Preface—Addressing challenges to advance sediment fingerprinting research

Smith, Hugh G.; Evrard, Olivier; Blake, William H.; Owens, Philip N.

doi:10.1007/s11368-015-1231-2

Cited by 32 publications

(26 citation statements)

References 31 publications

(38 reference statements)

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“…Minella et al () observed that the budget calculations indicate that rural small watershed had a sediment delivery ratio of ∼15%, whereas in our watershed study, a sediment delivery ratio was only 0.71% (Rodrigues, ). Changes in the contribution of sediment sources along with the stream network were attributed to variations in land use, erosion processes, and sediment storage that reduces the proportion of upstream sediments reaching a given measurement site (Smith et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…Contribution of sediment‐producing sources to total sediment yield has been investigated predominantly in agricultural watersheds (Nagle & Ritchie, ; Smith, Evrard, Blake, & Owens, ) with evidence of erosion and sediment deposition (Minella, Merten, & Clarke, ; Minella, Merten, Reichert, & Santos, ; Minella, Merten, Walling, & Reichert, ; Minella, Walling, & Merten, ; Tiecher, Caner, Minella, & Santos, ; Tiecher et al, ); deforestation to prepare land for agriculture production (Bai, Zhang, Long, Liu, & Siyu, ); and in forest areas harvested and prepared for new planting (Schuller et al, ). However, information about sediment‐producing sources is incipient in areas of forestry production along the cultivation cycle.…”

Section: Introductionmentioning

confidence: 99%

“…Studies in watersheds under agricultural production, using fingerprinting technique for source identification of sediment transported in suspension, have been conducted successfully in several countries and have been expanding rapidly (Slimane et al, ; Smith et al, ). Studies were carried out in small rural watersheds in Southern Brazil, in sloping areas cropped mainly with tobacco in small family‐farm units (Minella et al, ; Minella et al, ; Minella, Merten, & Clarke, ; Minella, Merten, Walling, et al, ; Tiecher, Caner, Minella, Bender, et al, ; Tiecher, Caner, Minella, & Santos, ), for no‐till annual crops (Tiecher et al, ), on hillsides with shallow soils and non‐intensive agriculture (Miguel, Dalmolin, Pedron, & Moura‐Bueno, ; Miguel, Dalmolin, Pedron, Moura‐Bueno, & Tiecher, ), and for urban watersheds (Franz, Makeschin, Wei, & Lorz, ; Poleto, Merten, & Minella, ).…”

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Coarse and fine sediment sources in nested watersheds with eucalyptus forest

et al. 2018

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Knowledge on sediment behavior in watersheds contributes to properly implement soil and water conservation practices and solve sediment‐related environmental problems. We examined the effects of suspended and bedload sediments and location of potential sediment sources. We used geochemical properties to calculate sediment contributions, with sediment fingerprinting approach, in a watershed (0.98 km2) and in a sub‐watershed (0.39 km2) cropped to commercial eucalyptus along with native forest. The sediment sources evaluated were eucalyptus stand, stream bank along the main channel, and unpaved roads in each watershed. Two size‐fractions of source and sediment samples including fine (<0.063 mm) and coarse (0.063–2 mm) particles were analyzed. Particle size and source location in the watershed are major factors affecting the contribution of sediment sources. The closer the sediment source, the higher the sediment source contribution, especially for coarse particles. Runoff connectivity probably explains large changes in sediment source contributions over very short distances between sampling sites. The stream bank had the greater relative contribution to sediment yield, suggesting that the focal point for erosion control practices should be the channel rather than on the slopes with eucalyptus or riparian vegetation. Future studies should consider uncertainties in grouping sediment sources with a much better signal‐to‐noise ratio.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Coarse and fine sediment sources in nested watersheds with eucalyptus forest

et al. 2018

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“…Recently, substantial research effort has been made to evaluate and improve fingerprinting methods (Smith et al, 2015). Various sources of uncertainty, including sampling methods, source discrimination, tracer selection, and mixing models, have been studied in detail (Belmont et al, 2014;Foster, 2000;Koiter et al, 2015;Sherriff et al, 2015;Smith and Blake, 2014).…”

Section: Introductionmentioning

confidence: 99%

A new method for fingerprinting sediment source contributions using distances from discriminant function analysis

Liu

Storm

Zhang

et al. 2016

CATENA

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An extended Bayesian sediment fingerprinting mixing model for the full Bayes treatment of geochemical uncertainties

Cooper

Krueger

2017

Hydrological Processes

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Recent advances in sediment fingerprinting research have seen Bayesian mixing models being increasingly employed as an effective method to coherently translate component uncertainties into source apportionment results. Here, we advance earlier work by presenting an extended Bayesian mixing model capable of providing a full Bayes treatment of geochemical uncertainties. The performance of the extended full Bayes model was assessed against the equivalent empirical Bayes model and traditional frequentist optimisation. The performance of models coded in different Bayesian software (‘JAGS’ and ‘Stan’) was also evaluated, alongside an assessment of model sensitivity to reduced source representativeness and non-conservative fingerprint behaviour. Results revealed comparable accuracy and precision for the full and empirical Bayes models across both synthetic and real sediment geochemistry datasets, demonstrating that the empirical treatment of source data here represents a close approximation of the full Bayes treatment. Contrasts in the performance of models coded in JAGS and Stan revealed that the choice of software employed can impact significantly upon source apportionment results. Bayesian models coded in Stan were the least sensitive to both reduced source representativeness and non-conservative fingerprint behaviour, indicating Stan as the preferred software for future Bayesian sediment fingerprinting studies. Whilst the frequentist optimisation generally yielded comparable accuracy to the Bayesian models, uncertainties around apportionment estimates were substantially greater and the frequentist model was less effective at dealing with non-conservative behaviour. Overall, the effective performance of the extended full Bayes mixing model coded in Stan represents a notable advancement in source apportionment modelling relative to previous approaches. Both the mixing model and the software comparisons presented here should provide useful guidelines for future sediment fingerprinting studies

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Preface—Addressing challenges to advance sediment fingerprinting research

Cited by 32 publications

References 31 publications

Coarse and fine sediment sources in nested watersheds with eucalyptus forest

Coarse and fine sediment sources in nested watersheds with eucalyptus forest

A new method for fingerprinting sediment source contributions using distances from discriminant function analysis

An extended Bayesian sediment fingerprinting mixing model for the full Bayes treatment of geochemical uncertainties

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