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39 results for “coastal flooding”
CFMDG: a Coastal Flood Modelling Dataset in Gâvres (France) to support risk prevention and metamodels development
<p>Along most of the coastal areas, detailed coastal flood observations (e.g. inland water depths) are scarce, and when they are available, this for a limited number of events. Given recent scientific advances, <strong>coastal flooding</strong> events can be properly modelled, even in complex environments and under the action of wave overtopping, and thus provide detailed information. However, such models are computationally expensive, which prevents their use for instance for forecasting and warning. At the same time, metamodelling techniques have been explored for coastal hydrodynamics and have shown promising results. Metamodels are functions that aim to reproduce the behaviour of a “true” model (e.g., a numerical hydrodynamic model) for given input variables (for instance, offshore conditions). Within the RISCOPE research project (<a href="http://perso.math.univ-toulouse.fr/riscope">https://perso.math.univ-toulouse.fr/riscope</a>/) aiming at exploring to which extent such metamodelling techniques may allow to forecast coastal floods with a good accuracy, a <strong>simulated flood database</strong> has been built for the site of Gâvres (France), characterised by a significant effect of wave overtopping processes.</p> <p>The <strong>CFMDG dataset </strong>compiles a set of post-processed coastal flood simulations on the site of Gâvres. The dataset includes 250 scenarios. Each scenarios is defined by 6h time series centered on high tide, with one time series per forcing variables. The forcing variables (called X) are: local relative mean sea-level, tide, atmospheric storm surge, the offshore wave characteristics and the offshore wind. These scenarios combine past real (flood and no flood) events in the 1900-2021 time span with extreme statistics based events, and some complementary fictive events. The post-processed outputs (called Y) includes, for each scenario, the maximal flooded area (m²) and the maximal water depth (m) in each of the 64 618 inland model grid points.</p> <p>The modelling chain that allowed building this dataset relies on the joint use of a spectral wave model (WW3) to propagate the waves to the coast, and a non-hydrostatic wave-flow model (SWASH) to simulate the nearshore hydrodynamics and the flooding. The spatial and temporal resolution of the SWASH configuration validated on the Gâvres site are respectively 3 m and more than 10Hz. All the results are obtained for a Digital Elevation Model corresponding to the 2018 configuration of the site. </p> <p>Such type of dataset is of use for local knowledge, risk prevention, metamodel testing/training, and local coastal flood forecast. </p> <p>Part of this dataset has already been used in (<a href="http://www.mdpi.com/2077-1312/9/11/1191">Idier et al., 2021</a>; <a href="http://www.sciencedirect.com/science/article/pii/S0951832021006293?via%3Dihub">López-Lopera et al., 2021</a>; <a href="https://hal.science/hal-02536624">Betancourt et al., 2022</a>), to develop metamodels and set up a coastal flood forecast and early warning prototype.</p> <p>We hope and expect that making this dataset accessible will trigger further developments/investigations for improving risk knowledge on the considered site as well as methodological developments on machine-learning/metamodel-based techniques to support flood forecast.</p> <p>The table below summarizes the variables contained in the dataset, for each scenario.</p> <table> <tbody> <tr> <td> <p><strong>Variable name</strong></p> </td> <td> <p><strong>Description and unit </strong></p> </td> <td> <p><strong>Comment</strong></p> </td> </tr> <tr> <td> <p>Scenario n°</p> </td> <td> <p>Number of the scenario.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>INPUTS (X)</strong></p> </td> </tr> <tr> <td> <p>NM</p> </td> <td> <p>Relative mean sea level, referenced to the French vertical datum (m, IGN69)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>T</p> </td> <td> <p>Tidal water level (m), referenced to the relative mean sea level</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>S</p> </td> <td> <p>Atmospheric storm surge (m)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Hs</p> </td> <td> <p>Significant wave height (m)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Tp</p> </td> <td> <p>Wave peak period (s)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Dp</p> </td> <td> <p>Wave peak direction (° in nautical