Hazards

Potential dangers from both natural processes (e.g., earthquakes, floods, and climate change) and human impacts on the environment.
This category is also used for catastrophes, disasters, emergency management resources, environmental hazards, and geologic hazards.

293 results listed by similarity [list alphabetically]
maui_stm - Storm Hazard Intensity Level in the coastal zone of Maui, Hawaii

Storm Hazard Intensity Level in the coastal zone of Maui, Hawaii

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oahu_wav - High Wave Hazard Intensity Level in the coastal zone of Oahu, Hawaii

High Wave Hazard Intensity Level in the coastal zone of Oahu, Hawaii

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oahu_vol - Volcanic and Seismic Hazard Intensity Level in the coastal zone of Oahu, Hawaii

Volcanic and Seismic Hazard Intensity Level in the coastal zone of Oahu, Hawaii

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oahu_tsu - Tsunami Hazard Intensity Level in the coastal zone of Oahu, Hawaii

Tsunami Hazard Intensity Level in the coastal zone of Oahu, Hawaii

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oahu_stm - Storm Hazard Intensity Level in the coastal zone of Oahu, Hawaii

Storm Hazard Intensity Level in the coastal zone of Oahu, Hawaii

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oahu_sfl - Stream Flooding Hazard Intensity Level in the coastal zone of Oahu, Hawaii

Stream Flooding Hazard Intensity Level in the coastal zone of Oahu, Hawaii

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oahu_sea - Sea Level Hazard Intensity Level in the coastal zone of Oahu, Hawaii

Sea Level Hazard Intensity Level in the coastal zone of Oahu, Hawaii

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oahu_oha - Overall Hazard Assessment in the coastal zone of Oahu, Hawaii

Overall Hazard Assessment in the coastal zone of Oahu, Hawaii

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oahu_ero - Erosion Hazard Intensity Level in the coastal zone of Oahu, Hawaii

Erosion Hazard Intensity Level in the coastal zone of Oahu, Hawaii

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molo_wav - High Wave Hazard Intensity Level in the coastal zone of Molokai, Hawaii

High Wave Hazard Intensity Level in the coastal zone of Molokai, Hawaii

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molo_vol - Volcanic and Seismic Hazard Intensity Level in the coastal zone of Molokai, Hawaii

Volcanic and Seismic Hazard Intensity Level in the coastal zone of Molokai, Hawaii

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molo_tsu - Tsunami Hazard Intensity Level in the coastal zone of Molokai, Hawaii

Tsunami Hazard Intensity Level in the coastal zone of Molokai, Hawaii

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molo_stm - Storm Hazard Intensity Level in the coastal zone of Molokai, Hawaii

Storm Hazard Intensity Level in the coastal zone of Molokai, Hawaii

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molo_sfl - Stream Flooding Hazard Intensity Level in the coastal zone of Molokai, Hawaii

Stream Flooding Hazard Intensity Level in the coastal zone of Molokai, Hawaii

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molo_sea - Sea Level Hazard Intensity Level in the coastal zone of Molokai, Hawaii

Sea Level Hazard Intensity Level in the coastal zone of Molokai, Hawaii

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molo_oha - Overall Hazard Assessment in the coastal zone of Molokai, Hawaii

Overall Hazard Assessment in the coastal zone of Molokai, Hawaii

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molo_ero - Erosion Hazard Intensity Level in the coastal zone of Molokai, Hawaii

Erosion Hazard Intensity Level in the coastal zone of Molokai, Hawaii

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maui_wav - High Wave Hazard Intensity Level in the coastal zone of Maui, Hawaii

High Wave Hazard Intensity Level in the coastal zone of Maui, Hawaii

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maui_vol - Volcanic and Seismic Hazard Intensity Level in the coastal zone of Maui, Hawaii

Volcanic and Seismic Hazard Intensity Level in the coastal zone of Maui, Hawaii

Info
maui_tsu - Tsunami Hazard Intensity Level in the coastal zone of Maui, Hawaii

Tsunami Hazard Intensity Level in the coastal zone of Maui, Hawaii

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sand_ero - Erosion Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

Erosion Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

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maui_sfl - Stream Flooding Hazard Intensity Level in the coastal zone of Maui, Hawaii

Stream Flooding Hazard Intensity Level in the coastal zone of Maui, Hawaii

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maui_sea - Sea Level Hazard Intensity Level in the coastal zone of Maui, Hawaii

