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110 results for “river sediment”
Nar/Nxr/Nap and Nos tree files from Columbia River hyporheic sediments
<p>Nar/Nxr/Nap and Nos tree files from Columbia River hyporheic sediments pertaining to publication titled: <strong>Genome-resolved metaproteomics decodes the microbial and viral contributions to coupled carbon and nitrogen cycling in river sediments</strong></p> <p> </p>
Assembly-mapped metaproteomics gene annotations from Columbia River hyporheic sediments
<p>Annotations for the full set of genes that recruited unique peptides from the Columbia River hyporheic zone sediments manuscript.</p>
The research of River Morphology transition and Sediment variation: Shule River, Northwest of China
<p>The morphological features of the Shule River DFS are summarized based on remote sensing image data. It is fan-shaped and nearly symmetric, with a radius of 59.6 km, arc length of 128 km, chord length of 113 km and fan apex angle of 96°, covering an overall area of 3660 km<sup>2</sup>. According to the differences in geomorphological features, slope and river morphology, the Shule River DFS can be divided into the proximal, medial and distal portions (Fig 2). The proximal portion is geomorphologically characterized by the coverage of gravel Gobi, the medial portion by the presence of psammophytic vegetations, and the distal portion by oasis plain. There is a trend of descending slope from the proximal portion through the medial proportion to distal portion (Fig.2). River morphology grades from large-scale braided at the proximal portion through bifurcating braided at the medial portion to meandering at the distal portion. The differences in type of sedimentary microfacies and sedimentary characteristics allow us to determine the distribution law and distribution model of sedimentary facies under modern branched river system.</p>
Evaluation of Grain size, Amorphous Fe-Mn Oxides, and Chemical Weathering Effects on Geochemical Identification of the Yangtze River and Yellow River Sediments
<p>The data of grain size of the Yangtze River and Yellow River sediments are given in ds01.xls. Trace and REEs in the HCl- and AC-residual size-differentiated components of the Yangtze River and Yellow River sediments are given in ds02.xls. Major elements in the AC-residual <5 µm fractions of the Yangtze River and Yellow River sediments are given in ds03.xls. Major, trace, and REEs in the HCl- and AC-leachable fractions of the Yangtze River and Yellow River sediments are given in ds04.xls.</p>
Soil Erosion and Sediment Yield Modeling - Southern Caspian Sea River Basins
<p>Soil Erosion and Sediment Yield Modeling - Southern Caspian Sea River Basins</p>
The Dataset of the paper "Seasonal and spatial variability and mechanisms of suspended sediment concentration in the Yellow River mouth and adjacent waters"
<div> <p>The data for "Seasonal and spatial variability and mechanisms of suspended sediment concentration in the Yellow River mouth and adjacent waters". The file names correspond to the figures in the paper.</p> </div>
Sediment routing and anthropogenic impact in the Huanghe River catchment, China: an investigation using Nd isotopes of river sediments
<p>Table A1. Sampling locations, Sr-Nd isotopes, geochemical compositions and grain size parameters of Huanghe and loess sediments investigated.</p> <p>Table A2. Annual sediment load (Mt/yr) at major gauging stations along the Huanghe mainstream.</p> <p>Table A3. Nd isotopic compositions of major tectonic terranes and sources in the Huanghe basin (literature data).</p> <p>Table A4. Nd isotopes and geochemical compositions of rocks in the North China Craton.</p> <p>Table A5. Nd model ages of rocks from North China Craton (NCC), only rocks with Th/Sc, Th/Cr, Th/Co and Sm/Nd ratios within the range of references for NCC-UC have been selected.</p> <p>Table A6. Nd isotopes mixing of sediments from the lower Huanghe.</p>
Quantifying flow velocities in river deltas via remotely sensed suspended sediment concentration
<p><strong>bathymetry and model velocity field</strong></p>
Sediment exchange between southern Yellow Sea and Yangtze River Estuary in response to storm events
<p>Sediment exchange pattern between Yangtze River Estuary and southern Yellow Sea has been a crucial and controversial scientific question, which limits our understanding on the future prediction of morphological evolution of the radical sand ridges and Jiangsu tidal flats. This study investigates various processes including tides, winds and waves to identify a dominant factor controlling sediment exchange between the southern Yellow Sea and the Yangtze River Estuary under storm conditions using a validated numerical model. Our results show that tide is the dominant force controlling hydrodynamics and sediment transport between the two systems. Tide controlled residual currents and sediment fluxes are towards northwest from the Yangtze River Estuary to the southern Yellow Sea, while wind and wave-induced currents under normal wind condition (Beaufort 3 scale, 3.4m s<sup>-1</sup>) adjust net sediment flux by less than 10% and 40%, respectively. The influences of winds and waves on sediment transport vary with wind directions. South or southeast winds enhance the tide-induced northwestward sediment transport, while north or northeast winds reduce the northwestward sediment transport. Under strong winter storm condition, however, the associated northern winds and waves become more important than tides in sediment transport, leading to southeastern directed residual currents and net sediment fluxes. Sediment transport is more active in nearshore area than offshore areas, with little vertical variations.</p>