convention)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>U</p> </td> <td> <p>Wind speed (m/s)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>DU</p> </td> <td> <p>Wind direction (° in nautical convention)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>t</p> </td> <td> <p>Relative time centered on the high tide of each event (min)</p> </td> <td> <p>Not Concerned</p> </td> </tr> <tr> <td> <p>High Tide date</p> </td> <td> <p>UTC date for scenarios corresponding to past real events</p> </td> <td> <p>Not Concerned</p> </td> </tr> <tr> <td> <p><strong>OUTPUTS (Y)</strong></p> </td> </tr> <tr> <td> <p>Smax</p> </td> <td> <p>Maximum flooded area during the event (m²)</p> </td> <td> <p>Post-processed scalar output</p> </td> </tr> <tr> <td> <p>Hmax</p> </td> <td> <p>Maximum water depth reached during the event (m), provided for each inland location</p> </td> <td> <p>Post-processed functional (map) output</p> </td> </tr> <tr> <td> <p>longitude</p> </td> <td> <p>Longitude (°, WGS84)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>latitude</p> </td> <td> <p>Latitude (°, WGS84)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>XL93</p> </td> <td> <p>Longitude (m, Lambert 93)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>YL93</p> </td> <td> <p>Latitude (m, Lambert 93)</p> </td> <td> <p>For each inland location point</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><br> </p>
Modelling NBSs for coastal erosion and marine flooding: the Emilia-Romagna case studies
<p>The study was conducted in the context of the OPEn-air laboRAtories for Nature baseD solUtions to Manage environmental risks (OPERANDUM) project which is an H2020 project which aims at providing tools and methodologies for the assessment of NBS efficiency around the world. Two NBs were tested via modelling simulations on the Bellocchio Beach at Lido di Spina (Italy) located in the northern part of the Emilia-Romagna coast (northern Adriatic Sea): an artificial dune built with natural materials and a marine seagrass meadow.</p> <p>The artificial dune is an engineered structure that will mimic the functioning of natural dunes. Its aims are reducing both natural dune erosion and flooding in adjacent coastal lowlands. It consists of a barrier between the sea and land, in a similar way to a seawall. Unlike the latter, the NBS are ‘dynamic’, i.e. the dune/beach system interacts a great deal and is constantly undergoing small adjustments in response to changes in wind and wave climate or sea level. Its construction involves the placement of sediment from dredged sources on the beach and it will be reinforced with a structure composed of biodegradable material. Different typologies of experimental solutions are foreseen.</p> <p>The second NBS consists of an alongshore seagrass belt located in front of the coastal area. It was investigated as a potential mechanism for wave amplitude reduction. Among the few species that can live in the northern Adriatic Sea, Zostera Marina was chosen due to its ability to live in a marine environment influenced by freshwaters. A more detailed description can be found in (Pillai et al., 2021).</p> <p>The numerical model chain, specifically developed for the study, consists of an Ocean Circulation model, so-called SHYFEM (Umgiesser et al., 2004), a wave model, so-called WWIII (Alves and Ardhuin, 2016), and a morphological model, so-called XBeach (Roelvink et al., 2009). Ten years of XBeach simulations have been executed to simulate the morphological impacts on the coastal strip for the present (2010-19) and future climate (2040-49). For each 10 years period, four scenarios were simulated: the baseline scenario without NBS (baseline_run), the scenario with the dune (dune_run), the scenario with the seagrass effect (seagrass_run) and the scenario with the two NBS integration (dune_seagrass_run).XBeach was forced with sea level and wave time series predicted by the SHYFEM and WWIII models respectively.</p> <p>The model domain consists in a curvilinear structured grid of about 3.2 km (longshore) x 2.8 km (cross-shore) covering the coastal stretch of Bellocchio beach at Lido di Spina (Italy) and extends seaward up to about 10 m depth.</p> <p>The performance of the NBSs and their impact on coastal erosion and marine flooding were investigated. For both present and future scenarios (201-2019 and 2040-2049), the reduction in wave intensity obtained with the seagrass provided greater benefits in terms of erosion mitigation and flood reduction. The analysis highlighted the limited scale of the dune intervention, in particular under present conditions, highlighting that the longer the artificial dune implemented, the larger the beach and dune area protected. For the future scenarios, the results are still significant and even small projects are expected to help in mitigating coastal erosion and marine flooding.