Sea Level Hazard Intensity Level in the coastal zone of Maui, Hawaii

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maui_oha - Overall Hazard Assessment in the coastal zone of Maui, Hawaii

Overall Hazard Assessment in the coastal zone of Maui, Hawaii

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maui_ero - Erosion Hazard Intensity Level in the coastal zone of Maui, Hawaii

Erosion Hazard Intensity Level in the coastal zone of Maui, Hawaii

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lanai_wav - High Wave Hazard Intensity Level in the coastal zone of Lanai, Hawaii

High Wave Hazard Intensity Level in the coastal zone of Lanai, Hawaii

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lanai_vol - Volcanic and Seismic Hazard Intensity Level in the coastal zone of Lanai, Hawaii

Volcanic and Seismic Hazard Intensity Level in the coastal zone of Lanai, Hawaii

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lanai_tsu - Tsunami Hazard Intensity Level in the coastal zone of Lanai, Hawaii

Tsunami Hazard Intensity Level in the coastal zone of Lanai, Hawaii

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lanai_stm - Storm Hazard Intensity Level in the coastal zone of Lanai, Hawaii

Storm Hazard Intensity Level in the coastal zone of Lanai, Hawaii

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lanai_sfl - Stream Flooding Hazard Intensity Level in the coastal zone of Lanai, Hawaii

Stream Flooding Hazard Intensity Level in the coastal zone of Lanai, Hawaii

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lanai_sea - Sea Level Hazard Intensity Level in the coastal zone of Lanai, Hawaii

Sea Level Hazard Intensity Level in the coastal zone of Lanai, Hawaii

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lanai_oha - Overall Hazard Assessment in the coastal zone of Lanai, Hawaii

Overall Hazard Assessment in the coastal zone of Lanai, Hawaii

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lanai_ero - Erosion Hazard Intensity Level in the coastal zone of Lanai, Hawaii

Erosion Hazard Intensity Level in the coastal zone of Lanai, Hawaii

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kauai_wav - High Wave Hazard Intensity Level in the coastal zone of Kauai, Hawaii

High Wave Hazard Intensity Level in the coastal zone of Kauai, Hawaii

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kauai_vol - Volcanic and Seismic Hazard Intensity Level in the coastal zone of Kauai, Hawaii

Volcanic and Seismic Hazard Intensity Level in the coastal zone of Kauai, Hawaii

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kauai_tsu - Tsunami Hazard Intensity Level in the coastal zone of Kauai, Hawaii

Tsunami Hazard Intensity Level in the coastal zone of Kauai, Hawaii

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kauai_stm - Storm Hazard Intensity Level in the coastal zone of Kauai, Hawaii

Storm Hazard Intensity Level in the coastal zone of Kauai, Hawaii

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kauai_sfl - Stream Flooding Hazard Intensity Level in the coastal zone of Kauai, Hawaii

Stream Flooding Hazard Intensity Level in the coastal zone of Kauai, Hawaii

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kauai_sea - Sea Level Hazard Intensity Level in the coastal zone of Kauai, Hawaii

Sea Level Hazard Intensity Level in the coastal zone of Kauai, Hawaii

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kauai_oha - Overall Hazard Assessment in the coastal zone of Kauai, Hawaii

Overall Hazard Assessment in the coastal zone of Kauai, Hawaii

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kauai_ero - Erosion Hazard Intensity Level in the coastal zone of Kauai, Hawaii

Erosion Hazard Intensity Level in the coastal zone of Kauai, Hawaii

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hawaii_wav - High Wave Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

High Wave Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

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hawaii_vol - Volcanic and Seismic Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

Volcanic and Seismic Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

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hawaii_tsu - Tsunami Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

Tsunami Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

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hawaii_stm - Storm Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

Storm Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

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hawaii_sfl - Stream Flooding Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

Stream Flooding Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

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hawaii_sea - Sea Level Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

Sea Level Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

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hawaii_oha - Overall Hazard Assessment in the coastal zone of Hawaii, Hawaii

Overall Hazard Assessment in the coastal zone of Hawaii, Hawaii

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hawaii_ero - Erosion Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

Erosion Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

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National Assessment of Hurricane-Induced Coastal Erosion Hazards: 2021 Update

This dataset contains information on the probabilities of hurricane-induced erosion (collision, inundation and overwash) for each 1-kilometer (km) section of the United States [Gulf of Mexico and Atlantic] coast for category 1-5 hurricanes. The analysis is based on a storm-impact scaling model that uses observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast will respond to the direct landfall of category 1-5 hurricanes. Hurricane-induced water levels, due ...

