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151 results for “extreme events”
Data from: Extreme climate events counteract the effects of climate and land-use changes in Alpine treelines
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Data from: Early snowmelt by an extreme warm event affects understory more than overstory trees in Japanese temperate forests
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Data in support "Turbidity hysteresis in an estuary and tidal river following an extreme discharge event", manuscript submitted to Geophysical Research Letters,
<p>Data set in support of manuscript "Turbidity hysteresis in an estuary and tidal river following an extreme discharge event" submitted to Geophysical Research Letters. Submitted March 2020, revised and resubmitted April 2020. Matlab script (makeFigs_hysteresis_upload.m) is used to generate the figures from the manuscript. Data files (*.mat) correspond with each figure (*.png). For questions or additional information please contact D. Ralston. </p>
Data and code for Synoptic mechanisms of large-scale extreme precipitation events over southeastern China (Version 1.1.0)
<p>All data generated for the article <em>Synoptic mechanisms of large-scale extreme precipitation events over South China</em> and the code used to reproduce the article analysis and figures.</p>
Repository for: "Extreme statistic and extreme events in dynamical models of turbulence"
<div>This repository contains underlying data, post-processing scripts and figure scripts, corresponding to the article "Extreme statistic and extreme events in dynamical models of turbulence", X.M. de Wit, G. Ortali, A. Corbetta, A.A. Mailybaev, L. Biferale, F. Toschi, 2024, Phys. Rev. E 109 (5), 055106.</div> <div> </div> <div><strong>Data</strong></div> <div>Raw data of the obtained moments and histogram of the structure function are provided in 'PRODUCTION/STAT_RUNS/' and 'VALIDATION/STAT_RUNS/' respectively for the production runs and validation runs.</div> <div> </div> <div><strong>Post-processing</strong></div> <div>Various post-processing routines for e.g. the computation of the anomalous scaling exponents are provided in the Jupyter notebooks 'PROD_process.ipynb' and 'VALI_*_process.ipynb' respectively for the production runs and validation runs. The computation of the singularity spectrum is provided in 'PROD_singularity_spec.ipynb'.</div> <div> </div> <div><strong>Figures</strong></div> <div>Reproduction of the figures as appearing in the paper can be done using the corresponding Jupyter notebooks labeled as 'PAPER_*.ipynb'.</div>
The interaction between subpolar and subtropical jet stream leads to extreme rainfall events over North India in 2013 and 2023
<p>..ini.csv: These files contain the starting location of the Lagrangian trajectories</p> <p>..out.csv: These files contain the ending location of the Lagrangian trajectories</p> <p>..run.csv: These files contain the trajectory location at each spatial grid crossing</p> <p>era5_air*.csv: These are the trajectory files for the simulation where we have backtracked the upper level air southwards to identify the subpolar and subtropical jet stream interaction</p> <p>era5_north_air*.csv: These are the trajectory files for the simulation where we have backtracked the upper level air northwards from the flood locations to identify the wind pathways responsible for upper level meridional wind divergence.</p> <p>era5_water*.csv: These are the trajectory files for the simulation where we have backtracked the surface precipitation to identify atmospheric water sources and pathways.</p>
Database of extreme events, test cases selection and available data, Deliverable 5.1 – ECFAS Project (GA 101004211), www.ecfas.eu
<p>The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.</p> <p>The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the <strong>European Commission H2020 Framework Programme</strong> within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. </p> <p><em><strong>Reference literature:</strong></em></p> <p><strong>Souto-Ceccon, P. E., Montes, J., Duo, E., Ciavola, P., Fernández-Montblanc, T., and Armaroli, C.: A European database of resources on coastal storm impacts, Earth Syst. Sci. Data, 17, 1041–1054, <a href="https://doi.org/10.5194/essd-17-1041-2025">https://doi.org/10.5194/essd-17-1041-2025</a>, 2025.</strong></p> <p><strong>Description of the product</strong></p> <p>Deliverable 5.1 is a comprehensive inventory of extreme coastal events that produced flooding at different locations along the European coastline. It includes the collection and identification of events, locations and available information on the test cases.</p> <p>The purpose of the ECFAS database is to provide a source of information on extreme coastal events and locations that experienced coastal flooding, considering both hazard and impact aspects. Thus, the database collects events, sites and available information to support further investigation on specific test cases. Test cases are defined here as specific sites where an extreme event that generated flooding and damage occurred. The time frame considered for the analysis is between 2010 and 2020 in order to use recent satellite imagery with good resolution and including, if possible, overlap with Sentinel missions.</p> <p>The product includes three files: 1) an Excel file with the inventory; 2) an accompanying report that includes the guidelines to use the inventory, other relevant information on the method used to implement the inventory and the sources of information; 3) the test cases polygons in .geojson format.</p> <p>This ECFAS Database is made available under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/odbl/1.0/">http://opendatacommons.org/licenses/odbl/1.0/</a>. Any rights in individual contents of the database are licensed under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/dbcl/1.0/">http://opendatacommons.org/licenses/dbcl/1.0/</a>.</p> <p>This <strong>Report</strong> is made available under the <strong>Creative Commons Attribution 4.0 International License</strong>.</p> <p> </p> <p><em><strong>Disclaimer:</strong></em></p> <p>ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.</p> <p>This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211</p> <p> </p>
