High‐temporal resolution fluvial sediment source fingerprinting with uncertainty: a Bayesian approach

Cooper, Richard J.; Krueger, Tobias; Hiscock, Kevin M.; Rawlins, Barry G.

doi:10.1002/esp.3621

Cited by 72 publications

(68 citation statements)

References 67 publications

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“…As indicated in Section 2, materials can be eroded and subsequently deposited before they finally reach the river (Cooper et al, 2014a;Vale et al, 2016). In sediment fingerprinting, only the primary sediment source is identified, without providing information about sediment transport rates and the complexity of the pathways (i.e.…”

Section: Methodological Challengesmentioning

confidence: 99%

“…In sediment fingerprinting, only the primary sediment source is identified, without providing information about sediment transport rates and the complexity of the pathways (i.e. locations and duration of intermediate sediment storage in the catchment and in the river) (Cooper et al, 2014a;Koiter et al, 2013b;Poulenard et al, 2009). This problem is clearly illustrated by a hydromorphological assessment study of the River Frome (UK) (Grabowski and Gurnell, 2016).…”

Section: Methodological Challengesmentioning

confidence: 99%

“…Finally, traditional sediment fingerprinting approaches based on geochemical analysis techniques are very time-and cost-consuming, limiting the use of the technique at a high temporal resolution (Brosinsky et al, 2014a;Cooper et al, 2014a;Poulenard et al, 2012). Recently, studies have demonstrated the promise of spectral reflectance-based (visible and (near) infrared) fingerprinting methods as a quicker and less costly alternative for sediment source apportionment, with considerable potential to expand the temporal resolution of sediment fingerprinting analyses during high-flow events (e.g.…”

Section: Methodological Challengesmentioning

confidence: 99%

“…The approach A final empirical approach to gain insights into SS dynamics is to compare temporal variations in sediment source contributions to the total sediment load in the river (Brosinsky et al, 2014b;Carter et al, 2003;Cooper et al, 2014a;Fang et al, 2015;Poulenard et al, 2009). Fryirs (2013 noted that information on the preferential delivery of certain sources of sediment and the loss of other sources should be taken into account when assessing sediment delivery within a catchment, because sediment sources may vary between and during high flow events.…”

Section: Sediment Fingerprintingmentioning

confidence: 99%

“…Belmont et al, 2011;Chen et al, 2016) and during individual events (e.g. Cooper et al, 2014a;Evrard et al, 2013;Poulenard et al, 2012). Therefore, applying sediment fingerprinting on multiple timescales can provide information on possible variations and shifts in the dominant sediment source over short to long term timescales, which can help to better understand the interactions between the factors underlying SS transport.…”

Section: Accounting For Temporal Variabilitymentioning

confidence: 99%

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

Vercruysse

Grabowski

Rickson

2017

Earth-Science Reviews

329

181

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Suspended sediment is a natural part of river systems and plays an essential role in structuring the landscape, creating ecological habitats and transporting nutrients. It is also a common management problem, where alterations to sediment quantity and quality negatively impact ecological communities, increase flood hazard and shorten the lifespan of infrastructure. To address these challenges and develop appropriate sustainable management strategies, we need a thorough understanding of sediment sources, pathways and transport dynamics and the drivers that underlie spatial and temporal variability in suspended sediment transport in rivers. However, research to date has not sufficiently addressed the temporal complexity of sediment transport processes, which is limiting our ability to disentangle the hydro-meteorological, catchment, channel and anthropogenic drivers of suspended sediment transport in rivers. This review critically evaluates previously published work on suspended sediment dynamics to demonstrate how the interpretation of sediment sources and pathways is influenced by the temporal scale and methodology of the study. To do this, the review (i) summarizes the main drivers of temporal variation in suspended sediment transport in rivers; (ii) critically reviews the common empirical approaches used to analyze and quantify sediment sources and loads, and their capacity to account for temporal variations; (iii) applies these findings to recent case studies to illustrate how method and timescale affect the interpretation of suspended sediment transport dynamics; and finally (iv) synthesizes the findings of the review into a set of guidelines for a multi-timescale approach to sediment regime characterization. By recognizing a priori that study design and temporal scale have an impact on the interpretation of SS dynamics and employing methods that address these issues, future research will be better able to identify the drivers of suspended sediment transport in rivers, improve sediment transport modelling, and propose effective, sustainable solutions to sediment management problems.

