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76 results for “flood modelling”
CFMDG: a Coastal Flood Modelling Dataset in Gâvres (France) to support risk prevention and metamodels development
<p>Along most of the coastal areas, detailed coastal flood observations (e.g. inland water depths) are scarce, and when they are available, this for a limited number of events. Given recent scientific advances, <strong>coastal flooding</strong> events can be properly modelled, even in complex environments and under the action of wave overtopping, and thus provide detailed information. However, such models are computationally expensive, which prevents their use for instance for forecasting and warning. At the same time, metamodelling techniques have been explored for coastal hydrodynamics and have shown promising results. Metamodels are functions that aim to reproduce the behaviour of a “true” model (e.g., a numerical hydrodynamic model) for given input variables (for instance, offshore conditions). Within the RISCOPE research project (<a href="http://perso.math.univ-toulouse.fr/riscope">https://perso.math.univ-toulouse.fr/riscope</a>/) aiming at exploring to which extent such metamodelling techniques may allow to forecast coastal floods with a good accuracy, a <strong>simulated flood database</strong> has been built for the site of Gâvres (France), characterised by a significant effect of wave overtopping processes.</p> <p>The <strong>CFMDG dataset </strong>compiles a set of post-processed coastal flood simulations on the site of Gâvres. The dataset includes 250 scenarios. Each scenarios is defined by 6h time series centered on high tide, with one time series per forcing variables. The forcing variables (called X) are: local relative mean sea-level, tide, atmospheric storm surge, the offshore wave characteristics and the offshore wind. These scenarios combine past real (flood and no flood) events in the 1900-2021 time span with extreme statistics based events, and some complementary fictive events. The post-processed outputs (called Y) includes, for each scenario, the maximal flooded area (m²) and the maximal water depth (m) in each of the 64 618 inland model grid points.</p> <p>The modelling chain that allowed building this dataset relies on the joint use of a spectral wave model (WW3) to propagate the waves to the coast, and a non-hydrostatic wave-flow model (SWASH) to simulate the nearshore hydrodynamics and the flooding. The spatial and temporal resolution of the SWASH configuration validated on the Gâvres site are respectively 3 m and more than 10Hz. All the results are obtained for a Digital Elevation Model corresponding to the 2018 configuration of the site. </p> <p>Such type of dataset is of use for local knowledge, risk prevention, metamodel testing/training, and local coastal flood forecast. </p> <p>Part of this dataset has already been used in (<a href="http://www.mdpi.com/2077-1312/9/11/1191">Idier et al., 2021</a>; <a href="http://www.sciencedirect.com/science/article/pii/S0951832021006293?via%3Dihub">López-Lopera et al., 2021</a>; <a href="https://hal.science/hal-02536624">Betancourt et al., 2022</a>), to develop metamodels and set up a coastal flood forecast and early warning prototype.</p> <p>We hope and expect that making this dataset accessible will trigger further developments/investigations for improving risk knowledge on the considered site as well as methodological developments on machine-learning/metamodel-based techniques to support flood forecast.</p> <p>The table below summarizes the variables contained in the dataset, for each scenario.</p> <table> <tbody> <tr> <td> <p><strong>Variable name</strong></p> </td> <td> <p><strong>Description and unit </strong></p> </td> <td> <p><strong>Comment</strong></p> </td> </tr> <tr> <td> <p>Scenario n°</p> </td> <td> <p>Number of the scenario.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>INPUTS (X)</strong></p> </td> </tr> <tr> <td> <p>NM</p> </td> <td> <p>Relative mean sea level, referenced to the French vertical datum (m, IGN69)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>T</p> </td> <td> <p>Tidal water level (m), referenced to the relative mean sea level</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>S</p> </td> <td> <p>Atmospheric storm surge (m)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Hs</p> </td> <td> <p>Significant wave height (m)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Tp</p> </td> <td> <p>Wave peak period (s)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Dp</p> </td> <td> <p>Wave peak direction (° in nautical convention)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>U</p> </td> <td> <p>Wind speed (m/s)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>DU</p> </td> <td> <p>Wind direction (° in nautical convention)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>t</p> </td> <td> <p>Relative time centered on the high tide of each event (min)</p> </td> <td> <p>Not Concerned</p> </td> </tr> <tr> <td> <p>High Tide date</p> </td> <td> <p>UTC date for scenarios corresponding to past real events</p> </td> <td> <p>Not Concerned</p> </td> </tr> <tr> <td> <p><strong>OUTPUTS (Y)</strong></p> </td> </tr> <tr> <td> <p>Smax</p> </td> <td> <p>Maximum flooded area during the event (m²)</p> </td> <td> <p>Post-processed scalar output</p> </td> </tr> <tr> <td> <p>Hmax</p> </td> <td> <p>Maximum water depth reached during the event (m), provided for each inland location</p> </td> <td> <p>Post-processed functional (map) output</p> </td> </tr> <tr> <td> <p>longitude</p> </td> <td> <p>Longitude (°, WGS84)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>latitude</p> </td> <td> <p>Latitude (°, WGS84)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>XL93</p> </td> <td> <p>Longitude (m, Lambert 93)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>YL93</p> </td> <td> <p>Latitude (m, Lambert 93)</p> </td> <td> <p>For each inland location point</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><br> </p>
Data needed to reproduce the flood hazard modeling of Pollack et al., 2024
<p>This repository contains some of the data needed (Data_Flood_Modeling) to reproduce the flood hazard modeling of Pollack et al., 2024 "[Funding rules that promote equity in climate adaptation outcomes](https://osf.io/preprints/osf/6ewmu)." which are required to run the codes https://github.com/CoRE-Lab-UCF/Pollack_et_al_2024.git </p> <p>Specifically, this repository contains:</p> <ol> <li>dem_subgrid_1m_nbd.asc (DEM at 1m resolution in ascii format. Source DEM is CoNED, see Supporting material of Pollack et al., 2024)</li> <li>Gloucester_street_light_utm.tif (Basemap in UTM coordinates, UTM18N with EPSGcode=26918)</li> <li>sfincs.inp (Model file of SFINCS)</li> <li>Unique_Land_Classes_CN.xls (Table including the land cover classes and corresponding Manning coefficients used for surface roughness)</li> </ol>
Modelling NBSs for coastal erosion and marine flooding: the Emilia-Romagna case studies
<p>The study was conducted in the context of the OPEn-air laboRAtories for Nature baseD solUtions to Manage environmental risks (OPERANDUM) project which is an H2020 project which aims at providing tools and methodologies for the assessment of NBS efficiency around the world. Two NBs were tested via modelling simulations on the Bellocchio Beach at Lido di Spina (Italy) located in the northern part of the Emilia-Romagna coast (northern Adriatic Sea): an artificial dune built with natural materials and a marine seagrass meadow.</p> <p>The artificial dune is an engineered structure that will mimic the functioning of natural dunes. Its aims are reducing both natural dune erosion and flooding in adjacent coastal lowlands. It consists of a barrier between the sea and land, in a similar way to a seawall. Unlike the latter, the NBS are ‘dynamic’, i.e. the dune/beach system interacts a great deal and is constantly undergoing small adjustments in response to changes in wind and wave climate or sea level. Its construction involves the placement of sediment from dredged sources on the beach and it will be reinforced with a structure composed of biodegradable material. Different typologies of experimental solutions are foreseen.</p> <p>The second NBS consists of an alongshore seagrass belt located in front of the coastal area. It was investigated as a potential mechanism for wave amplitude reduction. Among the few species that can live in the northern Adriatic Sea, Zostera Marina was chosen due to its ability to live in a marine environment influenced by freshwaters. A more detailed description can be found in (Pillai et al., 2021).</p> <p>The numerical model chain, specifically developed for the study, consists of an Ocean Circulation model, so-called SHYFEM (Umgiesser et al., 2004), a wave model, so-called WWIII (Alves and Ardhuin, 2016), and a morphological model, so-called XBeach (Roelvink et al., 2009). Ten years of XBeach simulations have been executed to simulate the morphological impacts on the coastal strip for the present (2010-19) and future climate (2040-49). For each 10 years period, four scenarios were simulated: the baseline scenario without NBS (baseline_run), the scenario with the dune (dune_run), the scenario with the seagrass effect (seagrass_run) and the scenario with the two NBS integration (dune_seagrass_run).XBeach was forced with sea level and wave time series predicted by the SHYFEM and WWIII models respectively.</p> <p>The model domain consists in a curvilinear structured grid of about 3.2 km (longshore) x 2.8 km (cross-shore) covering the coastal stretch of Bellocchio beach at Lido di Spina (Italy) and extends seaward up to about 10 m depth.