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49 results for “Debris flow”
Experimental study on the benefits of nature-based solutions for debris-flow mitigation via synergistic eco-geotechnical measures
<p>This supporting information provides the supplementary data (Raw data and videos) which were used to describe the effects of integrated eco-geotechnical measures on debris flow severity, flow rate, velocity, and particle size by various small-scale flume experiments.</p>
Digital Elevation Models (DEMs) after the Shovi (Caucasus) debris flow, 13 August 2023
<p>===========================</p> <p>Introduction</p> <p>On 4 August 2023, a large debris flow struck the mountain resort town of Shovi in Georgia. More on this event here : <a href="https://eos.org/thelandslideblog/the-4-august-2023-debris-flow-at-shovi-in-georgia">https://eos.org/thelandslideblog/the-4-august-2023-debris-flow-at-shovi-in-georgia</a></p> <p>Two Pléiades stereo images were acquired after the event on 13 August 2023, thanks to the activation of the CIEST² (<a href="https://www.poleterresolide.fr/ciest-2-nouvelle-generation-2/">https://www.poleterresolide.fr/ciest-2-nouvelle-generation-2/</a>) scheme. 2023-08-13a covers the lower part, 2023-08-13b the upper/source area.</p> <p>We share here the Pléiades DEMs derived from these images. The collection includes four files:</p> <p>Shovi_2023-08-13a_DEM_2m.tif</p> <p>Shovi_2023-08-13a_DEM_20m.tif</p> <p>Shovi_2023-08-13b_DEM_2m.tif</p> <p>Shovi_2023-08-13b_DEM_20m.tif</p> <p> </p> <p>===========================</p> <p>Methods</p> <p>The Pléiades stereo-images were processed using the Ames Stereo Pipeline (ASP, Beyer et al., 2018), yielding a DEM in 2x2m and 20x20 m GSD and an orthoimage in 0.5x0.5m GSD. The processing was done using as only input the stereo-images and their orientation information, as Rational Polynomial Coefficients (RPCs). The parallel_stereo routine performs all the steps needed in the correlation of the stereo-images, yielding a pointcloud which is then interpolated using the routine point2dem. We used the block matching algorithm and the set of processing parameters from Marti et al. (2016)</p> <p>Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline: NASA’s Open Source Software for Deriving and Processing Terrain Data, Earth and Space Science, 5, 537–548, https://doi.org/10.1029/2018EA000409, 2018.</p> <p>Marti, R. et al.: Mapping snow depth in open alpine terrain from stereo satellite imagery, The Cryosphere, 10(4), 1361–1380, doi:10.5194/tc-10-1361-2016, 2016.</p> <p> </p> <p>===========================</p> <p>Data Specifications:</p> <p>Cartographic projection: UTM zone 38N (EPSG:32638)</p> <p>Origin of Elevation: meters above WGS84 ellipsoid</p> <p>Raster data format: GeoTIFF</p> <p>NoData value : -9999</p> <p>Pléiades dataset includes only DEMs because a licence needs to be signed with CNES to access Pléiades imagery. Please contact the authors for further information on this.</p> <p> </p> <p>===========================</p> <p>Acknowledgements:</p> <p>Pléiades images were provided under the CIEST² initiative (CIEST2 is part of ForM@Ter (https://en.poleterresolide.fr/) (Pléiades © CNES 2023, distribution AIRBUS DS)</p> <p> </p> <p>===========================</p> <p>Dataset Attribution:</p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <p> </p> <p>===========================</p> <p>Citation:</p> <p>Please cite this repository as :</p> <p>Berthier E. (2023). Digital Elevation Models (DEMs) after the Shovi flood (Caucasus), 13 August 2023, Dataset distributed on Zenodo: 10.5281/zenodo.8252339</p>
Digital Elevation Models (DEMs) after the Shovi (Caucasus) debris flow, 13 August 2023
