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122 results for “sea level rise”
PIE LTER quadrat percent cover associated with marsh sites used in space for time sea level rise study, Rowley, MA.
To assess community structure, first each 1 square meter is first examined by moving grass around to scan the substrate for mussels, crab burrows, amphipod burrows, and Littorina littorea snails. Researchers then estimates the percent live cover of a variety of plant species, checking against a standard list of species. Species not on the list are also recorded, and, if unknown, for identification in the lab after sampling. Each species has its cover recorded individually, which could lead to greater than 100% cover. Percent cover of bare space, detritus, and wrack are also recorded. Last, Melampus bidentata snails are recorded in 10 cm x 10 cm at each corner of the quadrat.
Coastal SEES Collaborative Research: Coastal Sustainability: A cross-site comparison of salt marsh persistence in response to sea-level rise and feedbacks from social adaptations
Coastal ecosystems are often valued for decision-making purposes based on monetized market and non-market values of goods and services, and associated economic impacts. Examples include values of fishery landings, price changes for waterfront homes, and tourism revenues. Monetized quantities such as these do not provide a comprehensive characterization of the values provided by these ecosystems. Human reliance on the goods and services provided by ecosystems and the global decline in the health of many of these ecosystems suggests the need for ecosystem valuation to help inform decision-making and conservation policy. However, traditionally employed economic valuation methods are rarely able to capture the full scope of the benefits ecosystems provide, including benefits provided by "cultural" ecosystem services. Qualitative methods such as focus groups can provide insight on these values not available through quantitative methods alone. This research explores public perceptions of salt marsh value through the use of semi-structured focus groups in marsh-adjacent communities in Massachusetts, Virginia, and Georgia. The data include de-identified focus group transcripts from three 90-minute focus groups held in each state. Initial questions were drawn from the same semi-structured question list in each focus group, with exploratory follow-up questions based on participant responses. Results of text analysis suggest that in case study communities, outdoor experiences in salt marshes inspire serenity in Massachusetts, influence shore identities in Virginia, and promote stewardship cultivation in Georgia. Perceived threats to these benefits, such as the threat of residential development, industrial pollution, and increasing flood risk, together constitute the context for various community responses related to marsh protection. Results supplement information from extant economic valuations and show the importance of utilizing diverse methods to elicit information on soci
Synthesis of Sea level rise and carbon accumulation rates in United States tidal wetlands
Coastal wetlands accumulate soil carbon more efficiently than terrestrial systems, but sea level rise potentially threatens the persistence of this prominent carbon sink. Here, we combine a published dataset of 372 soil carbon accumulation rates from across the United States with new analysis of 131 sites in coastal Louisiana. The combined database featured 503 measurements of carbon accumulation, spanning broad gradients in mean annual temperature, tide range, and dominant vegetation.
Engaging the Business and Tourism Industry in Visualizing Sea Level Rise Impacts to Transportation Infrastructure in Waikiki, Hawaii
<p>Transportation planners in coastal communities plan for future hazards and risks of sea-level rise (SLR), and often, they communicate risk in public meetings via PowerPoint presentations with charts as well as two-dimensional (2D) maps that visualize information using Geographic Information Systems (GIS) technologies. The project investigates the use of immersive technology to communicate SLR risk, including the development of an immersive three-dimensional (3D) model of the Waikiki neighborhood of Honolulu, Hawaii. According to the project’s original methodology, participants would have experienced the model using virtual reality (VR). However, due to the COVID-19 pandemic, the team pivoted to creating and implementing an internet-based survey instrument with embedded 2D charts and video of the animated 3D model. The flooding projections were derived from National Oceanic and Atmospheric Administration (NOAA) data. NOAA supplies the SLR Viewer, a screening-level tool that uses the best-available national projections to map areas vulnerable to current and future flood risks.</p>
