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49 results for “Debris flow”
Fish and crayfish density and count data for Peeks Creek, Macon County, NC, USA 2005-2014, 2019, and 2022 following a catastrophic debris flow, as well as six reference streams
We followed the process of recovery of the fish and crayfish assemblage in Peeks Creek, a high-gradient second order stream in the Little Tennessee River watershed of North Carolina, after a debris flow devastated the channel and its riparian zone. After 15 years, the fish assemblage had recovered, and the channel and riparian zone had stabilized. Of the three major components of the fish assemblage, Rainbow Trout (Oncorhynchus mykiss (Walbaum)), a strong swimmer, reappeared in year 1. Longnose Dace (Rhinichthys cataractae (Valenciennes in Cuvier and Valenciennes)) reappeared in year 3. Mottled Sculpin (Cottus bairdii Girard), a weak swimmer, did not become established until year 6 and only resumed expected abundance in year 9. Appalachian Brook Crayfish (Cambarus bartonii cavatus Hay) numbers recovered quickly, though only one individual was found the year following the debris flow. Unassisted natural recovery occurred after a costly engineered restoration project had been rejected and arguably represents the preferable solution. However, recovery of the fish assemblage may not have been achieved if the stream flowed directly into an impoundment or low gradient river that lacked the source of species for recolonization, or if the stream had been located above a barrier to upstream movement.
Landslides from Space - Mocoa Debris Flow, Columbia (1st April 2017)
<p>More than 330 people died in a rainfall-triggered landslide which occured on 1st April 2017 in Mocoa, Columbia.<br> <br> The pre-event acquisition is from 13th February 2017 (Sentinel-2) and the post-event acquisition is from 4th April 2017 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image.<br> <br> <em>Contains modified Copernicus Sentinel data (2017)</em></p>
Debris Flow Carbon Mobilization Table
This file contains the Tongass National Forest Landslide Inventory ID, planimetric area, aboveground carbon amount, and soil carbon amount mobilized by each landslide. Carbon amounts were determined using two aboveground carbon models and a soil carbon model, available at (https://databasin.org/people/brianbuma/). These values were used to estimate the amount of carbon mobilized by debris flow type landslides in SE Alaska, within the Tongass National Forest.
SBC LTER: Beach: Distribution of terrestrial organic material in intertidal and nearshore marine sediment due to debris flow response efforts
These data describe the distribution and processing of terrestrial organic material observed in the Santa Barbara Channel (Goleta Bay) during the spring of 2018. Specifically, beach, slough, and marine sediments were sampled during and after the debris disposal event that took place between January and February 2018 following the Thomas Fire and the subsequent Montecito Debris Flow. Data are contained in one table, including organic carbon, carbon isotope, lignin phenol, and pyrogenic carbon measurements analyzed from sediment cores collected at different depths and locations in the nearshore region.
Spontaneous initiation of debris flow surges from sedimentary deposits
<p>Data used in figures of Research paper manuscript 'Spontaneous initiation of debris flow surges from sedimentary deposits', which has been contribution to <a href="http://www.letpub.com.cn/index.php?page=journalapp&view=detail&journalid=10219">JOURNAL OF GEOPHYSICAL RESEARCH-EARTH SURFACE</a> journals .</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>
Data for Roles of Granularity and Timescales in Debris Flow Hazards on Alluvial Fans
<p>This dataset includes the digital elevation models (DEM) for the 9 debris flow fan experiments and the slope map data for the 9 debris flow fan experiments and 2 field cases (the Straight Fan and Piute Fan in White Mountain, CA). These data are stored as GeoTIFF files that include information on mesh coordinates. Please read the Data_Information.pdf for the details of the data file contents, duration, sediment contents, flow/discharge/input rates, and mesh size. </p>
Supplementary Movies and Dataset for Debris Flows, Debris Avalanches and Rock Avalanches Impacting A Flexible Ring Net Barrier.
