Accounting for uncertainty in cumulative sediment transport using Bayesian statistics

Schmelter, Mark L.; Erwin, Susannah O.; Wilcock, Peter Richard

doi:10.1016/j.geomorph.2012.06.012

Cited by 19 publications

(22 citation statements)

References 38 publications

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“…The scope of this study includes three general objectives: (1) improve the previous Bayesian models published by Schmelter and others [4,5,12,13] extending the model from one to many fractions and by including a more suitable prior and a dimensionally scalable MCMC (Markov Chain/Monte Carlo) algorithm; (2) test the novel model using laboratory flume data and validate the Bayesian model results against previous work and other statistical methods; and (3) evaluate the model by examining differences between the Bayesian model output and the validation data analysis, by exploring what is gained by adopting a Bayesian approach over previous approaches, and developing conclusions regarding the suitability of the Bayesian multi-fraction transport model.…”

Section: Objectivesmentioning

confidence: 99%

“…The term "model" in this paper is used to describe both the sediment transport relations defined by the Wilcock-Crowe equations as well as the Bayesian statistical model. Readers are referred to [6,7] for further information on the Wilcock-Crowe equations and Schmelter et al [5,12] for further details on Bayesian sediment transport models.…”

Section: Model Formulationmentioning

confidence: 99%

“…The initial Bayesian models described in Schmelter et al [4,5,12] indicated that the inverse gamma, while conjugate, was not ideal for an uninformative prior. The multi-fraction model deviates from the original specification for variance distributions.…”

Section: −3mentioning

confidence: 99%

“…The models we have used in previous Bayesian models of sediment transport (Schmelter et al [4,5]) employed a two parameter MCMC algorithm consisting of a Gibbs sampler [19] for the variance parameter (because the prior for σ 2 was conjugate with the likelihood) and a Metropolis update for the reference/critical stress. The Metropolis update [20] was manually tuned.…”

Section: Mcmc Algorithmmentioning

confidence: 99%

See 3 more Smart Citations

Multi-Fraction Bayesian Sediment Transport Model

Schmelter

Wilcock

Hooten

et al. 2015

JMSE

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A Bayesian approach to sediment transport modeling can provide a strong basis for evaluating and propagating model uncertainty, which can be useful in transport applications. Previous work in developing and applying Bayesian sediment transport models used a single grain size fraction or characterized the transport of mixed-size sediment with a single characteristic grain size. Although this approach is common in sediment transport modeling, it precludes the possibility of capturing processes that cause mixed-size sediments to sort and, thereby, alter the grain size available for transport and the transport rates themselves. This paper extends development of a Bayesian transport model from one to k fractional dimensions. The model uses an existing transport function as its deterministic core and is applied to the dataset used to originally develop the function. The Bayesian multi-fraction model is able to infer the posterior distributions for essential model parameters and replicates predictive distributions of both bulk and fractional transport. Further, the inferred posterior distributions are used to evaluate parametric and other sources of variability in relations representing mixed-size interactions in the original model. Successful OPEN ACCESSJ. Mar. Sci. Eng. 2015, 3 1067 development of the model demonstrates that Bayesian methods can be used to provide a robust and rigorous basis for quantifying uncertainty in mixed-size sediment transport. Such a method has heretofore been unavailable and allows for the propagation of uncertainty in sediment transport applications.

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Section: Objectivesmentioning

confidence: 99%

Section: Model Formulationmentioning

confidence: 99%

Section: −3mentioning

confidence: 99%

Section: Mcmc Algorithmmentioning

confidence: 99%

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Multi-Fraction Bayesian Sediment Transport Model

Schmelter

Wilcock

Hooten

et al. 2015

JMSE

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“…; Schmelter et al. ; Schmelter and Stevens, ), allowing uncertainties in model parameters, data and model predictions to be rigorously quantified. However, the lack of separate process and data models in those studies means that they do not fit with the BHM framework used herein.…”

Section: Introductionmentioning

confidence: 99%

Assimilating catchment processes with monitoring data to estimate sediment loads to the Great Barrier Reef

et al. 2014

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Quantifying riverine sediment loads, and the uncertainty around these estimates, is important for monitoring the impact of land use on ecologically sensitive receiving waters such as the Great Barrier Reef lagoon. We used a Bayesian Hierarchical Modelling approach that assimilates information from a process model for runoff, a mechanistically motivated statistical model for sediment generation and observed runoff and sediment load data. The approach was trialled on a 10-year dataset collected from a 14-km 2 sub-catchment in the Burdekin basin, Australia. Using our model, we were able to estimate daily sediment concentrations, discharges and loads (with credible intervals) over a 10-year period, including 3 years where there were long periods of missing observational data. We found that for the high-frequency monitoring undertaken at the study site, credible intervals around sediment loads were narrow. Credible intervals were substantially wider in years where observational data were not available and load estimates relied on the underlying processes and neighbouring observations. The method presented here is the first attempt at assimilating discharge and concentration measurements with process models for the purpose of sediment load estimation. The potential for quantifying loads entering the Great Barrier Reef lagoon is promising, particularly for ephemeral streams that are typical of arid and semi-arid Australia. Copyright

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Input‐variable sensitivity assessment for sediment transport relations

Fernández

García

2017

Water Resources Research

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A methodology to assess input‐variable sensitivity for sediment transport relations is presented. The Mean Value First Order Second Moment Method (MVFOSM) is applied to two bed load transport equations showing that it may be used to rank all input variables in terms of how their specific variance affects the overall variance of the sediment transport estimation. In sites where data are scarce or nonexistent, the results obtained may be used to (i) determine what variables would have the largest impact when estimating sediment loads in the absence of field observations and (ii) design field campaigns to specifically measure those variables for which a given transport equation is most sensitive; in sites where data are readily available, the results would allow quantifying the effect that the variance associated with each input variable has on the variance of the sediment transport estimates. An application of the method to two transport relations using data from a tropical mountain river in Costa Rica is implemented to exemplify the potential of the method in places where input data are limited. Results are compared against Monte Carlo simulations to assess the reliability of the method and validate its results. For both of the sediment transport relations used in the sensitivity analysis, accurate knowledge of sediment size was found to have more impact on sediment transport predictions than precise knowledge of other input variables such as channel slope and flow discharge.

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Accounting for uncertainty in cumulative sediment transport using Bayesian statistics

Cited by 19 publications

References 38 publications

Multi-Fraction Bayesian Sediment Transport Model

Multi-Fraction Bayesian Sediment Transport Model

Assimilating catchment processes with monitoring data to estimate sediment loads to the Great Barrier Reef

Input‐variable sensitivity assessment for sediment transport relations

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