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54 results for “Sea-level Rise”
Coastal landcover change and the associated biomass trends in the mid-Atlantic sea-level rise hotspot
Climate change is driving worldwide landscape reorganization. In the coastal ecosystem, climate-driven sea level rise is forcing landward marsh migration and forest die-off, with potentially large consequences on coastal carbon balance. Here we used 30 m resolution Landsat images to study coastal landcover change from 1984 to 2020, and analyzed the Normalized Difference Vegetation Index (NDVI, a proxy of plant biomass) trend between 1984 and 2020 in the mid-Atlantic sea level rise hotspot. Our study region stretches across the entire Chesapeake Bay and the Delaware Bay to encompass all areas between 0-5m above sea level (total area ~12,500 km2). Specifically, the data package includes 3 raster datasets derived from the Landsat images. All datasets cover the identical mid-Atlantic region and have identical spatial resolution of 30 m. The two landcover datasets, named as 'Landcover_year1984.tif' and'Landcover_year2020.tif', respectively refer to landcover map in 1984 and 2020. Each of the maps has 7 landcover classes differentiated by different integers, and they are: water (0), farmland (1), urban area(2), upland forest (3), transition forest (4), marsh (5) and sandbar (6). Both landcover maps were generated using a combination of random forest classification and manual delineation, and the resultswere validated with high-resolution aerial photos and satellite images with an overall mapping accuracybeyond 90%. The third raster dataset, named as 'NDVItrend_1984to2020.tif', is the NDVI trend map. The value of each 30 by 30 m pixel in the map represents the slope of the NDVI trendline estimated using annual peak-growing season NDVI images acquired between 1984 and 2020. Negative values in the dataset represent decreases of NDVI (i.e. biomass loss, or ecosystem browning) from 1984 and 2020,whereas positive values correspond to an increase of NDVI (i.e. biomass gain, or ecosystem greening)between 1984 and 2020. The data package is completed.
Lateral and vertical forest retreat rate in the mid-Atlantic sea-level rise hotspot
Ghost forests consisting of dead trees adjacent to marshes are striking indicators of climate change. Here we quantify both the lateral and vertical rate of coastal forest retreat between 1984 and 2020 along the US mid-Atlantic coast. The study region includes areas between 0-5 m above sea level across the Chesapeake Bay and the adjacent Delaware Bay. Specifically, the data package includes 2 shapefile datasets derived from four decades of Landsat satellite observations of coastal treeline dynamics. The two datasets are generated on the same spatial-scale and have the same spatial resolution (0.075 km2), both stored as hexagon grids with a side length 170 m. Here we define "forest retreat" (as shown in the datasets as positive values) as the migration of coastal treeline landwards (lateral retreat) or upslope (vertical retreat), whereas "forest advance" (negative values) refers to treeline migration seawards (lateral advance) or downslope (vertical advance). The number '999999' in both datasets indicates areas of stable coastal treelines (i.e. no change) between 1984 and 2020.
Sea-level rise and freshwater management are reshaping coastal ecosystems in the Florida Everglades
Datasets include hydrology (water level and salinity), net ecosystem exchange of CO2, photosynthetically active radiation (PAR), and air temperature for a freshwater marl prairie, brackish marsh ecotone, and saline scrub mangrove forest. Data were derived from multiple sources, including two sites from the South Florida Water Management District (SFWMD) DBhydro web database, two sites from the Florida Coastal Everglades Long Term Ecological Research (FCE-LTER) program and three AmeriFlux sites in the Southeastern Everglades region. Ameriflux sites were co-located with FCE-LTER sites. To understand the effects of sea level rise and freshwater management on landscape carbon exchange (C), we measured the net ecosystem exchange of CO2 (NEE) between subtropical wetland ecosystems and the atmosphere along a dynamic salinity gradient. Ecosystems were representative of freshwater marl prairies, brackish marsh ecotones, and saline scrub mangrove forests. In the southeastern Everglades, the magnitude of environmental change was greatest along the coast, where mangrove scrub forests exhibited a greater capacity to maintain CO2 uptake with changing conditions.
