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8 results for “fluvial sediments”
Forestry roads in the Purapel fluvial catchment and related changes in sediment connectivity
<p>This dataset contains georeferenced data of forestry roads and sediment connectivity in the Purapel catchment, which drains the Chilean Coastal Range. The forestry road network consists of all the dirt and gravel roads mapped in QGIS by observing open satellite images and vectorial data available during January 2021. The observed data are maps that were listed in the QGIS OpenLayers plugin (<a href="https://github.com/sourcepole/qgis-openlayers-plugin">https://github.com/sourcepole/qgis-openlayers-plugin</a>), such as Google Satellite (Map data ©2015 Google) and OpenStreetMap <sup>1</sup>, the road network of the Chilean Congress National Library (<a href="https://www.bcn.cl/siit/mapas_vectoriales">https://www.bcn.cl/siit/mapas_vectoriales</a>) and compositions of Sentinel 2 images (European Space Agency, courtesy of the U.S. Geological Survey) of the post-2017 fire period.</p> <p>Sediment Connectivity maps were calculated on a 5 m resolution LiDAR DTM using the Connectivity Index<sup> 2</sup>. The maps were derived from the stand-alone, free and open-source executable SedInConnect 2.3<sup> 3</sup> using the Weighting factor of <sup>2</sup> and two different targets, which are available as tif files:</p> <ul> <li>ICs.tif contains <em>IC<sub>s</sub></em>, the Connectivity Index to the stream network.</li> <li>ICrs.tif contains <em>ICr<sub>s</sub></em>, the Connectivity Index to the road and the stream network.</li> </ul> <p>Here, the Road Connectivity, <em>RC </em>(dimensionless) is defined as the difference between both previous maps, with the aim to describe the change in sediment connectivity due to forestry road network:</p> <ul> <li><em>RC = IC<sub>rs</sub> - IC<sub>s</sub></em></li> </ul> <p>It is available as RC.tif file. The area of<em> high RC </em>was defined using the percentile 95 (3.12). File RC95.tif is a mask of <em>RC </em><em>≥</em><em> 3.12</em>.</p> <p>The contributing area <em>CA </em>(m<sup>2</sup>) was calculated using the multiple flow D-infinity approach <sup>4</sup> using TauDEM (https://hydrology.usu.edu/taudem/taudem5/downloads.html).</p> <p>The file CA_RC95.tif contains the contributing area (m<sup>2</sup>) of the surfaces with highest changes in sediment connectivity due to the road network. That is:</p> <ul> <li><em>CA_RC95 = </em>{<em>CA </em>|<em> RC </em><em>≥</em><em> 3.12</em>}</li> </ul> <p>The landscape distribution of those surfaces, in terms of proximity to the hilltops and valleys, is described by the density plot of the raster file CA_RC95.tif in R:</p> <pre><code>library("raster") library("ggplot2") CA_RC95<-raster("CA_RC95.tif") CA_RC95<-CA_RC95*0.0025 df = as.data.frame(CA_RC95) df = na.omit(df) ggplot(df,aes(CA_RC95)) + geom_histogram(aes(y=..count..*25),binwidth = 50)+ geom_density(aes(y=50 * ..count..*25), col="blue",size=2, adjust=10000)+ xlab("Contributing Area [ha] \n Hilltop Valley") + ylab("Area [m2]")+ theme(axis.text.x = element_text(face="bold", size=30), plot.title = element_text(color="black", size=40, face="bold",hjust=0.5), axis.title.x=element_text(color="blue", size=40, face="bold"), axis.text.y = element_text(face="bold", size=30), axis.title.y=element_text(color="blue", size=40, face="bold"))+ scale_y_continuous(trans = 'log10')+ ggtitle("Upstream area of surfaces with \n High Road Connectivity (RC > 3.12)") </code></pre> <p>Bibliography</p> <p>1. OpenStreetMap contributors. Planet dump retrieved from https://planet.osm.org. https://www.openstreetmap.org/ (2017).