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87 results for “inundation”
¿Cómo buscar las zonas inundables de Zamora en el Sistema Nacional de Cartografía de Zonas Inundables?
<p>Díez Herrero, A. y Díez Marcelo, P. (2020). <em>¿Cómo buscar las zonas inundables de Zamora en el Sistema Nacional de Cartografía de Zonas Inundables?</em> Videograbación muda en formato mp4. Duración 01:05 minutos. Estrategia de Comunicación del Riesgo de Inundaciones en Zamora (ESCORIZA), Proyecto DRAINAGE.</p>
¿Cómo ver los mapas de peligrosidad y riesgo de inundación de Zamora en el Sistema Nacional de Cartografía de Zonas Inundables?
<p>Díez Herrero, A. y Díez Marcelo, P. (2020). <em>¿Cómo ver los mapas de peligrosidad y riesgo de inundación de Zamora en el Sistema Nacional de Cartografía de Zonas Inundables?</em> Videograbación muda en formato mp4. Duración 01:24 minutos. Estrategia de Comunicación del Riesgo de Inundaciones en Zamora (ESCORIZA), Proyecto DRAINAGE.</p>
¿Cómo ver las zonas inundables de Zamora en el Visor Mírame de la Confederación Hidrográfica del Duero?
<p>Díez Herrero, A. y Díez Marcelo, P. (2020). <em>¿Cómo ver las zonas inundables de Zamora en el Visor Mírame de la Confederación Hidrográfica del Duero?</em> Videograbación muda en formato mp4. Duración 01:51 minutos. Estrategia de Comunicación del Riesgo de Inundaciones en Zamora (ESCORIZA), Proyecto DRAINAGE.</p>
Processed dataset used for developing tsunami inundation emulators(Part-III Simulation and Emulation Dataset)
<div> <div>This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078</div> <br> <div>This dataset contains some of the processed datasets covering tsunami inundation depths using simulation results and emulation results discussed in the article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effectwith project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.</div> <br> <div>The post-processed (numpy) files for the two test locations of Catania(CT) and Siracusa(SR) are provided in compressed gzip files(.gz):</div> <br> <div>Processed inundation depth files from simulation and emulation typically stored in model/CT/ptha or model/SR/ptha</div> <div> -<strong>pthaSR.tar.gz </strong>- max inundation depth1d(eve x m) for true(simulation and pred(emulation of different models))</div> <div> -<strong>pthaCT.tar.gz</strong> - max inundation depth1d(eve x m) for true(simulation and pred(emulation of different models))</div> <br> <div>The filename follows the nomenclature with:</div> -true - simulation results</div> <div>-pred - emulator with pretraining</div> <div>-pred_direct - emulator without pretraining</div> <div>-pred_nodeform - emulator without pertaining and deformation input</div> <div><br> <div> <div>More information on the attached readme, see project structure and code is available at:</div> <div><strong>https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators</strong></div> </div> </div> <p> </p>
Processed files used for OQ based tsunami loss modelling using HPC based inundation and emulators(Part-IV OQ Simulation and Emulation Hazard Dataset)
<div> <div>This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078</div> <br> <div>This dataset contains some of the processed OpenQuake files covering tsunami inundation depth hazard using simulation results and emulation results discussed in the article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effects with project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.</div> <div> </div> <div>The risk calculation and procedure is available in the main repo: <a href="https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk">https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk </a></div> <div> </div> <br> <div>The processed files for the test locations of Catania(CT) are provided in compressed gzip files(.gz):</div> <br> <div>Processed hazard information used to prepare and run OQ event based risk analysis are as below,</div> <div> -<strong>hazard.tar.gz </strong>- preliminary numpy files with sitcol, eventid and hazard magnitude info</div> <div> -<strong>loss.tar.gz</strong> -final hdf5 files with both event hazard and site info</div> <br> <div>The filename follows the nomenclature with:</div> </div> <div>ML4SicilyTsunami/risk/loss/tsunami_prob_892.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_1658.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_3454.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_7071.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_true.hdf5</div> <div><br> <div> <div>More information on the attached readme, see project structure and code is available at:</div> <div><strong>https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators</strong></div> <div><strong>https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk</strong></div> <div><strong>https://github.com/naveenragur/OQ-Tsunami</strong></div> </div> </div>
Data for figures of explosive volcanic tsunami generation, propagation and inundation around Lake Taupō
<p>Data for figures in a planned publication of scenario-based study of explosive volcanic tsunami generation, propagation and inundation around Lake Taupō.</p>
Multi-Level Monte Carlo Models for Flood Inundation Uncertainty Quantification - Dataset
<p>Dataset used for the analysis of Multi-level Monte Carlo methods for flood inundation uncertainty quantification. This includes:</p> <ol> <li>Flood model simulations for Dyce, Glasgow and Inverurie across three resolutions (5m/10m/20m).</li> <li>Data for violin plot figures with code.</li> </ol>
Inundation map of Mandra's flood event (Athens, Greece) 2017
