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17 results for “Flood Forecasting”
FUTURES land change forecasts in response to flooding in Charleston area, South Carolina (2019-2050)
<p>Climate-aware scenarios of FUTURES land change projections in Charleston area (Cherleston, Dorchester, Berkeley) for 2019-2050.</p> <p>Zipped folder <code>results</code> contains FUTURES v3 simulation runs for 3 counties in South Carolina, USA (Charleston, Berkeley, Dorchester) from 2019 to 2050. Included are five <em>climate-aware</em> scenarios (reactive, managed retreat, resist, polarized population, trapped population) and one scenario which does not take future climate conditions into account (<em>dynamic development</em>). Each folder contains 50 monte-carlo simulation results with <code>developed_seed_X.tif</code> and <code>adapted_seed_X.tif</code> where <code>X</code> goes from 1 to 50. Values of <code>developed_seed_X.tif</code> range from -31 to 31, where the positive values represent simulation step when an undeveloped pixel was developed and the negative values represent simulation step when a developed pixel was abandoned. Zero stands for initial development. For example, value -11 means the particular pixel was abandoned in 2030. Note that values of raster files in <code>Dynamic development</code> folder range from -1 to 31, where -1 means undeveloped and the rest is the same as above. Values of <code>adapted_seed_X.tif</code> range from 0 to 100, where 0 means trapped (flooded but no adaptation), the other values represent return period for which the pixel is adapted (e.g. 2-, 5-, 10-, 20-, 50- and 100-year flood). The files' CRS is Albers Conic Equal Area, NAD83(2011) datum.</p> <p>Additionally, we include <code>migration_matrix.csv</code> derived from IRS data, that contains probability values of moving from an origin county in the case study (row) to any other counties (columns).</p>
Quantitative precipitation estimate (QPE) and forecast (QPF) exceedance comparison with flash flood reports
<p class="ParagraphText">Flash flooding remains a challenging prediction problem, which is exacerbated by the lack of a universally accepted definition of the phenomenon. In this article, we extend prior analysis to examine the correspondence of various combinations of quantitative precipitation estimates (QPE) and precipitation thresholds to observed occurrences of flash floods, additionally considering short-term quantitative precipitation forecasts from a convection-allowing model. Consistent with previous studies, there is large variability between QPE datasets in the frequency of "heavy" precipitation events. There is also large regional variability in the best thresholds for correspondence with reported flash floods. In general, Flash Flood Guidance (FFG) exceedances provide the best correspondence with observed flash floods, except in the interior western US where recurrence interval thresholds (for the southwestern US) and static thresholds (for the northern and central Rockies) provide better correspondence. Six-hour QPE provides better correspondence with observed flash floods than 1-h QPE in all regions except the west coast and southwestern US. Exceedances of precipitation thresholds in forecasts from the operational High-Resolution Rapid Refresh (HRRR) generally do not correspond with observed flash flood events as well as QPE datasets, but they outperform QPE datasets in some regions of complex terrain and sparse observational coverage such as the southwestern US. These results can provide context for forecasters seeking to identify potential flash flood events based on QPE or forecast-based exceedances of precipitation thresholds. </p>
Global Flood Forecasting (GFF)
