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48 results for “flood risk”
Adaptation required to preserve future high-end river flood risk at present levels
<p>Dataset accompanying the publication</p> <p>S.N. Willner, A. Levermann, F. Zhao, K. Frieler, Adaptation required to preserve future high-end river flood risk at present levels. Sci. Adv. 4, eaao1914 (2018).</p> <p>The dataset includes the increase in flood protection that is required to keep the observed high-end flood risk of the past constant in the next 25 years as well as the affected population in both periods used (1971-2004 and 2035-2044).</p>
Unique Property Reference Number (UPRN) indicator: Flood risk (UPRN_1_1)
<p>This indicator covers three main flooding risks in the UK: fluvial (rivers and streams), coastal and surface water. The indicators were computed for all the Unique Property Reference Numbers (UPRNs) in the NHS Cheshire and Merseyside Integrated Care Board region (North West England). This region is formed by the following Local Authorities: Chester and Cheshire East, Cheshire West, Halton, Knowsley, Liverpool, Sefton, St. Helens, Warrington and Wirral.</p> <p> </p>
Dataset to analyze the psychometric and structural properties of a scale designed to measure attitudes to integrated flood risk management
<p>The content of this reference includes the dataset generated by means of a questionnaire survey to a representative sample (N=406) of a city of Zamora (Spain). It contains 39 columns (c) which represents the following variables: Flooding area (c1); Flood risk perception (c2 to c7); Attitudes toward flood mitigation measures (c8 to c22); Perceived efficiency of general flood mitigation measures (c23 to c28); Assessment of the adequacy of overall flood risk management in the city and neighborhood (c29 to c30); Safety, environment, and economy preferences in flood risk management (c31 to c33); Postal code (c34); Age (c35); Age intervals (c36); sex (c37); Factorial saved loadings (c38 to c39).</p>
Compound flood risk analysis in CONUS
<p>This repository includes the developed E3SM source code for running MOSART simulation with a downstream boundary and a suite of codes for performing the statistical analysis of compound flood risk assessments. For the access to the full MOSART simulation output, please contact the author. </p>
Data supporting the manuscript: Demonstrating the value of beaches for adaptation to future coastal flood risk
<p>*Forcing hydrograms used to compute the flooding maps in .mat format.</p> <p>*Geodatabase of Pre-storm flooding maps </p> <p>*Geodatabase of Post-storm flooding maps</p>
Leveraging spatial patterns in precipitation forecasts using deep learning to support risk-averse flood management
Open the record for dataset details and reuse information.
Impact of a workshop with visualization and ethics discussion on awareness of flood risk and intent to protect
Open the record for dataset details and reuse information.
Los Angeles 100-year flood risk
Open the record for dataset details and reuse information.
Detection and Estimation of Inundation and Associated Risks Using Traffic and Monitoring Cameras and Image Processing Under Extreme Flooding Conditions
<p>The main objective of this project is to develop an inundation detection and evaluation framework using images from traffic monitoring cameras and reliable flood monitoring under extreme precipitation conditions. This study presents a comparative assessment of image enhancement and segmentation techniques to automatically identify the flash flooding from the low-resolution images taken by traffic-monitoring cameras. Due to inaccurate equipment in severe weather conditions (e.g., raindrops or light refraction on camera lenses), low-resolution images are subject to noises that degrade the quality of information. De-noising procedures are carried out for the enhancement of images by removing different types of noises. After the de-noising, image segmentation is implemented to detect the inundation from the images automatically. In addition, the detection of the inundation using the image segmentation with and without de-noising techniques are compared. The results indicate that among de-noising methods, the Bayes shrink with the thresholding discrete wavelet transform shows the most reliable result. For the image segmentation, the Bayesian segmentation is superior to the others. The results demonstrate that the proposed image enhancement and segmentation methods can be effectively used to identify the inundation from low-resolution images taken in severe weather conditions. A new Bayesian filtering method will be devised and applied to estimate the inundation from low-resolution images that will allow traffic engineers to take preventive or proactive actions to improve the safety of drivers and protect and preserve the transportation infrastructure. This new observation with improved accuracy will enhance our understanding of dynamic urban flooding by filling an information gap in the locations where conventional observations have limitations.</p>
Supplementary Data:Risk Analysis for Real-time Flood Control Operation of a Multi-reservoir System Using a Dynamic Bayesian Network
<p>The files in this record contain data for risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic bayesian network considered for publication in Water Resources Research.</p> <p>The files consist of:</p> <ul> <li>Reservoir data and river flood routing parameters</li> <li>Flood data</li> <li>Code and results of the Monte Carlo simulations</li> <li>Code and results of the Bayesian network</li> </ul>
Refugee Camp Flood Risk - NASA ARSET Training Data
<p>This upload contains data and documentation to complete the practical exercise in <em>Part 1 - Assessing Flood Risk in Refugee Camp Settings</em> of the NASA ARSET <em>Earth Observations for Humanitarian Applications </em>training programme. Further details can be found at http://appliedsciences.nasa.gov/get-involved/training/english/arset-earth-observations-humanitarian-applications</p>
Dataset: Risk Transfer Model for Flood Risk Evolution in a Multi-reservoir System
