Evaluating sampling efficiency when estimating sediment source contributions to suspended sediment in rivers by fingerprinting

Abstract

Abstract:A general method is proposed which measures the increase in uncertainty when sampling effort is reduced in sediment fingerprinting. The method gives quantitative measures of how reduced sampling of material in one of the source areas, and/or of suspended sediment in streams, increases the uncertainties in the proportions of sediment contributed from the sources. Because the proportions of sediment contributed by the source areas must add to one, standard errors of the estimated proportions cannot be used as the usual measures of uncertainty: the paper uses instead the volume of the joint 95% confidence region for the estimated proportions. The paper shows how the uncertainty in this volume changes as numbers of suspended sediment samples, and the numbers of samples collected from cropped fields, are reduced by successive steps from 24 (20, in the case of cropped fields) to 16, 12, 8, 4 and 2 samples. As expected, uncertainty increases rapidly as the number of samples -whether of suspended sediment or from cropped fields -is reduced drastically. The pattern of increasing uncertainty is similar both for reductions in suspended sediment sampling, and for reduced sampling from cropped areas. When the number of suspended sediment samples, and the number of samples from cropped fields, are reduced to the same values, the increase in uncertainty from fewer suspended sediment samples was always slightly greater than the increased uncertainty from the reduced sampling of cropped areas, although this finding took no account of differences in the costs of field sampling and laboratory analysis.

Coarse and fine sediment sources in nested watersheds with eucalyptus forest

“…Thus, in many cases, the actual sediment provenance signal represented by the tracers is smaller than the within source group variability. An alternative might be trying the tracing with significantly different composite fingerprints and using the Monte Carlo uncertainty analysis method (Clarke & Minella, ). If the fingerprints produce inconsistent results with larger error, cluster analysis‐based sediment source classification methods of Walling and Woodward () could form sediment source groups with a much better signal‐to‐noise ratio.…”

Improving the design and implementation of sediment fingerprinting studies: summary and outcomes of the TRACING 2021 Scientific School

“…A compromise is to be found on the number of samples to collect, given the time, budget, field, and logistical constraints. However, the number of samples should be maximised, as a larger number of source samples will always provide a more robust basis for analysis, modelling, and discussion (Clarke and Minella 2016 ; Du and Walling 2017 ). As a community, we require a better articulation of this cost–benefit consideration and its implications for the methods adopted and the likely strength of conclusions (e.g., qualitative vs. quantitative estimates of source contributions).…”

Combining sediment source tracing techniques with traditional monitoring: The “Arvorezinha catchment” experience

“…Subsequently, the contribution of sediment sources in the Arvorezinha catchment was also the main topic of two additional doctoral dissertations (Maier, 2013;Tiecher, 2015). From these studies, 12 scientific articles have been published so far (Minella et al, 2004(Minella et al, , 2007(Minella et al, , 2014Clarke, 2015;Clarke & Minella, 2016;Tiecher et al, 2015, Tiecher et al, 2019…”

“…An important challenge in catchment research is defining the number of samples necessary to correctly represent the processes of interest. In the case of the sediment ‘fingerprinting’ approach, a mathematical‐statistical model was developed to determine the number of samples required for better characterization and reducing uncertainties related to the source (Clarke, 2015; Clarke & Minella, 2016). Defining the number of samples that minimizes sampling and analysis costs while maximizing their discrimination and classification ability is key to improving monitoring and modelling efficiency.…”