Replication package for "Why do people persist in sea-level rise threatened coastal regions? Empirical evidence on risk aversion and place attachment"
<p><strong>Steps to replicate the tables and figures in “Why do people persist in sea-level rise threatened coastal regions? Empirical evidence on risk aversion and place attachment”</strong></p> <p><em>by Ivo Steimanis, Matthias Mayer and Björn Vollan</em></p> <p><strong>General information:</strong></p> <ul> <li>Instructions for replication of the results using Stata. All do-files were created in Stata 16.</li> <li>There are 4 folders (DO-FILES, DTA-FILES, OUTPUT, XLS-FILES), in the replication package. Copy these folders to your computer in a common directory</li> </ul> <p> </p> <p><strong>Do-files:</strong></p> <ul> <li>In the DO-FILES folder run the <strong>“00_master.do”</strong> to replicate the results reported in the main manuscript and the supplementary materials. The results will be saved in the OUTPUT folder. All additional Stata packages will be automatically installed.</li> <li><strong>“01_merge_generate.do” </strong>merges the different datasets and creates additional variables using in the analysis</li> <li><strong>“02_analysis.do” </strong>provides the code to replicate all figures and tables reported in the main manuscript and supplementary materials</li> </ul> <p> </p> <p><strong>Data sets:</strong></p> <ul> <li>“bd_combine.dta”: cleaned survey data from Bangladesh</li> <li>“vn_combine.dta”: cleaned survey data from Vietnam</li> <li>“data_analysis.dta”: main data set with the survey data from Bangladesh and Vietnam merged</li> </ul>
Biogeomorphic modeling to assess the resilience of tidal-marsh restoration to sea level rise and sediment supply - Supporting code and data
<p>Code and data to reproduce figures and analyses of the paper:</p> <p>Gourgue, O., van Belzen, J., Schwarz, C., Vandenbruwaene, W., Vanlede, J., Belliard, J.-P., Fagherazzi, S., Bouma, T.J., van de Koppel, J., and Temmerman, S.: Biogeomorphic modeling to assess resilience of tidal marsh restoration to sea level rise and sediment supply, Earth Surf. Dynam., submitted.</p> <p>Standard Python dependencies:</p> <ul> <li>GDAL</li> <li>Geopandas</li> <li>Matplotlib</li> <li>NumPy</li> <li>Rasterio</li> <li>SciPy</li> <li>Seaborn</li> <li>Shapely</li> <li>scikit-learn</li> </ul> <p>Third-party Python dependencies:</p> <ul> <li>Centerline (https://github.com/fitodic/centerline)</li> <li>pputils (https://github.com/pprodano/pputils)</li> <li>pysheds (https://github.com/mdbartos/pysheds)</li> </ul> <p>In-house Python dependencies:</p> <ul> <li>Demeter 1.0.5 (https://doi.org/10.5281/zenodo.5205258)</li> <li>OGTools 1.1 (https://doi.org/10.5281/zenodo.3994952)</li> <li>TidalGeoPro 0.1 (https://doi.org/10.5281/zenodo.5205285)</li> </ul>
Extreme sea level rise along the Indian Ocean coastline: Observations and 21st century projections
<p>This file include all the data sets used to make the figures in the paper " <strong><em>Extreme sea level rise along the Indian Ocean coastline: Observations and 21<sup>st</sup> century projections</em></strong> "</p>
Data for publication "Committed sea-level rise under the Paris Agreement and the legacy of delayed mitigation action".
<p>Data underlying the publication "Committed sea-level rise under the Paris Agreement and the legacy of delayed mitigation action".</p> <p>Journal: Nature Communications</p> <p>Authors: <em>Matthias Mengel<sup>1*</sup></em><em>, Alexander Nauels</em><sup><em>2</em></sup><em>, Joeri Rogelj</em><sup><em>3,4</em></sup><em>, Carl-Friedrich Schleussner<sup>1,5</sup></em></p> <p>(1) Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, D-14412 Potsdam, Germany</p> <p>(2) Australian-German College of Climate & Energy Transitions, The University of Melbourne, Parkville, Victoria 3010, Australia</p> <p>(3) ENE Program, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg A-2361, Austria</p> <p>(4) Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, Zurich 8006, Switzerland</p> <p>(5) Climate Analytics, Ritterstr. 