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6 results for “Gravel bed rivers”
Laboratory data - physical modelling of gravel bed rivers under unsteady flow conditions
<p>Sediment transport and topography data from laboratory experiments under unsteady flow conditions.</p> <p>Data supporting the manuscript:<br> Redolfi, M., Bertoldi, W., Tubino, M., & Welber, M. (2018). Bed load variability and morphology of gravel bed rivers<br> subject to unsteady flow: A laboratory investigation. Water Resources Research, 54, 842–862. https://doi.org/<br> 10.1002/2017WR021143<br> Details about the physical model and the experimental procedure can be found in the paper.</p>
Fig. 526. Gravel extraction from river beds. A in Fig. 4 in Fig. 4 in Fig. 3 in Fig. 21. Sesarmops mora n in Paralbunea dayriti
Fig. 526. Gravel extraction from river beds. A: Seymareh River, B: Kangir River, C: Tajan River, D: Balekhlo chay River.
PhotoMOB: Automated GIS method for estimation of fractional grain dynamics in gravel bed rivers
<p><i><strong>PhotoMOB </strong></i>is a GIS toolbox automating the tasks to perform a spatial-temporal quantification of grain size distribution, bed stability and fractional mobility from photos of gravel bed river after hydrological events. Grain are classified as identical (i.e., stationary) or different (i.e., mobile/turned out). </p><p>The grain size distributions from the photos can be extracted in the form Area-by-Number or Grid-by-Number and are compatible with measurements obtained via other methods as pebble-count. </p><p>This folder contains data related to both papers in following subfolders: </p><ul><li><i>1_PhotoMOB_part1_Data</i>: data used for error assessment of grains characterisation<ul><li>Grain dataset collected in the field (1_Real),with measurement of the axes (a, b and c) measured using the Pebble-Box, measurement of the weight, estimation of the position of the grains on the surface of the bed.</li><li>Grain dataset digitised manually and identified automatically by the <i><strong>supervised</strong></i><strong> </strong>and <i><strong>automated PhotoMOB </strong></i>procedures as well as for<i> Basegrain</i> free stand-alone tool from Detert and Weitbrecht (2013) and <i>Sedimetrics Digital Gravelometer</i> from Graham (2005a, 2005b). </li></ul></li><li><i>2_PhotoMOB_part2_Data:</i> data used for error assessment of grain dynamics</li><li>3_Example_Data_ready_to_PhotoMOB_Extractor_App:<ul><li>This corresponds to a dataset of 3 hydrological events processed by the PhotoMOB GIS protocol (Part 1 +Part 2). These data are available as an example to be used in the PhotoMOB Extractor application (https://shiny.fannyville.com/photomob_extractor/) to obtain an overview of the output available from these data. </li></ul></li></ul><p> </p>
DATASET - Automated grain sizing from UAV imagery of a gravel-bed river: benchmarking of three object-based methods and analysis of particle-size clustering
<p>Dataset used to compute the grain size distributions from in-field line sampling and digitally on orthoimages with automated methodologies and by manual labelling. It also contains the data used to produce spatial statistics.</p>
Dataset on the dynamics of shallow wakes in gravel-bed floodplains: results of the field experiments on the Tagliamento River, Italy
<p>Shallow wakes in fluvial environments are hydrodynamic structures produced by in-stream obstructions such as boulders, log jams, patches of riparian and aquatic vegetation. Dynamics of wakes affect a range of biotic and abiotic processes including dispersal of aquatic organisms, mixing of pollutants, sediment deposition and therefore geomorphological changes in rivers. Despite their importance, fundamental mechanisms of shallow wakes development are not fully understood due to lack of experimental data collected in natural environments and free of scaling effects compared to laboratory studies. Present dataset provides explicit information on hydrodynamics of shallow wake flows produced by solid and porous instream obstructions. Controlled field experiments with 30 runs were conducted at the side-branch of the gravel-bed Tagliamento River in the North-Eastern Italy in the period from July till October 2019. During the experimental campaign we varied characteristics of the instream obstructions (their diameter, solid volume fraction, submergence and porosity at the leading edge) and approach velocity. Each run included: (1) measurements of mean velocity and turbulence in the longitudinal (25-30 sampling locations) and lateral transects (8-10 transects with 14 sampling locations) of the constructed experimental channel where model obstructions were placed; (2) detailed surveys of the free surface topography; and (3) flow visualizations and recordings of the wakes patterns using a drone. The dataset is intended to be used for understanding fundamental mechanisms of shallow wakes development in natural fluvial environments, examination of scaling effects and verification of numerical models.</p>
Improving predictions of critical shear stress in gravel bed rivers: identifying the onset of sediment transport and quantifying sediment structure: Dataset
<p>This dataset accompanies the paper: Hodge RA, Voepel HE, Yager EM, Leyland J, Johnson JPL, Sear DA, Ahmed S. Improving predictions of critical shear stress in gravel bed rivers: identifying the onset of sediment transport and quantifying sediment structure. In review for Earth Surface Processes and Landforms. </p> <p>These data are from Figure 3 to 6. The aim of this part of the paper was to assess different methods for measuring the grain-scale sediment structure of a gravel-bed river. The approaches used are direct measurements, terrestrial laser scanning, and CT scanning. </p> <p> </p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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