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39 results for “Storm surge”
Probabilistic-deterministic storm surge return level dataset for the Bengal delta
<p>Bengal delta shoreline, spanning Bangladesh and India, gets hit every 3 years on average by a major tropical cyclone. Although their occurrence is relatively moderate compared to other tropical regions (accounting for only 5% of global cyclones), the impact of these events is major, accounting for 50% of the victims recorded worldwide. This is due to the very low topography of the delta above sea level (less than 5 meters), high storm surge induced water level and flooding, combined with the high density of the vulnerable population. </p> <p>On one hand, the unavailability of long-term reliable water level data on a sparse tide-gauge network along the coastline has hindered the assessment of storm surge hazards. The application of hydrodynamic modelling to fill the data gap also suffers from the unavailability of a reliable long-term storm dataset over the region. The complex topography of the Bengal delta, with defence structures, and a dense network of rivers presents another modelling challenge. Finally, the interaction of tide, surge and wave further complicate the numerical complexity, needing a coupled modelling framework. </p> <p>Thanks to advancements made to acquire high-quality regional nearshore bathymetry and topography (Krien et al. 2016, Khan et al. 2019), as well as coupled storm surge modelling (Krien et al. 2017, Khan et al. 2021), the tidal and storm surge dynamics over the Bengal delta is now well captured by recent high-resolution coupled SCHISM-WWM Bay of Bengal model (Khan et al. 2021). To estimate the risk of storm surge and associated flooding across the Bengal delta, we have integrated the wave-coupled hydrodynamic model of Khan et al. (2021) for a large ensemble (~3600 cyclones, ~5000 years of storm activity) of synthetic cyclones generated through the statistical-deterministic method of Emanuel (2006). Our storm and surge ensemble covers the whole range of natural variability of storm frequency, size, intensity and track location, with a dense spatial distribution. The interactions among the tide, surge, and waves are modelled explicitly at high spatial resolution. The storm surge-induced water level at various return periods, up to 500 years, is then determined at high spatial resolution (250m at the coast) using a ranking-based technique.</p> <p>The dataset distributed here represents the storm surge water level estimate (e.g. total water level from the tide, surge, and wave computed dynamically through the model) at 25 to 500 year return period (25-year step). The corresponding variable in the self-describing netCDF data file is 'maxelev'. The estimated storm surge water level values are interpolated in a 30" (~1km at the equator) structured grid over the Bengal delta from the original unstructured-grid model outputs (250m resolution at the coast). </p> <p>This dataset is a part of a manuscript, currently being submitted to Natural Hazards and Earth System Sciences (https://nhess.copernicus.org/). Please cite the original paper, along with the dataset if used in your work as - Khan, M. J. U., Durand, F., Emanuel, K., Krien, Y., Testut, L., and Islam, A. K. M. S.: Storm surge hazard over Bengal delta: A probabilistic-deterministic modelling approach, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2021-329, in review, 2021.</p>
DeepSurge storm surge predictions for HighResMIP tropical cyclones
<p>DeepSurge is a newly presented deep-learning approach to modeling the storm surge generated by a tropical cyclone (TC). This dataset is a collection of DeepSurge outputs for synthetic TCs in the North Atlantic generated by the HighResMIP project (Haarsma et al. 2016) for a simulated historical (1950-2014) and future (2015-2050) climate under the climate scenario SSP585.</p> <p>The data generation process and data analysis is detailed in an upcoming publication. The storm surge data presented here intentionally does not include the effects of sea level rise, rainfall, or other factors, in order to isolate the effects of changing TC climatology on future storm surge risk.