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19 results for “rockfall”
Rockfall PE GR-FL gpkg
<p>Geopackage with stopping points and volumes of past rockfall events in Liechtenstein and the canton of Grisons (CH).</p> <p>This dataset belongs to the publication "Automated delimitation of rockfall hazard indication zones<br> using high resolution trajectory modelling at regional scale", by L. Dorren et al. (2023). Geosciences.</p>
Input data set for the statistical analsysis of rockfall reach probabilities
<p>These files contain reach probability values extracted from 3D rockfall simulations for field-mapped block deposits as well as a series of attributes characterising the deposits. They served for the statistical analysis of the reach probability values as a function of site, forest and rockfall characteristics. The results of the analysis are published in Dorren et al. 2022: Delimiting rockfall runout zones using reach probability values simulated with a Monte-Carlo based 3D trajectory model. Natural Hazards and Earth System Scienses.</p>
3-D view of a slope affected by rockfall
<p>In the compressed folder, there is a 3-D view of the slope affected by rockfall and its defence nearby the town of Lauria (South Italy) </p> <p>To create a 3-D interactive view of the mitigation works (that can be used with any browser without installing GIS or other software), we used the <a href="https://qgis2threejs.readthedocs.io/en/docs/">Qgis2threejs</a> plugin for QGIS. The LiDAR DTM was used as an elevation layer to create several high-resolution 3-D view models with different layers.</p> <ol> <li>Rockfall barriers </li> <li>Location of 2002 rockfall </li> <li>Area interested by 2017 wildfire</li> <li>building</li> </ol> <p>full Paper </p> <p> </p> <p><a href="https://www.mdpi.com/2073-445X/11/11/1951/htm">Merging Historical Archives with Remote Sensing Data: A Methodology to Improve Rockfall Mitigation Strategy for Small Communities</a></p>
Highly energetic rockfalls: Dataset of the 2015 event from the Mel de la Niva, Switzerland
<p>This is the dataset related to the back analysis of the 2015 rockfall event from the Mel de la Niva Mountain near Evolène village, Valais canton, Switzerland. It includes the digital surface model and orthophoto generated by structure from motion photogrammetry, the video files of the event, the reconstructed 3D trajectories and blocks, the shapefiles containing the rockfall paths segments and deposited blocks, and the simulation results.</p>
Camera Snapshots of Rockfalls at Dolomieu crater, Reunion Island
<p>Camera snapshots of rockfalls at Dolomieu crater, Piton de la Fournaise volcano, Reunion.</p> <p>The folders in the compressed file are structured by event date and camera.</p> <p> </p> <p>Available cameras: CBOC, DOEC, SFRC</p> <p>Snapshot interval: 0.5 s</p>
Data from: RockNet: Rockfall and earthquake detection and association via multitask learning and transfer learning
<p>Seismological data can provide timely information for slope failure hazard assessments, among which rockfall waveform identification is challenging for its high waveform variations across different events and stations. A rockfall waveform does not have typical body waves as earthquakes do, so researchers have made enormous efforts to explore characteristic function parameters for automatic rockfall waveform detection. With recent advances in deep learning, algorithms can learn to automatically map the input data to target functions. We develop RockNet via multitask and transfer learning; the network consists of a single-station detection model and an association model. The former discriminates rockfall and earthquake waveforms. The latter determines the local occurrences of rockfall and earthquake events by assembling the single-station detection model representations with multiple station recordings. RockNet achieves macro F1 scores of 0.990 and 0.981 in terms of discriminating earthquakes and rockfalls from other events with the single-station detection and association models, respectively.</p>
Data from: RockNet: Rockfall and earthquake detection and association via multitask learning and transfer learning
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Dataset for laboratory experiments of fragmenting rockfalls and rockslides
<p>This data set provides movies for impact-fragmentation of sliding blocks recorded using high-speed camera during laboratory experiments, which can be useful for a thorough understanding of the evolutions of internal rock damages. The calculated data of area covered by deposit, aspect ratio of deposit, the travel distance of the center of mass on the horizontal plane, the travel distance of sliding mass on the horizontal plane, and the relative breakage ratio are also provided. In addition, we also provided the data of velocity profiles of blocks with different structures at t=0.1 s from x=0 m to x = 0.8 m.</p> <p>Dataset_S1. Data of area covered by deposit, aspect ratio of deposit, the travel distance of the center of mass on the horizontal plane, the travel distance of sliding mass on the horizontal plane, and the relative breakage ratio. The velocities of each tests derived from the pictures took by high speed camera (PIVLab code in Matlab is used for those calculation). The deposit parameters was calculated based on digital surface model (DSM) of deposit.</p> <p><br> Dataset_S2. Videos of all tests.</p> <p><br> Dataset_S3. Orthophotos and DSM of deposits for all tests.</p>
