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82 results for “coastal change”
Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux -- Supplemental Data Set: Sea Level Sensitivity Kernels
<p><strong>Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux<br> SUPPLEMENTAL DATA SET: SEA LEVEL SENSITIVITY KERNELS</strong></p> <p>To accompany</p> <p> Jerry X. Mitrovica, Carling C. Hay, Robert E. Kopp, Christopher Harig, and<br> Konstantin Laytchev (2018). Quantifying the Sensitivity of Sea Level Change<br> in Coastal Localities to the Geometry of Polar Ice Mass Flux. Journal of<br> Climate. doi: 10.1175/JCLI-D-17-0465.1.</p> <p>We provide sea level kernels for ~740 tide gauge sites in the Permanent Service for Mean Sea Level (PSMSL) database (Holgate et al., 2013). Kernels associated with sensitivities to Greenland and Alaskan glacier melt are given on a spatial grid covering the globe, with 512 latitude rows (i=1,512) and 1024 longitude (j=1,1024) columns.</p> <p>Longitude values are evenly spaced moving eastward from Greenwich (the jth grid point has an east longitude value of (j-1)×360°/1024). Latitude values are Gauss-Legendre points beginning close to the North Pole and ending near the South Pole. Kernels associated with sensitivities to Antarctic melt are given on a spatial grid covering the globe, with 256 (Gauss-Legendre) latitude rows (i=1,256) and 512 longitude (j=1,512) columns. Longitude values are evenly spaced moving eastward from Greenwich.</p> <p>The format of the files is: </p> <p> grid_sitenumber_region.txt</p> <p>where “region” is either “green” (Greenland), “ant” (Antarctic) or “Alaska” (Alaska). The list of sites (and site numbers) is provided in the sites.txt file. The first 8 sites in this list were test sites and can be ignored.</p>
Arctic Coastal Change
<p><strong>General description</strong><br>The Arctic Coastal Change image collection consists of multitemporal coastal land cover data for the entire Arctic coast for the period 1984–2023. Coastal land cover is divided into land, water, and ice. Land cover is averaged over five-year time-steps, starting from 1984–1988 and ending in 2019–2023. The dataset has been produced using the full Landsat collection in Google Earth Engine (namely, Landsat 5, Landsat 7 and Landsat 8).</p> <p>This is considered a beta version, as the paper describing the data processing is currently under review.</p> <p><strong>Data structure</strong><br>The pan-Arctic dataset has been divided into 24 large tiles. The tiles are described in the accompanying vector file '<em>ACC_tiles.geojson</em>' and distributed as zip files. The dataset consists of one image for each of the eight time-steps, indicated with the image property 'timestep'. Each image has two bands: 1) land cover and 2) the number of valid observations. The spatial resolution of the dataset is 30 meters.</p> <p>The pixel and property values indicate following:</p> <p>Band: '<em>landcover</em>'<br>1 = land<br>2 = water<br>3 = ice<br>NA = not enough valid satellite data for reliable land cover classification or outside 10-km coastal zone</p> <p>Band: '<em>observations</em>'<br>1 = 5–9<br>2 = 10–14<br>3 = 15–29<br>4 = 30 or more<br>NA = less than 5 valid observations</p> <p>Property: '<em>timestep</em>'<br>1984 = time-step 1984–1988<br>1989 = time-step 1989–1993<br>1994 = time-step 1994–1998<br>1999 = time-step 1999–2003<br>2004 = time-step 2004–2008<br>2009 = time-step 2009–2013<br>2014 = time-step 2014–2018<br>2019 = time-step 2019–2023</p>
Biomarker assessment of spatial and temporal changes in the composition of flocculent material (floc) in the subtropical wetland of the Florida Coastal Everglades (FCE) from May 2007 to December 2009
Flocculent material (floc) is an important energy source in wetlands. In the Florida Everglades, floc is present in both freshwater marshes and coastal environments and plays a key role in food webs and nutrient cycling. However, not much is known about its environmental dynamics, in particular its biological sources and bio-reactivity. We analysed floc samples collected from different environments in the Florida Everglades and applied biomarkers and pigment chemotaxonomy to identify spatial and seasonal differences in organic matter sources. An attempt was made to link floc composition with algal and plant productivity. Spatial differences were observed between freshwater marsh and estuarine floc. Freshwater floc receives organic matter inputs from local periphyton mats, as indicated by microbial biomarkers and chlorophyll-a estimates. At the estuarine sites, the floc is dominated by mangrove as well as diatom inputs from the marine end-member. The hydroperiod (duration and depth of inundation) at the freshwater sites influences floc organic matter preservation, where the floc at the short-hydroperiod site is more oxidised likely due to periodic dry-down conditions. Seasonal differences in floc composition were not consistent and the few that were observed are likely linked to the primary productivity of the dominant biomass (periphyton in the freshwater marshes and mangroves in the estuarine zone). Molecular evidence for hydrological transport of floc material from the freshwater marshes to the coastal fringe was also observed. With the on-going restoration of the Florida Everglades, it is important to gain a better understanding of the biogeochemical dynamics of floc, including its sources, transformations and reactivity.
