1. EAF - GW
The EU Water Framework Directive: Statistical aspects of the identification of groundwater pollution trends, and aggregation of monitoring results
Financed by:
Federal Ministry of Agriculture, Forestry, Environment and Water Management - Austria
European Commission, DG Environment
in kind contributions by partner countries
Project co-ordination
Federal Environment Agency - Austria

2. Partners and observers involved
Federal Environment Agency, Austria
Administration for Environment, Nature, Land and Water Management, Belgium
Geological Survey of Denmark and Greenland, Denmark
Agence de l ’eau Artois Picardie, France
Hessisches Ministerium für Umwelt, Landwirtschaft und Forsten, Germany
Institute of Geology and Mineral Exploration, Greece
EPA - Environmental Protection Agency, Ireland
RIVM-Nat. Institute for Public Health and the Environment, The Netherlands
Instituto da Água, Portugal
Ministerio de Medio Ambiente, Spain
Environment Agency (England and Wales), United Kingdom
quo data, Germany (Subcontractor)
Observers from: Norway, Italy, Finland, Sweden and Hungary

3. Objectives of the project
Development of proposals for:
algorithm for data aggregation and assessment of groundwater chemical status
algorithm for assessment of trends and trend reversal
length of time series and starting point for trend calculations
treatment of <LOQ values

4. Groundwater bodies Groundwater bodies DK200 DK100 DK300 UK006 NL005
DE001
ES0409
ES0812
ES0829
FR001
FR202
GR001
PTM5
DK200
DK100
DK300
PTA2
PTM2

5. Side Products summary of current practice in Member States
GW-body characterisation
based on provisions in WFD, Annex II
web-based form for data collection
land use description based on Corine Landcover data
GIS maps of GW-bodies
development of data exchange formats (quality data and GW-body description)
development of databases

6. Chemical status (A. V, 2.4.5)
Interpretation and presentation of groundwater chemical status (Annex V, 2.4.5)
In assessing status, the results of individual monitoring points within a groundwater body shall be aggregated for the body as a whole for those chemical parameters for which environmental quality standards have been set in Community legislation
the mean values of the results of monitoring at each point in the groundwater body or group of bodies shall be calculated; and
in accordance with Art. 17 these mean values shall be used to demonstrate compliance with good groundwater chemical status

7. Need for aggregated data
surveillance monitoring
identification of risk failing to meet objectives in Art. 4
operational monitoring
status assessment
trend assessment
trend calculations are carried out based on mean values
starting point

8. Data aggregation Requirements on algorithm: statistically correct
development of a pragmatic way
one data aggregation method (suitable for small, large and groups of GW-bodies)
applicability for all types of parameters

9. Data aggregation Candidate methods tested in the project
Temporal aggregation (within year and station)
Spatial aggregation (over all stations in GW body) by one of the following candidate methods
median
(empirical) 70% percentile
arithmetic mean
upper confidence limit of the arithmetic mean
kriging mean
upper confidence limit of the kriging mean
maximum likelihood mean
maximum likelihood 70 percentile

10. Data aggregation Proposal for general procedure (1)
Check whether the GW body consists of several sub bodies with different sampling site densities
If no, examine the monitoring network with regard to the network criteria
If yes, examine the monitoring networks within subbodies with regard to the network criteria
If the network criteria for the monitoring network(s) is not fulfilled, the monitoring network has to be adapted accordingly.
For hydrogeologically inhomogeneous gw-bodies: representative network with regard to hedrogeology

11. Sampling site distribution

12. Data aggregation Proposal for general procedure (2)
Use AM or the weighted AM (in case of several subbodies) to estimate the spatial mean (pragmatic approach).
If the action limit is exceeded by CLAM, one may use CLKM alternatively (which can be considerably smaller in case of spatial correlation and high variability of the concentration level)

13. Trend assessment (A. V, 2.4.4) Identification of trends in pollutants
long-term anthropogenically induced upward trends and trend reversal
base year or period from which trend identification is to be calculated
calculation of trends for a body or group of bodies of groundwater
statistical demonstration of trend reversal and level of confidence

14. Trend assessment Requirements on algorithm
for the selection of the methods the following criteria were taken into consideration:
applicability for all types of parameters
extensibility to assess and incorporate cause - effect relationship
sufficient power for the detection of trend/reversal robustness was considered less important than power and extensibility (data validation will be responsibility of MS)
development of a pragmatic way
proposal for starting point and length of time series

15. Trend assessment
Criterion: Power to detect an increase of 30% should be close to 90% (probability to detect a trend)
Preliminary conclusion:
Method to be applied: Loess smoother
Starting point: CL95=75% of action (limit) value

16. Trend assessment -starting point

17. Trend analysis
Minimum length of time series for the detection of an upward trend
Annual data: Length >= 8 years and >= 8 values
Half-yearly data: Length >= 5 years and >=10 values
Quarterly data: Length >= 5 years and >=15 values

18. Analysis of a trend reversal
Preliminary conclusion:
Method to be applied: Two-sections model
Minimum length of time series
Annual data: Length >= 14 years and >= 14 values
Half-yearly data: Length >= 10 years and >=18 values
Quarterly data: Length >= 10 years and >=30 values

19. Trend reversal

20. Links to other Working Groups
monitoring network design (e.g density)
analytical requirements for LOQ and LOD
monitoring frequency
guidance for delimitation of GW-bodies
data exchange format (GW-body description, quality data)
characterisation of GW-bodies
identification of risk
presentation of results
groundwater action values
.....

22. Results and products
algorithm for data aggregation, trend and trend reversal assessment
software tool available for free
summary report
website including all information elaborated within the project
web-GIS-presentation of results