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33 results for “Urban flooding”
Urban pluvial flood maps under different green cover scenarios
<p>This dataset provides pluvial flood water depth maps for the cities of Logroño, Spain; Gdynia, Poland; Milan Italy; and Athens Greece as a part of the REACHOUT project. The maps are generated using a Pluvial Flood Tool for different return periods estimated based on observations and EURO-CORDEX future climate change scenarios (Logroño only) under different nature-based green cover scenarios, depending on the city.</p> <p>Technical Info</p> <p>The pluvial flood hazard maps are generated for each event using rainfall intensity as input for the hydrostatic inundation model SaferRAIN (Samela et al., 2020). This is a simplified raster-based model based on a hierarchical filling and spilling algorithm, identifying inundated areas on the basis of high-resolution digital elevation model. It accounts for spatially distributed rainfall input and infiltration, building upon the pixel-based Green-Ampt model (Green and Ampt, 1911). It is suitable for applications over large urban areas.</p> <p>Rainfall input for the pluvial flood model is computed for return periods (RPs) of 2-, 5-, 10-, 25-, 50-, 100-, 200-years based on the historical rainfall data. Different datasets have been utilized in various cities to tailor the analysis to their specific needs. More specifically:</p> <ul> <li> <p>In the city of Gdynia, historical local station data (Climate data IMGW 1960-2021: https://danepubliczne.imgw.pl/) are used to estimate RPs and assess different precipitation events. </p> </li> </ul> <ul> <li> <p>For the cities of Milan and Athens, 2.2-km ERA5 downscaled data are employed to assess historical precipitation events under different RPs (Essenfelder et al., 2021). </p> </li> <li> <p>In the city of Logroño, historical local station data (SOS-Logroño precipitation data 1999-2022: https://www.larioja.org/emergencias-112/es/meteorologia/datos-actuales-rioja/detalle-estacion?homepage=9&cod_muni=89) are used to estimate RPs and assess different precipitation events. Additionally, here, future climate change projections have been analyzed. These projections are based on the precipitation Intensity-Duration-Frequency (IDF) curves computed from the EURO-CORDEX data (Pal J et al., 2024 - <a href="https://doi.org/10.5281/zenodo.14035736" target="_blank" rel="noopener">10.5281/zenodo.14035736</a>). Observations are then scaled according to the changes simulated between future and historical scenarios, using the median and 90th percentile values estimated from the EURO-CORDEX ensemble.</p> </li> </ul> <p>Different urban green cover maps are used as input for the model to simulate the pluvial flood maps under the current land cover conditions and for different nature-based adaptation scenarios for each city to estimate their benefits. Nature-based adaptation scenarios are the result of codesign processes carried out within REACHOUT, involving local stakeholders, experts and representatives of local administrations. Urban green cover scenarios were identified based on areas that could be converted from built-up areas and concrete surfaces (no water infiltration) to green areas allowing for rainwater infiltration. In addition, during this process, local station precipitation, high-resolution digital elevation model and high-resolution land cover data were collected to configure and run the pluvial flood model.</p> <p>Short description of the datase:</p> <p>This dataset contains urban pluvial flood maps for return periods of 2-, 5-, 10-, 25-, 50-, 100-, 200-years for hourly and 15-minute events for different urban green cover scenarios and climate change scenarios depending on the city.</p> <p>Format:</p> <p>The format of this dataset is organized in a ZIP file: PluvialFloodMap_{Cityname}.zip. The zip file is organised into sub-folders, one for each urban green cover scenario, including raster (Tiff) files for the rainfall event associated with each return period.</p> <p>Logrono:</p> <ul> <li> <p>Precipitation events historical: 15-minute events – 9.79 mm (RP2), 13.51mm (RP5), 16.27 mm (RP10), 20.15 mm (RP25), 23.33 mm (RP50), 26.77 mm (RP100), 30.50 mm (RP200)</p> </li> <li> <p>Precipitation events climate change: 15-minute events – CC_Q50 (median): 10.49 mm (RP2), 14.91 mm (RP5), 18.32 mm (RP10), 23.18 mm (RP25), 26.61 mm (RP50), 31.25 mm (RP100), 35.40 mm (RP200): CC_Q90 (90th percentile): 11.83 mm (RP2), 16.76 mm (RP5), 20.96 mm (RP10), 26.87 mm (RP25), 32.27 mm (RP50), 38.99 mm (RP100), 46.65 mm (RP200)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS planned: baseline + additional 4 bioswales/ponds (= 29,850 m3) and a green corridor (5.3 km x 5 m) in the southern part of the city.</p> </li> <li> <p>NBS planned plus: NBS planned scenarios + additional small ponds/rain gardens (depth 0.5 m, 13,500 m3)</p> </li> <li> <p>All Green: baseline + all open spaces converted to green</p> </li> </ul> <p>Milan</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 33.36 mm (RP5), 38.52 mm (RP10), 45.04 mm (RP25), 49.88 mm (RP50), 54.68 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>DMG_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise economic damage reduction</p> </li> <li> <p>POP_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise exposed population reduction</p> </li> </ul> <p>* Each green conversion scenario considers four different incremental conversion percentages: 25%, 50%, 75%, and 100% of all potential green areas.