Extreme precipiation events
<p>The obtained EPE were grouped as follows: М—Mongolia, SES-M—border of Mongolia and south of Eastern Siberia, SES—south of Eastern Siberia. For the identified EPE, a temporal specification was suggested. We divided EPE according to the number of days with 99th percentile—1-day precipitation (1-day EPE) and 2 or 3-day precipitation (2-day EPE). So we operated with six based groups: М2 and 1, SES-M1 and 2, SES1 and 2. Index 1 and 2 correspond to 1-day and 2 or more-day EPE.</p>
Extreme events changes over China under 1.5-4°C global warming targets: projected by an ensemble of regional climate model simulations
<p>This file is for the upload of data for 2019JD031057R.</p>
Future NG-IDF Dataset for CONUS Design Extreme Event Projections
<p><strong>Future NG-IDF dataset</strong> consists of projections of design extreme events in future climates across the CONUS. Future climates are represented by an ensemble of 10 downscaled CMIP5 global climate models (GCMs) under the RCP8.5 emission scenario. The dataset covers over 200,000 locations at a 1/16th-degree resolution. This dataset extends Sun et al. (<a href="10.5281/zenodo.5827028">2019</a>) by including future projections following the Next-Generation Intensity-Duration-Frequency (NG-IDF) approach (Yan et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR021290">2018</a>). In contrast to traditional precipitation-based IDF methods such as NOAA Atlas 14, which neglects snow processes, NG-IDF accounts for the total water reaching the land surface (<strong>AWR</strong>) from rainfall, snowmelt, and rain-on-snow (ROS) events.</p><p><strong>Downscaled GCM</strong> climate forcing was derived through the Multivariate Adaptive Constructed Analogs (MACA) method for statistical downscaling and were bias-corrected against the Livneh gridded meteorological dataset (Livneh et al., <a href="https://doi.org/10.1175/JCLI-D-12-00508.1">2013</a>), which is the climate forcing used for the historical CONUS NG-IDF (Sun et al., <a href="10.5281/zenodo.5827028">2019</a>). The selection of the 10 GCMs was based on their divergence in projecting future changes in temperature and precipitation for the CONUS, as well as their accuracy in representing historical climate conditions. The chosen GCMs are bcc-csm1-1, BNU-ESM, CCSM4, CNRM-CM5, CSIRO-Mk3-6-0, inmcm4, IPSL-CM5A-LR, IPSL-CM5A-MR, MRI-CGCM3, and NorESM1-M. The MACA downscaled GCM projections for future climate forcing are available at: <a href="http://thredds.northwestknowledge.net:8080/thredds/catalog/NWCSC_INTEGRATED_SCENARIOS_ALL_CLIMATE/macav2livneh/catalog.html">Northwest Knowledge Network</a>.</p><p><strong>Driving Mechanism Classification for Extreme AWR Events:</strong></p><p>Extreme AWR events at each location are classified by their driving mechanisms as follows:</p><blockquote><p>precip: Sum of snowfall and rain, with the assumption that both contribute instantaneously to runoff</p><p>rainfall: Precipitation on snow-free ground.</p><p>melt_only: Decreasing SWE, daily rainfall < 10 mm, and the sum of rainfall and snowmelt has < 20% contribution from rainfall.</p><p>ROS: Rain-on-snow - Decreasing SWE with at least 10 mm of daily rainfall falling on a snowpack with at least 10 mm daily SWE, and snowmelt contributes at least 20% of the total of rain and snowmelt.</p><p>AWR: Amount of water reaching the land surface from rain, snowmelt, and ROS events.</p></blockquote><p><strong>Folder Structure:</strong></p><blockquote><p>historical/matlab_matrix/: baseline period</p><p>rcp85/matlab_matrix/: future period</p></blockquote><p>For each GCM, there are 'historical' and 'rcp85' subfolders for baseline and future periods, respectively. Within the subfolder, files list discrete IDF values by event duration (24h, 48h, and 72h) and mechanism (<i>AWR</i>, <i>precip</i>, <i>rainfall</i>, <i>ROS</i>, and <i>melt_only)</i>.</p><p><strong>File Naming Convention: </strong></p><blockquote><p>The naming convention is [duration]_[mechanism].mat, e.g., 24hr_AWR.mat</p></blockquote><p><strong>Data Dimension</strong>: </p><blockquote><p>The data is structured in a three-dimensional array of 381 x 921 x 7. The first dimension corresponds to the latitude index, and the second to the longitude index, mapping to specific geographic coordinates. The third dimension represents NG-IDF values for return periods of 2, 5, 10, 25, 50, 100, and 500 years. 'NaN' indicates no runoff generated by a specific mechanism, such as ROS. </p></blockquote><blockquote><p><strong>Coordinate Reference:</strong></p></blockquote><blockquote><p>Each latitude and longitude index is directly mapped to the corresponding row or line number in the 'CONUS_livneh_lat.csv' and 'CONUS_livneh_lon.csv' files, which contain 381 and 921 lines, respectively.</p></blockquote><p><strong>Unit of NG-IDF Values</strong>:</p><blockquote><p>millimeter (mm)</p></blockquote><p> </p>
Response of hydrological processes to event- and annual-scale precipitation extremes in the critical zone of the hillside surface
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