Integrated risk assessment for WFD ecological status classification applied to Llobregat river basin (Spain). Part I—Fuzzy approach to aggregate biological indicators

Water Framework Directive (WFD) requirements and recommendations for Ecological Status (ES) classification of surface water bodies do not address all issues that Member States have to face in the implementation process, such as selection of appropriate stressor-specific environmental indicators, definition of class boundaries, aggregation of heterogeneous data and information and uncertainty evaluation. In this context the “One-Out, All-Out” (OOAO) principle is the suggested approach to lead the entire classification procedure and ensure conservative results. In order to support water managers in achieving a more comprehensive and realistic evaluation of ES, an Integrated Risk Assessment (IRA) methodology was developed. It is based on the Weight of Evidence approach and implements a Fuzzy Inference System in order to hierarchically aggregate a set of environmental indicators, which are grouped into five Lines of Evidence (i.e. Biology, Chemistry, Ecotoxicology, Physico-chemistry and Hydromorphology). The whole IRA methodology has been implemented as an individual module into a freeware GIS (Geographic Information System)-based Decision Support System (DSS), named MODELKEY DSS. The paper focuses on the conceptual and mathematical procedure underlying the evaluation of the most complex Line of Evidence, i.e. Biology, which identifies the biological communities that are potentially at risk and the stressors that are most likely responsible for the observed alterations. The results obtained from testing the procedure through application of the MODELKEY DSS to the Llobregat case study are reported and discussed.

► EU Water Framework Directive asks for ecological and chemical status assessment.
► One-Out, All-Out principle is suggested for Ecological Status (ES) classification.
► We developed an alternative Integrated Risk Assessment (IRA) methodology.
► IRA provides a more comprehensive, realistic and flexible ES classification.
► Part I focuses on the Fuzzy Inference System to evaluate biological status.