<p>===========================</p><p>Introduction</p><p>On 4 August 2023, a large debris flow struck the mountain resort town of Shovi in Georgia. More on this event here : <a href="https://eos.org/thelandslideblog/the-4-august-2023-debris-flow-at-shovi-in-georgia">https://eos.org/thelandslideblog/the-4-august-2023-debris-flow-at-shovi-in-georgia</a></p><p>Two Pléiades stereo images were acquired after the event on 13 August 2023, thanks to the activation of the CIEST² (<a href="https://www.poleterresolide.fr/ciest-2-nouvelle-generation-2/">https://www.poleterresolide.fr/ciest-2-nouvelle-generation-2/</a>) scheme.</p><p>We share here the Pléiades DEMs derived from these images. The collection includes four files:</p><p>Shovi_2023-08-13a_DEM_2m.tif</p><p>Shovi_2023-08-13a_DEM_20m.tif</p><p>Shovi_2023-08-13b_DEM_2m.tif</p><p>Shovi_2023-08-13b_DEM_20m.tif</p><p>===========================</p><p>Methods</p><p>The Pléiades stereo-images were processed using the Ames Stereo Pipeline (ASP, Beyer et al., 2018), yielding a DEM in 2x2m and 20x20 m GSD and an orthoimage in 0.5x0.5m GSD. The processing was done using as only input the stereo-images and their orientation information, as Rational Polynomial Coefficients (RPCs). The parallel_stereo routine performs all the steps needed in the correlation of the stereo-images, yielding a pointcloud which is then interpolated using the routine point2dem. We used the semi global matching algorithm and the set of processing parameters from Deschamps-Berger et al. (2020)</p><p>Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline: NASA's Open Source Software for Deriving and Processing Terrain Data, Earth and Space Science, 5, 537–548, https://doi.org/10.1029/2018EA000409, 2018.</p><p>Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M.: Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data, The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, 2020.<br><br>===========================</p><p>Data Specifications:</p><p>Cartographic projection: UTM zone 38N (EPSG:32638)</p><p>Origin of Elevation: meters above WGS84 ellipsoid</p><p>Raster data format: GeoTIFF</p><p>NoData value : -9999</p><p>Pléiades dataset includes only DEMs because a licence needs to be signed with CNES to access Pléiades imagery. Please contact the authors for further information on this.</p><p>===========================</p><p>Acknowledgements: </p><p>Pléiades images were provided under the CIEST² initiative (CIEST2 is part of ForM@Ter (https://en.poleterresolide.fr/) (Pléiades © CNES 2023, distribution AIRBUS DS)</p><p>===========================</p><p>Dataset Attribution:</p><p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p><p>===========================</p><p>Citation:</p><p>Please cite this repository as described below:</p><p>Etienne Berthier. (2023). Digital Elevation Models (DEMs) after the Shovi flood (Caucasus), 13 August 2023</p>
Experimental datasets for investigation of threshold of motion of coral debris particles under steady unidirectional flow
<p>The datasets for the following mansucript:</p> <p><span>A Comprehensive Criterion for Threshold of Motion of Bioclastic Sediments under Steady Unidirectional Flow</span></p>
Bank erosion process and distribution along channel caused by multiple debris flow surges
<p>This dataset contains point cloud data and Excel table data, which together describe the bank retreat process of 5 types of bank soils caused by multiple debris flow surges</p>
Data From "Radar-Based Deep Learning for Debris Flow Identification amid the Environmental Disturbances"
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A worldwide event-based debris-flow barrier dam dataset from 1800 to 2023
<p>Debris-flow barrier dams (DFBDs) not only reshape the original hydrological and geological conditions but may also trigger secondary disasters such as outburst floods and upstream aggradation. Data on DFBDs is crucial for related research. However, there is no dataset for DFBDs has been established. To fill this gap, we adopted a rigorous data collection and validation process, reviewed 2519 literatures and media reports, and constructed a dataset of 555 DFBD events. The dataset comprehensively records 38 attributes of the DFBDs, including location, country, trigger, the date of formation, reliability, debris flow density, debris flow velocity, dam length, height, width, volume, blocking mode, stability, longevity, dam material, lake length, lake volume, lake area, failure mechanism, peak discharge, and loss of life, etc.</p>
A Hydrodynamic-Based Physical Unified Modeling for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behavior
<p>Simulation results for manuscript "A Hydrodynamic-Based Physical Unified Modelling Framework for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behaviour", submitted to Water Resources Research.</p>