</p> <p>Gdynia</p> <ul> <li> <p>Precipitation events historical: 6-hours events – 24.89 mm (RP2), 35.93 mm (RP5), 43.55 mm (RP10), 53.19 mm (RP25), 60.60 mm (RP100), 75.74 mm (RP200) </p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + bioswales/ponds (+ 50,000 m3)</p> </li> <li> <p>All green: baseline + all open spaces converted to green</p> </li> <li> <p>NBS All green: all green + NBS</p> </li> </ul> <p>Athens</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 28.05 mm (RP5), 34.08 mm (RP10), 42.28 mm (RP25), 48.83 mm (RP50), 55.74 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + ponds/rain gardens in existing green spaces (depth 1m) in the northern district of the city</p> </li> <li> <p>All green: baseline + all open spaces (>100 m2) converted to green</p> </li> </ul>
Resilient and sustainable Permeable Pavements For urban Flood Mitigation
<p>Permeable pavements are more sustainable option towards climate change as they maintain the hydrological cycle. However, they suffer from problems such as lower strength and integrity. In this project, this issue is addressed in three main stages: a. development of high viscosity bitumen; b. evaluating different types of additives to be used in porous asphalt mixtures (surface course of permeable pavements); c. Development of a multi-criteria tool to implement the permeable pavement system using GIS software. </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>
Urban Flood Inventory in China
<p>This dataset is a catalog of urban floods in China from 2000 to 2022. The data is sourced from Chinese news text, with the BERT model used to extract information on the timing and location of flood events. The temporal scale is daily, with entries showing only the month for cases where specific dates could not be extracted. The spatial scale is at the county level in China. Details of the data collection and creation process are thoroughly explained in the relevant paper. Each year's flood events are stored in a CSV file named after that year, containing the following fields:</p> <ul> <li><strong>Year</strong>: the year the event occurred;</li> <li><strong>PAC</strong>: the administrative code for the county where the event occurred;</li> <li><strong>Province</strong>: the province of the affected area;</li> <li><strong>City</strong>: the city of the affected area;</li> <li><strong>County</strong>: the county where the event occurred;</li> <li><strong>Occurrence</strong>: the number of times floods occurred in that area during the year;</li> <li><strong>Time</strong>: the specific date of each flood event in that area.</li> </ul> <p>The shapefile.zip file, including ".shp," ".shx," ".dbf," and other files, is the shapefile of China's administrative regions used by the author to visualize the data.</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>
Supporting dataset for "Influence of urban forms on -long-duration urban flooding: laboratory experiments and computational analysis"
<p>In this dataset, we provide two parts of data:</p> <p>(1) Figures in format .fig that are included in the main text and supplementary material</p> <p>(2) Experimental datasets for the five configurations, including flow depth, discharge partition, and the flow surface velocity,</p> <p>- the data is written in a .h5 file that can be read by different languages (ex. Python),</p> <p>- a document PDF and a text file are available to visualize the data structure</p> <p>- a code of Python for reading the data in the .h5 file </p> <p> </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>
Estimates of high tide flooding on roadways within urban areas along the United States Atlantic coast
<p>Estimates of high tide flooding (HTF) on roadways in urban areas along the US Atlantic Coast. These estimates were calculated using NOAA HTF areal extent estimates, OpenStreetMap roadway data, and 2010 census-designated urban area and census block data. See the corresponding manuscript (Gold et al., 2021 - link coming soon) and <a href="https://github.com/acgold/HTF-on-roads">GitHub repository</a> for additional information about these data.</p>
A dynamic von Mises-based model to evaluate the impact of urbanization and climate change on flood timing in Yangtze and Huaihe River Basins, China
<p>The daily streamflow data extracted from 8 selected stations from the Huaihe and Yangtze River Basins, China.</p>
Quantitative assessment of the reservoir-induced and urbanization-induced impact on multivariate flood risk via a nonstationary vine Copula model
<p>Here we show the results of the characteristics of the floods at Huayuankou, Lanzhou and Toudaoguai gauges selected by AMS and POT mentod, respectively. Besides that, the inormation about the reservoirs and the imprevious layer in the control catchment of each station is also uploaded.</p>
Flood retention lakes in a rural-urban catchment: how can they be better used for flood mitigation?
