Title: Combination of multiple Biological Quality Elements into waterbody assessment of surface waters
Authors: CARONI RossanaVAN DE BUND WouterCLARKE Ralph T.JOHNSON Richard K.
Citation: HYDROBIOLOGIA vol. 704 no. 1 p. 437-451
Publisher: SPRINGER
Publication Year: 2013
JRC N°: JRC71218
ISSN: 0018-8158
URI: http://link.springer.com/article/10.1007/s10750-012-1274-y
http://publications.jrc.ec.europa.eu/repository/handle/JRC71218
DOI: 10.1007/s10750-012-1274-y
Type: Articles in periodicals and books
Abstract: The Water Framework Directive (WFD) requires EU Member States to classify the ecological status of surface waters by using multiple biological quality elements (BQEs). According to the WFD, a "one-out-all-out" (OOAO) rule should be applied when integrating multiple BQEs into an overall biological status of a waterbody, i.e. classification is determined by the lowest class status among all the BQEs. This principle has, however, been criticized for increasing the probability of committing a false positive error (erroneously downgrading a waterbody to a worse class). Through the use of both simulated and monitoring datasets, we have analyzed the effects of different combination rules in the classification outcome and classification reliability. The OOAO represented the strictest combination rule in terms of increased probabilities of waterbodies being in moderate or worse status in comparison to other rules such as average or median. The OOAO approach gave acceptable results when the different BQEs were complementary, showing the effects of different pressures, and when the level of uncertainty in the metrics used in the assessment was not too high. For higher levels of metric uncertainty, the average rule across BQEs produced better statistical results. Increasing the number of BQEs used in the assessment affected the classification outcome when using the OOAO approach. This was especially problematic if all BQEs address the same pressure and the effect was proportional to the level of uncertainty of the BQEs. Applying the average rule gave more reliable results in this case. Our study showed that grouping of metrics and metrics uncertainty has a large influence on classification outcomes and that this should be carefully considered to ensure that the final classification adequately reflects ecological status.
JRC Directorate:Sustainable Resources

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