Climate warming drives rockfall from an increasingly unstable mountain slope
<p>This readme file provides all data and R codes used to perform the analyses presented in Figs. 2-4 of the main text and Supplementary Information Figures S1-S2-S3.</p> <p><br> FIGURE 2<br> - Seasonally_dated_GDs.txt: Contains information on the timing (Season) of rockfall (GD) in a given tree (Id) and a given year (yr) over the past 100 years. Inv refers to the operators which analyzed growth disturbances in the tree-ring series. Lat / Long refers to the position of the tree in CH1903/ Swiss Grid projection. Intensity (1-4) refers to (1), intermediate (2) and strong (3) GD. Intensity 4 was attributed to injuries (I). Only the 408 GD rated 3 (strong TRD) and 4 (injuries) were used in Fig. 2. Acronyms used for Response_type read as follows: TRD: Tangential rows of traumatic resin ducts; I: Injuries. Acronyms used for Season refer to Dormancy (1_D), early (2_EE), middle (3_ME) and late (4_LE) earlywood, whereas a GD found in the latewood was attributed to either the early (5_EL) or late (6_LL) latewood.</p> <p>- Trends_in_seasonality_R1.R: The data contained in "Seasonally_dated_GDs" were processed with the R script "Trends_in_Seasonality.R". This seasonal trend analysis code is inspired by work published by Schlögl et al. (2021; https://doi.org/10.1016/j.crm.2021.100294) and Heiser et al. (2022; https://doi.org/10.1029/2011JF002262).</p> <p><br> FIGURE 3-4-S1<br> - Tasch_GD.txt: Contains the raw data on rockfall impacts (GD) in a given year (yr) as found in all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year.<br> - Rockfall_frequency_climate.R: The data contained in "Tasch_GD.txt" were processed with the R script "Rockfall_frequency_climate.R". <br> - The temperature (Imfeld23_tmp.txt) and precipitation (Imfeld23_prc.txt) data used in Fig. 3 are from the Imfeld et al. 2023 (10.5194/cp-19-703-2023) gridded dataset (1x1 km lat/long) and were extracted at the grid point centered on the Täschgufer site.<br> - The script set with temperature series enables to compute Fig. 4 (l.149:216) and Fig. 3 (l. 216:330); the script set with precipitation series enables to compute Fig. S1</p> <p><br> FIGURE S2<br> - Tasch_GD.txt: Contains the raw data on rockfall impacts (GD) at the Täschgufer site in a given year (yr) as found in all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year.<br> - Rockfall_frequency_borehole.R: is adapted from "Rockfall_frequency_climate.R" to work with the borehole dates. <br> - Corvatsch0_6R1: Contains the Corvatsch borehole temperature series (2000-2020, 0.6m depth) (Hoelzle, M. et al. https://doi.org/10.5194/essd-14-1531-2022, 2022).</p> <p><br> FIGURE S3<br> - Plattje_GD.txt: Contains the raw data on rockfall impacts (GD) at the Plattje site in a given year (yr) as found all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year.<br> - - Rockfall_frequency_climate_Plattje.R: The data contained in "Plattje_GD.txt" were processed with the R script "Rockfall_frequency_climate_Plattje.R". <br> - The temperature (Imfeld23_tmp_Plattje.txt) and precipitation (Imfeld23_prc_Plattje.txt) data used in Fig. 3 are from Imfeld et al. 2023 (10.5194/cp-19-703-2023) gridded dataset (1x1 km lat/long) and were extracted at the grid point centered on the Plattje site.</p> <p> </p>
Input Rockfall Simulations Täsch (RockyFor3D)
<p>Input data for the rockfall simulations with RockyFor3D in Täsch to derive disturbance probabilities and intensities (see Moos and Lischke, 2021 for details). The calculated disturbance probabilities and intensities per cell used in the forest simulations can be found in the text file (ta_distSmall.txt)</p>
Multi-method monitoring of rockfall activity along the classic route up Mont Blanc (4809ma.s.l.) to encourage adaptation by mountaineers
<p>The file Mourey et al._NHESS_2021_128.xlsx gathers the data used in the article "Multi-method monitoring of rockfall activity along the classic route up Mont Blanc (4809ma.s.l.) to encourage adaptation by mountaineers" at an hourly time scale. </p>
Seismic analysis of the detachment and impact phases of a rockfall and application for estimating rockfall volume and free-fall height
<p>Digital Elevation models of the Mount Granier and Mount Saint-Eynard.</p> <p>Mount Saint-Eynard DEMs were carried out using an Optech Ilris-LR laser scanner.</p> <p>Mount Granier DEMs were carried out by photogrammetry.</p> <p> </p>
Data from: Geologic and geomorphic controls on rockfall hazard: how well do past rockfalls predict future distributions?