</p> <p>For long-period simulations, no relevant improvements in reducing beach erosion was observed when the artificial dune was combined with the seagrass meadows with respect to the seagrass effects only. Instead, a dominant increase in sea levels will probably highlight the dune functions in hindering the marine ingression into the lagoon area behind and the consequent sediment redistribution.</p> <p>This dataset consists of XBeach model results, mainly:</p> <ul> <li>Morphological evolution of the coastal bottom at Bellocchio beach (Lido di Spina, Italy) in terms of initial and final bed levels, for the current (201-2019) and the future (2040-2049) scenarios. Results are available for the four NBS scenarios described above (and detailed in the Presentation.pdf)</li> <li>Maximum flood depth, defined as the non-simultaneous maximum water depth on the beach domain of Bellocchio (Lido di Spina, Italy) for the current (201-2019) and the future (2040-2049) scenarios. Results are available for the four NBS scenarios described above (and detailed in the Presentation.pdf) </li> <li>Erosion-deposition maps for the current (201-2019) and the future (2040-2049) scenarios. Results are available for the four NBS scenarios described above (and detailed in the Presentation.pdf).</li> </ul>
Data: Cutting the costs of coastal protection by integrating vegetation in flood defences.
<p>File: levee_crest_height_reduction_per_country_version_July2021.nc<br>Fields: (1) Crest height reduction m per km along the populated coastline susceptible to flooding (return period = 100 years)<br> (2) Crest height reduction cost saving per country in million USD<sub>2005</sub> PPP along the populated coastline susceptible to flooding (return period = 100 years)<br> (3) Cost savings as percentage of GDP<sub>2005</sub> along the urban populated coastline susceptible to flooding (return period = 100 years)</p> <p>File: transectdata_version_July2021.nc<br> Transectdata of vegetated transects within the study area.<br>Fields: <br>(1) rps = return period <br>(2) fid = id of the transects<br>(3) centroids = coordinates of the transects<br>(4) inun = (1) in area susceptible to flooding<br>(5) urban = (1) in urban area, (0) not in urban area<br>(6) veg_width = derived coastal vegetation belt width along the foreshore<br>(7) veg_type = derived coastal vegetation type along the foreshore (1: salt marshes, 2: mangroves)<br>(8) hsig = Offshore significant wave heights (multiple return periods) corresponding to the transects<br>(9) wave period = Offshore peak wave period (multiple return periods) corresponding to the transects<br>(10) surge = Extreme water level combination of surge and tide (m +MSL) (multiple return periods)<br>(11) veg_z0 = elevation at the start of the vegetated zone (m +MSL)<br>(12) hrms_end_noveg = root mean square wave height at the end of the foreshore (without vegetation) (multiple return periods)<br>(13) hrms_endveg = root mean square wave height at the end of the foreshore (with vegetation) (multiple return periods) <br>(14) pdens_15km = population density derived using buffer of 15 kilometre radius</p>
Monitoring NBS for coastal erosion and marine flooding: the Emilia-Romagna case study
<p>The study was conducted in the context of the OPERANDUM project which is an H2020 project which aims at providing tools and methodologies for the assessment of NBS efficiency around the world. As NBS will be tested an artificial dune built with natural materials. </p> <p>The artificial dune is an engineered structure that will mimic the functioning of natural dunes. Its aims are reducing both natural dune erosion and flooding in adjacent coastal lowlands. It consists of a barrier between the sea and land, in a similar way to a seawall. Unlike the latter, the NBS are ‘dynamic’, i.e. the dune/beach system interacts a great deal and is constantly undergoing small adjustments in response to changes in wind and wave climate or sea level. Its construction involves the placement of sediment from dredged sources on the beach and it will be reinforced with a structure composed of biodegradable material. Different typologies of experimental solutions are foreseen.