This article is published in Environmetrics as a special issue on Modern quantitative methods for environmental risk assessment, edited by Lelys Bravo de Guenni, Cómputo Científico y Estadística, Universidad Simón Bolívar, Valle de Sartenejas. Carretera Baruta-Hoyo de La Puerta, Caracas, Miranda 1080-A, Venezuela, and Susan J. Simmons, Mathematics and Statistics, UNCW, 601 South College Road, Wilmington, NC 28403, U.S.A.
Special Issue Paper
Functional clustering of water quality data in Scotland†
Article first published online: 20 NOV 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Special Issue: Modern quantitative methods for environmental risk assessment
Volume 23, Issue 8, pages 685–695, December 2012
How to Cite
Haggarty, R.A., Miller, C.A., Scott, E.M., Wyllie, F. and Smith, M. (2012), Functional clustering of water quality data in Scotland. Environmetrics, 23: 685–695. doi: 10.1002/env.2185
- Issue published online: 25 DEC 2012
- Article first published online: 20 NOV 2012
- Manuscript Accepted: 23 OCT 2012
- Manuscript Revised: 21 OCT 2012
- Manuscript Received: 30 APR 2012
- cluster analysis;
- functional data analysis;
- Water Framework Directive
Assessing quality and quantity of water is of crucial importance to identify risks to the environment, society and human health. The European Community Water Framework Directive establishes guidelines for the classification of all water bodies across Europe and requires that all sites attain ‘good’ status by 2015. Classifications are made on the basis of a range of chemical and biological determinands. Within the directive, standing waters can be grouped, and the classifications of all members of the group are then based on the classification of a single representative lake within that grouping. Classification is based on different chemical and biological determinands. A key question is therefore how to determine ‘appropriate’ groups. We investigate and develop univariate and multivariate functional clustering models to investigate the spatiotemporal structure of determinands in a set of 21 Scottish lakes. These approaches enable sites to be grouped on the basis of one or more determinands; however, unlike with standard clustering methods, the temporal dynamics of the determinands are also taken into account in the formation of the groups. Copyright © 2012 John Wiley & Sons, Ltd.