To evaluate the geospatial hazard relationships between recent (contemporary) rockfalls and their prehistoric predecessors, we compare the locations, physical characteristics, and lithologies of rockfall boulders deposited during the 2010-2011 Canterbury earthquake sequence (CES) (n=185) with those deposited prior to the CES (n=1093). Population ratios of pre-CES to CES boulders at two study sites vary spatially from ~5:1 to 8.5:1. This is interpreted to reflect (i) variations in CES rockfall flux due to intra- and inter-event spatial differences in ground motions (e.g. directionality) and associated variations in source cliff responses, (ii) possible variations in the triggering mechanism(s), frequency, flux, record duration, boulder size distributions, and post-depositional mobilization of pre-CES rockfalls relative to CES rockfalls, and (iii) geological variations in the source cliffs of CES and pre-CES rockfalls. On interfluves, CES boulders traveled approximately 100 to 250 m further downslope than prehistoric (pre-CES) boulders, interpreted to reflect reduced resistance to CES rockfall transport due to preceding anthropogenic hillslope de-vegetation. Volcanic breccia boulders are more dimensionally equant, rounded, larger, and traveled further downslope than coherent lava boulders, illustrating clear geological control on rockfall hazard. In valley bottoms, the furthest-traveled pre-CES boulders are situated further downslope than CES boulders due to (i) remobilization of pre-CES boulders by post-depositional processes such as debris flows, and (ii) reduction of CES boulder velocities and travel distances by collisional impacts with pre-CES boulders. A considered earth-systems approach is required when using preserved distributions of rockfall deposits to predict the severity and extents of future rockfall events.
Kinematics characteristics of rockfalls during earthquakes: Insights from shaking table tests
<p>This dataset contains the experimental results of runout distance and lateral displacement from all over 3000 tests, categorized and labeled based on different influencing factors, including Triangular prism, Quadrangular prism, Pentagonal prism, Hexagonal prism, Initial velocity, Slope type, Slenderness ratio.</p>
Pictures Rockfall Breistelweg, Schneggi Parking 2 (day)
<p>Pictures from rockfall illustrating the amount of rock and the damage at daytime.</p>
Pictures Rockfall Breistelweg, Schneggi Parking I (night)
<p>Pictures from rockfall illustrating the amount of rock and the damage. </p>
Data from: Geologic and geomorphic controls on rockfall hazard: how well do past rockfalls predict future distributions?
Open the record for dataset details and reuse information.
Simulation results "Täsch VS" - TreeMig with rockfall disturbance
<p>Text files with results of TreeMig simulations with rockfall disturbance, but without coupling (dat_cellSpp1_neu.txt) and with coupling (dat_cellSpp_xx) for each time step (year given in file name;xx) for the site Täsch.</p> <p>Please contact the author for further information on the files.</p>
Supporting Data for "Seismic Monitoring of Rockfalls Using Fiber-Optic Distributed Acoustic Sensing"
<p>This file contains data and MATLAB scripts to generate the figures presented in the manuscript "Seismic Monitoring of Rockfalls Using Fiber-Optic Distributed Acoustic Sensing".</p>
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