Porosity and size gradation of saturated gravel with percolated fines

Núñez‐González, Francisco; Vide, Juan Pedro Martín; Kleinhans, Maarten G.

doi:10.1111/sed.12257

Cited by 14 publications

(13 citation statements)

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“…If finer sediments are supplied at a small rate to a riverbed covered by a completely developed armor coat, the fine sediments can infiltrate into the interstitial spaces of the coarser bed material and move into void spaces. As a result of fine sediment exchange in void space, the ratio of void space over the total amount of gravel-bed and the porosity change [1,2]. The study of porosity variation plays an important role for fluvial geomorphology assessment and in river ecosystem management.…”

Section: Introductionmentioning

confidence: 99%

Combination of Discrete Element Method and Artificial Neural Network for Predicting Porosity of Gravel-Bed River

Bui

Rutschmann

2019

Water

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In gravel-bed rivers, monitoring porosity is vital for fluvial geomorphology assessment as well as in river ecosystem management. Conventional porosity prediction methods are restricting in terms of the number of considered factors and are also time-consuming. We present a framework, the combination of the Discrete Element Method (DEM) and Artificial Neural Network (ANN), to study the relationship between porosity and the grain size distribution. DEM was applied to simulate the 3D structure of the packing gravel-bed and fine sediment infiltration processes under various forces. The results of the DEM simulations were verified with the experimental data of porosity and fine sediment distribution. Further, an algorithm was developed for calculating high-resolution results of porosity and grain size distribution in vertical and horizontal directions from the DEM results, which were applied to develop a Feed Forward Neural Network (FNN) to predict bed porosity based on grain size distribution. The reliable results of DEM simulation and FNN prediction confirm that our framework is successful in predicting porosity change of gravel-bed.

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Section: Introductionmentioning

confidence: 99%

Combination of Discrete Element Method and Artificial Neural Network for Predicting Porosity of Gravel-Bed River

Bui

Rutschmann

2019

Water

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“…Núñez‐González et al . [] generalized Cui's equations to consider fine material with an extended grain size distribution. The derived equations for nonuniform fine sediment infiltration are summarized as follows.…”

Section: General Equations For Infiltration Of Sediment Mixturesmentioning

confidence: 99%

“…Time increments and q T were selected to assure the fulfillment of q T Δt / Δz < 0.001; for this condition, when performing simulations with a simplified version of the new model, Núñez‐González et al . [] showed that the influence of time and depth increments in the final infiltrated fines content is almost negligible.…”

Section: Model Calibrationmentioning

confidence: 99%

“…For the initial porosities λ u , values calibrated by Núñez‐González et al . [] for the same data set and a simplified version of the model, not dependent on β , are used. Using this calibrated values of λ u the final saturation conditions from the experiments are replicated in all the simulations here.…”

Section: Model Calibrationmentioning

confidence: 99%

“…Sediment infiltration has been previously studied by flume experiments [ Einstein , ; Beschta and Jackson , ; Diplas and Parker , ; Wooster et al ., ; Haynes et al, ; Gibson et al ., ; Khullar et al ., ], by field measurements [ Frostick et al ., ; Lisle , ; Sear , ] and theoretically [ Sakthivadivel and Einstein , ; Lauck , ; Cui et al ., ; Núñez‐González et al ., ]. Field and experimental observations for an initially clean gravel deposit have shown that for very fine intruding sediment in relation to the bed material, fines penetrate deep and fill the bed from underlying layers up (a process called unimpeded static percolation by Kleinhans [] and Gibson et al .…”

Section: Introductionmentioning

confidence: 99%

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Infiltration of fine sediment mixtures through poorly sorted immobile coarse beds

Núñez‐González

2016

Water Resources Research

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Percolation of fine sediment is a common process in gravel bed rivers, which often exhibit extended and overlapping grain size distributions of the bed and the supplied fine sediment. Yet existing sediment infiltration theory assumes well‐sorted fine material with smaller grain size than the bed pores, and as such, is not suitable for many situations encountered in gravel bed streams. Previous developments for infiltration of uniform material are here generalized to consider poorly sorted sediment mixtures. Governing equations and a numerical solution to model the vertical distribution of infiltrating sediment are presented. The equations are solved as a function of a trapping coefficient, dependent on the relative size of infiltrating fines in relation to bed material. A method is developed to generate equivalent grain size distributions to calculate the trapping coefficient, when grain sizes of the infiltrating and bed materials overlap. Moreover, a bed cutoff size is defined and computed with a semiempirical packing‐porosity model, to distinguish particles smaller than the bed pores. Published experimental data are used to test the new model and calibrate the trapping coefficient. It is shown that this coefficient is highly sensible to the fine and coarse tails of fine and coarse materials grain size distributions. Accordingly, calibrated values of the coefficient are set as a function of a mean size ratio, computed from the geometric mean of the tails of the size distributions. Incorporating this relation, the model performed well in reproducing indirect observations of sediment infiltration from experiments reported in the literature.

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The pernicious problem of streambed colmation: a multi‐disciplinary reflection on the mechanisms, causes, impacts, and management challenges

2017

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The accumulation of fine sediments in rivers is a pernicious problem with wide-ranging consequences for the healthy functioning of rivers throughout the world. It is linked to a range of landuse changes and human activities that have increased sediment inputs leading to elevated fine sediment loads that exceed the sediment transport capacities of rivers. Surficial deposits of fine material can also create the conditions for fine sediment to move into and accumulate within the coarser bed substrate, a process known as colmation and the focus of this review. Colmation, also referred to as clogging, fine sediment infiltration, fine sediment deposition, ingress, infilling, intrusion of fines, siltation, and the surface-subsurface exchange of particles, is particularly damaging to river habitats and ecosystems. It causes degradation through the physical effects of reduced porosity and flow connectivity and the biogeochemical changes arising from the hydraulic and hydrological impacts and the effects of sediment-bound contaminants, all of which can impact on river ecology. Different aspects of the phenomenon of colmation have been studied across a number of disciplines and over several decades and this paper synthesizes this wide literature to provide a multidisciplinary perspective on the mechanisms, causes and impacts of colmation and discusses some key management challenges.

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Porosity and size gradation of saturated gravel with percolated fines

Cited by 14 publications

References 50 publications

Combination of Discrete Element Method and Artificial Neural Network for Predicting Porosity of Gravel-Bed River

Combination of Discrete Element Method and Artificial Neural Network for Predicting Porosity of Gravel-Bed River

Infiltration of fine sediment mixtures through poorly sorted immobile coarse beds

The pernicious problem of streambed colmation: a multi‐disciplinary reflection on the mechanisms, causes, impacts, and management challenges

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