A Hydrodynamic-Based Physical Unified Modeling for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behavior
<p>Simulation results for manuscript "A Hydrodynamic-Based Physical Unified Modelling Framework for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behaviour", submitted to Water Resources Research.</p>
Erosion data of debris flows on banks with herbaceous plants
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Deposition characteristics of debris flows in wooded channel
<p>This dataset mainly contains point cloud data of debris flow deposition landforms in wooded channel and data reflecting the physical and movement characteristics of debris flows. These data were obtained through physical model experiments.</p>
An integrated method for assessing vulnerability of buildings caused by debris flows
<p>Two datasets are provided for the development of a method in assessing culnerability of buildings caused by future debris flows. The first dataset includes the debris-flow events that caused damages to the buildings, and it is used to develop a physical vulnerability matrix. The second dataset is composed of debris-flow events that occurred in areas without distribution of buildings, and therefore no property loss is caused by these events. This dataset in our study is used for model training and utilize this model to estimate debris-flow density in future scenarios. </p>
Supplementary materials for an AI-based method for estiamting the potential runout distance of post-seismic debris flows
<p>We included several figures and tables in this file to support the study on runout distance estimation using a AI-based method, mainly focusing on the precipitation downscaling, calibration, assessment, and rainfall threshold calculation. Additionally, the prediction results of all the debris flow catchments were included in a table when intraday rainfall ranges from 40 to 100 mm.</p>
Climate change has no apparent effect on debris flows in a supply-limited torrent
<h3><strong>This file contains all tree-ring data, growth disturbance data, final debris-flow chronology data and map background data used in the paper "A supply-limited torrent that does not feel the heat of climate change"</strong></h3> <p>Multetta-tree-ring raw data.rwl: Contains the raw measurement data from 761 tree-ring cores from 478 <em>P. mugo</em> trees.</p> <p>Growth Disturbances-Original data.xlsx: Contains detailed information on tree-ring cores, growth disturbances and their intensity. Zone area refers to the zonation (1-4 corresponds to SI-SIV) of the sampled trees in the study area. Tree ID refers to the name of the tree-ring core or cross section/wedge. The third and fourth columns refer to Y/latitude and X/longitude. Last ring refers to the year of the outermost ring and is mostly 2020, which is the year when the fieldwrok was carried out. In some cases, tree-ring cores were broken or they were taken from dead trees, so the year of the outermost ring is not 2020. Oldest ring refers to the year of the innermost available ring. In the age incomplete column, a value of 1 indicates that the oldest ring measured is not the innermost ring of the tree center. Age refers to the age of the trees, which is equals to the value of the last ring minus the value of the oldest ring. The Comments column indicates the wedge and the cross section. From the tenth column, the numbers 2020, 2019, 2018...... refer to different years corresponding to tree rings. Here, all values including 0, 1, 2, 3 and 4 indicate that there is a measured annual ring in the corresponding year. Blank indicates no data. GS refers to growth suppression. CW refers to compression wood. I refers to injury and CT refers to callus tissue. The number 0 means no growth disturbance. Numbers 1-4 mean intensity from weak to strong. For example, in the 2016 column, any core with '2GS' means the tree-ring showed growth suppression in 2016 and the corresponding intensity is 2. The growth disturbance (GDs) statistic is shown at the bottom, including all 1427 GDs, but GDs with intensity 1 were excluded from the analysis. Note: Samples mub74 and mul105 have data from both section and core, so 480 tree-ring series exist. </p> <p>Events-Final definition.xlsx: Contains all tree-ring