</p> <h4>Dataset format</h4> <p>The data comes in the form of maximum surge levels at 2846 near-coastal locations for each synthetic TC. Each TC is defined by the corresponding track in the HighResMIP TempestExtremes dataset (Roberts 2019). The data is presented in NetCDF format, with two dimensions: </p> <ul> <li>'nodes', the number of near-coastal locations, always 2846.</li> <li>'tracks', the number of tracks in the simulation, which is different in each file.</li> </ul> <p>There are 6 variables in each file:</p> <ul> <li>'lons' and 'lats', the coordinates of the nodes in degrees North and East respectively.</li> <li>'track_valid' is a binary indicator (zero for false, one for true) indicating whether the TC occurs within the region of interest (HighResMIP tracks are global, but we only simulate those in the North Atlantic)</li> <li>'track_done' is another binary indicator for whether the track has been simulated. It should indicate true for all tracks for which 'track_valid' is true.</li> <li>'max_zeta' provides the predicted maximum surge height, in meters, for each storm at all 2846 nodes. This data is only valid in entries for which the corresponding 'track_done' and 'track_valid' indicators are true.</li> <li>'years' is the year in which each simulated TC occurs.</li> </ul>
Methane and carbon dioxide flux in a tidal freshwater marsh recovering from three years of experimental seawater additions and following the Hurricane Irma storm surge
Methane (CH4) and carbon dioxide (CO2) flux rates were measured in a tidal freshwater marsh using static flux chambers. The experimental field site, SALTEx (Seawater Addition Long-Term Experiment) is part of the Georgia Coastal Ecosystems (GCE) LTER and is located on the Altamaha River, GA. The marsh was experimentally dosed with brackish water additions for 3 years, from 2014 – 2017. There are three treatments groups (Press, Pulse, and Fresh) and two control groups (with and without siding on the plots), each with six replicates. Press treatment plots received brackish water throughout the year, Pulse plots received brackish water in September and October and fresh water the rest of the year, Fresh plots received fresh river water throughout the year. The two control groups, one with siding on the plots and one without, received no water addition manipulations. All dosing ceased in January 2018, at which point we began to study the recovery of the marsh. In this study, the Hurricane Irma Rapid Grant evaluated additional effects of the Hurricane Irma storm surge that occurred in October 2017. Greenhouse gas measurements were taken seasonally beginning in March 2018 and ending in March 2019.
Emilia-Romagna coastal area NBS (OAL ITALY) for storm surge mitigation
<p>Within the framework of the OPEn-air laboRAtories for Nature baseD solUtions to Manage environmental risks (OPERANDUM) project, the seagrass NBS is presented within a simulation design methodology consisting of the comparison between validated wave numerical simulations for the present/ future climate, and modified wave simulations with marine seagrass. Ten years of WWIII simulations have been executed to generate the wave climatology, particularly over the Emilia-Romagna coastal strip for the present (2010-19) and future climate (2040-49) using MedCordex winds (based on RCP8.5). The WWIII model was modified to include a modified bottom dissipation stress due to submerged vegetation, thereby incorporating the NBS4 as a potential mechanism for wave amplitude reduction. The seagrass species <em>‘Zostera marina’</em> was chosen in this study and an along-shore seagrass belt was first inserted in WWIII and sensitivity experiments were carried out to assess the effects of different types of seagrass landscape designs in the Bellocchio beach. Simulation experiments with and without seagrass (NBS4) were carried out for the present and future climates. Based on the present and future climate simulations, it is noted that the seagrass landscaping is an important aspect in the numerical modelling of vegetation. A combination of broken vegetation stripes and clusters were seen to be effective in reduction of wave energy at the coast in comparison to other landscape designs. The wave height comparisons in the Bellocchio beach, with and without vegetation showed a considerable