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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,
Raster;
Tags: Atlantic Ocean,
Barrier Island,
Bayesian Network,
CMGP,
Cedar Island,
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,
Raster;
Tags: Atlantic Ocean,
Barrier Island,
Bayesian Network,
CMGP,
Charadrius melodus,
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,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: Atlantic Ocean,
Barrier Island,
CMGP,
Coastal and Marine Geology Program,
Edwin B. Forsythe NWR,
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,
Raster;
Tags: Atlantic Ocean,
Barrier Island,
Bayesian Network,
CMGP,
Charadrius melodus,
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,
Raster;
Tags: Atlantic Ocean,
Barrier Island,
Bayesian Network,
CMGP,
Charadrius melodus,
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,
Raster;
Tags: Atlantic Ocean,
Barrier Island,
Bayesian Network,
CMGP,
Cape Ann,
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,
CMGP,
Cape Hatteras,
Projected future wave-driven flooding depths on Roi-Namur Island on Kwajalein Atoll in the Republic of the Marshall Islands for a range of climate-change scenarios. This study utilized field data to calibrate oceanographic and hydrogeologic models, which were then used with climate-change and sea-level rise projections to explore the effects of sea-level rise and wave-driven flooding on atoll islands and their freshwater resources. The overall objective of this effort, due to the large uncertainty in future emissions (and thus climate change scenarios) that is largely irreducible, was to reduce risk and increase island resiliency by providing model simulations across a range of plausible future conditions. This...
Categories: Data;
Tags: CMGP,
Coastal and Marine Geology Program,
Elevation,
Kwajalein,
Marshall Islands,
This part of DS 781 presents bathymetric contours for several seafloor maps of the Monterey Canyon and Vicinity map area, California. The shapefile is included in "Contours_MontereyCanyon.zip," which is accessible from http://dx.doi.org/10.3133/ofr20161072. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series—Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016–1072,...
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: Bathymetry,
CMGP,
Coastal and Marine Geology Program,
Monterey County,
Moss Landing,
This part of the data release presents topography data from northern Monterey Bay, California collected in March 2017 using a tripod-mounted Riegl VZ-1000 lidar scanner (USGS Field Activity 2017-620-FA). For each area surveyed, the scanner was placed at several positions which were selected to provide maximum line-of-sight coverage of the area of interest. Scans were typically conducted in panoramic mode, creating a detailed point cloud of all unobstructed surfaces in a 360 degree view of the scanner. At each scan position, co-registered photographic imagery was also collected with a scanner mounted DSLR camera. Scanner registration was performed by placing four or more cylindrical or flat reflective tripod-mounted...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Bathymetry and Elevation,
CMGP,
Coastal and Marine Geology Program,
GPS (Global Positioning System),
LIDAR,
This portion of the USGS data release presents digital elevation models (DEMs) derived from bathymetric and topographic surveys conducted along northern Monterey Bay, California, in March 2016 (USGS Field Activity Number 2016-627-FA). Nearshore bathymetry data were collected using two personal watercraft (PWCs) equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected using an all-terrain vehicle equipped with a GNSS receiver and on foot with GNSS receivers mounted on backpacks. Additional topography data were collected with a terrestrial lidar scanner. Positions of the survey platforms were referenced to a GNSS base station placed...
This dataset consists of short-term (~31 years) shoreline change rates for the north coast of Alaska between the Hulahula River and the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2010. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate short-term rates.
Categories: Data;
Types: Citation,
Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: ANWR,
Arctic,
Arctic National Wildlife Refuge,
Beaufort Sea,
CMGP,
This dataset consists of long-term (~65 years) shoreline change rates for the north coast of Alaska between Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2012. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate long-term rates.
Categories: Data;
Types: Citation,
Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: Arctic,
Barrow,
CMGP,
Chukchi Sea,
Coastal and Marine Geology Program,
This dataset consists of short-term (~32 years) shoreline change rates for the north coast of Alaska between Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using an end point rate-of-change method based on available shoreline data between 1979 and 2011. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate short-term rates.
Categories: Data;
Types: Citation,
Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: Arctic,
Barrow,
CMGP,
Chukchi Sea,
Coastal and Marine Geology Program,
In 2012, US Geological Survey (USGS) and National Oceanic & Atmospheric Administration (NOAA) embarked on an ambitious project to digitize surficial seafloor data from existing National Ocean Service (NOS) smooth sheets in the Gulf of Alaska including numerous bays bordering the Gulf. USGS and NOAA are using the data for the nation-wide usSEABED project that seeks to compile and unify existing seafloor characterization point data into GIS-friendly data using the dbSEABED program (Jenkins, 1997; Reid and others, 2005; Buczkowski and others, 2006; Reid and others, 2006) and for the North Pacific Research Board’s Gulf of Alaska Integrated Ecosystems Research Program (NPRB, GOA-IERP, http://www.nprb.org/gulf-of-alaska-project)...
This data provides river turbidity measurements collected on the Carmel River, CA. Turbidity was measured to study any changes in the Carmel River’s sediment loads following the removal of the San Clemente Dam. The USGS-run DTS-12 turbidity sensor was deployed above the Sleepy Hollow Weir on the Carmel River, CA (instrument was located at 36.445250 degrees North, 121.710494 degrees West). Deployment began on December 9, 2014. After June 16, 2016, the instrument was removed for calibration. A new instrument was re-deployed on October 14, 2016, and continued to record until recovery on July 13, 2017. Due to the instrument removal and calibration, there exists an approximately 4-month long gap in data collection from...
Categories: Data;
Tags: CMGP,
Carmel River,
Carmel Valley,
Coastal and Marine Geology Program,
Monterey County,
Water depth and turbidity time-series data were collected in Little Holland Tract (LHT) from 2015 to 2017. Depth (from pressure) was measured in high-frequency (6 or 8 Hz) bursts. Burst means represent tidal stage, and burst data can be used to determine wave height and period. The turbidity sensors were calibrated to suspended-sediment concentration measured in water samples collected on site. The calibration and fit parameters for all of the turbidity sensors used in the study are tabulated and provided with the data. Data were sequentially added to this data release as they were collected and post-processed. Typically, each zip folder for a deployment period contains one file from an optical backscatter sensor...
Water depth, turbidity, and current velocity time-series data were collected in Little Holland Tract in 2016. Depth (from pressure) and velocity were measured in high-frequency (8 Hz) bursts. Burst means represent tidal stage and currents, and burst data can be used to determine wave height, period, and direction, and wave-orbital velocity. The turbidity sensors were calibrated to suspended-sediment concentration measured in water samples collected on site. The calibration and fit parameters for all of the turbidity sensors used in the study are tabulated and provided with the data. Data were sequentially added to this data release as they were collected and post-processed. Typically, each zip folder for...
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised to check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment...
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised to check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment...
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