Find research datasets worth reusing

opencc-zeroApr 2024View details →

Post-fire flood hazard model (PF2HazMo) version 1.0.0: Model scripts and parameterization and validation data

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.

zenodo40/100

First Street Foundation Flood Model Hazard Layers V1.3

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. 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. 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. 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>.

opencc-by-4.0Oct 2021View details →

zenodo40/100

Supplement to "Discriminating non-stationary flood hazard effects via probabilistic estimation of sparse residuals from rescued and rated stage–discharge data"

This supplement contains the data and script associated with “Sparse hydrometric data rescue for exploratory analyses of conveyance-driven flood hazard trends and controls,” which has been submitted to a journal for consideration. This supplement is deposited on Zenodo.   Code, Data, and Attribute Descriptions   Uploaded are five directories (indicated in bold font, with their contents detailed below) containing input data and various outputs of the analyses performed for our case study on the Pulangi River at Lumayong (Philippines). Please consider this description equivalent to an omnibus README file for the deposited files. Note that we collected and rescued the hydrometric data herein from the archives of the Water Projects Division (WPD) of the Philippine Department of Public Works and Highways (DPWH).   ●      Pln_R_code contains (1) Pln_RC.R, the R script file, and (2) 240601_Pln_RC.RData, which stores objects generated from the script based on the last execution (on 1 June 2024). The script consists of admittedly too many lines of code (e.g., for data wrangling, formal analyses, and producing figures used in the manuscript) that should have been split into multiple .R files. Apologies. Please be guided by the outline and the comments and kindly reach out lest issues with the code arise.   ●      Pln_GH_PDFs contains the scanned gaugekeeper’s reports of gauge heights (“stages”).    ○      The name of each file varies. For example, Pln-GH-1100.pdf contains daily stage readings for all months in the year 2011. But, if the last two digits are not zeroes, as in Pln-GH-1204.pdf, they refer to the month of that year; in this case, for example, the PDF file contains stage data for the month of April in the year 2012. ○      Each scanned sheet contains sub-daily (with readings at “AM,” “NOON,” and “PM”) and mean daily stages for a given month. ○      Also indicated are the gaugekeeper’s remarks on the daily weather (e.g., “fair,” “cloudy”). Under inclement weather, the gaugekeeper would note the duration of rainfall and its intensity and might, at times, record extra stage readings.   ●      Pln_DM_PDFs contains the following PDF files and a sub-directory: ○      Pln-DM-<XXXXXX>.pdf contains the scanned log for each direct stage—discharge measurement, otherwise known as “gaugings.” Each file is named according to the date of gauging (YYMMDD). For example, the log for the gauging performed on 24 February 2011 can be found in the file Pln-DM-110224.pdf. Data from these gaugings are summarized in Pln-DM-filtered.csv in the Pln_In_CSVs directory. ■      The readability of each file varies based on the original quality of the original paper-format data. ■      The first page in each file contains, on the left side, a summary of the gauging data and metadata (e.g., date of measurement, number of gauging verticals or “sections” used, method of crossing or measuring the cross-section), and on the right side, the velocity—area readings at each gauging vertical. ■      The second page in each file contains the plotted cross-section of the channel at the time of measurement. ○      Pln-RC.pdf contains various other paper-format data and some annotations relevant to the historical rating at the station. ■      P. 1: The hydrographic engineer’s comment (dated 19 January 2012) on the evaluation of the discharge data, specifically detailing the periods of validity of the rating curves developed for different sub-periods of monitoring. ■      PP. 2—3: A summary table of all the gaugings performed from 1983 to 2010 whose logs were no longer retrievable (and hence not included as a DM-PDF file in this directory). ■      PP. 4—5: Plots of the official stage—discharge rating curves developed by DPWH hydrographers. ■      PP. 6—8: Rating tables used to convert daily mean stages to deterministic discharge estimates. Two of these rating tables were digitized and can be found in the Pln_RatingTables sub-directory in Pln_In_CSVs. ■      PP. 9—22: Daily stage (m) and its corresponding daily discharge (L/s) for the 2004—2010 sub-period. The stages were digitized and included in Pln-H_arch.csv in the Pln_In_CSVs directory. ○      Pln_DM_unused contains the PDF files of logs (including data and metadata) corresponding to the gaugings that were excluded from our analysis following our filtering step for gauging location consistency.   ●      Pln_In_CSVs contains the following CSV files and two sub-directories; these files were used as inputs to the R script for formal analyses: ○      Pln-H_arch.csv contains the mean daily stage values [“H_bar_arch”] (m) for every day in the 2004—2020 sub-period [“Date”] (YYYY-MM-DD). The stages in this file are already corrected for gross errors. ○      Pln-DM-filtered.csv contains the following information on the gaugings performed on the Pulangi at Lumayong (1983—2020): (i) date [“Date”] (YYYY-MM-DD); (ii) stage [“H”] (m); (iii) discharge in L/s [“Q_lps”] and m3/s [“Q_cms”]; (iv) wetted area [“A_sqm”] (m2); (v) mean flow velocity [“Vel_mps”] (m/s); (vi) channel width [“W_m”] (m); (vii) mean flow depth [“D_ave_m”] (m); (viii) location of the measurement cross-section with respect to the staff gauge, with negative values meaning downstream of the gauge and positive values meaning upstream of the gauge [“XS_loc_wrt_gage”]; (ix) maximum flow depth [“Max_Depth_m”] (m); and (x) minimum streambed elevation [“MINSBE”] (m). Note that this file includes only gaugings that passed our filtering step for measurement location consistency. ○      Pln-DM_oview.csv contains information on the temporal coverage (bounded by “Start_date” and “End_date”) of the available hydrometric data from the station archives. ○      Pln-H_sample_GrossE_corr.csv contains a sample sub-period (2017-06-15 through 2017-11-30) and the corresponding values of stages (m), uncorrected [“H_uncorrected”] and corrected for gross errors [“H_corrected”]. This CSV file was used as an input to the R script to produce one of the figures in the manuscript.  ○      Pln-XS-csv.csv contains data on the gauging transects: gauging ID [“GaugingID”]; date [“Date”] (YYYY-MM-DD); lateral distance from a fixed initial point [“Lat_distance”] (m); width of the gauging vertical [“Width_vert..m.”] (m); depth relative to the water surface [“Depth..m.”] (m); stage at the gauging vertical [“H_m_vert..m.”] (m); and elevation with respect to a fixed arbitrary datum at the station [“Elev..m.”] (m). ○      Pln_Rating_Tables contains two rating tables, Pln-DM - RatingTable_A.csv and Pln-DM - RatingTable_B.csv, prepared and used by DPWH hydrographers for converting stage values to deterministic discharge estimates for the Pulangi River at Lumayong for the 1980s—early 2000s sub-period. Each rating table contains columns for stage [“H”], discharge in L/s [“Q_lps”], and discharge in m3/s [“Q_cms”]. ○      Pln_Q_rated contains two CSV files: Pulangi.csv has the columns “YEAR”, “DAY”, and every month of the year [“JAN” through “DEC”] for the 1983—2003 sub-period, with the values under each month column indicating the deterministic discharge estimates in L/s; Pulangi_trunc.csv contains similarly formatted data, but for the 2009—2010 sub-period.   ●      Pln_Out_CSVs contains two CSV files, the primary outputs of the hydrometric data rescue effort. ○      Pln_Q_recon.csv contains the reconstructed discharge time series (1983—2020) and its associated uncertainties at the 95% credibility interval. It has the following columns: date [“Date”] (YYYY-MM-DD); daily stage [“H”] (m); lower bound of the discharge estimate [“Q_lwr”] (m3/s); median discharge estimate [“Q_med”] (m3/s); and the upper bound of the discharge estimate [“Q_upr”] (m3/s). Pln_H_recon.csv contains the reconstructed and quality-controlled stage time series (1983—2020). It has the following columns: date [“Date”] (YYYY-MM-DD); mean daily stage that has been corrected for gross errors, but not yet filtered through other quality checks [“H_bar_arch”] (m); quality check for low outliers [“Low_Outlier”] (TRUE/FALSE); quality check for flatliners [“Flatliner”] (TRUE/FALSE); difference between the stage values on day i and day i-1 [“Daily_Step”] (m); quality check for large steps [“Large_Step”] (TRUE/FALSE); and the corrected and quality-controlled mean daily stage values [“H”] (m).

