Temporal and spatial analysis of phosphate concentrations in surface waters in the 'Gelderse Vallei', the Netherlands.

Abstract

High phosphate concentrations in surface waters are regarded as bad for the ecosystem integrity and leads to loss of aesthetic, ecological and economic value of the system. Therefore the European Union set targets for phosphate concentrations in the Water Framework Directive, which must be met by 2015 and ultimately by 2027. For the majority of the surface waters in the ‘Gelderse Vallei’ these targets are not met at the moment. This study focuses on the temporal and spatial trends of phosphate concentrations in the ‘Gelderse Vallei’, with respect to the Water Framework Directive. An extensive dataset with phosphate concentrations is provided by water board ‘Vallei & Eem’ and is analyzed by using ordinary linear regressions and ANOVAs for the temporal trends. For the spatial analyses maps are made to provide quick visual output of spatial patterns. For the temporal analyses no clear statistical trends are shown, however 70% of all ordinary linear regressions were negative, possibly indicating decreasing phosphate concentrations in the ‘Gelderse Vallei’. From the spatial analyses phosphate concentrations tend to decrease in northern direction. The change in phosphate between time periods does not show a clear spatial pattern. Considering the standards from the Water Framework Directive, respectively 60% and 50% of the studied surface waters will not meet the standards by 2015 and 2027. For water board ‘Vallei & Eem’ this means that measures should be taken to meet these standards. Furthermore, more attention should be given to the south of the ‘Gelderse Vallei’, for phosphate concentrations in the north are meeting the standards by 2015 and 2027 while in the south most surface waters do not meet the standards by 2015 and 2027. Extending the dataset, extending the amount of analyzed surface waters and extending research on explanations of the trends can contribute to a better understanding of the trends and therefore to stronger conclusions and indicating possible measures.