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.
Assessment of vulnerability to climate change of coastal communities in the Gulf of California and the Yucatan Peninsula: vulnerability outputs
<p>The dataset includes the outputs of the project: "Assessment of vulnerability to climate change of coastal communities in the Gulf of California and the Yucatan Peninsula: vulnerability outputs" funded by the David and Lucille Packard Foundation and awarded to H. Reyes-Bonilla (UABCS). </p> <p>This study analyzed vulnerability of fisheries-dependent coastal communities based on three components: a) adaptive capacity (84 indicators), which reflect the ability of a community to respond and recover after adverse events; b) susceptibility (11 indicators) which was determined based on fishing dependence; and c) exposure (31 indicators) that was evaluated with current environmental data. Future vulnerability was determined for a 2050 horizon and based on two climate change scenarios: SSP126, which represents low emissions, and SSP585, which takes into consideration that the amount of greenhouse gases will continue to increase. These data come from the Coupled Model Intercomparison Project 6 (CMIP6), which serves as the basis for the 6th IPCC report. We evaluated vulnerability using indicators what were available at the local scale.</p>
Warming drives phenological changes in coastal zooplankton
<p>This repository contains zooplankton observations and model data used in the article Forsblom, Stoffers, Lindén, Lehtiniemi and Engstöm-Öst (2024) Warming drives phenological changes in coastal zooplankton. Marine Biology (in press).</p>
Probabilistic state-level estimates of US coastal storm property damages from climate change
<p>Probabilistic estimates of property damage (2010 USD) by damage mechanism, sea level measure, region, and time period. Values are derived from <em>Estimates of US coastal damages by local sea level </em>(doi: https://doi.org/10.5281/zenodo.820149) using the code at https://github.com/ClimateImpactLab/acp-impacts</p> <p>Direct (surge + wind) and business-interruption storm damage estimates as well as estimated property below sea level are projected by estimating damages as a function of local sea level (LSL). These damage functions are then applied to probabilistic estimates of local sea level from Kopp et al (2014) using a Monte Carlo simulation. Percentiles of the resulting distributions are presented in the included files. Estimates of property below sea level are calculated for both mean sea level (MSL) as well as mean higher high water (MHHW).</p> <p>In each period, estimates of the cumulative value of inundated property, or property below sea level, are removed from the exposure data set when computing future storm damages; therefore, projections of future storm damages differ between the two measures of sea level.</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: Land use change and coastal water darkening drive synchronous dynamics in phytoplankton and fish phenology on centennial time scales
<p>At high latitudes, the suitable window for timing reproductive events is particularly narrow, promoting tight synchrony between trophic levels. Climate change may disrupt this synchrony due to diverging responses to temperature between e.g. the early life stages of higher trophic levels and their food resources. Evidence for this is equivocal, and the role of compensatory mechanisms are poorly understood. Here, we show how a combination of ocean warming and coastal water darkening drive long-term changes in phytoplankton spring bloom timing in Lofoten Norway, and how spawning time of Northeast Arctic cod responds in synchrony. Spring bloom timing was derived from hydrographical observations dating back to 1936, while cod spawning time was estimated from weekly fisheries catch and roe landing data since 1877. Our results suggest that land use change causing coastal water darkening has gradually delayed the spring bloom up to 1990 after which ocean warming has caused it to advance. The cod appear to track phytoplankton dynamics by timing gonadal development and spawning to maximize overlap between offspring hatch date and predicted resource availability. This finding emphasises the importance of land-ocean coupling for coastal ecosystem functioning, and the potential for fish to adapt through phenotypic plasticity.</p>
CLIMATE CHANGE EFFECTS ON A SUBTROPICAL COASTAL SHALLOW LAKE FROM HEATWAVE INDEXES
<p>This zipped folder contains the files used to generate the results of this article, submitted to the journal Earth Systems and Environment.</p>
Figure 1. A in Structural and diversity changes in coastal dunes from the Mexican Caribbean: the case of the invasive Australian pine (Casuarina equisetifolia)
Figure 1. A) Location of Cozumel Island within the Yucatán Peninsula. B) Study area on the north side of Cozumel Island. C) The distribution of Casuarina equisetifolia, shown as dark gray polygons and the sampling plots (numbered circles). Plots 1, 6–9 are the invaded, while plots 2–5, 10 are non-invaded.