<h3>Dataset from paper: <strong><em>Off to new Shores: A Dataset & Benchmark for (near-)coastal Flood Inundation Forecasting</em></strong></h3> <p>Consists of target flood maps and various inputs used to predict said flood maps. Each input modality is contained in a separate zip file. Each zip file contains a collection of raster data, one file per ROI.</p> <p><em><strong>Files:</strong></em></p> <ul> <li><strong>base.zip</strong> - Contains flood maps and meta-data (e.g. geolocations). Includes <a href="https://github.com/Orion-AI-Lab/KuroSiwo" target="_blank" rel="noopener">Kuro Siwo labels</a>.</li> <li><strong>s1.zip</strong> - Contains Copernicus Sentinel-1 data from 2014-2020 obtained through Alaska Foundation, using an EarthData account.</li> <li><strong>era5.zip</strong> - Contains ERA5 and ERA5-Land data products obtained from Google Earth Engine (GEE), which was originally generated using Copernicus Climate Change Service information 2014-2020.<em><br></em></li> <li><strong>glofas.zip</strong> - Contains <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical" target="_blank" rel="noopener">GLOFAS v4.0 data</a> from Copernicus Climate Change Service's Climate Data Store (CDS).</li> <li><strong>hydroatlas.zip</strong> - Contains <a href="https://www.hydrosheds.org/hydroatlas" target="_blank" rel="noopener">HydroATLAS v10 data</a>. Rasterized and resampled to match ERA5-Land.</li> <li><strong>hand.zip</strong> - Contains <a href="https://gis.asf.alaska.edu/arcgis/rest/services/GlobalHAND/GLO30_HAND/ImageServer" target="_blank" rel="noopener">HAND data</a> (CC0 license). Resampled to match Sentinel-1.</li> <li><strong>dem.zip</strong> - Contains <a href="https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model" target="_blank" rel="noopener">CopDEM GLO-30 data</a>. Resampled to match Sentinel-1 and ERA5-Land.</li> <li><strong>extras.zip</strong> - Contains 4 folders of extra data: <ul> <li><em>vit-snunet-extra</em> - Flood maps, meta-data and S1 of ROIs that fell outside era5 availability.</li> <li><em>vit-snunet-raw</em> - Raw floodmaps generated by Kuro Siwo model (after removing tiling effects, before other post-processing)</li> <li><em>vit</em> - Alternate flood maps; generated using FloodViT model only</li> <li><em>snunet</em> - Alternate flood maps; generated using SNUNet model only</li> </ul> </li> <li><strong>worldcover.zip</strong> - Contains <a href="https://worldcover2021.esa.int/download" target="_blank" rel="noopener">worldcover data</a> (CC-BY license) resampled (nearest) to Sentinel-1. Only used at evaluation time. </li> </ul> <p><em><strong>Instructions</strong></em>: </p> <ol> <li>Download whichever inputs you want to use.</li> <li>Extract all in the same folder.</li> <li>Point <a href="https://github.com/Multihuntr/gff" target="_blank" rel="noopener">the code</a> to that folder.</li> </ol> <p><em><strong>Licenses</strong></em>: The redistributed data products (s1, era5, hydroatlas, hand, dem and worldcover) retain their original licenses. They are, however, very permissive. Only HydroATLAS is not necessarily available for commercial use.</p>
Arno River flooding 1966 | WRF-ENS forecasts
<p>Contains WRF-ENS forecasts for the Arno River flooding event occurred in Italy in November 1966. Files format is binary with auxiliary description file.</p>
A multi-system comparison of forecast flooding extent using a scale-selective approach [data]
<p>A multi-system comparison of forecast flooding extent using a scale-selective approach, data.</p> <p>Creator: Helen Hooker[1] Publication Year: 2023</p> <p>Organisation(s): 1. Department of Meteorology, University of Reading, U.K</p> <p>Description: This dataset contains:</p> <p>- SAR-derived observed flood maps used in the study.</p> <p>- CSM validation maps for forecast flood maps from Food Foresight (FF), GloFAS Rapid Flood Mapping (RFM) and FFWC Super Model (FFWC) predicting flooding extent on 25 July 2020 for the Jamuna River, Bangladesh compared against Sentinel-1 SAR-derived flooding extent. File format is e.g. CSM_FF_forecastdate_rundate.tif. CSM maps for Flood Foresight following system improvement are labelled with ..._reas.tif.</p> <p>Helen Hooker. (2023). A multi-system comparison of forecast flooding extent using a scale-selective approach (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7509980</p> <p>Related publications:</p> <p>A multi-system comparison of forecast flooding extent using a scale-selective approach; 2023; Hydrology Research (in preparation) Helen Hooker[1], Sarah L. Dance[1,2,3], David C. Mason[4], John Bevington[5], and Kay Shelton[5]</p> <ol> <li>Department of Meteorology, University of Reading, UK.</li> <li>Department of Mathematics and Statistics, University of Reading, UK.</li> <li>NCEO, University of Reading, UK.</li> <li>Department of Geography and Environmental Science, University of Reading, UK.</li> <li>JBA Consulting, UK.</li> </ol> <p>Correspondence: Helen Hooker (<a href="mailto:h.hooker@pgr.reading.ac.uk">h.hooker@pgr.reading.ac.uk</a>)</p>