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Section: Methodological Challengesmentioning

confidence: 99%

Section: Methodological Challengesmentioning

confidence: 99%

Section: Methodological Challengesmentioning

confidence: 99%

Section: Sediment Fingerprintingmentioning

confidence: 99%

Section: Accounting For Temporal Variabilitymentioning

confidence: 99%

See 3 more Smart Citations

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

Vercruysse

Grabowski

Rickson

2017

Earth-Science Reviews

329

181

View full text Add to dashboard Cite

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Sensitivity of fluvial sediment source apportionment to mixing model assumptions: A Bayesian model comparison

Cooper

Krueger

Hiscock

et al. 2014

Water Resources Research

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Mixing models have become increasingly common tools for apportioning fluvial sediment load to various sediment sources across catchments using a wide variety of Bayesian and frequentist modeling approaches. In this study, we demonstrate how different model setups can impact upon resulting source apportionment estimates in a Bayesian framework via a one-factor-at-a-time (OFAT) sensitivity analysis. We formulate 13 versions of a mixing model, each with different error assumptions and model structural choices, and apply them to sediment geochemistry data from the River Blackwater, Norfolk, UK, to apportion suspended particulate matter (SPM) contributions from three sources (arable topsoils, road verges, and subsurface material) under base flow conditions between August 2012 and August 2013. Whilst all 13 models estimate subsurface sources to be the largest contributor of SPM (median ∼76%), comparison of apportionment estimates reveal varying degrees of sensitivity to changing priors, inclusion of covariance terms, incorporation of time-variant distributions, and methods of proportion characterization. We also demonstrate differences in apportionment results between a full and an empirical Bayesian setup, and between a Bayesian and a frequentist optimization approach. This OFAT sensitivity analysis reveals that mixing model structural choices and error assumptions can significantly impact upon sediment source apportionment results, with estimated median contributions in this study varying by up to 21% between model versions. Users of mixing models are therefore strongly advised to carefully consider and justify their choice of model structure prior to conducting sediment source apportionment investigations.Key PointsAn OFAT sensitivity analysis of sediment fingerprinting mixing models is conductedBayesian models display high sensitivity to error assumptions and structural choicesSource apportionment results differ between Bayesian and frequentist approaches

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An enhanced Bayesian fingerprinting framework for studying sediment source dynamics in intensively managed landscapes

Abban

Papanicolaou

Cowles

et al. 2016

Water Resources Research

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An enhanced revision of the Fox and Papanicolaou (hereafter referred to as “F‐P”) (2008a) Bayesian, Markov Chain Monte Carlo fingerprinting framework for estimating sediment source contributions and their associated uncertainties is presented. The F‐P framework included two key deterministic parameters, α and β, that, respectively, reflected the spatial origin attributes of sources and the time history of eroded material delivered to and collected at the watershed outlet. However, the deterministic treatment of α and β is limited to cases with well‐defined spatial partitioning of sources, high sediment delivery, and relatively short travel times with little variability in transport within the watershed. For event‐based studies in intensively managed landscapes, this may be inadequate since landscape heterogeneity results in variabilities in source contributions, their pathways, delivery times, and storage within the watershed. Thus, probabilistic treatments of α and β are implemented in the enhanced framework to account for these variabilities. To evaluate the effects of the treatments of α and β on source partitioning, both frameworks are applied to the South Amana Subwatershed (SASW) in the U.S. midwest. The enhanced framework is found to estimate mean source contributions that are in good agreement with estimates from other studies in SASW. The enhanced framework is also able to produce expected trends in uncertainty during the study period, unlike the F‐P framework, which does not perform as expected. Overall, the enhanced framework is found to be less sensitive to changes in α and β than the F‐P framework, and, therefore, is more robust and desirable from a management standpoint.

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High‐temporal resolution fluvial sediment source fingerprinting with uncertainty: a Bayesian approach

Cited by 72 publications

References 67 publications

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

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

Sensitivity of fluvial sediment source apportionment to mixing model assumptions: A Bayesian model comparison

An enhanced Bayesian fingerprinting framework for studying sediment source dynamics in intensively managed landscapes

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