3, 10969 Berlin, Germany</p> <p>(*) email matthias.mengel@pik-potsdam.de</p> <p>Abstract:</p> <p>Sea-level rise is a major consequence of climate change that will continue long after emissions of greenhouse gases have stopped. The 2015 Paris Agreement aims at reducing climate-related risks by reducing greenhouse gas emissions to net zero and limiting global-mean temperature increase. Here we quantify the effect of these constraints on global sea-level rise until 2300 including Antarctic ice-sheet instabilities. We estimate median sea-level rise between 0.7 and 1.2m if net zero greenhouse gas emissions are sustained until 2300, varying with the pathway of emissions during this century. Temperature stabilization below 2°C is insufficient to hold median sea-level rise until 2300 below 1.5m. We find that each 5-year delay in near-term peaking of CO2 emissions increases median year-2300 sea-level rise estimates by ca. 0.2m, and extreme sea-level rise estimates at the 95th percentile by up to 1m. Our results underline the importance of near-term mitigation action for limiting long-term sea-level rise risks.</p> <p> </p> <p>Large zip files provides data. Small zip file python code for plotting and writing supplementary data.</p>
Variable vertical land motion for sea level rise projections
<h1><strong>Data for Govorcin et al., 2024: Variable vertical land motion for sea level rise projections [submitted for publication].</strong></h1> <p><strong>Disclaimer:</strong> Data is subject to change due to the review process.</p> <p><strong>Repository Contains:</strong></p> <ul> <li> <p><strong>Vertical Land Motion over California</strong></p> <ul> <li><strong>Reference:</strong> International Terrestrial Reference System, solution 2014 (ITRF2014)</li> <li><strong>Period:</strong> 2015-2023</li> <li><strong>Data Type:</strong> GeoTIFF</li> <li><strong>Unit:</strong> mm/yr</li> </ul> </li> <li> <p><strong>Vertical Land Motion (Propagated) Formal Uncertainties (Rates Std.) over California</strong></p> <ul> <li><strong>Reference:</strong> International Terrestrial Reference System, solution 2014 (ITRF2014)</li> <li><strong>Period:</strong> 2015-2023</li> <li><strong>Data Type:</strong> GeoTIFF</li> <li><strong>Unit:</strong> mm/yr</li> </ul> </li> <li> <p><strong>Vertical Land Motion Temporal Variability over California</strong></p> <ul> <li><strong>Period:</strong> 2015-2023</li> <li><strong>Data Type:</strong> GeoTIFF</li> <li><strong>Unit:</strong> mm/yr</li> </ul> </li> <li> <p><strong>Archive: Output HDF5 (Mintpy format) and GNSS Files</strong></p> <ul> <li>Includes <code>velocity.h5</code>, <code>geometry.h5</code>, <code>gnss_model</code>, <code>calibrated_velocity.h5</code>, <code>CA_3D_rates.h5</code>, and <code>temporal variability</code> per track and merged, projected to vertical. See <strong>README</strong> for more information.</li> </ul> </li> </ul> <h2>Citation:</h2> <p>If you use this data in your work, research or publication, please cite the following article:</p> <p>Govorcin, M. Bekaert, D., Hamlington, B., Sangha, S., Sweet, W. (2024). Variable Vertical Land Motion for Sea Level Rise Projections, 01 August 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-4676043/v1]</p> <h2>Acknowledgment</h2> <p>The research was conducted at the Jet Propulsion Laboratory, California Institute of Technology. This research was supported by the Observational Products for End-Users from Remote Sensing Analysis (OPERA) project (<a href="https://www.jpl.nasa.gov/go/opera" target="_blank" rel="noopener">https://www.jpl.nasa.gov/go/opera</a>), managed by the Jet Propulsion Laboratory and funded by the Satellite Needs Working Group, that is creating remote sensing products to address Earth observation needs across U.S. civilian federal agencies.</p>
Virtual tide gauges for predicting relative sea level rise supporting data
<p>Data and results from the publication</p> <p> Hawkins R., Husson L., Choblet G., Bodin T. and Pfeffer J.,<br> "Virtual tide gauges for predicting relative sea level rise",<br> JGR: Solid Earth,<br> 2019 (submitted)<br> </p> <p>Software available from </p> <p>https://github.com/rhyshawkins/TransTessellate2D/</p> <p> </p>
Vanishing glaciers: a cause of sea-level rise and a threat to water supply
<p><span>The video discusses the contribution of glaciers to sea-level rise and their importance for humans. Due to their proximity to 0°C temperature, glaciers respond much faster to global warming than ice sheets, making their mass loss a significant contributor to sea-level rise during the 20th century and beyond. To determine the health state of glaciers and their contribution to sea-level rise, glaciologists calculate their mass budget, which has been largely negative for several decades now, indicating that glaciers are losing mass year after year, causing them to retreat. The video emphasizes the need for immediate reductions of greenhouse gas emissions to preserve these crucial and vulnerable water resources and natural heritage.</span></p>