<p>In this dataset we manually reproduced and we share as a shape file the flood extent derived by the National Observatory of Athens BEYOND team, through the processing of a WordlView-4 satellite high resolution (31 cm) image on 21/11/2017 and with ground observations, of the flood event which hit Mandra, Athens, Greece in 15th of November 2017 (Bellos et al., 2020).</p> <p>It is available online by BEYOND team at <a href="http://beyond">http://beyond</a> <a href="http://eocenter.eu/index.php/web-services/floodhub">eocenter.eu/index.php/web-services/floodhub</a>). </p> <p>Another one flood dataset is provided in:</p> <p>https://zenodo.org/record/7140750</p> <p>References</p> <p>Bellos, V., Papageorgaki, I., Kourtis, I., Vangelis, H., Kalogiros, I., Tsakiris, G. (2020).<br> Reconstruction of a flash flood event using a 2D hydrodynamic model under spatial and temporal variability of storm.<br> Natural Hazards, 101(3), 711-726.</p> <p>The dataset is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-sa/4.0/): CC BY-NC-SA) You are free to: Share — copy and redistribute the material in any medium or format; Adapt — remix, transform, and build upon the material, for not commercial use, under the following terms:</p> <p>1) Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made.</p> <p>2) You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.</p> <p>3) ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.</p>
Simulation data for a semi-empirical approach for tsunami inundation in South Italy
<p>The archive contains data used for the simulations devised for a semi-empirical method for the evaluation of tsunami wave propagation and inundation along the coasts of South Italy.</p> <p>UPDATE - October 19, 2022<br> - Added readme files (in English and Italian) with an explanation of the files of the repository.<br> - Added text files for values of alpha also for the case of perpendicular tsunami fronts (as per Figures 5d and 5f of the paper); only files for realistic tsunami fronts (as per Figures 5c and 5e of the paper) were present in the previous version of the repository. </p>
Fig. 1 in Coniferous Forest Annual Growth Under Impact Of Beaver-Made Inundations In Dobele Forestry, Latvia
Fig. 1. Scheme of transect layout in influenced stand research.
Time-Dependent Probabilistic Tsunami Inundation Assessment Using Mode Decomposition to Assess Uncertainty for an Earthquake Scenario
<p>This is the dataset of the manuscript submitted to JGR Ocean (Feb 2021).</p>
Vulnerability of sea turtle nesting sites to erosion and inundation: a decision support framework to maximize conservation
<p>Sandy beaches provide essential nesting habitat for sea turtles but are threatened globally by a rapidly changing climate. Identifying which nesting sites are at greatest risk from erosion and inundation remains an important goal of sea turtle conservation globally. Yet, efforts to identify at-risk sites have been hindered by the ability to model complex processes and incomplete information on nesting distribution and abundance. To assess the erosion and inundation risk to the reproductive success of a discrete genetic stock of flatback turtles (<em>Natator</em> <em>depressus</em>) across its nesting range in the Pilbara region of Western Australia, we used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) Coastal Vulnerability Model. A relative exposure index was calculated for 402 nesting beaches in terms of six geophysical variables: wind and wave exposure, surge potential, relief, observed sea level rise and coastal geomorphology, and coupled with published information on the distribution and abundance of turtle tracks in the region. </p> <p>The majority of beaches (74%) had an intermediate to high exposure. In particular, 36% of beaches with a high abundance of flatback tracks (the top 25% of the frequency distribution) had a high exposure (the top 25% of the frequency distribution). This suggests that coastal exposure is a key vulnerability to the reproductive success of sea turtles that nest in this region. Promisingly, five beaches with a high abundance of turtle tracks also had a low exposure (bottom 25% of the frequency distribution) and these beaches may be critical for the long-term resilience of the stock against sea level rise and severe storms. Exposure varied across nesting sites and the approach presented here allows for a rapid and broadscale assessment of relative erosion and inundation risks at a scale most relevant to management. </p>
Inundation Street 360-video
<p>360-video file for Inundation Street. This is an experimental immersive storytelling experience to show viewers what it might be like to find their home flooded and guide them through some actions to reduce the impact.</p> <p>The video was funded by the EPSRC DARE project.</p> <p>The video can also be viewed at https://www.youtube.com/watch?v=8oSf7G21fzw </p>
Inundation Maps - Polyphytos open surface water reservoir - WQeMS raster products
<p>Within this dataset, inundation maps of the Polyphytos open surface water reservoir in Greece for the years 2021 and 2022 are available in GeoTIFF raster format. They were generated by the Land Water Transition Zone Change Detection service of the WQeMS project as intermediate products. Each raster file in the dataset is named according to the date in which the processing was performed. Copernicus Sentinel-2 data was utilized for the generation of the inundation maps.</p>
Climate change causes declines and greater extremes in wetland inundation in a region important for wetland birds