</p> <p>The Bellocchio Beach at Lido di Spina (Italy) was initially chosen for the study, however the Volano beach was selected as the new study area because of the strong erosion caused by an intense storm event in December 2020 at Bellocchio. The dune was built on the Volano beach and monitoring surveys were carried out on this new site. </p> <p>A morphological monitoring aimed to assess the beach evolution and the performance of the NBS were performed. Monitoring of morphology evolution of shoreline and inland area provide information about impact of the NBS on coastal erosion. Furthermore, the changes in the form of the work give information about the resistance of the NBS to wave attacks. Sedimentological campaigns have been planned in order to provide information regarding the texture of the sediments present in the area detected and possibly highlight changes after the construction of the dune.</p> <p>Three monitoring campaigns were carried out before, immediately after and six months later the construction of the dune (January, May and October 2022). All data were analysed to assess local coastal dynamics and NBS evolution. </p> <p>The monitoring consisted of: </p> <ul> <li> <p>topographic and bathymetric surveys (GNSS and multibeam/singlebeam echosounder) to generate DTMs of the entire area (10 m cell size); </p> </li> <li> <p>aerial photogrammetric surveys by UAV for the production of orthophotos and high resolutions DTMs of the emerged beach (1m cell size) and of the dune area (0.2 m cell size); </p> </li> <li> <p>sediment sampling and grain size analysis. </p> </li> </ul> <p>Surveys show that morphological and sedimentological changes are determined mostly by anthropic actions to the beach and seabed maintenance (artificial winter banks and Sacca di Goro channel). </p> <p>Regarding the dune area no significant changes in morphology were observed due to the limited period between the surveys. Appreciable signals were detected, such as the natural recolonization by pioneer plant species and the slight sand accumulation on the dune foot.</p> <p>This dataset consists of data related to monitoring activities. </p>
Joint series underlying the paper " Compound coastal-riverine flooding of St. Lawrence River coasts under sea level rise conditions"
<p>Compound coastal-riverine flooding, known as flooding events caused by the co-occurrence of high streamflow and coast water levels, can have substantial economic and social implications in low-lying coastal regions. Recent studies over Canada’s coasts have shown that neglecting the interdependency between flood drivers can underestimate the risk of flooding by up to 50%. However, to date, such interdependency and its effect on the frequency of compound riverine-coastal flooding has not been investigated for the coasts of the St. Lawrence River, Estuary, and Gulf system (StL), where Sea Level Rise (SLR), along with intensified river peaks, are already threatening communities. In this study, a copula-based bivariate frequency analysis (AND hazard scenario) was applied to quantify the differences between joint return periods computed under dependent and independent assumptions, for 26 sites along the StL. Furthermore, design pairs for 100-year joint events in the historical period (1986-2020) were compared with the 2100 horizon, where the SLR associated with the RCP8.5 emission scenario was incorporated into the water level time series. Results show that 1) the independence assumption can underestimate the frequency of compound flooding in the Fluvial Section of the StL by up to 30 times and 2) the SLR can increase the frequency of compound flooding by up to 50 times in the Estuary and the Gulf and by up to 5 times in the Fluvial Section of the StL. This study highlights the need for explicit consideration of the dependence between flood drivers and of SLR in the delineation of flood maps along all of the coasts of the St. Lawrence.</p>
Climate and vegetation change in a coastal marsh: two snapshots of groundwater dynamics and tidal flooding at Piermont Marsh, NY spanning 20 years
<p>Groundwater hydrology plays an important role in coastal marsh biogeochemical function, in part because groundwater dynamics drive the zonation of macrophyte community distribution. Changes that occur over time, such as sea level rise and shifts in habitat structure are likely altering groundwater dynamics and eco-hydrological zonation. We examined tidal flooding and marsh water table dynamics in 1999 and 2019 and mapped shifts in plant distributions over time, at Piermont Marsh, a brackish tidal marsh located along the Hudson River Estuary near New York City. We found evidence that the marsh surface was flooded more frequently in 2019 than in 1999, and that tides were propagating further into the marsh in 2019, although marsh