Figure 3 in Structural and diversity changes in coastal dunes from the Mexican Caribbean: the case of the invasive Australian pine (Casuarina equisetifolia)
Figure 3. Grouping of invaded (triangles) and non-invaded (squares) sampling plots according to their species composition similarity (PERMANOVA). The numbers close to the symbols are the assigned sampling plot number. The dotted lines represent the scores' standard deviation of each group. The solid black lines represent the distance (similarity) between sampling plots.
Research data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems"
<p><strong>Research Data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in <em>Earth's Future</em></strong></p> <p>Dear reader,</p> <p>reasearch data are provided for the research article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in <em>Earth's Future</em>. The authors hope that the research data allows for a better understanding of the modeling workflow. Questions regarding the modeling approach etc. can be directed to the authors, see contact details below.</p> <p>The research data covers the following files:</p> <ul> <li>iMOD-Python (Visser and Bootsma, 2019) scripts to create the iMOD-WQ (Verkaik et al., 2021) input files for the n=566 model variants. Subfolders for each model variant and corresponding files are stored in the subfolder 'model_variants'. An overview regarding the set-up of the model variants is presented in the .xlsx spreadsheet 'model_variants_overview.xlsx' in the folder 'model_variants'.</li> <li>Base data files, used as input files to iMOD-WQ (Verkaik et al., 2021), stored in the subfolder 'imod_input'. However, in most cases no consent for re-distribution of these data sets exists, and they cannot be made freely available through this publication. Please, consider the corresponding meta-data files and/or get in touch with one of the authors for further information.</li> <li>Post-processed model output data, which was further used for model evaluation, stored in the subfolder 'model_output'.</li> <li>Figure files and the corresponding .py scripts, stored in the subfolder 'figures'.</li> </ul> <p>Meta-data files are provided with data files in the different subfolders for clarification.</p> <p>iMOD-WQ (Verkaik et al., 2021) input and .run-files were executed on the University Oldenburg High-Performance Cluster 'Rosa', funded by DFG through its Major Research Instrumentation Program, INST 184/225-1 FUGG, and the Ministry of Science and Culture (MWK) of the Lower Saxony State.</p> <p>Further information on the iMOD suite can be found here: https://deltares.github.io/iMOD-Documentation/</p> <p>The DFG is thanked for SALTSA project funding (DFG project number MA 3274/9-1) within the Priority Programme ‘Regional Sea Level Change and Society (SeaLevel)’. Research related to this article further benefited from funding of the projects WAKOS (BMBF; support code 01LR2003E) and the DFG research unit FOR 5094: The dynamic deep subsurface of high-energy beaches (DynaDeep).</p> <p>Literature:</p> <p>Verkaik, J., Hughes, J. D., van Walsum, P. E. V., Oude Essink, G. H. P., Lin, H. X., & Bierkens, M. F. P. (2021). Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument. Environmental Modelling & Software, 143, p.105092.</p> <p>Visser, M., & Bootsma, H. (2019). iMOD-Python: Work with iMOD MODFLOW models in Python. Retrieved from https://imod.xyz/</p> <p>Seibert, S. L., Greskowiak, J., Oude Essink, G. H. P., & Massmann, G. (2024). Understanding climate change and anthropogenic impacts on the salinization of low‐lying coastal groundwater systems. Earth's Future, 12, e2024EF004737. https://doi.org/10.1029/2024EF004737<br><br><strong>Contact one of the authors if you have further questions</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de), Gualbert H.P. Oude Essink (Gualbert.OudeEssink@deltares.nl) or Gudrun Massmann (gudrun.massmann@uol.de)</p>
Figure 3 in Phytoplankton adaptation strategies under the influence of climatic changes and anthropogenic pressure on the Black Sea coastal ecosystems on the example Sevastopol Bay
Figure 3. Multiannual dynamics: a, d – concentrations of chlorophyll a (1), phytoplankton biomass (2) and water temperature (3), b, e – concentrations of nitrates (1), silicon (2), phosphates (3) and ammonium (4), c, f- diatoms contribution (1), dinoflagellates (2) and coccolithophorides (3) in the total phytoplankton biomass in Sevastopol Bay in summer and autumn.