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BEWARE database: A Bayesian-based system to assess wave-driven flooding hazards on coral reef-lined coasts

A process-based wave-resolving hydrodynamic model (XBeach Non-Hydrostatic, ‘XBNH’) was used to create a large synthetic database for use in a “Bayesian Estimator for Wave Attack in Reef Environments” (BEWARE), relating incident hydrodynamics and coral reef geomorphology to coastal flooding hazards on reef-lined coasts. Building on previous work, BEWARE improves system understanding of reef hydrodynamics by examining the intrinsic reef and extrinsic forcing factors controlling runup and flooding on ...

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sand_wav - High Wave Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

High Wave Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

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sand_vol - Volcanic and Seismic Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

Volcanic and Seismic Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

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sand_tsu - Tsunami Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

Tsunami Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

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sand_stm - Storm Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

Storm Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

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sand_sfl - Stream Flooding Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

Stream Flooding Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

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sand_sea - Sea Level Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

Sea Level Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

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sand_oha - Overall Hazard Assessment in the coastal zone of Sand Island (Oahu), Hawaii

Overall Hazard Assessment in the coastal zone of Sand Island (Oahu), Hawaii

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September 2007 Southwest Florida Division of Emergency Management Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2007 Southwest Florida ...

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Hurricane Sandy Assessment of Potential Coastal Change Impacts: NHC Advisory 29, 1100 AM EDT MON OCT 29 2012

This dataset defines hurricane-induced coastal erosion hazards for the Delaware, Maryland, New Jersey, New York, and Virginia coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Sandy in October 2012. Hurricane-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the ...

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PACIFIC - Coastal Vulnerability to Sea-Level Rise: U.S. Pacific Coast

The goal of this project is to quantify, at the National scale, the relative susceptibility of the Nation's coast to sea-level rise through the use of a coastal vulnerability index (CVI). This initial classification is based upon the variables geomorphology, regional coastal slope, tide range, wave height, relative sea-level rise and shoreline erosion and accretion rates. The combination of these variables and the association of these variables to each other furnish a broad overview of regions where ...

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Hurricane Nate Assessment of Potential Coastal Change Impacts: NHC Advisory 12, 0800 AM EDT SAT OCT 07 2017

This dataset defines storm-induced coastal erosion hazards for the Louisiana, Mississippi, Alabama and Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Nate in October 2017. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three ...

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Hurricane Michael Assessment of Potential Coastal Change Impacts: NHC Advisory 15, 0400 AM CDT WED OCT 10 2018

This dataset defines storm-induced coastal erosion hazards for the Alabama and Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Michael in October 2018. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: ...

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Hurricane Joaquin Assessment of Potential Coastal Change Impacts: NHC Advisory 27, 0800 AM EDT SUN OCT 04 2015

This dataset defines storm-induced coastal erosion hazards for the North Carolina, Virginia, Maryland, Delaware, New Jersey, New York, Rhode Island and Massachusetts coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Joaquin in October 2015. Storm-induced water levels, due to both surge and waves, were compared to beach and dune ...

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Subtropical Storm Alberto Assessment of Potential Coastal Change Impacts: NHC Advisory 8, 0800 AM EDT SUN MAY 27 2018

This dataset defines storm-induced coastal erosion hazards for the Louisiana, Mississippi, Alabama and Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Subtropical Storm Alberto in May 2018. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the ...

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Extratropical Storm Jan2016 Assessment of Potential Coastal Change Impacts: 1200 PM EST FRI JAN 22 2016

This dataset defines storm-induced coastal erosion hazards for the Virginia, Maryland, Delaware, New Jersey and New York coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct impact of the Extratropical Storm in January 2016. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities ...

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Hurricane Irma Assessment of Potential Coastal Change Impacts: NHC Advisory 41, 800 AM EDT SAT SEPT 9 2017

This dataset defines storm-induced coastal erosion hazards for the Florida, Georgia and South Carolina coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Irma in September 2017. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of ...