reduction in wave heights specifically in the higher ranges for both the present and future climates. There exists a strong seasonality in the attenuation rates along the coastal belt with higher attenuations during winter and comparatively lower in summer. In comparison to the present climate, a slightly increased rate of mean attenuation is expected in the future scenarios. Overall, the Zostera Marina seagrass applied for the Emilia-Romagna coastal belt was found to be efficient in reduction of wave energy (> 50%). The limitation being that the experiments were done with rigid seagrass and in the future, we look for advanced parameterization using flexible seagrass.</p> <p>This dataset contains wave model outputs for the OAL-ITALY, mainly:</p> <ul> <li>Bathymetry of the model domain, Spatial maps of mean significant wave height (Hs in m) for present (2010-19) and future climate (2040-49), Seagrass belt position in the Bellocchio beach, Time-series comparison of Hs, with & without vegetation, and Wave attenuation maps.</li> </ul> <ul> <li>Selected locations (station map) for the time series in the Emilia-Romagna coastal belt during the period 2010-19, and 2040-49 (8 stations), Selected locations (station map) in the Emilia-Romagna coastal belt for the time series comparison (with and without vegetation) during the period 2010-19, and 2040-49 (5 stations).</li> </ul> <ul> <li>WW3 time series of wave parameters (wave height, peak period, & direction) for 8 stations in the Emilia-Romagna coastal belt (2010-19, present climate).</li> <li>WW3 time series of significant wave height (Hs in metres) with and without vegetation for 5 stations in the Emilia-Romagna coastal belt (2010-19, present climate).</li> <li>WW3 time series of wave parameters (wave height, peak period, & direction) for 8 stations in the Emilia-Romagna coastal belt (2040-49, future climate).</li> <li>WW3 time series of significant wave height (Hs in metres) with and without vegetation for 5 stations in the Emilia-Romagna coastal belt (2040-49, future climate).</li> </ul>
Data supporting: "Trends in Europe storm surge extremes match the rate of sea-level rise"
<p><strong>Data supporting the paper:</strong></p> <p><strong>Calafat, F. M., T. Wahl, M. G. Tadesse, & S. Sparrow. Trends in Europe storm surge extremes match the rate of sea-level rise. <em>Nature</em> 603, 841-845.</strong></p> <p>Please cite the paper above when using this data set.</p> <p><em>Data description:</em></p> <ul> <li><strong>Bayesian_solutions_historical_total.nc</strong>: Bayesian estimates (posterior draws) of the GEV parameters, including trends in the GEV location parameter, at both tide gauge sites and prediction locations. This file also contains the observed surge annual maxima from tide gauge records on which these estimates are conditioned.</li> <li><strong>Bayesian_solutions_historical_contributions.nc</strong>: Bayesian estimates (posterior draws) of the contributions from external forcing and internal climate variability to the trends in the GEV location parameter.</li> <li><strong>Surge_annual_max_ensemble.nc</strong>: ensemble of surge annual maxima used to extract the pattern of response to external forcing.</li> </ul>
Compound flood potential from river discharge and storm surge extremes at the global scale
<p>This dataset presents the results presented in <a href="https://doi.org/10.5194/nhess-20-489-2020">Couasnon et al. (2019) - Measuring compound flood potential from river discharge and storm surge extremes at the global scale</a>. For more information about the methods, please refer to the paper. This dataset was created using as input <a href="https://zenodo.org/record/3552820#.XmIdoVxKhaQ">time series of discharge and maximum storm surge at river mouths globally from 1980 - 2014</a>.</p> <p>If using this data, please cite: </p> <p>Couasnon, A., Eilander, D., Muis, S., Veldkamp, T. I. E., Haigh, I. D., Wahl, T., Winsemius, H. C., and Ward, P. J.: Measuring compound flood potential from river discharge and storm surge extremes at the global scale, Nat. Hazards Earth Syst. Sci., 20, 489–504, https://doi.org/10.5194/nhess-20-489-2020, 2020.</p>
Using neural networks to predict hurricane storm surge and to assess the sensitivity of surge to storm characteristics.