Figure 2 in Phytoplankton adaptation strategies under the influence of climatic changes and anthropogenic pressure on the Black Sea coastal ecosystems on the example Sevastopol Bay
Figure 2. Multiannual dynamics: a, d – concentrations of chlorophyll a (1), phytoplankton biomass (2) and water temperature (3), b, e – concentrations of nitrates (1), silicon (2), phosphates (3) and ammonium (4), c, f- diatoms contribution (1), dinoflagellates (2) and coccolithophorides (3) in the total phytoplankton biomass in Sevastopol Bay in winter and spring
Figure 4 in Influence of Climatic Factors on the Interannual Changes of Gonadosomatic Index of the Red Mullet Mullus barbatus ponticus in the Coastal Crimean Waters
Figure 4. The relative size distribution of females (1) and males (2) in the spawning of 2016 -2019 (gonads on V maturity stage).
Figure 6 in Influence of Climatic Factors on the Interannual Changes of Gonadosomatic Index of the Red Mullet Mullus barbatus ponticus in the Coastal Crimean Waters
Figure 6. The average weight – length relationship for the red mullet that lived in the coastal waters of Crimea in the spring and summer of 2016–2019.
Figure 2 in Influence of Climatic Factors on the Interannual Changes of Gonadosomatic Index of the Red Mullet Mullus barbatus ponticus in the Coastal Crimean Waters
Figure 2. The average annual GSI values (1) of females and males of red mullet, the average monthly temperature of water (2) during the spawning period, and their standard deviations.
Supplementary materials for paper "Untangling the drivers of change and policy impact in coastal wetland area in the Yangtze Estuary using causal inference"
<h1>Annual coastal wetland vegetation maps of the Yangtze Estuary from 1986 to 2021</h1> <p> </p> <h2><strong>Basic information</strong></h2> <p>Using remotely sensed data from Google Earth Engine, we generated a 30 m resolution annual dataset of Yangtze Estuary wetland vegetation for the period 1986-2021. This dataset includes three dominant vegetation types (<em>Spartina alterniflora</em>, <em>Phragmites australis</em>, and <em>Scirpus mariqueter</em>) and tidal flat areas. We combined fieldwork data and high-resolution images for accuracy assessment, achieving an overall accuracy exceeding 80% in different years. Detailed information about the mapping methods can be found in our paper and accompanying supplementary materials.</p> <h2><strong>Notes:</strong></h2> <p>In the image classification scheme: 0-Tidal flats, 1-<em>Spartina alterniflora, </em>2-<em>Phragmites australis, </em>3-<em>Scirpus mariqueter.</em></p> <h2><strong>Usage Policy:</strong></h2> <p>This dataset is a collaborative effort between East China Normal University and Deakin University. If you plan to use our data in <strong>a scientific analysis paper or other research work</strong>, we strongly recommend contacting us in advance to seek our opinions, <strong>citing the unique DOI of this dataset</strong>, and considering acknowledging our contributions or including us as co-authors.</p>
Data for: Dynamic coastal pelagic habitat drives rapid changes in growth and condition of juvenile sockeye salmon (Oncorhynchus nerka) during early marine migration
<p>Migrating marine taxa encounter diverse habitats that differ environmentally and in foraging conditions over a range of spatial scales. We examined body (RNA/DNA, length-weight residuals) and nutritional (fatty acid composition) condition of juvenile sockeye salmon (<em>Oncorhynchus</em> nerka) in British Columbia while migrating through oceanographically variable waters. Fish were sampled in the stratified northern Strait of Georgia (NSoG); the highly mixed Johnstone Strait (JS); and the transitional zone of Queen Charlotte Strait (QCS). In 2015, body and nutritional condition were high in the NSoG but rapidly declined to reach the lowest levels in JS where prey availability was low, before showing signs of compensatory growth in QCS. In 2016, juvenile salmon had a significantly lower condition in the NSoG than in 2015, although zooplankton biomass was similar, condition remained low in JS, and no compensatory growth was observed in QCS. We provide evidence that differences in juvenile salmon condition between the two years were due to changes in the food quality available to juvenile fish. We propose that existing hypotheses about fish survival need to be extended to incorporate food quality in addition to quantity to understand changes in fish condition and survival between years.</p>
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