Mangrove diversity loss under sea-level rise triggered by bio-morphodynamic feedbacks and anthropogenic pressures
<p>To whom concerned,</p> <p>This dataset is the supplementary dataset for the publication in <em>Environmental Research Letters</em> entitled '<a href="https://dx.doi.org/10.1088/1748-9326/abc122"><em>Mangrove diversity loss under sea-level rise triggered by bio-morphodynamic feedbacks and anthropogenic pressures</em></a>' authored by Danghan Xie, et al. in 2020. The publication can be freely downloaded here: <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abc122">https://iopscience.iop.org/article/10.1088/1748-9326/abc122</a>. The dataset consists of both model results and corresponding codes that one can easily reproduce figures either in the manuscript or the supplementary document. </p> <p>To use the code, one needs to pre-install the Matlab (R2017a) and changes the pre-set route (in the code) to the directory where the dataset is stored. The figure shapes may vary with the size of the user's monitor so output figures may be either squeezed or extended in unpredictable ways, but the window size of the figure can be adjusted to match the shape and the results will not be affected.</p> <p>The author is appreciated that any potential concerns or questions regarding our research from any party or person, so please contact me through the email: <a href="mailto:d.xie@uu.nl">d.xie@uu.nl</a> or <a href="mailto:xiedanghan@gmail.com">xiedanghan@gmail.com</a>. To know more about my research, you can also follow the <a href="https://www.researchgate.net/profile/Danghan_Xie">ResearchGate</a>.</p> <p>With Kind Regards,</p> <p>Danghan</p> <p>11th of November, 2020</p>
'Subglacial Water Amplifies Antarctic Contributions to Sea-Level Rise' by Chen Zhao et al.
<p>Code and data used to produce figures in "Subglacial Water Amplifies Antarctic Contributions to Sea-Level Rise" by Chen Zhao et al.</p> <p>We are happy to help with anybody with any problems with this code, please get in touch (chen.zhao@utas.edu.au) or raise an issue.</p>
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>
Supplementary data for "Single extreme storm sequence can offset decades of predicted shoreline retreat by sea-level rise"
<p>This dataset comprises topography and bathymetric data at three coastal locations in Australia (Narrabeen), UK (Perranporth) and used for the publication "Single extreme storm sequence can offset decades of predicted shoreline retreat by sea-level rise". Please refer to readme files for metadata</p>
Data from article "Is the Atlantic a Source for Decadal Predictability of Sea-Level Rise in Venice?"
<p>Data from the article Zanchettin D., et al.: Is the Atlantic a Source for Decadal Predictability of Sea-Level Rise in Venice?, Earth and Space Science, article number 2022EA002494</p> <p>The dataset contains:</p> <p>- annual time series of October-March average of relative sea level in Venice corrected for vertical land movement (VLMcorrectedRSL) with associated standard error of the mean (LMcorrectedRSL_SEM) for the period 1873-2019;</p> <p>- annual time series of estimate of subsidence in Venice (Subsidence) for the period 1873-2019</p> <p>- modeled state of Venice sea level with associated uncertainty, provided as mean (delta_mean), 1st percentile (delta_1_percentile) and 99th percentile (delta_99_percentile), for the period 1873-2019</p> <p>- modeled local stochastic trend of Venice sea level with associated uncertainty, provided as mean (beta_mean), 1st percentile (beta_1_percentile) and 99th percentile (beta_99_percentile), for the period 1873-2019</p>
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
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>
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>
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>
Data: Salt marsh litter quality and decomposition under sea-level rise scenarios: from leaves to fine absorptive roots
<p>litter chemical characteristic in salt marshes, including fine absorptive roots, fine transportive roots, rhizomes and leaves. </p> <p>mass loss of litter and chemical characteristics of those litter under sea level scenarios (manipulated in situ)</p>
Data supplementing article "Tidal response to sea-level rise in different types of estuaries: the importance of length, bathymetry, and geometry" submitted to Geophysical Research Letters
<p>These data supplement the article "Tidal response to sea-level rise in different types of estuaries: the importance of length, bathymetry, and geometry". </p> <p>contact: Jiabi Du, jiabi.du@gmail.com</p> <p>Below are descriptions of the data files included here:</p> <p>1. ReadMe.txt</p> <p>- the description of each model configuration is listed in this file, inlcuding the estuarine geometry type, length, and bathymetry type. </p> <p>2. model grid and surface elevation output</p> <p>- In each directory named after the grid_name, a grid file with .gr3 format is included. <br> The .gr3 file contains two parts: the location of each node (first part), and the node number for each triangle grid (second part).</p> <p>- Two sub_directory name 'base' and 'slr' contains the time series of water level. </p> <p>3. MatlaScript ExtractWL</p> <p>- a function named "f_extract_wl.m" are used to extract the station output</p> <p> </p>
Data & Code for Sea-level rise induced threats depend on size of tide-influenced estuaries worldwide
<p>Data & Code for the article: "Data & Code for Sea-level rise induced threats depend on size of tide-influenced estuaries worldwide"</p> <p>- 1D Hydrodynamic model</p> <p>- Along-channel width profiles of the estuaries used in this study</p> <p>- Input data used for the tool and model in this study</p> <p>- Output data used for the figures</p>
Coastal marsh vulnerability to sea-level rise is exacerbated by plant species invasion
<p>In this dataset, it compasses the data and mat code file to visualize figures in the manuscript. </p>
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