</p> <p>The performance of the NBSs and their impact on coastal erosion and marine flooding were investigated. For both present and future scenarios (201-2019 and 2040-2049), the reduction in wave intensity obtained with the seagrass provided greater benefits in terms of erosion mitigation and flood reduction. The analysis highlighted the limited scale of the dune intervention, in particular under present conditions, highlighting that the longer the artificial dune implemented, the larger the beach and dune area protected. For the future scenarios, the results are still significant and even small projects are expected to help in mitigating coastal erosion and marine flooding.</p> <p>For long-period simulations, no relevant improvements in reducing beach erosion was observed when the artificial dune was combined with the seagrass meadows with respect to the seagrass effects only. Instead, a dominant increase in sea levels will probably highlight the dune functions in hindering the marine ingression into the lagoon area behind and the consequent sediment redistribution.</p> <p>This dataset consists of XBeach model results, mainly:</p> <ul> <li>Morphological evolution of the coastal bottom at Bellocchio beach (Lido di Spina, Italy) in terms of initial and final bed levels, for the current (201-2019) and the future (2040-2049) scenarios. Results are available for the four NBS scenarios described above (and detailed in the Presentation.pdf)</li> <li>Maximum flood depth, defined as the non-simultaneous maximum water depth on the beach domain of Bellocchio (Lido di Spina, Italy) for the current (201-2019) and the future (2040-2049) scenarios. Results are available for the four NBS scenarios described above (and detailed in the Presentation.pdf) </li> <li>Erosion-deposition maps for the current (201-2019) and the future (2040-2049) scenarios. Results are available for the four NBS scenarios described above (and detailed in the Presentation.pdf).</li> </ul>
HANZE catalogue of modelled and historical floods in Europe, 1950-2020
<p>The HANZE dataset covers riverine, pluvial, coastal and compound floods that have occurred in 42 European countries. It contains:</p> <ul> <li>2521 historical floods with impact data (1870-2020);</li> <li>237 further historical floods with significant impacts, but without precise impact data (1950-2020)</li> <li>Nearly 15,000 modelled floods with a potential to cause significant impacts, classified by actual historical occurrence or non-occurrence impacts (1950-2020).</li> </ul> <p>Historical floods and the classification of modelled floods was completed by extensive data-collection from more than 900 sources ranging from news reports through government databases to scientific papers. Impact data collected or modelled include area inundated, fatalities, persons affected or economic loss. Economic losses were inflation- and exchange-rate adjusted to 2020 value of the euro. The historical catalogue (lsit A) also includes losses in the original currencies and price levels. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union’s Nomenclature of Territorial Units for Statistics (NUTS), level 3. Apart from the possibility to download the data, the database can be viewed, filtered and visualized online: <a href="https://naturalhazards.eu">https://naturalhazards.eu</a>. </p> <p>The dataset contains the following files (CSV comma-delimited, UTF8, and ESRI shapefiles in zipped folders):</p> <p>HANZE_historical_floods_catalogue_listA.csv - historical floods with impact data (1870-2020)</p> <p>HANZE_historical_floods_catalogue_listB.csv - historical floods without impact data (1950-2020)</p> <p>HANZE_potential_flood_catalogue_all.csv - modelled potential floods (1950-2020)</p> <p>HANZE_list_of_references.csv - List of all references used in the catalogues</p> <p>HANZE_model_completness_analysis.csv - Comparison between modelled and reported footprints of historical floods</p> <p>Regions_v2010_simplified.zip - Map of subnational regions (v2010)</p> <p>Regions_v2021_simplified.zip - Map of subnational regions (regions v2021)</p> <p> </p> <p>v1.2: corrected NUTS regions v2021 for a few events, which were accidently coded with v2010 regions.</p> <p>v1.1: errors in two records in "HANZE_historical_floods_catalogue_listB.csv" (wrong country code in event ID 8227 and wrong start date in event ID 8237) were corrected.</p>
Benchmarking (multi)wavelet-based dynamic and static non-uniform grid solvers for flood inundation modelling (Simulation results)
