Determining sources of fine-grained sediment for a reach of the Lower Little Bow River, Alberta, using a colour-based sediment fingerprinting approach

Abstract

Identifying the predominant sources of sediment is a key requirement for soil erosion control within watersheds. A 4 yr study from 2009 to 2012 was conducted to apportion sediment sources in a subcatchment of the Lower Little Bow River watershed, AB, Canada. This study catchment lies along a 6 km reach of the river, having an upstream inlet and downstream outlet; as such, it represents the first application of the sediment fingerprinting technique in a reach setting. Six monitoring stations were established along this reach of river to collect sediments using passive time-integrated sediment samplers. Source material samples were collected throughout the study catchment. Reflectance spectra of source and sediment samples were determined using a diffuse reflectance spectrometer. Source materials were classified into five sources using canonical discriminant analysis. Following the normality test, analysis of variance and stepwise discriminant function analysis, four colour coefficients were selected and fitted to the Bayesian mixing model of stable isotope analysis in R. The results indicated that the unknown upstream sediment source was the single largest (37.4%) contributor of sediments, suggesting that there is sediment coming from a significant source or sources not represented within the study catchment. Irrigation return flow channels were the second largest contributor (25.6%), while the third largest contributor, agricultural land, had a minimal contribution (9.2%). Source contribution had a small spatial but large temporal variation. The results indicated that identifying the origin of secondary sediment sources of upstream and irrigation flow channels is a critical step for soil erosion control in the study subwatershed.Key words: colour coefficient, fingerprinting, MixSIAR, sediment sources.Résumé : Identifier les sources principales de sédiments est une exigence capitale lorsqu'on veut combattre l'érosion du sol dans un bassin hydrographique. De 2009 à 2012, les auteurs ont entrepris une étude visant à établir les sources de sédimentation d'un bassin versant secondaire de la Lower Little Bow River, en Alberta. Le bassin en question se situe le long d'un tronçon de six kilomètres de la rivière et compte une entrée en amont ainsi qu'un exutoire en aval. En tant que telle, l'étude correspond à la première application d'une technique de cartographie des sédiments dans un bief. Six postes de surveillance ont été aménagés le long du bief et les sédiments ont été recueillis au moyen de systèmes d'échantillonnage passif intégré dans le temps. Les échantillons de matériaux d'origine ont été glanés à la grandeur du bassin versant à l'étude. Le spectre de réflectance des échantillons prélevés à la source et des sédiments a été établi avec un spectromètre à réflectance diffuse. Les matériaux d'origine ont été répartis entre cinq sources par analyse discriminante canonique. À partir des résultats du test de normalité, de l'analyse de la variance et de l'analyse discriminante des fonctions pas à pas, les ...

Determining contemporary and historical sediment sources in a large drainage basin impacted by cumulative effects: the regulated Nechako River, British Columbia, Canada

“…MixSIAR is a Bayesian mixing model framework that was originally designed to estimate the proportion of prey sources ingested by a predator based on biological tracer data; for a detailed description, see Parnell et al (2013). The effectiveness of Bayesian sediment fingerprinting models to estimate the proportion of sediment sources based on geochemical signatures has been demonstrated in several previous studies (e.g., Koiter et al 2013a;Cooper and Krueger 2017;Blake et al 2018;Liu et al 2018) and they have been evaluated positively by Davies et al (2018). The probabilistic Bayesian hierarchical model with Markov Chain Monte Carlo (MCMC) sampling was run using the JAGS software (Just Another Gibbs Sampler; v. 4.3.0; Plummer 2003) interfaced with the R software.…”

Improving the quantification of sediment source contributions using different mathematical models and spectral preprocessing techniques for individual or combined spectra of ultraviolet–visible, near- and middle-infrared spectroscopy

“…1). They include studies in the ultraviolet-visible (UV-Vis) and near-infrared (NIR) ranges in the French Alps (Legout et al, 2013) and the Southern France (Uber et al, 2019), Luxemburg (Martínez-Carreras et al, 2010c, 2010b, 2010a, Spain (Brosinsky et al, 2014a(Brosinsky et al, , 2014b, Ethiopia (Verheyen et al, 2014), South Africa (Pulley et al, 2018;Pulley and Rowntree, 2016), Argentina (Batistelli et al, 2018), the United Kingdom (Collins et al, 2014), Canada (Barthod et al, 2015;Boudreault et al, 2018;Liu et al, 2017), Brazil (Tiecher et al, 2016(Tiecher et al, , 2015Valente et al, 2020) and Iran (Nosrati et al, 2020). In the middle infrared region (MIR), sediment fingerprinting studies were carried out in France (Poulenard et al, 2012(Poulenard et al, , 2009, Mexico (Evrard et al, 2013), the United Kingdom (Vercruysse and Grabowski, 2018), China (Liu et al, 2019), Brazil (Tiecher et al, 2017) and in a transnational river catchment covering part of Switzerland, France and Germany (Chapkanski et al, 2019).…”

Tracing total and dissolved material in a western Canadian basin using quality control samples to guide the selection of fingerprinting parameters for modelling

“…Accordingly, there has been a significant uptake of stable isotope mixing models in sediment fingerprinting research (e.g. Astorga et al, 2018;Bahadori et al, 2019;Barthod et al, 2015;Brandt et al, 2016;Bravo-Linares et al, 2018;Dutton et al, 2013;Glendell et al, 2018;Jantzi et al, 2019;Liu et al, 2017;McCarney-Castle et al, 2017). One key innovation has been the development of functions in the R programming language that allow for the 'de-convoluted un-mixing' of sediment sources (Blake et al, 2018).…”