<p>Data for "Lockwood, J. W., Lin, N., Oppenheimer, M., & Lai, C.-Y. (2022). Using neural networks to predict hurricane storm surge and to assess the sensitivity of surge to storm characteristics. Journal of Geophysical Research: Atmospheres, 127, e2022JD037617. https://doi.org/10.1029/2022JD037617"</p>
GTSM-ERA5-E dataset - Data underlying the paper "Global dataset of storm surges and extreme sea levels for 1950-2024 based on the ERA5 climate reanalysis"
<p>Extreme sea levels, generated by storm surges and high tides, have the potential to cause coastal flooding and erosion. Global datasets are instrumental for mapping of extreme sea levels and associated societal risks. Harnessing the backward extension of the ERA5 reanalysis, we present a dataset containing the statistics of water levels based on a global hydrodynamic model (GTSMv3.0) covering the period 1950-2024. This is an extension of a previously published dataset for 1979-2018 <a href="https://www.frontiersin.org/articles/10.3389/fmars.2020.00263/full" target="_blank" rel="noopener">(Muis et al. 2020)</a>. The timeseries (10-min, hourly mean and daily maxima) are available via the Climate Data Store of ECMWF at DOI: 10.24381/cds.a6d42d60. Using this extended ERA5 dataset, we calculate percentiles and estimate extreme water levels for various return periods globally. The percentiles dataset includes the 1, 5, 10, 25, 50, 75, 90, 95 and 99th percentiles. The extreme water levels include return values for 1, 2, 5, 10, 25, 50, 75 and 100 years, and they are estimated using POT-GPD method applied with a threshold of 99th percentile of the timeseries and using a 72-hour window for declustering peak events, and MLE method for fitting the GPD parameters. The parameters (shape, scale and location) are also supplied with this dataset.</p> <p>Validation of the underlying timeseries and the statistical values shows that there is a good agreement between observed and modelled sea levels, with the level of agreement being very similar to that of the previously published dataset. The extended 75-year dataset allows for a more robust estimation of extremes, often resulting in smaller uncertainties than its 40-year precursor. The present dataset can be used in global assessments of flood risk, climate variability and climate changes.</p> <p>Global modelling of water levels and extreme value analysis are associated with a number of uncertainties and limitations, that are particularly important to consider when conducting local assessments. Please refer to the Usage Notes in the corresponding manuscript (Aleksandrova et al. 2025, paper currently under review) for an overview of limitations.</p>
Paired time series of daily discharge and storm surge
<p>This dataset presents daily time series of discharge and maximum storm surge at river mouths globally from 1980 - 2014. </p> <p>Daily river discharge is the product of routing the mean daily runoff of the JULES model from the eartH2Observe WRR2 reanalysis data at 0.5° resolution (Best et al., 2011; Clark et al., 2011; Schellekens et al., 2017) with CaMa-Flood at a 0.25° resolution (Yamazaki et al., 2011). The maximum daily storm surge is obtained from the Global Tide and Surge Model (GTSM) (Muis et al., 2016; Verlaan et al., 2015). Each discharge location at the river mouth of coastal catchments larger than 1,000 km<sup>2</sup> is paired with the nearest (≤ 75 km) GTSM output location (Eilander et al., 2019). </p> <p> </p>
Dataset of Last Interglacial climate from publication "Modeled storm surge changes in a warmer world: the Last Interglacial" by P. Scussolini et al.
<p>Results from the simulation of Last Interglacial (Eemian) climate with climate model CESM1.2. Variables are: sea-level pressure (PSL); meridional wind (V), and zonal wind (U). Time step is 6-hourly.</p> <p>Detailed description of the methods are in the original publication:</p> <p>Scussolini, P., Dullaart, J., Muis, S., Rovere, A., Bakker, P., Coumou, D., Renssen, H., Ward, P. J., and Aerts, J. C. J. H.: Modelled storm surge changes in a warmer world: the Last Interglacial, EGUsphere, 2022, 1-20, 10.5194/egusphere-2022-101, 2022.</p>
Dataset of pre-industrial climate from publication "Modeled storm surge changes in a warmer world: the Last Interglacial" by P. Scussolini et al.
