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The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal Services Center. This 2018 update includes two new mean high water (MHW) shorelines for the Massachusetts...
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The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal Services Center. This 2018 update includes two new mean high water (MHW) shorelines for the Massachusetts...
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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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This metadata describes a digital elevation model (DEM) created from bathymetric and topographic data collected between 2017 and 2019 in the Sacramento River Deep Water Ship Channel (DWSC), northern Sacramento-San Joaquin Delta, California. We merged the newly collected bathymetric and topographic data presented in this data release (DOI:10.5066/P9AQSRVH) with 2019 surveys by the California Department of Water Resources (DWR) and 2017 USGS Sacramento Delta Lidar, to produce a seamless digital elevation model of the DWSC at a grid resolution of 1 m.
Tags: Bathymetry,
Bathymetry and Elevation,
CMHRP,
Coastal and Marine Hazards and Resources Program,
PCMSC, All tags...
Pacific Coastal and Marine Science Center,
Sacramento River,
Sacramento River Deep Water Ship Channel,
Sacramento-San Joaquin Delta,
San Joaquin River,
Seafloor Topography,
State of California,
U.S. Geological Survey,
USGS,
altimetry,
bathymetry,
bathymetry measurement,
digital elevation models,
field observation,
geographic information systems,
geoscientificInformation,
geospatial datasets,
lidar,
marine geophysics,
multibeam sonar,
sea-floor characteristics,
seabed,
topographic mapping,
topography, Fewer tags
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DATA ACCESS: See below under 'Attached Files' - click to download zip file package. Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern...
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