surface elevation gains were largely matching that of sea level rise. The changes in groundwater hydrology that we observed are likely due to the high tide rising at a rate that is greater than that of mean sea level. In addition, we reported on changes in plant cover by <em>P. australis</em>, which has displaced native marsh vegetation at Piermont Marsh. Although <em>P. australis</em> has increased in cover, wrack deposition and plant die off associated Superstorm Sandy allowed for native vegetation to rebound in part of our focus area. These results suggest that climate change and plant community composition may interact to shape ecohydrologic zonation. Considering these results, we recommend that habitat models consider tidal range expansion and groundwater hydrology as metrics when predicting the impact of sea level rise on marsh resilience.</p>
Data for "Neglecting the coupled effect of coastal flooding and erosion can lead to spurious projections and maladaptation"
<p>Data for the reproduction of the figures in the manuscript "Neglecting the coupled effect of coastal flooding and erosion can lead to spurious projections and maladaptation".</p>
Estimating household preferences for coastal flood risk mitigation policies under ambiguity
<p>Risk mitigation policies (like dike rising) are essential to address increasing coastal flood risks due to global warming. Furthermore, the optimal level of risk mitigation policy should be determined by public preferences for risk reduction. However, it is difficult to reveal public preferences for coastal flood risk reduction because projections of coastal flood risks inevitably involve uncertainty. This study aims to estimate household preference for coastal flood reduction under ambiguity and multiple projections of coastal flood risks. By coupling storm surge inundation simulations and stated preference experiments with decision models, we estimate the expected loss reduction, risk premium, and ambiguity premium for coastal flood risk mitigation policies. Results of the study show that ignoring the ambiguity premium causes significant undervaluation of coastal flood risk mitigation, and the ambiguity premium stems from households' over-concern about the worst projection, which may lead to an over-allocation of resources to prevent inundation damage caused from the worst-case flood before a disaster. The study concludes that a risk mitigation policy combining public insurance for the worst projection and pre-disaster prevention measures can be effective and efficient.</p>
Output Data for "Effective adaptation options to alleviate nuisance flooding in coastal megacities – learning from Ho Chi Minh City, Vietnam"
<p>This data repository pertains to the research article "Effective adaptation options to alleviate nuisance flooding in coastal megacities - learning from Ho Chi Minh City, Vietnam" by Leon Scheiber, Nivedita Sairam, Mazen Hoballah Jalloul, Kasra Rafiezadeh Shahi, Christian Jordan, Jan Visscher, Tara Evaz Zadeh, Laurens J.N. Oostwegel, Danijel Schorlemmer, Ngo Thanh Son, Hong Nguyen Quan, Torsten Schlurmann, Matthias Garschagen, Heidi Kreibich, published in Earth’s Future, 2024.</p> <p>The provided output data comprise:<br>- Risk Components (incl. inundation depths, exposed households and building values as well as relative losses)<br>- Expected Annual Damage (incl. 25/50/75th percentiles)<br>- Annually Affected Households </p> <p> </p>
Data from: Detecting the effects of rapid tectonically-induced subsidence on Mayotte Island since 2018 on beach and reef morphology, and implications for coastal vulnerability to marine flooding
<p>This dataset contains data from the monitoring morphological evolution of beaches and coral reefs in Mayotte island. Mayotte, part of the coral reef-fringed Comoro archipelago in the SW Indian Ocean, experienced in 2018 and 2019 an intense seismic crisis. The repeated earthquake activity since May 2018 has been associated with deformation of the surface of Mayotte, resulting in land subsidence.</p> <p>The earlier 2006-2008 profiles were realized using a Leica TC 407® total station, and referenced to local IGN 50 benchmarks. The more recent 2019, 2020, and 2021 surveys were carried out using a GNSS differential Trimble R8S® system. Given the rapid subsidence that has affected Mayotte, the benchmarks used in this study, like others in Mayotte, need to be recalibrated by the IGN (French Institut Géographique National) and SHOM. This has still not yet been done, as the final outcome of the vertical island movements is still not clear.</p>
Projections of the Timing of Decreasing Coastal Flood Protection
