scholarly journals Sediment grain-size distribution in San Francisco Bay, California; January, February, and August 1973

1981 ◽  
Author(s):  
Janet Kay Thompson
Keyword(s):  
Grain Size ◽  
Size Distribution ◽  
San Francisco ◽  
Grain Size Distribution ◽  
San Francisco Bay ◽  
Sediment Grain Size

scholarly journals Sediment transport modelling in riverine environments: on the importance of grain-size distribution, sediment density and boundary conditions

2018 ◽  
Author(s):  
Jérémy Lepesqueur ◽  
Renaud Hostache ◽  
Núria Martínez-Carreras ◽  
Emmanuelle Montargès-Pelletier ◽  
Christophe Hissler
Keyword(s):  
Grain Size ◽  
Sediment Transport ◽  
Size Distribution ◽  
Suspended Sediment ◽  
Grain Size Distribution ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Small Scale ◽  
Sediment Grain Size ◽  
Model Based

Abstract. Hydromorphodynamic models are powerful tools to predict the potential mobilization and transport of sediment in river ecosystems. Recent studies even showed that they are able to satisfyingly predict suspended sediment matter concentration in small river systems. However, modelling exercises often neglect suspended sediment properties (e.g. particle site distribution and density), even though such properties are known to directly control the sediment particle dynamics in the water column during rising and flood events. This study has two objectives. On the one hand, it aims at further developing an existing hydromorphodynamic model based on the dynamic coupling of TELEMAC-3D (v7p1) and SISYPHE (v7p1) in order to enable an enhanced parameterisation of the sediment grain size distribution with distributed sediment density. On the other hand, it aims at evaluating and discussing the added-value of the new development for improving sediment transport and riverbed evolution predictions. To this end, we evaluate the sensitivity of the model to sediment grain size distribution, sediment density and suspended sediment concentration at the upstream boundary condition. As a test case, the model is used to simulate a flood event in a small scale river, the Orne River in North-eastern France. The results show substantial discrepancies in bathymetry evolution depending on the model setup. Moreover, the sediment model based on an enhanced sediment grain size distribution (10 classes) and with distributed sediment density outperforms the model with only two sediment grain size classes in terms of simulated suspended sediment concentration.

scholarly journals Basal-crevasse-fill origin of laminated debris bands at Matanuska Glacier, Alaska, U.S.A.

2001 ◽  
Vol 47 (158) ◽  
pp. 412-422 ◽  
Author(s):  
Staci L. Ensminger ◽  
Richard B. Alley ◽  
Edward B. Evenson ◽  
Daniel E. Lawson ◽  
Grahame J. Larson
Keyword(s):  
Grain Size ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Suspended Load ◽  
Isotopic Ratios ◽  
Sediment Grain Size ◽  
Ice Flow ◽  
Stable Isotopic ◽  
Matanuska Glacier ◽  
Thick Layers

AbstractThe numerous debris bands in the terminus region of Matanuska Glacier, Alaska, U.S.A., were formed by injection of turbid meltwaters into basal crevasses. The debris bands are millimeter(s)-thick layers of silt-rich ice cross-cutting older, debris-poor englacial ice. The sediment grain-size distribution of the debris bands closely resembles the suspended load of basal waters, and of basal and proglacial ice grown from basal waters, but does not resemble supraglacial debris, till or the bedload of subglacial streams. Most debris bands contain anthropogenic tritium (3H) in concentrations similar to those of basal meltwater and ice formed from that meltwater, but cross-cut englacial ice lacking tritium. Stable-isotopic ratios (δ18O and δD) of debris-band ice are consistent with freezing from basal waters, but are distinct from those in englacial ice. Ice petrofabric data along one debris band lack evidence of active shearing. High basal water pressures and locally extensional ice flow associated with overdeepened subglacial basins favor basal crevasse formation.

scholarly journals Spatial Mapping of Riverbed Grain-Size Distribution Using Machine Learning

2020 ◽  
Vol 2 ◽  
Author(s):  
Huiying Ren ◽  
Zhangshuan Hou ◽  
Zhuoran Duan ◽  
Xuehang Song ◽  
William A. Perkins ◽  
...  
Keyword(s):  
Machine Learning ◽  
Grain Size ◽  
Physical Properties ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Numerical Models ◽  
Influential Factors ◽  
Velocity Shear ◽  
Sediment Grain Size ◽  
Substrate Size

Recent alluvial sediments in riverbeds play a significant role in controlling hydrologic exchange flows (HEFs) in river systems. The alluvial layer is usually associated with strong heterogeneity in physical properties (e.g., permeability and hydraulic conductivity), which affects local HEFs and therefore biogeochemical processes. The spatial distribution of these physical properties needs to be determined to inform the numerical models used to reveal the realistic hydro-biogeochemical behaviors. Such information can be obtained based on the intrinsic link between sediment grain-size distribution and hydraulic properties where sediment texture information is available. However, grain-size measurements are usually spatially sparse and do not have adequate coverage and resolution, particularly for a relatively large domain such as the Hanford Reach of the Columbia River. In this paper, we adopted machine learning (ML) approaches for categorizing and mapping the spatial distributions of riverbed substrate grain size and filling in missing areas of substrate data using the ML models along the reach. Such ML models for substrate size mapping were trained at 13,372 locations using measured substrate sizes along with observed and simulated attributes, including bathymetric attributes (e.g., elevation, slope, and aspect ratio) from LIDAR and bathymetric surveys, and hydrodynamic properties (e.g., water depth, velocity, shear stress, and their statistical moments). An ensemble bagging-based ML technique, Random Forest, was adopted to identify the most influential factors as predictors to develop the predictive models with over-fitting issues addressed. The models were evaluated with respect to each individual substrate size class and the lumped group, and then used to generate the final substrate size maps covering all the grid cells in the numerical modeling domain.

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