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GULF - Coastal Vulnerability to Sea-Level Rise: U.S. Gulf Coast

The goal of this project is to quantify, at the National scale, the relative susceptibility of the Nation's coast to sea-level rise through the use of a coastal vulnerability index (CVI). This initial classification is based upon the variables geomorphology, regional coastal slope, tide range, wave height, relative sea-level rise and shoreline erosion and accretion rates. The combination of these variables and the association of these variables to each other furnish a broad overview of regions where ...

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Hurricane Florence Assessment of Potential Coastal Change Impacts: NHC Advisory 57, 1100 AM EDT THU SEP 13 2018

This dataset defines storm-induced coastal erosion hazards for the Georgia, South Carolina, North Carolina, Virginia, Maryland, Delaware, New Jersey and New York coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Florence in September 2018. Storm-induced water levels, due to both surge and waves, were compared to beach and dune ...

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Hurricane Matthew Assessment of Potential Coastal Change Impacts: NHC Advisory 037, 800 AM EDT FRI OCT 07 2016

This dataset defines storm-induced coastal erosion hazards for the Florida, Georgia, South Carolina and North Carolina coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Matthew in October 2016. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of ...

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Tropical Storm Hermine Assessment of Potential Coastal Change Impacts: NHC Advisory 20, 0500 AM EDT FRI SEP 02 2016

This dataset defines storm-induced coastal erosion hazards for the Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Tropical Storm Hermine in September 2016. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: ...

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Model parameter input files to compare locations of coral reef restoration on different reef profiles to reduce coastal flooding

This dataset consists of physics-based XBeach Non-hydrostatic hydrodynamic models input files used to study how coral reef restoration affects waves and wave-driven water levels over coral reefs, and the resulting wave-driven runup on the adjacent shoreline. Coral reefs are effective natural coastal flood barriers that protect adjacent communities. Coral degradation compromises the coastal protection value of reefs while also reducing their other ecosystem services, making them a target for restoration. ...

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Hurricane Harvey Assessment of Potential Coastal Change Impacts: NHC Advisory 020, 700 AM CDT FRI AUG 25 2017

This dataset defines storm-induced coastal erosion hazards for the Texas and Louisiana coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Harvey in August 2017. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: ...

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ATLANTIC - Coastal Vulnerability to Sea-Level Rise: A Preliminary Database for the U.S. Atlantic Coast

The goal of this project is to provide a preliminary overview, at a National scale, the relative susceptibility of the Nation's coast to sea-level rise through the use of a coastal vulnerability index (CVI). This initial classification is based upon the variables geomorphology, regional coastal slope, tide range, wave height, relative sea-level rise and shoreline erosion and accretion rates. The combination of these variables and the association of these variables to each other furnish a broad overview of ...

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Hurricane Maria Assessment of Potential Coastal Change Impacts: NHC Advisory 41, 0800 AM EDT TUE SEPT 26 2017

This dataset defines storm-induced coastal erosion hazards for the North Carolina, Virginia, Maryland and Delaware coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Maria in September 2017. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the ...

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Tropical Storm Colin Assessment of Potential Coastal Change Impacts: NHC Advisory 4, 0500 AM EDT MON JUN 06 2016

This dataset defines storm-induced coastal erosion hazards for the Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Tropical Storm Colin in June 2016. Storm-induced water levels, due to both surge and waves, are compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision ...

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Delineated Coastal Cliff Transects Derived from Post-Hurricane Maria Lidar Elevation Data Collected from Puerto Rico: 2018

The National Assessment of Coastal Change Hazards project aims to understand and forecast coastal landscape change. This dataset consists of delineated coastal cliff transects that may be used to assess the hazard posed by eroding coastal cliffs on the islands of Puerto Rico, Culebra, and Vieques. The delineation of cliff tops and toes can be used as an input into cliff hazard metrics and to measure overall cliff changes over time. Cliff tops and cliff toes were identified along three-dimensional (3D) ...

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Delineated Coastal Cliff Tops Derived from Post-Hurricane Maria Lidar Elevation Data Collected from Puerto Rico: 2018

The National Assessment of Coastal Change Hazards project aims to understand and forecast coastal landscape change. This dataset consists of delineated coastal cliff tops that may be used to assess the hazard posed by eroding coastal cliffs on the islands of Puerto Rico, Culebra, and Vieques. The delineation of cliff tops and toes can be used as an input into cliff hazard metrics and to measure overall cliff changes over time. Cliff tops and cliff toes were identified along three-dimensional (3D) transects ...

