|
Step 1
|
Select query selecting data for determinand "Chlorophyll-a", and including Sea Region, WaterbaseID, date and SamplingDepth.
Include data for:
- Depth less than or equal to 10 metres and
- Month = 6,7,8,9 (Jun.-Sep.) for stations north of latitude 59 degrees in the Baltic Sea (Gulf of Bothnia and Gulf of Finland)
- Month = 5,6,7,8,9 ( May-Sep.) for all other stations
For each combination of WaterbaseID*Station*Date, calculate arithmetic mean of chlorophyll-a over depths.
|
|
Step 2
|
For each combination of WaterbaseID*Year, calculate the arithmetic mean over the depth averages from Step 1.
Export result to Aggregate database as table 't_Base_Metadata_Chl_a'
|
3. Classification of Chl-a concentration levels, for each station
For each (sub)regional sea, the observed concentrations are classified as Low, Moderate or High. Concentrations are classified as Low when they are lower than the 20-percentile value of concentrations within a (sub)region. Concentrations are classified as High when they are higher than the 80-percentile value of concentrations within a (sub)region. The classification boundaries therefore change between regional and/or sub-regional seas.
Trend analysis of Chl-a concentrations
Consistent time series are used as the basis for assessment of changes over time. The trend analyses are based on time series from 1985 onwards. Selected stations must have at least data in the last four years of the current assessment (2007 or later), and 5 or more years in the overall assessment period (since 1985). Trend detection for each time series was done with the Mann-Kendall Statistics using a two-sided test with a significance level of 5% (Sokal & Rohlf 1995).
The Mann-Kendall method is a non-parametric test suggested by Mann (1945) and has been extensively used for environmental time series (Helsel and Hirsch, 2002; Hipel and McLeod, 2005). Mann-Kendall is a test for monotonic trend in a time series y(x), which in this analysis is chlorophyll concentration (y) as a function of year (x). The test is based on Kendall's rank correlation, which measures the strength of monotonic association between the vectors x and y. In the case of no ties in the x and y variables, Kendall's rank correlation coefficient, tau, may be expressed as tau=S/D where S = sum {i<j} (sign(x[j]-x[i])*sign(y[j]-y[i])) and D = n(n-1)/2. S is called the score and D, the denominator, is the maximum possible value of S. The p-value of tau is computed by an algorithm given by Best and Gipps (1974). The tests reported here are two-sided (testing for both increasing and decreasing trends). Data series with p-value < 0.05 are reported as significantly increasing or decreasing. The test analyzes only the direction and significance of the change, not the size of the change.
Methodology for gap filling
n/a
Methodology references
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Sokal, R.R. & Rolhf, F.J. 1995. Biometry (3rd edition).
W. H. Freeman, New York
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Carletti A, Heiskanen AS. 2009. Water Framework Directive intercalibration technical report.
Part 3: Coastal and Transitional waters. JRC-IES EUR 23838 EN/3
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Ferreira JG, Andersen JH, Borja A, Bricker SB, Camp J, Cardoso da Silva M, Garcés E, Heiskanen AS, Humborg C, Ignatiades L, Lancelot C, Menesguen A, Tett P, Hoepffner N, Claussen U, 2011.
Overview of eutrophication indicators to assess environmental status within the European Marine Strategy Framework Directive. Estuarine Coastal and Shelf Science 93, 117-131.
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Helsel, D.R., Hirsch, R.M., 2002. Statistical methods in water resources. Techniques of Water Resources Investigations
Book 4, chapter A3. U.S. Geological Survey. 522 pp.
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Hipel K.W. & McLeod A.I., 2005. Time Series Modelling of Water Resources and Environmental Systems
Electronic reprint of our book originally published in 1994.
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Mann H.B., 1945. Nonparametric tests against trend
Econometrica 13, 245-259
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