</p> <p>2. Cavalli, M., Trevisani, S., Comiti, F. & Marchi, L. Geomorphometric assessment of spatial sediment connectivity in small Alpine catchments. <em>Geomorphology</em> <strong>188</strong>, 31–41 (2013).</p> <p>3. Crema, S. & Cavalli, M. SedInConnect: a stand-alone, free and open source tool for the assessment of sediment connectivity. <em>Computers and Geosciences</em> <strong>111</strong>, 39–45 (2018).</p> <p>4. Tarboton, D. G. A new method for the determination of flow directions and upslope areas in grid digital elevation models. <em>Water Resources Research</em> <strong>33</strong>, 309–319 (1997). </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>
Impacts of Post-fire Debris Flows on Fluvial Morphology and Sediment Transport in a California Central Coast Stream
<p>Structure from Motion orthoimagery, lidar differencing products, and grain size data to be published with the submission of "Impacts of Post-fire Debris Flows on Fluvial Morphology and Sediment Transport in a California Central Coast Stream" to <em>Journal of Geophysical Research: Earth Surface.</em> </p> <p> </p> <p>2016, 2021, and 2022 orthoimagery for Upper Big Creek:</p> <p>J_2016.tif, J_2021.tif, J_2022.tif, K_2016.tif, K_2021.tif, K_2022.tif, L_2016.tif, L_2021.tif, L_2022.tif</p> <p>Files titled K_[year].tif encompass our upstream study reach; files titled J_[year].tif encompass our middle study reach; files titled L_[year].tif encompass our downstream study reach.</p> <p> </p> <p>2016, 2021, and 2022 orthoimagery for Devil's Creek:</p> <p>G_2016.tif, G_2021.tif, G_2022.tif, _2016.tif, H_2021.tif, H_2022.tif, I_2016.tif, I_2021.tif, I_2022.tif</p> <p>Files labeled G_[year].tif encompass our upstream study reach; files labeled H_[year].tif encompass our middle study reach; files labeled I_[year].tif encompass our downstream study reach.</p> <p> </p> <p>2016, 2021, and 2022 grain size data for Upper Big Creek with units in meters:</p> <p>BC_2016.csv, BC_2021.csv, BC_2022.csv</p> <p> </p> <p>2016, 2021, and 2022 grain size data for Devil's Creek with units in meters:</p> <p>DC_2016.csv, DC_2021.csv, DC_2022.csv</p> <p> </p> <p>Differenced lidar digital terrain models for Big Creek and Devil's Creek with units in meters:</p> <p>DoD_11_22.tif (difference between 2011 and 2022 lidar DTMs), DoD_11_15.tif (difference between 2011 and 2022 lidar DTMs)</p> <p> </p> <p>This work was funded by the Geological Society of America, the National Center for Airborne Laser Mapping, the Washington Section of the American Water Resources Association, the Western Washington University Research and Sponsored Programs Office, and the Western Washington University Geology Department.</p>
Experimental data on "Sediment storage and fluvial sediment transport linkages across an experimental flood sequence"
<p>The repository contains data used in manuscript "Sediment storage and fluvial sediment transport linkages across an experimental flood sequence" by Hassan, Pierce, Chartrand. </p>
Data for: The pace of global river meandering influenced by fluvial sediment supply
Open the record for dataset details and reuse information.
