Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
11
datasets available to search
ShareScore release 0.9.0
Dataset results
11 results for “meandering river”
Floodplain Sediment Storage Times in a Simulated Meandering River
<p>This dataset contains four files which enable another researcher to bypass the most computationally intensive parts of the simulations (in MATLAB) associated with the publication of my dissertation thesis. These files result as the analysis of the Simulation of the Long-Term Evolution of a Meandering River (https://doi.org/10.5281/zenodo.5651840).</p> <p>The first of these files contain the storage time and age distributions of simulated sediments from the upstream reach of the simulated river, captured after analyzing the upstream 16 mini reaches of the floodplain.</p> <p>The second file contains the same distributions from two reaches after analyzing 36 mini reaches (the downstream reach values can be found by subtraction of the data in these two files)</p> <p>The third file contains other useful parameters tracked throughout the simulation</p> <p>The fourth file contains the X & Y coordinates which mark the boundaries of each mini reach</p> <p>See the accompanying dissertation document (to be referenced once the official reference is available) and the GitHub repository which includes the code required.</p>
Simulation of the Long-Term Evolution of a Meandering River
<p>This dataset contains three files which accompany the simulations (in MATLAB) associated with the publication of my dissertation. The first is a small input file which sets the initial planform of the channel of a meandering river. The second and third files are the resulting evolution through time, saved at 30 year time intervals but offset so that the actual temporal sequence is the first element from one file, then the first element from the other file then the second element from each file.</p> <p>These output files can serve as a check for other simulations, or save the simulation time required to generate them.</p> <p>See the accompanying dissertation document (to be referenced once the official reference is available) and the GitHub repository which includes the code required.</p> <p>The core 'engine' of this simulation was originally published as dowloadable code files in the supplementary online materials associated with this paper:</p> <p>Schwenk, J., Lanzoni, S., & Foufoula‐Georgiou, E. (2015). The life of a meander bend: Connecting shape and dynamics via analysis of a numerical model. <em>Journal of Geophysical Research: Earth Surface</em>, <em>120</em>(4), 690-710. https://doi.org/10.1002/2014JF003252</p>
Data for: The pace of global river meandering influenced by fluvial sediment supply
<p>Meandering rivers move gradually across the floodplains, and this river movement presents socioeconomic risks along river corridors and regulates terrestrial biogeochemical cycles. Experimental and field studies suggest that fluvial sediment supply can exert a primary control on lateral migration rates of rivers. However, we lack an understanding of the relative importance of environmental boundary conditions, such as floodplain vegetation and sediment supply, in setting the pace of river meandering across different environmental settings. Here, we combine the analysis of satellite imagery and global-in-scale sediment and water discharge models to evaluate the controls on lateral migration rates of 139 meandering rivers that span a wide range in size, climate, and bank vegetation. We show that migration rates normalized by the channel width monotonically increase with the volumetric sediment flux normalized by the characteristic size of the river. This relation is consistent across rivers in vegetated and unvegetated catchments, indicating that enhanced lateral migration rates in unvegetated basins is likely not only facilitated by lower bank mechanical strength, but also by higher normalized sediment supply in ephemeral rivers. Using three case examples, we also demonstrate that width-normalized meander migration rates respond to spatial gradients in sediment supply caused by river impoundments, highlighting the prominent role of sediment supply in setting the pace of meander migration. Our results suggest that sediment-supply variations caused by climate, land-cover and land-use changes can lead to predictable changes in meandering river evolution and ultimately drive architectural changes in sedimentary stratigraphy.</p>
Numerical model and natural river data for the timescale analysis of meandering channel migration
<p>This is the archive of the numerical model and river centerline data used for analyzing the timescale related to meandering channel migration, which is tied to the manuscript submitted to Journal of Geophysical Research: Earth Surface: Li, Y., and Limaye, A. B., Timescale of the morphodynamic feedback between planform geometry and lateral migration of meandering rivers.</p> <p>Running this model needs a MATLAB® software environment. The model can be launched by the wrapper scripts saved under the folder "software code/example wrappers". The wrapper script called "wrapper01a_channelOnly_runModel.m" is used to generate all model simulations in this study.</p>
Data for: The pace of global river meandering influenced by fluvial sediment supply
Open the record for dataset details and reuse information.