<p>Wetland ecosystems are vital for maintaining global biodiversity, as they provide important stopover sites for many species of migrating wetland-associated birds. However, because weather determines their hydrologic cycles, wetlands are highly vulnerable to the effects of climate change. Although changes in temperature and precipitation resulting from climate change are expected to reduce inundation of wetlands, few efforts have been made to quantify how these changes will influence availability of stopover sites for migratory wetland birds. Additionally, few studies have evaluated how climate change will influence inter-annual variability or the frequency of extremes in wetland availability. For spring and fall bird migration in 7 ecoregions in the south-central Great Plains of North America, we developed predictive models associating abundance of inundated wetlands with a suite of weather and landcover variables. We then used these models to generate predictions of wetland inundation at the end of the century (2069–2099) under future climate change scenarios. Climate models predicted the average number of inundated wetlands will likely decline during both spring and fall migration periods, with declines being greatest in the eastern ecoregions of the Great Plains. However, the magnitude of predicted declines varied considerably across climate models and ecoregions, with uncertainty among climate models being greatest in the High Plains ecoregion. Most ecoregions also were predicted to experience more-frequent extremely dry years (i.e., years with extremely low wetland abundances), but the projected change in inter-annual variability of wetland inundation was relatively small and varied across ecoregions and seasons. Because the south-central Great Plains represents an important link along the migratory routes of many wetland-dependent avian species, future declines in wetland inundation and more frequent periods of only a few wetlands being inundated. resulting in an uncertain future for migratory birds as they experience reduced availability of wetland stopover habitat across their migration pathways. </p>
Inundation and salinity regimes support blue carbon conditions in Australian temperate supratidal forests
Open the record for dataset details and reuse information.
Vulnerability of sea turtle nesting sites to erosion and inundation: a decision support framework to maximize conservation
Open the record for dataset details and reuse information.
Climate change causes declines and greater extremes in wetland inundation in a region important for wetland birds
Open the record for dataset details and reuse information.
Detection and Estimation of Inundation and Associated Risks Using Traffic and Monitoring Cameras and Image Processing Under Extreme Flooding Conditions
<p>The main objective of this project is to develop an inundation detection and evaluation framework using images from traffic monitoring cameras and reliable flood monitoring under extreme precipitation conditions. This study presents a comparative assessment of image enhancement and segmentation techniques to automatically identify the flash flooding from the low-resolution images taken by traffic-monitoring cameras. Due to inaccurate equipment in severe weather conditions (e.g., raindrops or light refraction on camera lenses), low-resolution images are subject to noises that degrade the quality of information. De-noising procedures are carried out for the enhancement of images by removing different types of noises. After the de-noising, image segmentation is implemented to detect the inundation from the images automatically. In addition, the detection of the inundation using the image segmentation with and without de-noising techniques are compared. The results indicate that among de-noising methods, the Bayes shrink with the thresholding discrete wavelet transform shows the most reliable result. For the image segmentation, the Bayesian segmentation is superior to the others. The results demonstrate that the proposed image enhancement and segmentation methods can be effectively used to identify the inundation from low-resolution images taken in severe weather conditions. A new Bayesian filtering method will be devised and applied to estimate the inundation from low-resolution images that will allow traffic engineers to take preventive or proactive actions to improve the safety of drivers and protect and preserve the transportation infrastructure. This new observation with improved accuracy will enhance our understanding of dynamic urban flooding by filling an information gap in the locations where conventional observations have limitations.</p>
Data from: Vegetation recovery in tidal marshes reveals critical slowing down under increased inundation
A declining rate of recovery following disturbance has been proposed as an important early warning for impending tipping points in complex systems. Despite extensive theoretical and laboratory studies, this 'critical slowing down' remains largely untested in the complex settings of real-world ecosystems. Here, we provide both observational and experimental support of critical slowing down along natural stress gradients in tidal marsh ecosystems. Time series of aerial images of European marsh development reveal a consistent lengthening of recovery time as inundation stress increases. We corroborate this finding with transplantation experiments in European and North American tidal marshes. In particular, our results emphasize the power of direct observational or experimental measures of recovery over indirect statistical signatures, such as spatial variance or autocorrelation. Our results indicate that the phenomenon of critical slowing down can provide a powerful tool to probe the resilience of natural ecosystems.
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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)
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DANDI Archive for NWB datasets
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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.