<p>This data package is companion to the research paper of the same title. It includes details of frequency analysis and event design, event-based numerical calibration results, simulated performances of retention lakes at specific locations and maximum inundation maps.</p>
Towards transferable data-driven models to predict urban pluvial flood water depth in Berlin, Germany
<p>The attached files include the predictive features and the water depth from 2D hydrodynamic simulations that were used to train data driven models to predict water depth in Berlin.</p>
Comparison among three different Digital Surface Models and their respective hydraulic outcomes in the flood-prone urban area of Navaluenga (Ávila, Spain)
<p>Three different Digital Surface Models (DSMs) generated from LiDAR data are presented. The LiDAR information has been considered as raw data (DSM3) and subjected to some transformations to better represent the urban environment (DSM1). DSM2 is an intermediate state between DSM1 and DSM3. </p> <p>On the other hand, a hydraulic model has been run for each DSM and for two return periods (25 and 500 years), obtaining in all cases the graphical outputs of depths, velocities, Froude numbers and hazard. </p> <p>The different DSMs are named DSM1, DSM2 and DSM3, which can be downloaded in TIN format. The hydraulic outputs associated with the different DSMs can be downloaded in raster format and are named as follows: the Digital Surface Model to which it refers, the return period considered and the type of hydraulic output (depth, velocity, Froude number and hazard).</p> <p>DSM1: Digital Surface Model 1 (TIN format).<br> dsm1_25depth: Depths obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25froud: Froude numbers obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25haz: Hazard obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25veloc: Velocities obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format). <br> dsm1_500depth: Depths obtained when considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500froud: Froude numbers obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500haz: Hazard obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500veloc: Velocities obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).</p> <p>DSM2: Digital Surface Model 2 (TIN format).<br> dsm2_25depth: Depths obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25froud: Froude numbers obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25haz: Hazard obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25veloc: Velocities obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format). <br> dsm2_500depth: Depths obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500froud: Froude numbers obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500haz: Hazard obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500veloc: Velocities obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).</p> <p>DSM3: Digital Surface Model 2 (TIN format).<br> dsm3_25depth: Depths obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25froud: Froude numbers obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25haz: Hazard obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25veloc: Velocities obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format). <br> dsm3_500depth: Depths obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500froud: Froude numbers obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500haz: Hazard obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500veloc: Velocities obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).</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>
Data: Parameter regionalization with donor catchment clustering improves urban flood modeling in ungauged urban catchments
<p>Data: Parameter regionalization with donor catchment clustering improves urban flood modeling in ungauged urban catchments for wrr</p> <p>Chen Hu, Jun Xia, Dunxian She</p> <p>!===<br> This dataset provides flood information for the paper submitted to wrr "Parameter regionalization with donor catchment clustering improves urban flood modeling in ungauged urban catchments",2023WR035071<br> </p>
Supporting dataset for "Numerical insights into the effects of model geometric distortion in laboratory experiments of urban flooding"
<p>The supporting datasets includes:</p> <p>(1) All the Figures in the manuscript in .fig format </p> <p> - Figures in main text</p> <p> - Figures in SI</p> <p>(2) Original matrice data for the results of each run</p> <p> - A file that explains detailed data content in tree structure;</p> <p> - 4 sub-repositories which contain data for models in different scales</p>
CoUDlabs_WP8_T811_UOS_001. Investigating geometrical effects on hydraulic energy losses during sewer to surface flow interactions during urban floods
<p>This document describes the dataset used in CO UD-labs JRA3 (WP 8) Task 8.1.1. This considers the hydraulic exchange (surcharge) from a piped drainage system to surface flood flow through a manhole. The dataset includes measurements of pressure, flow rate and depth from a physical scale model. The effect of changing the manhole lid properties on flow exchange (surcharge) and pressure in the experimental system is quantified over a range of flow rates. </p>
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>
Supporting dataset for: "Technical note: Laboratory modelling of urban flooding: strengths and challenges of distorted scale models"
<p>Li, X., Erpicum, S., Bruwier, M., Mignot, E., Finaud-Guyot, P., Archambeau, P., Pirotton, M., Dewals, B., (2019). Laboratory modelling of urban flooding: strengths and challenges of distorted scale models. Hydrology and Earth System Sciences, 2019.</p> <p>Included in this repository the data used in:</p> <p>- Figure 3: Upscaled water depth profiles in prototype in street 4 of Araud (2012).</p> <p>- Figure 4: Spatial distributions and scatter plot of the differences between the upscaled water depths</p> <p>- Figure 6: Upscaled outflow discharge in each street compared to the corresponding value deduced from the observations in the less distorted model.</p>
Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning
<p>Here is the data and original code for the paper titled <em>Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning</em>. If you have any questions, please contact <a rel="noopener">202331470015@mail.bnu.edu.cn</a>.</p>
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