Data from article "Sea-level rise in Venice: historic and future trends (review article)"
<p>Data from the article Zanchettin D., et al.: Sea-level rise in Venice: historic and future trends (review article), Nat. Hazards Earth Syst. Sci., 21, 1–35, 2021, https://doi.org/10.5194/nhess-21-1-2021</p> <p>The dataset contains:</p> <p>- Historical tide gauge data for Venice (relative sea level, or RSL, and RSL corrected for vertical land movement) for the winter (JFM) and autumn (OND) seasons.</p> <p>- sea-level projections for Venice for the RCP2.6, the RCP8.5 and a high-end scenario.</p>
Database of US Regional Sea-level Rise Assessment Reports (Current for 2021)
<p>Database of regional sea-level rise assessment reports in the U.S. The data set includes nearly 400 projections from 31 reports for 54 locations in the U.S. and Puerto Rico, and accompanies the publication "Evaluating Knowledge Gaps in Sea-level Rise Assessments from the United States", Garner et al., <em>Earth's Future</em>. The data set is comprised of the most recent published assessment reports for each location (deadline of December 31<sup>st</sup>, 2021). Fields included in the database are listed below. </p> <p>Though substantial effort was made to ensure that all available and relevant assessment reports were included in the database, it is perhaps inevitable that a small number of reports were overlooked and may not be included here. </p> <p>1) Title of the Assessment Report</p> <p>2) Region of focus for the projection</p> <p>3) Broader geographical region for the projection (U.S. Northeast, U.S. South, or U.S. West)</p> <p>4) Latitude of the projection</p> <p>5) Longitude of the projection</p> <p>6) Lead Author of the report</p> <p>7) Sectors with which the authors are affiliated</p> <p>8) Third-party report flag (Yes = not locally produced, No = locally produced)</p> <p>9) Year the report was published</p> <p>10) Year the previous iteration of the report was published, if applicable</p> <p>11) Methodology of the projection</p> <p>12) Emission scenario used for the project</p> <p>13) Baseline year for the projection</p> <p>14) End year for the projection</p> <p>15) Lower estimate of sea-level rise</p> <p>16) Definition of the lower estimate of sea-level rise</p> <p>17) Central estimate of sea-level rise</p> <p>18) Definition of the central estimate of sea-level rise</p> <p>19) Upper estimate of sea-level rise</p> <p>20) Definition of the upper estimate of sea-level rise</p> <p>21) Vertical Land Motion (Yes = included, No = excluded)</p> <p>22) Land Water Storage (Yes = included, No = excluded)</p> <p>23) Greenland Ice Sheet (Yes = included, No = excluded)</p> <p>24) Antarctic Ice Sheet (Yes = included, No = excluded)</p> <p>25) Glaciers (Yes = included, No = excluded)</p> <p>26) Thermal Expansion (Yes = included, No = excluded)</p> <p>27) Ocean Dynamics (Yes = included, No = excluded)</p> <p>28) Link to the report containing the projection</p> <p>29) Notes relevant to the projection's database entry</p>
Modelling the response of mangroves and saltmarshes to sea-level rise: model development and validation
<p>Data used to parameterise and calibrate a model (IWEM0D) of the response of coastal wetlands to sea-level rise. Model parameterisation with core data from Westernport Bay, Victoria, Australia.</p> <p>IWEM0D (Intertidal Wetland Evolution Model - 0D) simulates how mangrove forests and saltmarsh wetlands respond to sea-level rise. The model framework, as detailed in Rogers et al. (in review), treats surface elevation change over time as a function of:</p> <ul> <li>Present elevation <code>E</code></li> <li>Inorganic/mineral matter accumulation rate <code>MAR</code></li> <li>Organic matter addition rate <code>OAR</code> for mangroves and saltmarsh</li> <li>Autocompaction <code>AC</code></li> </ul> <p>Specifically, incremental change in surface elevation <code>E</code> over time <code>t</code> is modelled as:</p> <p><code>E[t+1] = E[t] + MAR[t] + OAR[t] - AC[t]</code></p>
The evolving landscape of sea-level rise science from 1990 to 2021
