QUALINET: an innovative GIS-based tool to assess groundwater quality in the framework of the WFD
Abstract
The European Water Framework Directive lays down an objective of good qualitative status of groundwater bodies. To reach this objective, historical data must be analyzed with powerful statistical tests which could appear complex for non-specialists. A guidance document is available but methods are not specified. In order to provide support to governmental agencies, we developed in collaboration with the Seine-Normandy Water Agency an integrated tool which allows a simple implementation of various statistical tests: QUALINET.
QUALINET is accessible through a GIS interface, developed with the ArcGIS software. The interface allows making complex selection of points and having a spatial representation of results (results can be displayed at each point or aggregated by entity as water body). QUALINET is linked to the French national database on groundwater (www.ades.eaufrance.fr), so monitoring data gathered can directly be processed. The tool automatically runs R-encoded algorithms. It is designed to be used by people that are familiar but not expert in statistics.
Groundwater quality series have characteristics (unevenly spaced data; sparse time series; data reported below limit of quantification) which make many methods not powerful or not applicable. QUALINET integrates powerful statistical methods which were selected after bibliographic and case studies conducted in the field of groundwater quality assessment. These tests can be adequately applied in order to assess status, trends and trend reversals which are WFD requirements. Development and test of the tool highlighted several issues regarding the implementation of WFD in the field of groundwater quality assessment. Geochemical background, spatial and temporal representativeness of data and inertia of groundwater bodies were all subjects under in-depth review. The tool also proposes methods to identify explanatory factors of contaminations, with specific tests implemented to analyze quality data against other environmental metadata (piezometry, rainfall, anthropogenic pressure…).