<p>The files in this record contain data for real-time optimal flood control decision making and risk propagation under multiple uncertainties considered for publication in Water Resources Research.</p> <p> </p> <p>The files consist of:</p> <p> </p> <p>Data:</p> <ul> <li>Figure 11;</li> <li>Figure 12;</li> <li>Figure S1;</li> <li>Figure S4</li> <li>Relative prediction error</li> <li>Reservoir information</li> <li>Streamflow</li> </ul> <p>Model code:</p> <ul> <li>Calculation of entropy</li> <li>Forecasting error simulation model</li> <li>LHS</li> <li>Analytic code of transfer model</li> </ul>
Interactive maps: Unveiling hidden risks in healthcare from flood-induced transportation disruption
<p>We provide interactive maps in HTML format, viewable in standard web browsers, to illustrate the impact of flood events (indicated by the index in the title) on specific regions, focusing on hospitals at risk. This collection includes 321 maps, each representing a hospital where the change in service population exceeds 0.3. Hovering over the hospital symbols (marked with a plus) reveals meta information, including service population details before and after a flood event. Additionally, hovering over the polygons provides further insights into the hospital in focus, offering a detailed view of each facility’s status and impact zone.</p>
Global asymmetries in the influence of ENSO on flood risk based on 1600 years of hybrid simulations - Submission - Data Supplement
<p>This contribution contains data and analysis scripts for the manuscript "Global asymmetries in the influence of ENSO on flood risk based on 1600 years of hybrid simulations" by Lenin Del Rio Amador, Mathieu Boudreault and David A. Carozza.</p>
Quantitative Assessment of the Impact of Future Land Use Changes on Flood Risk Using Remote Sensing, Machine Learning, and a Hydraulic Model
<p> </p> <p>The RF Machine learning code </p> <p>Topological, geomorphology, geology, metrological information of the Tajan watershed.</p> <p>Land use land cover images of the Tajan watershed</p> <p>River, transportation roads, villages map </p> <p>Global damage function datasets.</p>
Risk and Equity Metrics for the NYC Flood Risk Digital Twin
<p>These datasets contain the Risk and Equity metrics used to quantify the impact of pluvial flooding in NYC. They are obtained combining several sources (US Census data, New York State Traffic data, etc.) with the NYC Stormwater Flood Map corresponding to an extreme rain event.</p>
Data supporting the manuscript: "Demonstrating the value of beaches for adaptation to future coastal flood risk"
<p>* TWL scenarios used to force the flooding model in a .mat structure</p> <p>* Flooded areas obtained for the different TWL scenarios in a .mat structure considering the topobathymetry at the maximum TWL instant and just after the storm</p> <p>* Flooded damages obtained for the different TWL scenarios in a .mat structure at the maximum TWL instant and just after the storm</p> <p> </p>
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>
Distribution records: Reconciling biodiversity conservation and flood risk reduction: the new strategy for freshwater protected areas
<p><span><strong>Aim:</strong> </span><span>Natural disaster risk reduction (DRR) is becoming a more important function of protected area (PAs) for current and future global warming. However, biodiversity conservation and DRR have been handled separately and their interrelationship has not been explicitly addressed. This is mainly because, due of prevailing strategies and criteria for PA placement, a large proportion of PAs are currently located far from human-occupied areas, and habitats in human-occupied areas have been largely ignored as potential sites for conservation despite their high biodiversity. If intensely developed lowland areas with high flooding risk overlap with important sites for biodiversity conservation, it would be reasonable to try to harmonize biodiversity conservation and human development in human-inhabited lowland areas. Here, we examined whether extant PAs can conserve macroinvertebrate and freshwater fish biodiversity and whether human-inhabited lowland flood risk management sites might be suitable to designate as freshwater protected areas (FPAs).</span></p> <p><span><strong>Location:</strong> </span><span>Across Japan</span></p> <p><span><strong>Methods:</strong> </span><span>We examined whether extant PAs can conserve macroinvertebrate and freshwater fish biodiversity and analyzed the relationship between candidate sites for new FPAs and flood disaster risk and land use intensity at a national scale across Japan based on</span><span> distribution data for 131 freshwater fish species and 1395 macroinvertebrate species.</span></p> <p><span><strong>Results:</strong> </span><span>We found that extant PAs overlapped with approximately 30% of conservation-priority grid cells (1 km<sup>2</sup>) for both taxa. Particularly for red-listed species, only one species of freshwater fish and three species of macroinvertebrate achieved the representation target within extant PAs.</span><span> Moreover, more than 40% of candidate conservation-priority grid cells were located in </span><span>flood risk and human-occupied areas for both taxa.</span></p> <p><span><strong>Main conclusions:</strong> </span><span>Floodplain conservation provides suitable habitat for many freshwater organisms and helps control floodwaters, so establishing new FPAs in areas with high flood risk could be a win-win strategy for conserving freshwater biodiversity and enhancing ecosystem-based DRR (eco-DRR).</span></p>
Integrated machine learning and GIS-based bathtub models to assess the future flood risk in the Kapuas River Delta, Indonesia
<p>To use the data and the code, please cite the following article: </p> <p>Joko Sampurno, Randy Ardianto, Emmanuel Hanert; Integrated machine learning and GIS-based bathtub models to assess the future flood risk in the Kapuas River Delta, Indonesia. <em><em>Journal of Hydroinformatics</em></em> 2022; jh2022106. DOI: <a href="https://doi.org/10.2166/hydro.2022.106">https://doi.org/10.2166/hydro.2022.106</a></p>
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International Brain Laboratory public data
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OpenNeuro
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