<p>This dataset contains the bibliometric information (e.g., list of authors, keywords, journals, references, etc) for 14,951 sea-level rise related articles published between 1990 and 2021, as retrieved from the Web of Science.</p> <p>This dataset was used to scrutinise the evolution of sea-level rise science:</p> <ul> <li>Khojasteh D, Haghani M, Nicholls R, Moftakhari H, Sadat-Noori M, Mach K, Fagherazzi S, Vafeidis A, Barbier E, Shamsipour A, Glamore W. The evolving landscape of sea-level rise science from 1990 to 2021. <em>Communications Earth & Environment</em>. 2023.</li> </ul> <p>The zip file contains 30 text files where the bibliometric information is stored (each text file comprises the bibliometric information of maximum 500 sea-level rise articles). This dataset also includes an Excel file with data required to reproduce the figures presented in the manuscript. </p>
Sea level rise along China coast from 1950 to 2020 (datasets)
<p>this dataset contain the reconstructed sea level changes along China coast from 1950-2022;<br> the file name consists of two parts: PSMSL + ID;<br> PSMSL means Permanent Service for Mean Sea Level (https://psmsl.org/);<br> the ID is assigned by PSMSL to the tide gauges, users can find more information on the tide gauges from PSMSL website;<br> Each file contains five columns data:<br> first column: time year<br> second column: tide gauge records<br> third column: the reconstructed sea level<br> fourth column: the upper bound<br> fifth column: the lower bound<br> more details can be found in the following paper:<br> Dapeng Mu, Tianhe Xu, Haoming Yan, 2023. Sea level rise along China coast from 1950-2020, Science China-Earth Sciences<br> Any questions can be addressed to: mdp321@126.com or mdp@sdu.edu.cn</p>
PIE LTER location and sample dates for sites used in space for time sea level rise study, Rowley, MA.
This dataset contains the GPS-ed locations of the quadrats used for the 2017-2018 Space for Time substitution experiment in tidal creek marshes off the Rowley River and Plum Island Sound in Rowley Massachusetts. The space for time study uses an intensive and comprehensive approach to compare low elevation, Spartina alterniflora marsh areas to higher elevation Spartina patens marsh areas. Other related data files include: HTL-RO-ST-MAR-Biomass, HTL-RO-ST-MAR-Birds, HTL-RO-ST-MAR-Quads, HTL-RO-ST-MAR-Sediments, HTL-RO-ST-MAR-Bites, HTL-RO-ST-MAR-Sticky, HTL-RO-ST-MAR-Decomp, HTL-RO-ST-MAR-Traps, HTL-RO-ST-MAR-Deep_pitfalls
PIE LTER bird observations associated with marsh sites used in space for time sea level rise study, Rowley, MA.
This dataset contains observations of birds foraging at high and low tide at space for time substition plots in tidal creek marshes off the Rowley River and Plum Island Sound in Rowley Massachusetts. The space for time study uses an intensive and comprehensive approach to compare low elevation, Spartina alterniflora marsh areas to higher elevation Spartina patens marsh areas. Birds were observed using timed interval observations, with one sampling bout per tide per site. Other related data files include: HTL-RO-ST-MAR-Sites, HTL-RO-ST-MAR-Biomass, HTL-RO-ST-MAR-Quads, HTL-RO-ST-MAR-Sediments, HTL-RO-ST-MAR-Bites, HTL-RO-ST-MAR-Sticky, HTL-RO-ST-MAR-Decomp, HTL-RO-ST-MAR-Traps, HTL-RO-ST-MAR-Deep_pitfalls
PIE LTER predation and herbivory rates associated with marsh sites used in space for time sea level rise study, Rowley, MA.
This dataset contains aggregated observations of predation and herbivory rates on tethered bait in each quadrat of the space for time substitution observations in salt marsh sites in Rowley and Newbury, MA.
MARTREC Data from Visualizing Sea Level Rise Impacts in Transportation Planning
<p>This research focuses on a study area in Fort Lauderdale--a two-block stretch of Las Olas Blvd. between Southeast 9th Ave. and Southeast 11th Ave. where researchers expect mean high tides up to 36 inches higher in the year 2100. The project investigates a community planning process in which a combination of high- and low-tech visualization methods—a Geographic Information System (GIS) and a human artist—was used to increase public participation and draw out local knowledge which helps the decision-making process for the future. Mixed reality technologies such as Microsoft Hololens (augmented reality) and Samsung VR Gear (virtual reality) offer immersive educational and engagement experiences, which may convey information in a more meaningful way. Using a quasi-experimental methodology of before-and-after surveys, we compare the degree to which virtual reality technologies improve<br> (or impede) constituents’ absorption of information regarding sea-level rise risks to roadway infrastructure in their communities.</p>
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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.