<p>The supplementary movies S1, S2, and S3 (presented in Figures 1, S4, and S6) show typical debris flow, debris avalanche, and rock avalanche impacting a flexible ring net barrier with = 6 m/s, respectively.</p> <p>The experimental data used for comparison in Figure 2b from the large-scale flume test V6-B1 with a flexible ring net barrier was published open access in the below article (Vicari <em>et al.,</em> 2021).</p> <p>Vicari, H., Ng, C. W., Nordal, S., Thakur, V., De Silva, W. R. K., Liu, H., & Choi, C. E. (2021). The Effects of Upstream Flexible Barrier on the Debris Flow Entrainment and Impact Dynamics on a Terminal Barrier. <em>Canadian Geotechnical Journal</em>, <em>59</em>(6), 1007-1019. <a href="https://doi.org/10.1139/cgj-2021-0119">https://doi.org/10.1139/cgj-2021-0119</a></p>
Data on elevation of Heixiluo gully after a debris flow event
<p>The data is the elevation of gully after the debris flow in the Heixiluo, which is used in the paper submitted to Geophysical Research Letters (Modeling Progressive Erosion of Consolidated Landslide Dams by Debris Flows, [Paper # 2022GL101764]). </p>
JJG Debris flow data
<p><a name="OLE_LINK4"></a>Supplementary lists</p> <p> </p> <p>The supplementary materials include the data sources (in xlsx files) and the related figures, and the codes for the Poisson process simulating the surge sequences.</p> <p> </p> <p>1 SM1, the 63 events of debris flows analyzed in the text, including the occurring date and surge number for reach event.</p> <p> </p> <p>2 SM2, the 'Debris flow data.xlsx' file, including several sheets:</p> <p>S2-1, discharge for the 63 events;</p> <p>S2-2, time interval for the 63 events;</p> <p>S2-3, velocity for the 63 events</p> <p>S2-4, sediment delivery for the 63 events</p> <p>Each event is denoted by its occurring date, e.g., No.990810 means the event occurring on 10 August, 1999.</p> <p> </p> <p>3 SM3, probability distributions for the parameters</p> <p> S3-1, distribution of discharge for each event, illustrating that the curves for all the 63 events collapse upon the same exponential curve;</p> <p>S3-2, distribution of sediment yield</p> <p>S3-3, distribution of flow velocity</p> <p>S3-4, distribution of time interval</p> <p>Fig9-11 in the text are just some examples from these.</p> <p> </p> <p>4 SM4, the code of the Poisson process model, including numerical simulation code (matlab) that produce the surge sequences.</p> <p>5 SM5, R language code for calculating the power spectral analysis exponent of a sequence and plotting the PSA curve.</p> <p>6 SM6, R language code for sequence preprocessing with moving average and DFA-1/2/3 exponent calculation</p>
Sitka Debris Flow Inundation Model
This raster file contains the results of a debris flow inundation model that simulated 100,000+ debris flows from potential initiation sites across an area near Sitka, AK. Initiation locations were selected based on a landslide initiation susceptibility raster, and calibrated to debris flows which were triggered during a 2015 storm. Results were smoothed using a 50 meter gaussian filter.
Debris flow inventory and data for regionally modelling runout in the upper Maipo river basin, Chile
<p>This dataset contains mapped debris flow source points, runout track polygons and elevation data for the upper Maipo river basin, Chile</p>
Data and scripts for Journal of Geophysical Research – Earth Surface publication: Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina
<p>This data source contains scripts and data associated with the JGR Earth Surface publication <strong>“Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina”</strong> by A. Mueting, B. Bookhagen, and M. R. Strecker. The Digital Elevation Model (DEM) of the lower part of the Quebrada del Toro and Río Capilla catchment in the NW Argentinian Andes was generated from SPOT-7 tri-stereo images using Ames Stereo Pipeline. The final dataset has a spatial resolution of 3 m. A full description of the DEM generation process and accuracy assessment can be found in the associated paper. The scripts are also available at https://github.com/UP-RS-ESP/DEM_ConnectedComponents.</p>
River bed sediment and debris flow deposit lithology and Schmidt Hammer Rock Strength dataset, Suiattle River, Washington State, USA
<p>This dataset includes measurements of river bed sediment lithology and Schmidt Hammer Rock Strength (SHRS), as well as debris flow deposit lithology, grain size, and SHRS, at sites along the Suiattle River, North Cascades, Washington State, USA. See Pfeiffer et al. (2022, JGR-ES) for further description of the collection methodology and site description.</p>
Numerical simulation results of debris flow under different condition in Chutou gully
