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13 results for “flash flood”
Flash Flood Severity Index (Flashiness) dataset for the United States
<p>(Saharia et al., 2017)</p> <p>Flash floods, a subset of floods, are a particularly damaging natural hazard worldwide because of their multidisciplinary nature, difficulty in forecasting, and fast onset that limits emergency responses. In this study, a new variable called “flashiness” is introduced as a measure of flood severity. This work utilizes a representative and long archive of flooding events spanning 78 years to map flash flood severity, as quantified by the flashiness variable. Flood severity is then modeled as a function of a large number of geomorphological and climatological variables, which is then used to extend and regionalize the flashiness variable from gauged basins to a high-resolution grid covering the conterminous United States. Six flash flood “hotspots” are identified and additional analysis is presented on the seasonality of flash flooding. The findings from this study are then compared to other related datasets in the United States, including National Weather Service storm reports and a historical flood fatalities database.</p>
RGB orthophoto mosaic, DSM, 3d point cloud and LIDAR LAZ of the flash flood damages in Karavelovo and Bogdan vilages, Bulgaria- September 2, 2022
<p>The present dataset contains geospatial resources aimed at investigating and assessing the consequences of a flash flood of debris flow character, relatively significant in extent and magnitude of damage, in the area of two villages in the Municipality of Karlovo, located in central Bulgaria, which happened on September 2, 2022. For this purpose, an integrated approach based on the combination of digital photogrammetry with high spatial resolution and spatial accuracy, based on a fixed wing unmanned aerial system, and laser altimetry (LIDAR), based on a multirotor unmanned platform, was used. The data collection was carried out 2 days after the occurrence of the disaster, resulting in the generation of valuable information resources that allow not only to spatially and quantitatively determine the damage of the disaster, but also to reveal the mechanism of occurrence of the phenomenon: 1) orthophoto mosaic, Digital surface model-DSM and 3D point cloud (from photogrammetry) 2) Classified 3D point cloud- from LIDAR survey.</p>
Mapping a novel metric for Flash Flood Recovery using Interpretable Machine Learning
<p>This data is supplementary to the paper titled "Mapping a novel metric for Flash Flood Recovery using Interpretable Machine Learning". The file contains the main results.<br><br>For any queries, please visit <a href="https://hydrosense.iitd.ac.in" target="_blank" rel="noopener">Hydrosense Lab (IIT Delhi)</a>.</p>
Flash Flood Risk Map Hallstatt / Gosau / Bad Goisern: Risk Map
Damage Risk Map of FFRM catchment Hallstatt / Gosau / Bad Goisern based on max. water depth in all timesteps, zoning plan, building density and specific damage function
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>
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>
Database of vulnerability indicators used in the holistic characterization of vulnerability to flash floods in the region of Castilla y León (Spain)
<p>Database containing the vulnerability indicators used in the holistic analysis of vulnerability to flash floods in the region of Castilla y León (Spain), considering all its dimensions (social, economic, ecosystem, physical, institutional and cultural) and components (exposure, susceptibility and resilience). The database contains a total of 496 variables, of which 216 characterize social vulnerability, 180 economic vulnerability, 49 ecosystem vulnerability, 22 physical vulnerability, 23 institutional vulnerability and 6 cultural heritage vulnerability. The Excel file contains two sheets for each vulnerability dimension. The first sheet (whose name is composed with the name of the dimension and the suffix '_Variables') contains the information of the variables, i.e., the names of the municipalities, the province to which they belong and the values of the variables for each municipality. The variables on this sheet are identified as codes, whose definition and description (unit in which they are expressed, reference year of the information, information source and the link to the information) are found on the second sheet of each dimension (suffix '_Data_sources').</p>
Resilience indicators used in the multidimensional characterization of the resilience of urban areas prone to flash floods in the region of Castilla y León (Spain)
<p>Database containing resilience indicators used in the characterization of all dimensions of resilience (social, economic, ecosystemic, physical, institutional and cultural) in those municipalities susceptible to flash floods in the region of Castilla y León (Spain). The database includes a total of 191 resilience indicators, of which 48 correspond to social resilience, 32 to economic resilience, 34 to ecosystem resilience, 44 to physical resilience, 27 to institutional resilience and 6 to cultural resilience.</p> <p>The Excel (.xlsx) file is organized by dimensions, where the prefix "SOC_" corresponds to the social dimension, "ECON_" to the economic dimension, "ECOS_" to the ecosystemic dimension, "PHY_" to the physical dimension, "INS_" to the institutional dimension and "CUL_" to the cultural dimension. Each dimension of resilience occupies two tabs: the first tab (suffixes "_data") contains the description of the indicators included (i.e., indicator code, unit of measurement, year of information, source of information, direct link to the information and bibliographic references that support the consideration of the different indicators); while the second tab (suffixes "_variables") contains the values of the different indicators (identified by their codes, which appear in the first tab) for each unit of analysis. </p>
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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Historical data of flash flood and trend analysis information for Uttarakhand, India
<p>Historical data is always useful in interpreting any hazard-affected location. In this article historical data were gathered from various literature reviews, journals, newspapers, reports, and other sources to generate a flash flood map for Uttarakhand state, India. Between 1970 and 2020, a total of 122 sites were identified as being at risk of flash flooding. Moreover, several studies on rainfall trends at various scales have concluded that global warming is increasing extreme precipitation as well as extreme weather-related occurrences and risks. Therefore, high spatial resolution (0.25*0.25 degree) daily gridded rainfall data from the India Meteorological Department (IMD) was utilized to analyse the change in percentage from 1970 to 2020 for annual, pre-monsoon, monsoon, post-monsoon, and winter seasons.</p>
Flood Extent for Flash Floods in Lilongwe (2019)
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The data for "Entrapment risk precedes and is more fatal than instability risk in mountainous flash floods"
<p><span>The DEM and inundation depth data of the central area of Liulin Town were obtained by field measurement after the “8.12” flood event.</span></p>
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