<p>Simulation result data for Environment Agency benchmark test 5, Thamesmead hypothetical flood, and Carlisle 2005 case studies, using uniform DG2, adaptive MWDG2, adaptive HWFV1, non-uniform DG2, non-uniform FV1 and non-uniform ACC solvers. </p> <p>Model results are archived in 3 zip files:</p> <ul> <li>EA5.zip contains results of Environment Agency test 5 (Néelz and Pender, 2013)</li> <li>Thamesmead.zip contains results of Thamesmead hypothetical flood (Liang et al., 2008)</li> <li>Carlisle.zip contains results of Carlisle 2005 flooding (Neal et al., 2009)</li> </ul> <p>The results are stored with the following file extensions:</p> <ul> <li>".wd" for 2D flood inundation maps in ESRI ASCII format</li> <li>".stage" for water depth or water level time-series at staging points in tabulated text format</li> <li>".velocity" for velocity time-series at staging points in tabulated text format</li> </ul> <p>Model outputs are stored under directories named for each solver.</p> <p><strong>References</strong></p> <p>Néelz, S., & Pender, G. (2013). Benchmarking the latest generation of 2D hydraulic modelling packages. <em>Environment Agency: Bristol, UK</em>.</p> <p>Liang, Q., Du, G., Hall, J. W., & Borthwick, A. G. (2008). Flood Inundation Modeling with an Adaptive Quadtree Grid Shallow Water Equation Solver. <em>Journal of Hydraulic Engineering</em>, <em>134</em>(11), 1603–1610. https://doi.org/10.1061/(ASCE)0733-9429(2008)134:11(1603)</p> <p>Neal, J. C., Bates, P. D., Fewtrell, T. J., Hunter, N. M., Wilson, M. D., & Horritt, M. S. (2009). Distributed whole city water level measurements from the Carlisle 2005 urban flood event and comparison with hydraulic model simulations. <em>Journal of Hydrology</em>, <em>368</em>(1–4), 42–55. https://doi.org/10.1016/j.jhydrol.2009.01.026</p> <p> </p>
Tracing and visualisation of contributing water sources in a model of flood inundation: video supplement
<p>These are video supplement files to Wilson & Coulthard (2021), produced using version 1.8f-WS of CAESAR-Lisflood software, <a href="https://doi.org/10.5281/zenodo.5541122">available on Zenodo here</a>. For a full description of the methodology and case studies, please refer to the paper which is available here: <a href="https://doi.org/10.5194/gmd-2021-340">https://doi.org/10.5194/gmd-2021-340</a>.</p> <p>Video animations (no audio) for the following case studies are included:</p> <p>1. <strong>Carlisle, United Kingdom</strong> (carlisleanimation-sourcetracing.avi and carlisleanimation-depthonly.avi):</p> <ul> <li>Simulation of the January 2005 flood event at the confluence of the Rivers Caldew, Petteril and Eden, using a 5 m grid.</li> <li>Both water source tracing and depth only versions are provided.</li> <li>In the water tracing version, blue colours represent flows from the River Eden, reds are from the River Petteril and greens are from the River Caldew; darker shades represent deeper water. Available on YouTube here: <a href="https://youtu.be/xOtOi06cXvA">https://youtu.be/xOtOi06cXvA</a></li> <li>In the depth only version, darker shades of blue represent deeper water, with no information about the water source in a grid cell. Available on YouTube here: <a href="https://youtu.be/aFz-sPRGHVE">https://youtu.be/aFz-sPRGHVE</a></li> </ul> <p>2. <strong>Avon-Heathcote estuary in Christchurch, New Zealand</strong> (avonheathcoteanimation.avi):</p> <ul> <li>Simulation for July 2017, which included a high flow event on 22 July, using a model grid of 10 m.</li> <li>Blue colours represent flows from tide, reds are from the River Avon and greens are from the Heathcote River; darker shades represent deeper water.</li> <li>Available on YouTube here: <a href="https://youtu.be/Fczr5tczzXU">https://youtu.be/Fczr5tczzXU</a></li> </ul> <p>3. <strong>Amazon </strong>(amazonanimation.avi):</p> <ul> <li>Simulation at the confluence of the Solimões (mainstem Amazon) and Purus rivers in the central Amazon, Brazil, for the period of 1 October 2013 through December 2014, using a ~270 m model grid.</li> <li>Red colours are from the Solimões, green colours are from the Purus; darker shades represent deeper water.</li> <li>Available on YouTube here: <a href="https://youtu.be/PknAL_8fd1I">https://youtu.be/PknAL_8fd1I</a></li> </ul> <p>4. <strong>Planar slope</strong> (planaranimation.avi):</p> <ul> <li>A simple test case consisting of a 2000 x 1000 m planar slope (0.001 m/m), with walls added at 250 m intervals across the slope, each of which has several gaps through which water can flow. Model grid was 5 m.</li> <li>Eight water sources were traced in total, with three visualised in the animation: red = source 2, green = source 4, blue = source 6. Depths are shown in the middle plot.</li> <li>Available on YouTube here: <a href="https://youtu.be/DTw8ysJtx8o">https://youtu.be/DTw8ysJtx8o</a></li> </ul> <p>Please feel free to use these animations, under the terms of the CC-BY-4.0 license. Please provide a link back to this site and a citation to Wilson & Coulthard (2021).</p> <p>Reference:</p> <p>Wilson, M. D. and Coulthard, T. J.: Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation, Geosci. Model Dev. Discuss. [preprint], <a href="https://doi.org/10.5194/gmd-2021-340">https://doi.org/10.5194/gmd-2021-340</a>, in review, 2021</p>
Using Hydroclimate Modeling and Social Science to Enhance Flood Resilience on Lake Ontario through the Climate Smart Communities Program