Sediment gravity-flow drives the buildup of the modern Huanghe (Yellow River) delta front
<p>The dataset contains the output data of the numerical model and the source code for processing the data.</p> <p>1 Model validation </p> <p>The simulation time period covers 1996 to 2010 CE for H1, 1986 to 2009 CE for H2, and 1986 to 2008 CE for H3.</p> <p>1.1 The grain size distribution of the H1-H3 profile .grain</p> <p>1.2 The seafloor topography of the H1-H3 profile .bin</p> <p> </p> <p>2 Sensitivity numerical experiments</p> <p>2.1 The grain size distribution of the YD01-YD03 cores .grain</p> <p>We selected three cores (YD01, YD02, and YD03) at the H2 profile, simulated for the period of 1986-2006.</p> <p>2.2 The seafloor topography of the H2 profile .bin</p> <p>The model runs over timespans of 10 years (1986 to 1996), 20 years (1986 to 2006), and 30 years (1986 to 2015).</p> <p>2.3 The measured seafloor topography of the H2 profile .xlsx</p> <p>2.4 Original experimental data related to soil core samples .xlsx</p> <p> </p> <p>3 The plot_ grain.m and plot_ elevation.m file is used to process and analyze grain size distribution and topography, respectively</p>
Suspended sediment concentration mapping of a multi-channel system Songzi River of the Yangtze River Basin
<p>We used Google Earth Engine and observed hydrological data to create a multiple linear regression model to map SSC in the multi-channel system Songzi River of the Yangtze. <br> <br> Hydrological data from gauging stations and Landsat surface reflectance data were collected in the study area. Water discharge and suspended sediment data were obtained from four hydrological stations, including Zhicheng, Xinjiangkou, Shadaoguan and Guanwan. Such water and sediment data were obtained from the Changjiang Water Resources Commission (CWRC). Before these datasets are released to the public, they undergo rigorous verification following the relevant government protocols. <br> <br> The Landsat program with nine satellites launched from 1972 to 2021 operated by National Aeronautics and Space Administration (NASA) and United States Geological Survey (USGS) is aimed at to detect changes in the Earth's resources and environments (https://earthexplorer.usgs.gov/). It provides the longest, continuous, moderate resolution earth observation data, which is currently free, globally available. According to the data availability the GEE platform, our study used the surface reflectance data ("LANDSAT/LT05/C01/T1_SR" and "LANDSAT/LC08/C01/ T1_SR") of Landsat 5 and Landsat 8 to investigate the spectral characteristics of SSC for the Songzi River.</p>
Sediment-Mining-Sites-Shi-ting-River
<p>The “kml” file for the locations of mining sites and dredging machines in 2015, 2018 & 2022 in the Shi-ting River</p> <p>We provide the “kml” file for the locations of the mining sites and dredging machines that we observed from the historical satellite images (2015, 2018, and 2022) in Google Earth.</p>
Dataset accompanying the publication: Reservoir mud releasing may suboptimize fluvial sand supply to coastal sediment budget: Modeling the impact of Shihmen Reservoir case on Tamsui River estuary
<p>Delft3D model input and output files for scenario simulations (Scenario 1, Scenario 2, and Scenario 3)</p>
Riparian cottonwood trees and adjacent river sediments have different microbial communities and produce methane with contrasting carbon isotope compositions
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Dataset and codes for "BaHSYM: parsimonious Bayesian Hierarchical Model to predict river Sediment Yield"
<p>This folder contains:</p> <ul> <li>R project file</li> <li>R code for Best Fit model</li> <li>R code for temporal cross-validation</li> <li>R code for spatial cross-validation</li> <li>R code for cluster analysis</li> <li>dataset containing all input variables for the river gauges (and catchments) used for the development and testing of the BaHSYM model in Austria</li> </ul> <p>It also contains the same codes and datasets adapted to reproduce the model by de Vente et al. (2011), i.e. with the same structure but with the variables used in such model.</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>
The proportion of REEs in the chemical reactive phase of river sediments serves as an index for silicate weathering intensity
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Experimental study of flow and sediment transport in a river confluence with ice cover
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Time series of electrical conductivity, temperature, relative stream stage and total pressure recorded in surface water and streambed sediments of River Erpe and River Ammer, Germany, and the Sturt River, South Australia
<p>Time series of electrical conductivity (mS cm<sup>-1</sup>), temperature (degC), relative stream stage (cm) and total pressure (hPa) recorded in the surface water and in streambed sediments (depth in cm) of River Erpe and River Ammer, Germany, and the Sturt River, South Australia.</p>
Data from: Ceramsite production from sediment in Beian River: characterization and parameter optimization
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.