The effects of sample scheduling and sample numbers on estimates of the annual fluxes of suspended sediment in fluvial systems

Abstract

Abstract:Since the 1970s, there has been both continuing and growing interest in developing accurate estimates of the annual fluvial transport (fluxes and loads) of suspended sediment and sediment-associated chemical constituents. This study provides an evaluation of the effects of manual sample numbers (from 4 to 12 year À1) and sample scheduling (random-based, calendar-based and hydrology-based) on the precision, bias and accuracy of annual suspended sediment flux estimates. The evaluation is based on data from selected US Geological Survey daily suspended sediment stations in the USA and covers basins ranging in area from just over 900 km 2 to nearly 2 million km 2 and annual suspended sediment fluxes ranging from about 4 Kt year À1 to about 200 Mt year À1 . The results appear to indicate that there is a scale effect for random-based and calendar-based sampling schemes, with larger sample numbers required as basin size decreases. All the sampling schemes evaluated display some level of positive (overestimates) or negative (underestimates) bias. The study further indicates that hydrology-based sampling schemes are likely to generate the most accurate annual suspended sediment flux estimates with the fewest number of samples, regardless of basin size. This type of scheme seems most appropriate when the determination of suspended sediment concentrations, sedimentassociated chemical concentrations, annual suspended sediment and annual suspended sediment-associated chemical fluxes only represent a few of the parameters of interest in multidisciplinary, multiparameter monitoring programmes. The results are just as applicable to the calibration of autosamplers/suspended sediment surrogates currently used to measure/estimate suspended sediment concentrations and ultimately, annual suspended sediment fluxes, because manual samples are required to adjust the sample data/measurements generated by these techniques so that they provide depth-integrated and cross-sectionally representative data. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

Suspended sediment transport dynamics in rivers: Multi-scale drivers of temporal variation

“…First, hydro-meteorological conditions are dominant drivers of SS transport processes, both on long and short timescales (Horowitz et al, 2014). Precipitation and subsequent overland flow are the main agents of soil erosion and sediment transport (Perks et al, 2015;Yellen et al, 2014).…”

“…Sediment rating curves from different rivers demonstrate varying relationships between discharge and SS concentration over different orders of magnitude, depending on the location, as indicated in Section 2.2 ( Figure 2). Sediment rating curves are popular because they are fairly simple to construct and they can be established with a discrete and relatively small dataset (Horowitz et al, 2014). In the case where only a few sediment samples are available, turbidity (calibrated with SS concentration data) can be used as a proxy variable for SS concentration to develop sediment rating curves (Bilotta and Brazier, 2008;Gao, 2008).…”

“…For example, Horowitz (2008) showed that sediment rating curves tend to underestimate high and overestimate low SS concentrations because high flows are generally less common than low flows, while regression techniques tend to reduce the importance of outliers. Horowitz et al (2014) evaluated the effects of sample numbers and sampling scheduling on the precision and accuracy of annual SS load estimations based on daily SS data from monitoring stations in the USA. Their study demonstrates that instead of sampling at fixed points in time, hydrology-based sampling (i.e.…”

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

“…hydrology) to inform sampling (e.g. Horowitz et al, 2015;Lewis and Eads, 2008), or to compensate for sampling bias (Phillips et al, 1999), the long term condition of the system may only be adequately characterised through daily, or sub-daily sampling. Grosmont.…”

“…Horowitz et al, 2015;Johnes, 2007;Thompson et al, 2014). Alternative approaches for characterising the temporal and spatial variability of sediment fluxes are required (Littlewood and Marsh, 2005).…”