One of FAIRWAY's major research themes is »Monitoring & indicators in which we evaluate and develop transparent agri-drinking water indicators (ADWIs) to monitor and assess the impact of measures and good practices on drinking water quality.

Data and information on ADWIs collected for the area of the Lower Saxony case study was extracted from Eionet (the public European database http://cdr.eionet.europa.eu/). Their use in the harmonized indicator database is described here.

Harmonized indicator database

1. Research highlights

In »Harmonized indicator database we describe the preparation of harmonized datasets for water quality monitoring of drinking water resources, and the development of a readily usable database from these harmonized datasets.

The development of the database has mainly been driven by existing datasets of the FAIRWAY case studies. The database contains near 390,000 rows of data from the 13 case study sites, with more than 65 parameters and more than 500 sub-parameters. One of the challenges throughout the task of database development has been to find ways to harmonize as much as possible the datasets obtained from those various sources.

• We provide access to the database and describe it in terms of its general structure,
  »Indicator database
• describe its development from the data supplied by the case studies,
  »Database development process
• and detailed structure.
  »Detailed structure of the database
• Possible uses of the database are then mentioned along with examples of some interesting data series and instructions on using the database efficiently.
  »Using the database
• Finally, the major problems and limitations encountered throughout this work are discussed (including definition of boundaries (of particular relevance to Lower Saxony), local data through European databases, timescale of monitoring data and institutions with different operational aims (e.g. the Lower Saxony case study is involved in the pre-existing project on manure treatment and export so the FAIRWAY team could not supply information on the water quality for specific wells without asking a third party).
  »Conclusions

2. Conclusions

Some of the major challenges identified in building the database are that:

1. Definitions of ‘boundary’ are different from the pressure and state perspectives. The catchment area defines the hydrogeological boundary, but the agricultural boundary is an administrative boundary (at least are displayed as that). Moreover, there is generally a lag time (delay) between pressure and state indicators. Consequently, pressure data and state data do not overlap in most cases, and thus they cannot be linked directly.
2. Because of the difference in those definitions, the scale of the collected data is also different. The state data (mainly hydrogeochemical data on water quality) can be point or catchment scale while the pressure data is ideally at the field plot scale but actually most often at administrative levels (municipal, regional, or even national level).
3. Therefore, it is time-consuming to collect these large sets of data and process the data to a comparable form between state and pressure for a case study and between the case studies.