<p>This repository contains several data products associated with the New York Sea Grant project R/CHD-15 entitled <em>Using Hydroclimate Modeling and Social Science to Enhance Flood Resilience on Lake Ontario through the Climate Smart Communities Program.</em><strong><em> </em></strong>These products include:</p> <p>1. Estimates of the 25-year, 50-year, and 100-year flood across the New York coastline of Lake Ontario. These design events (reported in feet) are for still water levels that take into account both average water levels across the lake as well as local variations in water level due to storm surge. Wave setup and wave run-up are not considered in these design events. The design events incorporate the effects of water level regulation and the potential impacts of climate change on water supplies to Lake Ontario, and they are tailored for 79 unique locations along the shoreline (identified based on longitude and latitude). These flood levels are presented in an online flood risk assessment tool at: https://kts48.users.earthengine.app/view/lake-ontario-water-level-scenarios</p> <p>2. Protocols and summary of results for a series of focus groups and structured telephone interviews with local officials from communities along the Lake Ontario shoreline to assess barriers to participation in the New York State Climate Smart Communities Program.</p> <p>3. A Crosswalk between activities and administrative requirements of the New York State Climate Smart Communities Program and other federal and state flood resiliency programs. </p> <p>4. A final report summarizing the products above. </p>
Dataset for the article "Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes"
<p>This dataset include the data needed to create the socio-hydrological model to simulate human evacuation processes via a transportation network before a flood hits the residential area. Source code, in JAVA, for generating households in the agent-based model are also provided. </p>
A global flood risk modeling framework built with climate models and machine learning - Submission - Data Supplement
<p>This contribution contains data, fitted statistical models, and an analysis script for the submitted manuscript "A global flood risk modeling framework built with climate models and machine learning" by David A. Carozza and Mathieu Boudreault.</p>
Analyzing the sensitivity of a flood risk assessment model towards its input data, twelve damage scenarios
<p>This dataset contains the output shapefiles of twelve different risk assessment scenarios for the case study of Annotto Bay, Jamaica. These assessments were performed in the context of the research 'Analyzing the sensitivity of a flood risk assessment model towards its input data', published in the journal Natural Hazards and Earth System Sciences. More information on the input data and methodology can be found in this paper.</p>
Post-fire flood hazard model (PF2HazMo) version 1.0.0: Model scripts and parameterization and validation data
<p>Human development at the foot of the mountains faces sediment-laden flood hazards characterized by high-velocity, erosive flows carrying mud and debris, and when flood control infrastructure that protects communities fills with sediment, it loses capacity. The estimation and management of sediment-laden floods have proven challenging because cycles of wildfire, precipitation, and infrastructure sedimentation are still poorly understood. Efforts to model compound hazards such as post-fire floods are relatively new, and existing models do not consider the role of flood control infrastructure, such as debris retention basins and flood channels, in the development of post-fire floods. Here we present data sources and calibration methods to estimate sediment-laden flood hazards downstream of infrastructure on a catchment-by-catchment basis using the Post-Fire Flood Hazard Model (PF2HazMo), a stochastic modeling approach that utilizes continuous simulation to resolve the effects of antecedent conditions and system memory. Data sources provide parameter ranges needed for stochastic modeling, and several performance measures are considered for model calibration. With application to three catchments in Southern California, we show that PF2HazMo predicts the median of the simulated distribution of peak bulked flows within the 95% confidence interval of observed flows, with an order of magnitude range in bulked flow estimates depending on the performance measure used for calibration. Using infrastructure overtopping data from a post-fire wet season, we show that PF2HazMo accurately predicts the number of flood channel exceedances. Model applications to individual watersheds reveal whether existing infrastructure is undersized to contain present-day and future overtopping hazards based on current design standards.</p>
First Street Foundation Flood Model Hazard Layers V1.3
