Using high-frequency water quality data to assess sampling strategies for the EU Water Framework Directive

Abstract

Abstract. The EU Water Framework Directive (WFD) requires that the ecological and chemical status of water bodies in Europe should be assessed, and action taken where possible to ensure that at least "good" quality is attained in each case by 2015. This paper is concerned with the accuracy and precision with which chemical status in rivers can be measured given certain sampling strategies, and how this can be improved. High-frequency (hourly) chemical data from four rivers in southern England were subsampled to simulate different sampling strategies for four parameters used for WFD classification: dissolved phosphorus, dissolved oxygen, pH and water temperature. These data sub-sets were then used to calculate the WFD classification for each site. Monthly sampling was less precise than weekly sampling, but the effect on WFD classification depended on the closeness of the range of concentrations to the class boundaries. In some cases, monthly sampling for a year could result in the same water body being assigned to three or four of the WFD classes with 95 % confidence, due to random sampling effects, whereas with weekly sampling this was one or two classes for the same cases. In the most extreme case, the same water body could have been assigned to any of the five WFD quality classes. Weekly sampling considerably reduces the uncertainties compared to monthly sampling. The width of the weekly sampled confidence intervals was about 33 % that of the monthly for P species and pH, about 50 % for dissolved oxygen, and about 67 % for water temperature. For water temperature, which is assessed as the 98th percentile in the UK, monthly sampling biases the mean downwards by about 1 • C compared to the true value, due to problems of assessing high percentiles with limited data. Low-frequency measurements will generally be unsuitable for assessing standards expressed as high percentiles. Confining sampling to the working week compared to all 7 days made little difference, but a modest improvement in precision could be obtained by sampling at the same time of day within a 3 h time window, and this is recommended. For parameters with a strong diel variation, such as dissolved oxygen, the value obtained, and thus possibly the WFD classification, can depend markedly on when in the cycle the sample was taken. Specifying this in the sampling regime would be a straightforward way to improve precision, but there needs to be agreement about how best to characterise risk in different types of river. These results suggest that in some cases it will be difficult to assign accurate WFD chemical classes or to detect likely trends using current sampling regimes, even for these largely groundwaterfed rivers. A more critical approach to sampling is needed to ensure that management actions are appropriate and supported by data.

Use of spatially distributed time-integrated sediment sampling networks and distributed fine sediment modelling to inform catchment management

“…These were established using a Monte Carlo approach whereby the SSC record was sampled n times; where n is the a prioiri determined frequency of measurements over the entire monitoring period. For each measurement frequency the record was resampled 1000 times to produce an ensemble of measurements from which population statistics could be obtained (Skeffington et al, 2015). Following each sampling run, the number of samples was monotonically increased by one, with sample frequencies ranging from one sample per year, through to one every 15-min.…”

“…Grove et al, 2015;Johnes, 2007;Skeffington et al, 2015). This has implications not only for the characterisation of background levels of suspended sediment, but also particulate borne contaminants.…”

Changing climate and nutrient transfers: Evidence from high temporal resolution concentration-flow dynamics in headwater catchments

“…There is, therefore, increasing interest in the use of in-stream high resolution water quality monitoring to assemble higher resolution catchment-scale datasets (e.g. Bowes et al, 2015;Halliday et al, 2012;Jordan et al, 2012;Mellander et al, 2012;Mellander et al, 2014;Outram et al, 2014;Skeffington et al, 2015;Wade et al, 2012), which can be used to understand key catchment processes in terms of hydrology, diffuse pollution transfer and trophic impacts and how these may alter under a changing climate.…”

Do current European lake monitoring programmes reliably estimate phytoplankton community changes?

“…Carstensen (2007) concluded that the required monitoring efforts to ensure a precise quality assessment are substantially higher than envisaged in the WFD and, for phytoplankton and nutrients, may be as high as 500 observations to characterise a water body. However, a trade-off exists between the improved precision obtained by sampling more frequently and the economics of monitoring (Skeffington et al, 2015).…”