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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes a compilation of previously published historical shoreline positions for Virginia spanning 148 years (1849-1997), and two new mean high water (MHW) shorelines extracted from lidar data collected...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes a compilation of previously published historical shoreline positions for Virginia spanning 148 years (1849-1997), and two new mean high water (MHW) shorelines extracted from lidar data collected...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes a compilation of previously published historical shoreline positions for Virginia spanning 148 years (1849-1997), and two new mean high water (MHW) shorelines extracted from lidar data collected...
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This data release contains reference baselines for primarily open-ocean sandy beaches along the west coast of the United States (California, Oregon and Washington). The slopes were calculated while extracting shoreline position from lidar point cloud data collected between 2002 and 2011. The shoreline positions have been previously published, but the slopes have not. A reference baseline was defined and then evenly-spaced cross-shore beach transects were created. Then all data points within 1 meter of each transect were associated with each transect. Next, it was determined which points were one the foreshore, and then a linear regression was fit through the foreshore points. Beach slope was defined as the slope...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Coastal wetlands are major global carbon sinks, however, they are heterogeneous and dynamic ecosystems. To characterize spatial and temporal variability in a New England salt marsh, static chamber measurements of greenhouse gas (GHG) fluxes were compared among major plant-defined zones (high marsh dominated by Distichlis spicata and a zone of invasive Phragmites australis) during 2013 and 2014 growing seasons. Two sediment cores were collected in 2015 from the Phragmites zone to support previously reported core collections from the high marsh sites (Gonneea and others 2018). Collected cores were up to 70 cm in length with dry bulk density ranges from 0.04 to 0.33 grams per cubic centimeter and carbon content 22.4%...
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Transport of material in an estuary is important for water quality and hazards concern. We studied these processes in the Hudson River Estuary, located along the northeast coast of the U.S. using the COAWST numerical modeling system. A skill assessment of the COAWST model for the 3-D salinity structure of the estuary has been successfully studied in the past, and the present research extended that understanding to look at both physical and numerical mixing. The model grid extends from the south at the Battery, NY to the north in Troy, NY. The simulation is performed from March 25 to July 11, 2005 (111 days). For more information see: https://doi.org/10.5066/P95E8LAS.
Categories: Data; Types: Map Service, NetCDF OPeNDAP Service, OGC WMS Layer; Tags: CMG_Portal, Earth Science > Human Dimensions > Natural Hazards > Floods, Earth Science > Oceans > Marine Sediments >Sediment Transport, Earth Science > Oceans > Ocean Circulation > Ocean Currents, Earth Science > Oceans > Ocean Temperature > Potential Temperature, All tags...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
Categories: Data; Types: Downloadable, GeoTIFF, Map Service, OGC WFS Layer, OGC WMS Layer, Raster, Shapefile; Tags: Atlantic Ocean, Barrier Island, Bayesian Network, CMHRP, Coastal Erosion, All tags...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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The development of Submerged Aquatic Vegetation (SAV) growth model within the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model leads to a change in SAV biomass. The SAV biomass is computed from temperature, nutrient loading and light predictions obtained from coupled hydrodynamics (temperature), bio-geochemistry (nutrients) and bio-optical (light) models. In exchange, the growth of SAV sequesters or contributes nutrients from the water column and sediment layers. The presence of SAV modulates current and wave attenuation and consequently affects modelled sediment transport. The model of West Falmouth Harbor in Massachusetts, USA was simulated to study the seagrass growth/dieback pattern in a hypothetical...
Categories: Data; Types: Map Service, NetCDF OPeNDAP Service, OGC WMS Layer; Tags: CMG_Portal, Earth Science > Human Dimensions > Natural Hazards > Floods, Earth Science > Oceans > Marine Sediments >Sediment Transport, Earth Science > Oceans > Ocean Circulation > Ocean Currents, Earth Science > Oceans > Ocean Temperature > Potential Temperature, All tags...


map background search result map search result map Numerical model of salinity transport and mixing in the Hudson River Estuary DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Cedar Island, VA, 2012–2013 ElevMHW: Elevation adjusted to local mean high water: Cedar Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Edwin B. Forsythe NWR, NJ, 2012 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2010 Numerical model of Submerged Aquatic Vegetation (SAV) growth dynamics in West Falmouth Harbor without nitrate loading DisOcean: Distance to the ocean: Monomoy Island, MA, 2014 Development: Development delineation: Cape Lookout, NC, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Assawoman Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Fisherman Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Parramore Island, VA, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Parramore Island, VA, 2014 DisOcean: Distance to the ocean: Smith Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Wreck Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Wreck Island, VA, 2014 Static chamber gas fluxes and carbon and nitrogen isotope content of age-dated sediment cores from a Phragmites wetland in Sage Lot Pond, Massachusetts, 2013-2015 A GIS compilation of vector shorelines for the Virginia coastal region from the 1840s to 2010s Long-term shoreline change rates for the Virginia coastal region, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Intersects for coastal region of Virginia generated to calculate short-term shoreline change rates using the Digital Shoreline Analysis System version 5.1 Reference baselines used to extract shorelines for the West Coast of the United States Static chamber gas fluxes and carbon and nitrogen isotope content of age-dated sediment cores from a Phragmites wetland in Sage Lot Pond, Massachusetts, 2013-2015 Numerical model of Submerged Aquatic Vegetation (SAV) growth dynamics in West Falmouth Harbor without nitrate loading DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Wreck Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Wreck Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Fisherman Island, VA, 2014 Development: Development delineation: Cape Lookout, NC, 2014 DisOcean: Distance to the ocean: Smith Island, VA, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Parramore Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Edwin B. Forsythe NWR, NJ, 2012 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2010 Intersects for coastal region of Virginia generated to calculate short-term shoreline change rates using the Digital Shoreline Analysis System version 5.1 Long-term shoreline change rates for the Virginia coastal region, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 A GIS compilation of vector shorelines for the Virginia coastal region from the 1840s to 2010s Numerical model of salinity transport and mixing in the Hudson River Estuary Reference baselines used to extract shorelines for the West Coast of the United States