<p>Results from the simulation of pre-industrial climate with climate model CESM1.2. Variables are: sea-level pressure (PSL); meridional wind (V), and zonal wind (U). Time step is 6-hourly.</p> <p>Detailed description of the methods are in the original publication:</p> <p>Scussolini, P., Dullaart, J., Muis, S., Rovere, A., Bakker, P., Coumou, D., Renssen, H., Ward, P. J., and Aerts, J. C. J. H.: Modelled storm surge changes in a warmer world: the Last Interglacial, EGUsphere, 2022, 1-20, 10.5194/egusphere-2022-101, 2022.</p>
Dataset for "Assessing Storm Surge Multi-Scenarios based on Ensemble Tropical Cyclone Forecasting" paper
<p>1000 ensemble track forecast of tropical cyclone Hagibis (2019) is provided in NetCDF format and the computed storm surge forecast is provided in the Excel file.</p>
Data in support of manuscript "Impacts of storm surge barriers on drag, mixing, and exchange flow in a partially mixed estuary" submitted to JGR-Oceans
<p>Data set in support of manuscript "Impacts of storm surge barriers on drag, mixing, and exchange flow in a partially mixed estuary" submitted to JGR-Oceans in November 2021. Matlab script (makeFigs_barDragMix_upload.m) is used to generate the figures from the manuscript. Data files (*.mat) correspond with each figure (*.png). For questions or additional information please contact D. Ralston.</p>
Wave climate simulations for Denmark - for paper 'Coinciding storm surge and wave setup: A regional assessment of sea level rise impact'
<p>This wave climate dataset are the results for the paper titled 'Coinciding storm surge and wave setup: A regional assessment of sea level rise impact'.</p> <p>The operational wave forecasting service provided by DMI-WAM uses the WAM Cycle version 4.5.4, a third-generation spectral wave model. DMI-WAM is used for the wave climate simulations. The meteorological forcing used in this study was obtained from the regional climate model DMI-HIRHAM, developed by the Danish Meteorological Institute (DMI). It is a component of the CORDEX (Coordinated Regional Climate Downscaling Experiment) ensemble in Europe. Regarding the selection of the time frame and IPCC scenarios in our study, we adhered to the recommendations provided by municipalities. Municipalities are keenly interested in obtaining near-future wind wave data for the specific purpose of using them for risk management. Therefore, the examination of forthcoming weather extremes in the near future within the context of the high greenhouse gas emission scenario (RCP8.5 scenario) is of significance within this investigation. We conduct simulations that encompass two distinct time periods: the historical period spanning from 1976 to 2005, and the near-future period from 2041 to 2070. We analyse the WAM model results for wave climate under both present climate conditions (1976-2005) and future climate scenarios (2041-2070) under the RCP8.5 scenario. Furthermore, note that while our wave climate simulations provide valuable insights into the dynamics of wind-induced waves, the mean SLR is not explicitly taken into account. The mean SLR component is considered in the storm surge simulations.</p> <p>Description of files:</p> <p><a href="../api/records/11052226/draft/files/sla.swh.slope.hist.final.max.nc/content" target="_blank" rel="noopener noreferrer">sla.swh.slope.hist.final.max.nc</a> - Maximum sea level, significant wave height, wave length and slope for the historical period.</p> <p><a href="../api/records/11052226/draft/files/sla.swh.slope.rcp85.final.max.MSLR35.nc/content" target="_blank" rel="noopener noreferrer">sla.swh.slope.rcp85.final.max.MSLR35.nc</a> - Maximum sea level, significant wave height, wave length and slope for the RCP8.5 period.</p> <p><a href="../api/records/11052226/draft/files/wavesetup.hist.final.max.nc/content" target="_blank" rel="noopener noreferrer">wavesetup.hist.final.max.nc</a> - Maximum wave setup for the historical period.</p> <p><a href="../api/records/11052226/draft/files/wavesetup.rcp85.final.max.MSLR35.nc/content" target="_blank" rel="noopener noreferrer">wavesetup.rcp85.final.max.MSLR35.nc</a> - Maximum wave setup for the RCP8.5 period.</p> <p><a href="../api/records/11052226/draft/files/wam.grib.his.swh.98p.nc/content" target="_blank" rel="noopener noreferrer">wam.grib.his.swh.98p.nc</a> - 2% exceedence of significant wave height for the historical period.</p> <p><a href="../api/records/11052226/draft/files/wam.grib.rcp8.swh.98p.nc/content" target="_blank" rel="noopener noreferrer">wam.grib.rcp8.swh.98p.nc</a> - 2% exceedence of significant wave height for the RCP8.5 period.</p>