<p>We release four datasets that reflect the numerical simulation results of debris flow with a recurrence period of 100 years under different check dam conditions, including with and without check dams and the check dam breakage. The simulation parameters and topography were taken from the Chutou gully, Miansi Town, Wenchuan County, Sichuan Province, Southwestern China. The first dataset indicates the final flow depth distribution under the check dam breakage. The second dataset indicates the final flow depth distribution with check dams. The third dataset indicates the final flow depth distribution without check dams. The fourth dataset indicates the flow velocity evolution at different locations, columns A, E, I and M refer to different times, the rest refer to the flow velocity at different locations at corresponding times. The datasets can be used to analyse the effect of the check dam on debris flows or deposit evolution. The first three datasets can be viewed or edited through the ArcGIS software; the last dataset be viewed or edited through the Excel software.</p>
Digital elevation model of differences of two debris flow event in Chutou gully
<p>We release two datasets that reflected the height alteration of channel deposits during the debris flow events, which occurred on 20 August 2019 and 17 August 2020 in Chutou gully, Miansi Town, Wenchuan County, Sichuan Province, Southwestern China. The first dataset indicates the height alteration during the 2019 debris flow, whereas the second indicates the 2020 debris flow. The datasets can be used to analyse the deposit evolution during the debris flow events. The datasets can be viewed or edited through the ArcGIS software</p>
Two multi-temporal datasets to track debris flow after the 2008 Wenchuan earthquake
<p>We provide two datasets for tracking the debris flow induced by the 2008 Wenchuan Mw 7.9 earthquake on a section of the Longmen mountains on the eastern side of the Tibetan plateau (Sichuan, China). The database was obtained through a literature review and field survey reports in the epicenter area, combined with high-resolution remote sensing imagery and extensive data collection and processing. The first dataset covers an area of 892 km<sup>2</sup>, including debris flows from 2008 to 2020. 186 debris flows affecting 79 watersheds were identified. 89 rainfall stations were collected to determine the rainfall events for the post-earthquake debris flow outbreak. The second database is a list of mitigation measures for post-earthquake debris flows, including catchment name, check dam number, coordinates, construction time, and successful debris flow mitigation date. This two datasets can aid different applications, including the early warning monitoring and engineering prevention of post-earthquake debris flow, as well as provide valuable data support for research in related disciplines.</p>
Supplementary Videos for ``Influence of Fine Particle Content in Debris Flows on Alluvial Fan Morphology"
<p>This dataset includes 8 videos for the 6 debris flow fan experiments:</p> <p>Video V1. Video record (side view) of the experiment process of a continuous (15 minute) flow with 6\% clay mixture.<br> Video V2. Video record (side view) of the experiment process of three successive (5 minute) flow with 6\% clay mixture.<br> Video V3. Video record (side view) of the experiment process of a continuous (15 minute) flow with 4\% clay mixture.<br> Video V4. Video record (side view) of the experiment process of three successive (5 minute) flow with 4\% clay mixture.<br> Video V5. Video record (side view) of the experiment process of a continuous (15 minute) flow with 8\% clay mixture.<br> Video V6. Video record (side view) of the experiment process of three successive (5 minute) flow with 8\% clay mixture.<br> Video V7. Video record (front view) of the experiment process of three successive (5 minute) flow with 4\% clay mixture.<br> Video V8. Video record (front view) of the experiment process of three successive (5 minute) flow with 8\% clay mixture.</p>
Supporting material for "Benford's law as debris flow detector in seismic signals"
<p>Supporting material list,</p> <p>0, Source code for Random_Forest_model;</p> <p>1, Random_Forest_model.pkl, trained machine learning model;</p> <p>2, FiguresSI.zip, 58 debris flow events with Benford's law features, and Exp. fitting curve;</p> <p>3, Table2DebrisFlowEventDetails.xlsx, details of 58 debris flow events and expentional fitting parameters.</p> <p>Note, you can check our GitHub repository<br>https://github.com/Nedasd/Benfords_law_as_mass_movements_detector.git</p> <p> </p>
Relevant data for numerical simulation of Xiangjiao post-fire debris flow
<p>This is a data set used for numerical simulation analysis of post-fire debris flow in Xiangjiao catchment, Sichuan Province, including the original rainfall data on the day of the debris flow event, the interpolated rainfall data, and some relevant experimental data. If you have any questions in the process of using these data, you can contact the email: 622200090027@mails.cqjtu.edu.cn.</p>
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