Bed topography of the large-scale meandering rivers at dynamic equilibrium state
<p>The simulation results for the bed topography of the 42 large-scale testbed rivers at dynamic equilibrium (along with instantaneous bed shear stress distributions) are presented.</p>
Mean bed shear stress of large-scale meandering rivers
<p>The source files (in Tecplot, and readable by ParaView) are the mean bed shear stress distribution of the 42 testbed rivers.</p>
Global database of river width, slope, catchment area, meander wavelength, sinuosity, and discharge
<p><strong>1.Summary</strong></p> <p>This document describes the database that accompanies the article written by the authors of this dataset and accepted by Geophysical Research Letters (doi: 10.1029/2019GL082027).The database is distributed as a set of shapefiles, containing polylines that define the geometry of river centerlines located between 60°N and 56°S, with attributes described below. The shapefiles are organized according to continent and further broken into major basins to allow for manageable file sizes. A more complete dataset is available in the netCDF format upon request (please email Renato Frasson at renato.prata.de.moraes.frasson@jpl.nasa.gov).</p> <p>This database was partially funded by the Algorithm Definition Team contract to the Ohio State University, University of North Carolina at Chapel Hill, and Remote Sensing Solutions, Inc.</p> <p><strong>2.Polyline geometry</strong></p> <p>The centerline geometry is defined by sets of points located approximately every 30 m based on the Global River Widths from Landsat (GRLW) database (Allen & Pavelsky, 2015; 2018). Each line describes a meander and features the following attributes.</p> <p><strong>3.Attribute description</strong></p> <ul> <li><strong>SegmentID:</strong> identification number of the river segment (segments are parts of a river delimited by confluences).</li> <li><strong>lakeFlag:</strong> 0 – river, 1 – lake, 2 – river under the influence of tide, 3 – canal, 4 – unable to connect GRWL with HydroSHEDs, 5 – dam, -9999 – no data.</li> <li><strong>Width:</strong> average width in the meander, disregarding small river widths assigned to locations undetected by Landsat but known to be inundated. Locations where no width could be produced are marked as -9999.</li> <li><strong>Elevation:</strong> mean elevation from SRTM (90m) per river meander in meters. SRTM pixels are assigned to equally spaced points (every ~30m) over the river centerlines using the nearest neighbor approach. The average elevation of all valid points per meander is reported here. Locations where no elevation could be produced are marked as -9999.</li> <li><strong>Slope:</strong> water surface slope in centimeter per kilometer. Slope is initially computed over 10 km reaches, then used to compute optimum reach lengths using a modified version of the equation proposed by LeFavour and Alsdorf (2005) in the form of RL=2σ /S, where RL is the optimum reach length, σ is the height uncertainty (5.51 m from LeFavour and Alsdorf, 2005) and S the initial slope estimate. Final slopes are computed over the optimum reach lengths using elevations assigned to the 30 m river points using either classic linear regression or the Theil-Sen estimator depending on which method produces the best coefficient of determination. Locations where no slope could be produced are marked as -9999.</li> <li><strong>Meandwave:</strong> Meander wavelength in meters. This is computed by first smoothing the 30 m resolution river centerlines using a 5-point moving average and then identifying inflection points on the smoothed river centerlines. Finally, the meander wavelength takes the value of twice the distance between consecutive inflection points according to the definition given by Leopold and Wolman (1960).</li> <li><strong>Sinuosity:</strong> Dimensionless sinuosity of each river meander computed the ratio of the length between meander endpoints measured along the river centerline to half the meander wavelength as defined by Leopold and Wolman (1960).</li> <li><strong>catch_area:</strong> Catchment area was derived from flow direction and corresponding flow accumulation grids based on HydroSHEDS (Lehner<em> et al.</em>, 2008). The flow accumulation grid describes, for any location (i.e. pixel), the number of upstream raster pixels that drain to that particular location. We translated flow accumulation given in number of pixels into catchment area (in m<sup>2</sup>) by multiplying the number of pixels flowing to a location by the average area of SRTM pixels according to the latitude of the centroid of the river segment.</li> <li><strong>QWBM:</strong> mean annual flow estimated with the water balance model WBMsed (Cohen<em> et al.</em>, 2014).</li> <li><strong>Strpwr_len:</strong> stream power normalized by width (W/m).</li> <li><strong>Strpwr_are:</strong> stream power normalized by area (W/m<sup>2</sup>).</li> </ul> <p><strong>Acknowledgements</strong></p> <p>Use of this database should be acknowledged appropriately.</p> <p>The WBM data used in this database were provided by Dr. Albert Kettner at INSTAAR, University of Colorado at Boulder.</p> <p><strong>References</strong></p> <p>Allen, G. H., and T. M. Pavelsky (2015), Patterns of river width and surface area revealed by the satellite-derived north american river width data set, <em>Geophysical Research Letters</em>, <em>42</em>(2), 395-402, doi: 10.1002/2014gl062764.</p> <p>Allen, G. H., and T. M. Pavelsky (2018), Global extent of rivers and streams, <em>Science</em>, doi: 10.1126/science.aat0636.</p> <p>Cohen, S., A. J. Kettner, and J. P. M. Syvitski (2014), Global suspended sediment and water discharge dynamics between 1960 and 2010: Continental trends and intra-basin sensitivity, <em>Glob. Planet. Change</em>, <em>115</em>, 44-58, doi: https://doi.org/10.1016/j.gloplacha.2014.01.011.</p> <p>LeFavour, G., and D. Alsdorf (2005), Water slope and discharge in the amazon river estimated using the shuttle radar topography mission digital elevation model, <em>Geophysical Research Letters</em>, <em>32</em>(17), doi: 10.1029/2005gl023836.</p> <p>Lehner, B., K. Verdin, and A. Jarvis (2008), New global hydrography derived from spaceborne elevation data, <em>EOS, TRANSACTIONS, AMERICAN GEOPHYSICAL UNION</em>, <em>89</em>(10), 93-94, doi: doi:10.1029/2008EO100001.</p> <p>Leopold, L. B., and M. G. Wolman (1960), River meanders, <em>Geological Society of America Bulletin</em>, <em>71</em>(6), 769-793, doi: 10.1130/0016-7606(1960)71[769:RM]2.0.CO;2.</p> <p> </p> <p> </p>
Steelquist et al., 2024 - The impact of bedrock meander cutoffs on 50 kyr-scale incision rates, San Juan River, Utah -- Datasets
<p>Datasets used in Aaron T. Steelquist, Gustav B. Seixas, Mary L. Gillam, Sourav Saha, Seulgi Moon, and George E. Hilley, 2024, <em>The impact of bedrock meander cutoffs on 50 kyr-scale incision rates, San Juan River, Utah, </em>Earth Surface Dynamics</p>
Meandering Rivers' Geometry
<p>These files correspond to the geometry of numerically generated 42 large-scale meandering rivers.</p>
Riverbank and centerline of meander river on the Tibetan Plateau
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.