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Model parameter input files to compare wave-averaged versus wave-resolving XBeach coastal flooding models for coral reef-lined coasts

This data release includes the XBeach input data files used to evaluate the importance of explicitly modeling sea-swell waves for runup. This was examined using a 2D XBeach short wave-averaged (surfbeat, XB-SB) and a wave-resolving (non-hydrostatic, XB-NH) model of Roi-Namur Island on Kwajalein Atoll in the Republic of Marshall Islands. Results show that explicitly modelling the sea-swell component (using XB-NH) provides a better approximation of the observed runup than XB-SB (which only models the time ...

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Delineated Coastal Cliff Toes Derived from Post-Hurricane Maria Lidar Elevation Data Collected from Puerto Rico: 2018

The National Assessment of Coastal Change Hazards project aims to understand and forecast coastal landscape change. This dataset consists of delineated coastal cliff toes that may be used to assess the hazard posed by eroding coastal cliffs on the islands of Puerto Rico, Culebra, and Vieques. The delineation of cliff tops and toes can be used as an input into cliff hazard metrics and to measure overall cliff changes over time. Cliff tops and cliff toes were identified along three-dimensional (3D) transects ...

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Tropical Storm Bill Assessment of Potential Coastal-Change Impacts: NHC Advisory 2, 0900 AM UTC MON JUN 16 2015

This dataset defines storm-induced coastal erosion hazards for the Texas and Louisiana coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Tropical Storm Bill in June 2015. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: ...

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Tropical Storm Gordon Assessment of Potential Coastal Change Impacts: NHC Advisory 8, 0700 AM CDT TUE SEP 04 2018

This dataset defines storm-induced coastal erosion hazards for the Louisiana, Mississippi, Alabama and Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Tropical Storm Gordon in September 2018. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the ...

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Extratropical Storm March 2018 Assessment of Potential Coastal Change Impacts: 0800 AM EST FRI MAR 02 2018

This dataset defines storm-induced coastal erosion hazards for the North Carolina, Virginia, Maryland, Delaware, New Jersey, New York, Rhode Island, Massachusetts, New Hampshire and Maine coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of an Extratropical Storm in March 2018. Storm-induced water levels, due to both surge and waves, were ...

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1998 Atlantic coast NASA/NOAA/USGS Spring ATM Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1998 Atlantic Coast ...

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1998 East Coast NASA/NOAA/USGS Winter ATM Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1998 Atlantic Coast ...

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2018 USGS Florida Panhandle Post-Michael Lidar-derived Dune Crest, Toe, and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2018 United States Army ...

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2018 Southeast Coast (VA, NC, SC) USACE NCMP Post-Florence Topobathy Lidar-Derived Dune Crest, Toe, and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline derived from the 2018 United States Army ...

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2018 East Coast (CT, MA, NC) USACE NCMP Topobathy Lidar Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline derived from the 2018 United States Army ...

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2018 South Texas USGS Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline derived from the 2018 United States ...

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Laboratory Observations of Variable Size and Shape Particles-Artificial Sand and Oil Agglomerates: June 2017 Video Data

Following marine oil spills, weathered oil can mix with sediment in the surf zone and settle to the seafloor to form mats up to hundreds of meters long. Wave action fragments these mats into 1- to 10-centimeter (cm) diameter sand and oil agglomerates (SOAs). SOAs can persist for years, becoming buried in or exhumed from the seafloor and/or transported cross-shore and alongshore (Dalyander and others, 2015). These fragments are angular near the source mat and become increasingly rounded as they are ...

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Laboratory Observations of Variable Size and Shape Particles-Artificial Sand and Oil Agglomerates: June 2017 Velocity Data

Following marine oil spills, weathered oil can mix with sediment in the surf zone and settle to the seafloor to form mats up to hundreds of meters long. Wave action fragments these mats into 1- to 10-centimeter (cm) diameter sand and oil agglomerates (SOAs). SOAs can persist for years, becoming buried in or exhumed from the seafloor and/or transported cross-shore and alongshore (Dalyander and others., 2015). These fragments are angular near the source mat and become increasingly rounded as they are ...

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2017 East Coast USACE/FEMA ATM Lidar-Derived Dune Crest, Toe and Shoreline, post-Hurricane Irma

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2017 Atlantic Coast ...

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2016 USACE Gulf Coast Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2016 U.S. Army Corps of ...

