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25 results for “flood hazards”
A deglacial hazard cascade exemplified by the landslide, tsunami and outburst flood at Elliot Creek, Southern Coast Mountains, British Columbia, Canada
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Multivariate hazard assessment for nonstationary seasonal flood extremes considering climate change
<p>Processed streamflow data used to create the plots shown in the paper ("Multivariate hazard assessment for nonstationary seasonal flood extremes considering climate change" published on JGR_A) for reproducibility</p>
Codes and data related to the article: Renard (2023). Use of a National Flood Mark Database to Estimate Flood Hazard in the Distant Past. Hydrological Sciences Journal.
<p>This package contains R codes and data related to the article:</p> <p>B. Renard. 2023. Use of a National Flood Mark Database to Estimate Flood Hazard in the Distant Past<em>. Hydrological Sciences Journal</em>. DOI: <a href="https://doi.org/10.1080/02626667.2023.2212165">10.1080/02626667.2023.2212165</a></p> <p><strong>CONTENTS</strong></p> <p>R scripts used to set up models, analyse results and prepare figures.</p> <ul> <li>France207_MAX.RData contain monthly maxima at 207 stations. See references below for the original sources.</li> <li>Folder data_raw/ contain exports from the flood marks database. See references below for the original sources.</li> <li>funk.R is a library of functions used by other scripts.</li> <li>0_setUp.R prepares the runs to estimate the model for one component. Estimation is performed using the RSTooDs package (<a href="https://zenodo.org/record/5075760">https://zenodo.org/record/5075760</a>). This script extracts the data, specifies the probabilitic models and write STooDs configuration files.</li> <li>1_margin.R analyses the runs that have been performed and estimates marginal distributions at each station. Note that the runs set up in the previous point need to have been performed before using this script.</li> <li>2_probMap.R computes estimated flood probabilities over the whole 1705-2015 period. The previous script needs to have been run before.</li> <li>3_sensitivityAnalysis.R prepares configuration files for sensitivity analysis experiments.</li> <li>4_ZenodoRelease.R prepares the released data and probability maps. The previous scripts needs to have been run before.</li> <li>Scripts named fig_XXX.R create the figures shown in the article.</li> </ul> <p><strong>REFERENCES</strong><br> Streamflow series at stations: <a href="https://www.hydro.eaufrance.fr/">https://www.hydro.eaufrance.fr/</a><br> developed by the "Service central d’hydrométéorologie et d’appui à la prévision des inondations" (Schapi) from the Ministry of the Ecological Transition</p> <p>Flood marks database: <a href="https://www.reperesdecrues.developpement-durable.gouv.fr/">https://www.reperesdecrues.developpement-durable.gouv.fr/</a><br> developed by the flood forecasting network "Vigicrues" from the Ministry of the Ecological Transition</p> <p> </p>
Global Flood Hazard Frequency and Distribution
The Global Flood Hazard Frequency and Distribution is a 2.5 minute grid derived from a global listing of extreme flood events between 1985 and 2003 (poor or missing data in the early/mid 1990s) compiled by Dartmouth Flood Observatory and georeferenced to the nearest degree. The resultant flood frequency grid was then classified into 10 classes of approximately equal number of grid cells. The greater the grid cell value in the final data set, the higher the relative frequency of flood occurrence. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).
Impact of storm propagation speed on coastal flood hazard induced by offshore storms in the North Sea
<p>This datasets include all numerical simulation results of the paper "Impact of storm propagation speed on coastal flood hazard induced by offshore storms in the North Sea".</p>
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