<p>This data set contains projections of the timing of decreasing coastal flood protection (i.e., projections of the timing of frequency amplifications of estimated flood protection standards) associated with Hermans et al. (in revision), The Timing of Decreasing Coastal Flood Protection Due to Sea-Level Rise. It contains the output of extreme value analysis of high-frequency GESLA3 tide gauge observations (daily maxima, generalized Pareto distribution fits and return curves), total AR6 sea-level projections interpolated to GESLA3 tide gauge locations, and the required sea-level rise for and the timing of frequency amplifications relative to estimated coastal flood protection standards (FLOPROS) or to the historical centennial event. Results are included for both automatically selected extremes thresholds and for a constant threshold of 98.8% at all locations.</p> <p>The manuscript that this data accompanies can be found at <a href="https://www.nature.com/articles/s41558-023-01616-5">https://www.nature.com/articles/s41558-023-01616-5</a>.</p> <p>The code used to produce this data can be found at <a href="https://github.com/Timh37/TimingAFs">https://github.com/Timh37/TimingAFs</a>.</p> <p><strong>Required Acknowledgements and Citations</strong></p> <p>Users of this dataset are asked to cite:</p> <ul> <li>The manuscript that this dataset accompanies: Hermans, T.H.J., Malagón-Santos, V., Katsman, C.A. <em>et al.</em> The timing of decreasing coastal flood protection due to sea-level rise. <em>Nat. Clim. Chang.</em> <strong>13</strong>, 359–366 (2023). https://doi.org/10.1038/s41558-023-01616-5</li> <li>Garner, G. G., T.H.J. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Global Mean Sea-Level Rise Projections. Version 20210809. Dataset accessed [YYYY-MM-DD] at <a href="https://doi.org/10.5281/zenodo.5914709">https://doi.org/10.5281/zenodo.5914709</a></li> <li>Tiggeloven, T., de Moel, H., Winsemius, H. C., Eilander, D., Erkens, G., Gebremedhin, E., Diaz Loaiza, A., Kuzma, S., Luo, T., Iceland, C., Bouwman, A., van Huijstee, J., Ligtvoet, W., and Ward, P. J.: Global-scale benefit–cost analysis of coastal flood adaptation to different flood risk drivers using structural measures, Nat. Hazards Earth Syst. Sci., 20, 1025–1044, <a href="https://doi.org/10.5194/nhess-20-1025-2020">https://doi.org/10.5194/nhess-20-1025-2020</a>, 2020</li> </ul> <pre><code>R.E.K. was supported by the National Science Foundation (NSF) as part of the Megalopolitan Coastal Transformation Hub (MACH) under NSF award ICER-2103754. T.H.J.H., V.M.-S. and A.B.A.S. were supported by PROTECT. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869304, PROTECT contribution number TBD.</code></pre> <p> </p>
Coastal flood maps and extreme sea levels for the German Baltic Sea coast
<p>The provided data was produced as part of the Ecas-Baltic project (2020 - 2023). The project is funded by the Federal Ministry of Education and Research in Germany (BMBF, funding code 03F0860H).</p> <p>The dataset contains information supporting the conclusions presented in the following publication (the final, revised version of the article will also be accessible via the preprint given below):</p> <p>Kiesel, J., Lorenz, M., König, M., Gräwe, U., and Vafeidis, A. T.: A new modelling framework for regional<br> assessment of extreme sea levels and associated coastal flooding along the German Baltic Sea coast,<br> Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2022-275, in review, 2023.</p> <p>The dataset contains:</p> <p>- the location and names of flood boundary stations</p> <p>- the boundary conditions provided by the coastal ocean model at each of the flood boundary stations for all storm surge events simulated in the study cited above</p> <p>- the flood maps containing both the maximum flood extent and maximum inundation depth at every grid cell of the coastal inundation model</p> <p>- the spatially explicit results of the extreme value analysis for every grid cell in the coastal ocean model</p> <p>- the modelled monthly peak water levels between 1961 and 2018 for every grid cell of the coastal ocean model</p> <p>- the modelled timeseries of water levels during the storm surge from January 2nd 2019 and the entire hindcast period (1961-2018) for all tide gauges along the German Baltic Sea coast</p> <p>For further information, we refer the reader to the readme file in this dataset or the publication itself.</p> <p> </p> <p> </p> <p> </p>
Estimating household preferences for coastal flood risk mitigation policies under ambiguity
Open the record for dataset details and reuse information.