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2016 Florida East Coast USACE Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2016 U.S. Army Corps of ...

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Laboratory Observations of Variable Size and Shape Particles-Artificial Sand and Oil Agglomerates: November 2016 Video Data

Following marine oil spills, weathered oil can mix with sediment in the surf zone and settle to the seafloor to form mats up to hundreds of meters long. Wave action fragments these mats into 1- to 10-centimeter (cm) diameter sand and oil agglomerates (SOAs). SOAs can persist for years, becoming buried in or exhumed from the seafloor and/or transported cross-shore and alongshore (Dalyander and others, 2015). These fragments are angular near the source mat and become increasingly rounded as they are ...

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Laboratory Observations of Variable Size and Shape Particles-Artificial Sand and Oil Agglomerates: November 2016 Velocity Data

Following marine oil spills, weathered oil can mix with sediment in the surf zone and settle to the seafloor to form mats up to hundreds of meters long. Wave action fragments these mats into 1- to 10-centimeter (cm) diameter sand and oil agglomerates (SOAs). SOAs can persist for years, becoming buried in or exhumed from the seafloor and/or transported cross-shore and alongshore (Dalyander and others, 2015). These fragments are angular near the source mat and become increasingly rounded as they are ...

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Archive of Ground Penetrating Radar and Differential Global Positioning System Data Collected in April 2016 from Fire Island, New York

Researchers from the U.S. Geological Survey (USGS) conducted a long-term, coastal morphologic-change study at Fire Island, New York, prior to and after Hurricane Sandy impacted the area in October 2012. The Fire Island Coastal Change project (https://coastal.er.usgs.gov/fire-island/) objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related impacts, post-storm beach response, and recovery. In April ...

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Beach Topography—Fire Island, New York, Pre-Hurricane Sandy, January 2012: Ground Based Lidar (ASCII XYZ Point Data)

The U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) and the U.S. Army Corps of Engineers Field Research Facility (USACE-FRF) of Duck, North Carolina collaborated to gather alongshore ground-based lidar beach topography at Fire Island, New York. This high-resolution, elevation dataset was collected on January 30, 2012, and was funded by SPCMSC. The USGS data release containing the aforementioned dataset includes the resulting, processed elevation point data (XYZ) and an ...

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Beach Topography—Fire Island, New York, Pre-Hurricane Sandy, January 2012: Ground Based Lidar (1-Meter Digital Elevation Model)

The U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) and the U.S. Army Corps of Engineers Field Research Facility (USACE-FRF) of Duck, North Carolina collaborated to gather alongshore ground-based lidar beach topography at Fire Island, New York. This high-resolution, elevation dataset was collected on January 30, 2012, and was funded by SPCMSC. The USGS data release containing the aforementioned dataset includes the resulting, processed elevation point data (XYZ) and ...

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2017 USGS Lidar: Chenier Plain, LA Point Cloud files with Orthometric Vertical Datum North American Vertical Datum of 1988 (NAVD88) using GEOID12B

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2001 Gulf Coast USGS ...

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2017 New York and New Jersey USACE NCMP Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2017 Atlantic Coast (New ...

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2016 USACE Post-Hurricane Matthew Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2016 U.S. Army Corps of ...

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2015 USACE Florida Gulf Coast Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2015 U.S. Army Corps of ...

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2015 Mississippi and Alabama USGS Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2015 Mississippi and ...

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2014 Mobile County, Alabama Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2014 Mobile County, ...

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2014 East Coast Maine USACE/NAE ATM Lidar-Derived Dune Crest, Toe and Shoreline, post-Hurricane Sandy

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2014 East Coast Maine ...

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2014 East Coast New Hampshire USACE/NAE ATM Lidar-Derived Dune Crest, Toe and Shoreline, post-Hurricane Sandy

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2014 East Coast New ...

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2014 East Coast Rhode Island NOAA/NGS ATM Lidar-Derived Dune Crest, Toe and Shoreline, post-Hurricane Sandy

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2014 East Coast Rhode ...

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2014 Post-Hurricane Sandy SC to NY NOAA NGS Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2014 Post-Hurricane ...

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2013 Maine USACE/NAE Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2013 Maine United States ...

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2013 USACE Oahu Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2013 U.S. Army Corps of ...

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2013 NOAA Oahu Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2013 National Oceanic ...