Leveraging spatial patterns in precipitation forecasts using deep learning to support risk-averse flood management
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Surrogate flash flooding: Probabilistic excessive rainfall predictions from the High Resolution Ensemble Forecast (HREF) system
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Quantitative precipitation estimate (QPE) and forecast (QPF) exceedance comparison with flash flood reports
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Data set for the manuscript "Uncertainty quantification and physics-informed forecasting for improved urban flood modeling"
<p>This is a data set for the manuscript "Uncertainty quantification and physics-informed forecasting for improved urban flood modeling."</p>
Arno River flooding 1966 | ENS forecasts | MARS ID experiment b09d
<p>Contains ENS forecasts for the Arno River flooding event occurred in Italy in November 1966. The dataset is also available at the ECMWF MARS Catalogue, experiment ID b09d. The data format is GriB version 1.</p>
FloodCastBench: A Large-Scale Dataset and Foundation Models for Flood Modeling and Forecasting
<div>Effective flood forecasting is crucial for informed decision-making and emergency response. Existing flood datasets mainly describe flood events but lack dynamic process data suitable for machine learning (ML). This work introduces the FloodCastBench dataset, designed for ML-based flood modeling and forecasting, featuring four major flood events: Pakistan 2022, UK 2015, Australia 2022, and Mozambique 2019. FloodCastBench provides comprehensive low-fidelity and high-fidelity flood forecasting datasets specifically for ML.</div> <div> </div> <div>This dataset comprises three folders: the low-fidelity flood forecasting folder, the high-fidelity flood forecasting folder, and the relevant data folder. The low-fidelity flood forecasting folder includes data on the 2022 Pakistan flood and the 2019 Mozambique flood, both with a spatial resolution of 480 m. The high-fidelity flood forecasting folder contains two subfolders: one for the 2022 Australia flood and the 2015 UK flood with a spatial resolution of 30 m, and another for the same floods with a spatial resolution of 60 m. All data files are stored in TIFF format, with a temporal resolution of 300 seconds, and file names are numbered sequentially, incremented every 300 seconds until the simulation endpoint. The relevant data folder includes five subfiles: DEM, land use and land cover, rainfall data, georeferenced files, and initial condition files. The DEM, land use and land cover, rainfall, and initial condition data are all provided in TIFF format. The rainfall data is organized in a format of year-month-day-hour-minute-second. Georeferenced files provide geographic extent and spatial reference to support viewing and analysis of the associated TIFF files in GIS.</div> <div> </div> <div>FloodCastBench details the process of flood dynamics data acquisition, starting with input data preparation (e.g., topography, land use, rainfall) and flood measurement data collection (e.g., SAR-based maps, surveyed outlines) for hydrodynamic modeling. We deploy a widely recognized finite difference numerical solution to construct high-resolution spatiotemporal dynamic processes with 30-m spatial and 300-second temporal resolutions. Flood measurement data are used to calibrate the hydrodynamic model parameters and validate the flood inundation maps. Furthermore, we establish a benchmark of foundational models for neural flood forecasting using FloodCastBench, validating its effectiveness in supporting ML models for spatiotemporal, cross-regional, and downscaled flood forecasting.</div>
Data used in "A novel response priority framework for an urban coastal catchment using global weather forecasts-based improved flood risk estimates"