based reconstructed debris-flow events for each zone (1-4 corresponds to SI-SIV) after careful examination of the spatial distribution of damaged trees. In the process of defining events, years were excluded from the analysis that (1) showed incoherent patterns of damaged trees (e.g. GDs evenly distributed on the cone, probably due to climatic extremes or insect pests), (2) were recorded in historical chronicles as snow avalanche years, or (3) were characterized by high mortality of <em>P. mugo</em> trees, as indicated by low tree-ring index (<1.5 average value) in the event cataster of the Canton of Grisons and the Swiss National Park (Bigler and Rigling, 2013).</p> <p><strong>Figures and the data used in figures are shown as below:</strong></p> <p>Information on tree location and age in fig.1D, GDs and sample depth (At) in fig.3A, reconstructed XXL events in fig.4A, GDs for affected regions (total of 16 regions) in fig.4B, GDs in different years for different zones in fig.S3 and GDs for different affected regions in fig.S4 is from the file: Growth Disturbances-Original data.xlsx.</p> <p>Information on the 56 defined events in fig.3B and the reconstructed debris-flow events in fig.5A, 5B is from the file: Events-Final definition.xlsx. Here, 75 events occurred in 4 zones, but actually all events occurred in 56 different years.</p> <p>The LiDAR DEM background in fig.1B, 1C and the hillshade view of the 2023 LiDAR DEM background in fig.S1, S2 are from Swisstopo (https://map.geo.admin.ch/).</p> <p>The R code used for the repose time pattern analysis in fig.5A, 5B is originally from the previously published paper (Heiser, M. et al., 2023) and is available on GitLab at https://gitlab.com/Rexthor/repose-time-patterns.</p>
Debris Flow Dataset for Debris Flow Velocity Inversion based on Farneback Optical Flow
<p>A velocity inventory of large-scale debris flow flume experimental data, published by USGS (Logan, 2018), was generated using the Debris Flow Velocity Inversion Method based on optical flow model (Farnebäck<span>, 2003</span>). This dataset includes raw data from three debris flow experiments conducted in 2007, 2015, and 2017. Each dataset corresponds to three relevant results: perspective transformation, optical flow analysis, and front position detection.</p>
Use of WRF-Hydro in postfire debris-flow hazard simulation
<p>This is the dataset used in a study named "Use of WRF-Hydro to Simulate Runoff-Generated Debris Flow Hazards in Burn Scars" by C. Li<sup>1</sup>, A. L. Handwerger<sup>2,3</sup>, J. Wang<sup>4</sup>, W. Yu<sup>5,6</sup>, X. Li<sup>7</sup>, N. J. Finnegan<sup>8</sup>, Y. Xie<sup>9,10</sup>, G. Buscarnera<sup>7</sup>, and D. E. Horton<sup>1</sup></p> <p><sup>1 </sup>Department of Earth and Planetary Sciences, Northwestern University</p> <p><sup>2 </sup>Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, CA, 90095, USA</p> <p><sup>3 </sup>Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA</p> <p><sup>4 </sup>Environmental Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA</p> <p><sup>5 </sup>Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder</p> <p><sup>6 </sup>NOAA/Global Systems Laboratory, 325 Broadway, Boulder, Colorado</p> <p><sup>7 </sup>Department of Civil and Environmental Engineering, Northwestern University</p> <p><sup>8 </sup>University of California Santa Cruz, Department of Earth and Planetary Sciences, Santa Cruz, CA, 95064, USA</p> <p><sup>9</sup> Program in Environmental Sciences, Northwestern University, 2145 Sheridan Road, Evanston, IL, 60208, USA</p> <p><sup>10 </sup>Department of Biological Sciences, Purdue University, 915 W State St, West Lafayette, IN 47907, USA</p>
Supplementary Information for "Deciphering controls of pore-pressure evolution on sediment bed erosion by debris flows"
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Experimental flume dataset for debris flow study
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Grains-energy release governs debris flow high mobility
<p>These files include the data to generate Figs. 1-2 and 4 and Figs. S7-S13 in supporting information</p>
Data From "Radar-Based Deep Learning for Debris Flow Identification amid the Environmental Disturbances"
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