<p>Up to 15 different hazard layers are available, representing 3 different time periods (2021, 2036, 2051) and 4-5 different return periods from the 2-year (coastal only) to the 500-year intervals.</p> <p>Data is delivered in GeoTIFF format and at a 3 meter resolution with each pixel representing depth of flooding in centimeters. This high resolution dataset allows you to visualize flood extents at multiple return periods both today and in the future.</p> <p>The hazard inundation layers are emailed through a clickable link that automatically starts the download of the datasets. The Version 1.3 hazards are available for the contiguous United States.</p> <p>You can download a sample of the hazard layers generated from First Street's Flood Model on this page. You can request access to the hazard layers for areas within the contiguous United States on the First Street website<a href="https://firststreet.org/data-access/paid-access/?utm_source=Hazard_Layers&utm_medium=Purchase_Data&utm_campaign=Zenodo#pricing-component"> here</a>. You can find the data dictionary which breaks down the data that is available with each hazard layer purchase<a href="https://firststreet.org/data-access/getting-started-with-first-street-data/documentation-hazard-dictionary/?utm_source=Hazard_Layers&utm_medium=Hazard_Dictionary&utm_campaign=Zenodo"> here</a>. If you are also interested in the flood risk statistics, you can find more information <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/data-dictionary/?utm_source=Hazard_Layers&utm_medium=Data_Dictionary&utm_campaign=Zenodo">here</a>.</p>
A tempοral Deep Convolutional Neural Network model on Sentinel-1 Image Time Series for pixel-wise Flood Classification (dataset)
<p>This is a dataset which has been designed to be used for flood time series classification. Each time series is annotated as flood or no-flood and represents a pixel-wise time series derived from stack of Sentinel-1 IW GRD images that have been pre-processed according to <a href="http://doi.org/10.5281/zenodo.6510223">https://doi.org/10.5281/zenodo.6510223</a>.</p>
Integrated Machine Learning model in Early Urban Flooding Warning System - Data
<p>AI_DATA.npy - Inundation data (mm) generated from MIKE+ model that has been converted to numpy array</p> <p>INDEX.npy - The index where inundation is > 0 </p> <p>source.tif - Source tif image for creating map from ML models</p>
Results and figures from "Evaluating the robustness of the ARIO model for a local disaster: 2021 Flooding in Germany"
<p>These files and notebook allow reproducing the figures from "Evaluating the robustness of the ARIO model for a local disaster: 2021 Flooding in Germany".</p> <p>The main result file is a pandas DataFrame saved in parquet format under "results/general-plot_df.parquet".</p> <p>Two notebooks allow post-processing these results and plot the figures.</p> <p>Reproduction of the raw results can be achieved with the Snakemake pipeline, available here: https://github.com/spjuhel/BoARIO-Sensitivity</p> <p> </p>
Large Language Model-Based Classification of Flash Flood Impacts Across the United States
<p>This repository contains the data sets used for the publication of the journal article titled <em>Large Language Model-Based Classification of Flash Flood Impacts Across the United States</em>.</p> <p>This is the first release of the data with a Zenodo DOI attached to the README.md file. </p> <p>Further information about the data can be found in the GitHub repository's README.md file.</p>
Experimental and numerical study of the effect of model geometric distortion on laboratory modelling of urban flooding
<p>The supporting datasets includes: </p> <p>(1) All the figures in the manuscript in .fig format (in case possible)</p> <p>- Figures in main text</p> <p>- Figure in Support Information</p> <p>(2) Datasets for generating the main outcomes of the manuscript</p> <p>- two files that explain detailed data content in three structure</p> <p>- 3 sub-repositories which contains the results for the three models</p> <p>- one repository contains the data for Figure 8</p> <p> </p>
INSYDE model - Testing empirical and synthetic flood damage models: the case of Italy
<p>This repository contains the R code for <a href="http://www.nat-hazards-earth-syst-sci.net/16/2577/2016/">INSYDE, a synthetic, probabilistic flood damage model based on explicit cost analysis</a>.</p> <p>Correspondence to: Rui Figueiredo (<a href="mailto:rui.figueiredo@iusspavia.it">rui.figueiredo@iusspavia.it</a>)</p>
Testing Flood Damage Models on three recorded flood events in Italy
<p>The package includes: 1) two excel files containing input, processing and output of the statistical analysis; 2) a geodata folder made of three sub-folders, each related to case study areas (Adda, Bacchiglione, Secchia). They include the output of flood hazard models and geometries of exposed buildings.</p>
Scientific data: Laboratory modelling of urban flooding
<p>These datasets include experimental data obtained in the hydraulic laboratory. A detailed description of the datasets is available in the article. </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.