Climate and vegetation change in a coastal marsh: two snapshots of groundwater dynamics and tidal flooding at Piermont Marsh, NY spanning 20 years
Open the record for dataset details and reuse information.
Archive Documents Covering Rhode Island Coastal Flood Protection (1954-2002)
<p>This is a repository of nearly 2000 primary and secondary documents covering the politics of coastal flood protection infrastructure projects form the 1950s through the end of the 20th century. Nearly all documents are from the 1950s and 1960s. They are in the form of mostly scans and photos and were collected from public and private archives between September and November 2019.</p> <p> </p> <p>Documents include internal memos, project-related materials, and newspaper clippings from the New England District of the USACE archived at the U.S. National Archives and Records Administration facility (Waltham, Massachusetts), personal papers from Congressman John E. Fogarty, Senator John Pastore, and Governor Dennis J. Roberts archived at Providence College, over three decades of newspaper articles on microfilm from the Providence Journal and Evening Bulletin archived at both the Rhode Island Historical Society and the Providence Public Library, and additional materials associated with the Fox Point Hurricane Barrier at the Providence City Archive (all Providence, Rhode Island).</p> <p> </p> <p>A selection of documents from this repository were used to reconstruct event sequences associated with the Fox Point Hurricane Barrier in Providence Rhode Island and the proposed (but never built) Narragansett Bay Hurricane Barrier.</p> <p> </p> <p>This selection of documents is in an organized folder tree, entitled "Rasmussen2022" and is used to support the arguments in a climate change adaptation study. As of February 2022, the study is under review with the Journal of Water Resources Policy and Management, "Coastal defense megaprojects in an era of sea-level rise: politically feasible strategies or Army Corps fantasies?", by D.J. Rasmussen, Robert E. Kopp, and Michael Oppenheimer.</p>
Evaluation of flood hazards in data-sparse coastal lowlands: highlighting the Ayeyarwady Delta (Myanmar)
<p>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):<br>Evaluating flood hazards in data-sparse coastal lowlands: highlighting the Ayeyarwady Delta (Myanmar). Environmental Research Letters.<br>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.</p>
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"
<p>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"<br><br><span><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<br><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<br></span></p>
Wave setup and wave-induced flooding dataset for "Coastal flooding and sea-level rise allowances in atoll island"
<p>This dataset contains the wave setup and wave-induced flooding from the simulations in a profile of a coral reef island. The complete description of the simulations can be found in "Coastal flooding and sea-level rise allowances in atoll island". The wave setup and flooding values correspond to the median values of the 3 simulations performed for each parameter combination. A measure of the dispersion of the three simulation is also given as the standard deviation of the three simulations.</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>
Supplementary Data Tables for 'The Global Drivers of Chronic Coastal Flood Hazards under Sea-Level Rise'
<p>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 </p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.