Probabilistic reanalysis of storm surge extremes in Europe
<p>This dataset contains the probabilistic reanalysis of storm surge extremes presented in the paper:</p> <p>Calafat, F. M., and M. Marcos (2020), Probabilistic reanalysis of storm surge extremes in Europe. Proc. Natl. Acad. Sci. U. S. A.</p> <p>Please cite that paper when using this dataset.</p> <p>This dataset includes estimates of the GEV parameters (location, scale and shape) and surge annual maxima at both gauged and ungauged locations.</p>
ASM-SS: The First Quasi-Global High Spatial Resolution Coastal Storm Surge Dataset Reconstructed from Tide Gauge Records
<p>The ASM-SS dataset is a high spatial resolution (every 10 km per node along the coastline), long-term (over 80 years from 1940 to 2020), quasi-global (within 45°S-45°N), hourly data-driven storm surge dataset. Each NetCDF file includes five parameters: longitude, latitude, nodes, time, and surge level. Longitude and latitude are the location information of nodes in degree; the unit of time is accumulated hours since 1900-01-01 00:00:00; surge levels are given in meters. Users can use longitude, latitude, and time as keywords to select surge levels at nodes of interest within a target period. </p>
Seamless Projections of Global Storm Surge and Ocean Waves under a Warming Climate
<p>This is dataset of global ocean waves and storm surges used in the paper "Seamless Projections of Global Storm Surge and Ocean Waves under a Warming Climate" by Shimura et al. (2022, Geophysical Research Letters).</p> <p>AnnualMaxSSH_SeamlessExperiment.nc contains the global annual maximum storm surge in the seamless experiment.</p> <p>AnnualMaxHs_SeamlessExperiment.nc contains the global annual maximum significant wave heights in the seamless experiment.</p> <p>AnnualMaxHs_TimesliceExperiment_Historical.nc contains the global annual maximum significant wave heights in the time-slice experiment (the historical climate simulations).</p> <p>AnnualMaxHs_TimesliceExperiment_ProjectedFuture.nc contains the global annual maximum significant wave heights in the time-slice experiment (the projected future climate simulations).</p>
Model data of tsunami and storm surge scenarios for Anegada, British Virgin Islands
Open the record for dataset details and reuse information.
CoDEC Dataset - Data underlying the paper "A high-resolution global dataset of extreme sea levels, tides and storm surges including future projections "
<p>The world’s coastal areas are increasingly at risk of coastal flooding due to sea-level rise (SLR). We present a novel global dataset of extreme sea levels, the Coastal Dataset for the Evaluation of Climate Impact (CoDEC), which can be used to accurately map the impact of climate change on coastal regions around the world. The third generation Global Tide and Surge Model (GTSM), with a coastal resolution of 2.5 km (1.25 km in Europe), was used to simulate extreme sea levels for the ERA5 climate reanalysis from 1979 to 2017, as well as for future climate scenarios from 2040 to 2100. The validation against observed sea levels demonstrated a good performance, and the annual maxima had a mean bias (MB) of -0.04 m, which is 50% lower than the MB of the previous GTSR dataset. The CoDEC-ERA5 dataset is the successor of GTSR <a href="https://www.nature.com/articles/ncomms11969">(Muis et al., 2016)</a> and is based on the next generation climate and hydrodynamic models. The main improvements are summarized in Table 2 of the accompanying paper <a href="https://www.frontiersin.org/articles/10.3389/fmars.2020.00263/abstract">(Muis et al., 2020)</a>.</p> <p><br> </p>
Dynamic Load Balancing for Predictions of Storm Surge and Coastal Flooding-Model setup and source code
<p>Source code and model setup/inputs for the paper titled "Dynamic Load Balancing for Predictions of Storm Surge and Coastal Flooding" article. Simulations were conducted using a modified version of ADCIRC+DLB (ADCIRC + Dynamic Load Balancing) on unstructured triangular meshes.</p> <p>Contains:</p> <ol> <li>Model input files. <ol> <li>ADCIRC model input files for the ideal channel setup and Hurricane Irene simulation (*.13, *.14, *.15)</li> </ol> </li> <li>Zipped archive of the ADCIRC code (adcirc-cg-DLB.zip) used to produce the simulations for the paper.</li> <li>Step-by-step compilation and usage instructions for ADCIRC+DLB. <ol> <li>Installation.html </li> <li>Usage.html</li> </ol> </li> </ol>
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