Uncertainties in selected river water quality data

Abstract

Abstract.Monitoring of surface waters is primarily done to detect the status and trends in water quality and to identify whether observed trends arise from natural or anthropogenic causes. Empirical quality of river water quality data is rarely certain and knowledge of their uncertainties is essential to assess the reliability of water quality models and their predictions. The objective of this paper is to assess the uncertainties in selected river water quality data, i.e. suspended sediment, nitrogen fraction, phosphorus fraction, heavy metals and biological compounds. The methodology used to structure the uncertainty is based on the empirical quality of data and the sources of uncertainty in data (van Loon et al., 2005). A literature review was carried out including additional experimental data of the Elbe river. All data of compounds associated with suspended particulate matter have considerable higher sampling uncertainties than soluble concentrations. This is due to high variability within the cross section of a given river. This variability is positively correlated with total suspended particulate matter concentrations. Sampling location has also considerable effect on the representativeness of a water sample. These sampling uncertainties are highly site specific. The estimation of uncertainty in sampling can only be achieved by taking at least a proportion of samples in duplicates. Compared to sampling uncertainties, measurement and analytical uncertainties are much lower. Instrument quality can be stated well suited for field and laboratory situations for all considered constituents. Analytical errors can contribute considerably to the overall uncertainty of river water quality data. Temporal autocorrelation of river water quality data is present but literature on general behaviour of water quality compounds is rare. For meso scale river catchments (500-3000 km 2 ) reasonable yearly dissolved load calculations can be achieved using biweekly sample frequencies. For suspended sediments none of the methods invesCorrespondence to: M. Rode (michael.rode@ufz.de) tigated produced very reliable load estimates when weekly concentrations data were used. Uncertainties associated with loads estimates based on infrequent samples will decrease with increasing size of rivers.

AnnAGNPS Model Application for Nitrogen Loading Assessment for the Future Midwest Landscape Study

“…AnnAGNPS simulated annual total N and computed annual total N using the observed daily stream flow and observed monthly total N concentration are displayed in Figure 5. NSE, RE and the index of agreement were not calculated because N concentration at a monthly interval is not good enough for model calibration and evaluation [42]. Results of BY simulation from different delineations are given in Table 6.…”

Modelling nitrogen and phosphorus loads in a Mediterranean river catchment (La Tordera, NE Spain)

“…From the end of the 1990s, as required by the Urban Waste Treatment Directive (UWWTD, 91/271/EEC), the Catalan government developed and implemented strategic plans for the treatment of all urban and industrial waste waters -in 1995 and 2002 for urban waste waters, and in 1994 for industrial waste waters (ACA, 2002a(ACA, , 2003. More recently, waste waters from all towns with more than 2000 inhabitants are treated, and point sources of nutrients have decreased substantially since the first plans for urban and industrial waste water were initiated (ACA, 2002a(ACA, , 2003Prat et al, 2005;Jubany, 2008). In contrast, agricultural diffuse N emissions remain largely unaddressed.…”

Diffuse contributions dominate over point sources of soluble nutrients in two sub-catchments of the Manawatu River, New Zealand

“…Nutrient loads can be calculated in several ways (Richards & Holloway 1987;Ferguson 1986Ferguson , 1987Richards 1998;USEPA 1999;Guo et al 2002;Aulenbach & Hooper 2006;Rhode & Suhr 2007) each of which tries to resolve a fundamental question: 'What were the loads at times when water quality was not measured?' Load calculation methods fit four main categories summarised by Aulenbach & Hooper (2006): (1) Regression/rating curve approaches: the regression relationship (or visualised rating curve) between flow and measured nutrient concentration is used to estimate a representative concentration for time between samples, from which a load for the period is calculated.…”