<p>The datasets used in "A novel response priority framework for an urban coastal catchment using global weather forecasts-based improved flood risk estimates" have been provided as rar files. Further details and instructions are provided in readme.txt in each folder of the rar file.</p>
Supplementary materials for "Spatial runoff updating based on hydrologic system differential response for flood forecasting"
<p>Supplementary material 1: Observations in the BSK basin including precipitation, evaporation and discharge. </p> <p>Supplementary material 2: Observations in the SW basin including precipitation, evaporation and discharge. </p> <p>Supplementary material 3: Results for floods 20071006 in the BSK basin</p> <p>Supplementary material 4: Results for floods 19890526 in the SW basin</p> <p>Supplementary material 5: Results with different lead time in the BSK and SW basins</p>
Social Media Alerts can Improve, but not Replace Hydrological Models for Forecasting Floods
<p>Social media can be used for disaster risk reduction as a complement to traditional information sources, and the literature has suggested numerous ways to achieve this. In the case of floods, for instance, data collection from social media can be triggered by a severe weather forecast and/or a flood prediction. By way of contrast, in this paper we explore the possibility of having an entirely independent flood monitoring system which is based completely on social media, and which is completely self-activated. This independence and self-activation would bring increased robustness, as the system would not depend on other mechanisms for forecasting. We observe that social media can indeed help in the early detection of some flood events that would otherwise not be detected until later, albeit at the cost of many false positives. Overall, our experiments suggest that social media signals should only be used to complement existing monitoring systems, and we provide various explanations to support this argument.</p> <p>This dataset contains the jupyter notebook and related data for running experiments described in the paper along with an additional table with full list of events considered</p>
Forecasting flood-related emergency phone calls
<p>AGUILERA, H.; GUARDIOLA-ALBERT, C.; GARROTE, J.; GUTIÉRREZ-PÉREZ, I.; VÁZQUEZ TARRÍO, D. & DÍEZ-HERRERO, A. (2021). Forecasting flood-related emergency phone calls (Ref: 237). <em>International Conference on Time Series and Forecasting</em> (ITISE 2021), Session B.4: Time Series for Natural Hazard Monitoring. 19th-21th July, 2021 Gran Canaria (Spain), Presentación oral en videograbación. Duración 13:11 minutos. DOI 10.5281/zenodo.5144911.eng</p>
Fine resolution flood forecasts and hindcast of Hurricane Harvey for Harris County, TX using PRIMo
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Spatial scale evaluation of forecast flood inundation maps
<p>Spatial scale evaluation of forecast flood inundation maps, data and code</p> <p>Creator: Helen Hooker[1] Publication Year: 2022</p> <p>Organisation(s): 1. Department of Meteorology, University of Reading, U.K</p> <p>Description: This dataset contains:</p> <p>- Python functions for a new scale-selective approach to forecast flood map evaluation.</p> <p>- SAR-derived observed flood maps used in the study.</p> <p>- JBA Consulting Flood Foresight forecast flood maps used in the study. </p> <p>Helen Hooker. (2022). Spatial scale evaluation of forecast flood inundation maps (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6011882</p> <p>Related publications:</p> <p>Spatial scale evaluation of forecast flood inundation maps; 2022; Journal of Hydrology (in preparation) Helen Hooker[1], Sarah L. Dance[1,2,3], David C. Mason[4], John Bevington[5], and Kay Shelton[5]</p> <ol> <li>Department of Meteorology, University of Reading, UK.</li> <li>Department of Mathematics and Statistics, University of Reading, UK.</li> <li>NCEO, University of Reading, UK.</li> <li>Department of Geography and Environmental Science, University of Reading, UK.</li> <li>JBA Consulting, UK.</li> </ol> <p>Correspondence: Helen Hooker (<a href="mailto:h.hooker@pgr.reading.ac.uk">h.hooker@pgr.reading.ac.uk</a>)</p>
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