opencc-by-4.0Jun 2024View details →

opencc-zeroJul 2024View details →

Comparing first street foundation and PRIMo flood hazard data across the Los Angeles metropolitan region

Extreme flooding events are becoming more frequent and costly, and impacts have been concentrated in cities where exposure and vulnerability are both heightened. To manage risks, governments, the private sector, and households now rely on flood hazard data from national-scale models that lack accuracy in urban areas due to unresolved drainage processes and infrastructure. The data in this repository supports an assessment of the uncertainties of First Street Foundation (FSF) flood hazard data, available across the U.S.. For the analysis, FSF data was compared to PRIMo-Drain, a flood hazard model that resolves drainage infrastructure and fine resolution drainage dynamics. In the linked journal manuscript, using the case of Los Angeles, California, we find that FSF and PRIMo-Drain estimates of population and property value exposed to 1%- and 5%-annual-chance hazards diverge at finer scales of governance, for example by 4- to 18-fold at the municipal scale. FSF and PRIMo-Drain data often predict opposite patterns of exposure inequality across social groups (e.g., Black, White, Disadvantaged). Further, at the county scale, we compute a Model Agreement Index of only 24%—a ~1 in 4 chance of models agreeing upon which properties are at risk. Collectively, these differences point to limited capacity of FSF data to confidently assess which municipalities, social groups, and individual properties are at risk of flooding within urban areas. These results caution that national-scale model data at present may misinform urban flood risk strategies and lead to maladaptation, underscoring the importance of refined and validated urban models.

publicApr 2024View details →

Post-fire flood hazard model (PF2HazMo) version 1.0.0: Model scripts and parameterization and validation data

Open the record for dataset details and reuse information.

publicJul 2024View details →

Comparing first street foundation and PRIMo flood hazard data across the Los Angeles metropolitan region

Open the record for dataset details and reuse information.

publicFeb 2025View details →

Data from: Compound post-fire flood hazards considering infrastructure sedimentation

Open the record for dataset details and reuse information.

opencc-by-4.0Mar 2020View details →

Adaptation time to magnified flood hazards underestimated when derived from tide gauge records [dataset]

This dataset contains the data supporting the manuscript Lambert et al., Adaptation time to magnified flood hazards underestimated when derived from tide gauge records, submitted to Environmental Research Letters. All files contain two main variables: AF is the amplification factor as a function of [station, time, scenario] DT is the doubling time as a function of [station, scenario] Note that these files contain data of all 299 available stations, rather than the subset of 130 presented in the manuscript. Filenames: diss_AWL.nc     Average Water Level at dissipative beaches diss_IWL.nc     Instantaneous Water Level at dissipative beaches grd_AWL.nc      Average Water Level at gentle rocky dikes grd_IWL.nc      Instantaneous Water Level at gentle rocky dikes harbour.nc      Average and Instantaneous Water Level at harbours, excluding wave contributions srd_AWL.nc      Average Water Level at steep rocky dikes srd_IWL.nc      Instantaneous Water Level at steep rocky dikes ssb_AWL.nc      Average Water Level at steep sandy beaches ssb_IWL.nc      Instantaneous Water Level at steep sandy beaches tidegauge.nc    Tide gauge-based analysis

opencc-by-4.0Dec 2023View details →

Evaluation of flood hazards in data-sparse coastal lowlands: highlighting the Ayeyarwady Delta (Myanmar)

This folder includes datasets that were produced to assess flood hazards and exposure in the Ayeyarwady Delta in Myanmar by applying the new standardised, integrative approach of Seeger, K., Peffeköver, A., Minderhoud, P. S. J., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, Brill, D. (2024): Evaluating flood hazards in data-sparse coastal lowlands: highlighting the Ayeyarwady Delta (Myanmar). Environmental Research Letters. The README includes the names of files to be used for citation as well as a brief explanation when necessary. All processing details are given in the paper and related supplementary material.

opencc-by-4.0Jul 2024View details →

Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique"

Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique" <a href="../api/records/12664900/draft/files/hmax_idai_ifs_rebuild_bc_hist_rain_surge_noadapt.tiff/content" target="_blank" rel="noopener noreferrer">hmax_idai_ifs_rebuild*</a> -> Flood maps <a href="../api/records/12664900/draft/files/spatial_idai_ifs_rebuild_bc_3c-hightide_rain_surge_retreat.gpkg/content" target="_blank" rel="noopener noreferrer">spatial_idai_ifs_rebuild*</a> -> Impacts

opencc-by-4.0Jul 2023View details →

Supplementary Data Tables for 'The Global Drivers of Chronic Coastal Flood Hazards under Sea-Level Rise'

Please refer to and cite the manuscript Hague et al. (2023), 'The Global Drivers of Chronic Coastal Flood Hazards under Sea-Level Rise', Earth's Future. https://doi.org/10.1029/2023EF003784

opencc-by-4.0Sep 2020View details →

100-Year Flood Hazard Map with Municipal Boundaries Overlay

100-Year Flood Hazard Map with Municipal Boundaries Overlay

opencc-by-4.0Jan 2022View details →

Aqueduct Floods Hazard Maps extract for Tanzania

This contains an extract from the <a href="https://www.wri.org/resources/data-sets/aqueduct-floods-hazard-maps">WRI Aqueduct global open flood dataset</a>, Version 2 (updated October 20, 2020), showing flood depth in metres for coastal and river flooding under both current baseline conditions and future projections in 2030, 2050, and 2080. The data has been clipped to a bounding box around Tanzania (29.321032 -11.731272 40.449392 -0.98583). Please see the <a href="https://www.wri.org/publication/aqueduct-floods-methodology">Technical Note</a> for further details of the full dataset and methodology used to create it. License: Creative Commons Attribution 4.0 International License. Full license text available at <a href="http://www.wri.org/publications/permissions-licensing">Creative Commons Attribution 4.0</a>

dryad32/100

A deglacial hazard cascade exemplified by the landslide, tsunami and outburst flood at Elliot Creek, Southern Coast Mountains, British Columbia, Canada

We describe and model the evolution of a recent landslide and outburst flood in the southern Coast Mountains, British Columbia, Canada. About 18 Mm3 of rock descended 1000 m from a steep valley wall and traveled across the toe of a glacier before entering a 0.6 km2 glacier lake and producing a >100-m high wave. Water overtopped the lake outlet and scoured a 10-km long channel before depositing debris on a 2 km2 fan below the lake outlet. Floodwater, organic detritus, and fine sediment entered a fjord where it produced a 70-km long turbidity current and altered turbidity, water temperature, and water chemistry for weeks. The outburst flood destroyed forest and culturally significant salmon spawning and rearing habitat. Physically based models of the landslide, the displacement wave, and the flood provide real-time simulations of the event and can improve understanding of similar hazard cascades and the risk they pose.

opencc-zeroFeb 2022View details →

opencc-by-4.0Sep 2022View details →

Historical flood reconstruction in a torrential alpine catchment (Saltina, Brig, Swiss Alps) and its implication for flood hazard assessments

EXCEL has three sheets : 1. Pas flood description 2. Past Engineering 3. Hydraulic modeling flood discharge (1331 to 1965) and systematic discharge from 1966 to 2020.   Word has two pictures   1. past old maps (1331,1888,1938,2017) 2. Walls, check dam, and lifting bridge

opencc-by-4.0Oct 2019View details →

Supplementary data: Hazard from Himalyan glacier lake outburst floods

We provide supplementary data to the manuscript by Veh, G., Korup, O., and Walz A.: "Hazard from Himalayan glacier lake outburst floods". These datasets (file ending .rds) are ready to use within the R software for statistical programming. Please copy all files into one single directory and follow the instructions given in the scripts. You can find the following scripts on the associated Github page: https://github.com/geveh/GLOFhazard bayes_lm_piecewise_regression_stan.R lm_piecewise_const.stan R_Script_lake_area_vs_max_depth.R R_Script_Hazard_from_GLOFs_PNAS_supp.R HDIofMCMC.R Outputs within the scripts are written to disk, again as R-objects, if not suppressed by the user. Make sure you have at least 100 GB of free disk space.