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>
Data and code for the publication "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments" - Part 2(2)
<p><strong>Background</strong></p><p>The dataset contains data on Microplastic transport experiments run in an experimental flume of the University of Bayreuth. It was analysed in the paper by J.P. Boos, F. Dichgans, J.H. Fleckenstein, B.S. Gilfedder and S. Frei, "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments", currently under review in Water Resources Research</p><p> </p><p><strong>Description of the dataset</strong></p><p>This dataset contains data used for individual particle detection, and is a companion of the main dataset (10.5281/zenodo.10083568). The files need to be downloaded and merged into the given folder structure. Put the folder "1Pix" along with the folder "10Pix" to the folder in "210812/Data-FIS/matlab/2_Experiment/exp/".</p><p> </p><p><strong>Disclaimer</strong></p><p>The data and code are provided as is without any warranty.</p><p> </p><p><strong>Funding</strong></p><p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -– Project Number 391977956 –- SFB 1357.</p>
Data and code for the publication "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments" - Part 1(2)
<p><strong>Background</strong></p><p>The dataset contains data on Microplastic transport experiments run in an experimental flume of the University of Bayreuth. It was analysed in the paper by J.P. Boos, F. Dichgans, J.H. Fleckenstein, B.S. Gilfedder and S. Frei, "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments", currently under review in Water Resources Research</p><p> </p><p><strong>Description of the dataset</strong></p><p>This dataset is the main dataset used for the analysis. There is a twin archive connected to this one, which contains the dataset which was used for the individual particle detection routines (10.5281/zenodo.10081788). The files have to be downloaded and merged into the folder structure.</p><p>The following data is included</p><ul><li>individual experimental data and results in the folders<ul><li><strong>210812</strong> (10 µm, coarse sand, low-flow)</li><li><strong>220727</strong> (1 µm, coarse sand, low flow)</li><li><strong>220803</strong> (3 µm, coarse sand, low-flow)</li><li><strong>220818</strong> (1 µm, fine sand, low-flow)</li><li><strong>220901</strong> (1 µm, coarse sand, high-flow)</li></ul></li><li><strong>Comparison</strong> (comparing individual results of the experiments)</li><li><strong>Scripts</strong> (contains the individual matlab scripts)</li><li><strong>labbook.xlsx</strong> (contains metadata on the experiments, which are read out in the matlab scripts)</li></ul><p> </p><p><strong>Description of the code</strong></p><p>The matlab scripts *.m contain the code to read and analyse all experimental data. The scripts are divided for the different input file types.</p><ul><li>Main scripts to analyze experimental data<ul><li><strong>Experiment_Main.m </strong>Main routine for individual experiments, reading and analysing Fluorometer, Levelogger, Flowmeter, Ultrasonics PIV</li><li><strong>Experiment_Main_Compare.m </strong>Comparison of individual experiment results</li></ul></li><li>FIS-dataset<ul><li><strong>FIS_Cal_Individual.m: </strong>Realizes individual calibrations of one experiment</li><li><strong>FIS_Cal_Result.m: </strong>Merges individual calibrations of one experiment</li><li><strong>Experiment_FIS.m: </strong>Load data of one experiment, detect interfaces. Followed by<ul><li><strong>Experiment_FIS_1pix</strong>: Individual particle detection (for 10 µm experiment, no binning)</li><li><strong>Experiment_FIS_10pix</strong>: Particle cloud analysis (all experiments, binning 10 Pix * 10 Pix)</li></ul></li><li><strong>Experiment_FIS_10pix_compare.m: </strong>Compare results of particle cloud analysis for all experiments.</li></ul></li><li>Fluo-data<ul><li><strong>Fluo_Cal.m </strong>Realizes calibration for Fluorometer devices</li></ul></li><li>PIV-dataset<ul><li><strong>PIV_individual.m </strong>Individual analysis of Particle Image Velocimetry (in total 9 different subdatasets, from 3 camera positions, and each 3 different illumination positions)</li><li><strong>PIV_merge.m </strong>Merge<strong> </strong>9 individual results of PIV for a result for one experiment</li></ul></li><li>Profiler-dataset<ul><li><strong>Profiler.m </strong>Analyses data from bedform profiling (merging individual measurements after the experiment)</li><li><strong>Profiler_Compare.m </strong>Compares bedform elevations and metrics between the 5 experiments (acquired after the experiment)</li><li><strong>Profiler_Time.m </strong>Analyses temporal change of bedform elevation during the experiment</li></ul></li></ul><p> </p><p><strong>Disclaimer</strong></p><p>The data and code are provided as is without any warranty.</p><p> </p><p><strong>Funding</strong></p><p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -– Project Number 391977956 –- SFB 1357.</p>
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