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2013-2014 Northeast USGS Lidar-Derived Dune Crest, Toe and Shoreline

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2013 Dauphin Island USGS Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2013 Dauphin Island ...

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2012 Post-Hurricane Sandy New Jersey USGS EAARL-B Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post-Hurricane ...

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2012 Pre-Sandy New York and New Jersey USGS Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Pre Hurricane Sandy ...

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2012 Pre-Hurricane Sandy Fire Island National Seashore, USGS Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post-Hurricane ...

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2012 Post-Hurricane Sandy Long Island, New York USACE Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post-Hurricane ...

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2012 Post-Hurricane Sandy Northeast Atlantic Coast USGS Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post ...

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2012 Post-Hurricane Sandy Fire Island, New York Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post-Hurricane ...

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2012 Post-Hurricane Isaac USGS Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post-Hurricane ...

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2011 East Coast New York/New Jersey NOAA/NGS Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2011 East Coast New York ...

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2011 USGS New York Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2011 Atlantic Coast ...

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2011 Northern Gulf Coast USACE Lidar-derived dune crest, toe and shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2011 Northern Gulf Coast ...

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July 2010 Dauphin Island USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Dauphin Island U.S. ...

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2010 Assateague Island National Seashore USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Assateague Island ...

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2010 Louisiana and Mississippi USACE Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Louisiana and ...

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2010 Maryland USACE Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Maryland U.S. Army ...

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2010 Delaware USACE Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Delaware U.S. Army ...

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2010 New Jersey USACE Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 New Jersey U.S. ...

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2010 New York USACE Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 New York U.S. Army ...

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2010 Virginia USACE Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Virginia U.S. Army ...

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2010 Florida West Coast USACE Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Florida West Coast ...

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2010 Southeast Atlantic USACE Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Southeast Atlantic ...

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2010 Alabama and Florida USACE Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Alabama and Florida ...

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2010 Northeast Atlantic USACE Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Northeast Atlantic ...

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2009 Post-Nor’Ida USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2009 Post-Nor’Ida USGS ...

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2009 Western Gulf of Mexico USACE Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2009 Western Gulf of ...

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2009 Florida USACE Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2009 Florida U.S. Army ...

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2009 North Carolina USACE Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2009 U.S. Army Corps of ...

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2009 Cape Canaveral USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2009 Cape Canaveral ...

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2008 Assateague Island USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2008 Assateague Island ...

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2008 South Louisiana USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2008 South Louisiana ...

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2008 North Carolina and Virginia NOAA/NGS Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2008 Atlantic Coast ...

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2008 USGS Post-Hurricane Ike Texas Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2008 USGS Post-Hurricane ...

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2008 Post-Hurricane Gustav Northern Gulf of Mexico USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2008 Post-Hurricane ...

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June 2008 Alabama and Florida USGS EAARL Lidar-derived dune crest, toe and shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the June 2008 Louisiana, ...

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September 2007 Northern Gulf of Mexico USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2007 Northern Gulf of ...

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2007 USACE Oahu Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2007 U.S. Army Corps of ...

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2007 South Florida FDEM Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2007 Florida Division of ...

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2007 Northeast Barrier Islands USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2007 Northeast Barrier ...

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September 2006 Mississippi and Alabama USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2006 USGS Mississippi ...

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March 2006 Mississippi and Alabama USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2006 USGS Mississippi ...

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2006 FEMA Oahu Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2006 Federal Emergency ...

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September 2006 Post-Hurricane Wilma Florida U.S. Army Corps of Engineers Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2006 Post-Hurricane ...

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2005 Padre Island USGS EAARL Lidar-derived dune crest, toe and shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005 Experimental ...

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2005 USGS Post-Hurricane Rita Texas and Louisiana Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005 USGS Post-Hurricane ...

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2005 Post-Hurricane Katrina EAARL Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005 Post-Hurricane ...

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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Wreck Island, VA, 2014

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 ...

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2005-2006 Atlantic Coast USACE Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005-2006 Atlantic Coast ...

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2005 East Coast (DE, MD,NJ, NY, NC, and VA) USACE NCMP Topobathy Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline derived from the 2005 U.S. Army Corps of ...

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2005 Post-Hurricane Dennis Florida U.S. Army Corps of Engineers Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005 USACE Post-Dennis ...

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2004 Post-Hurricane Jeanne USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 Post-Hurricane ...

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2004 Post-Hurricane Ivan Northern Gulf of Mexico EAARL Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 USGS Post-Ivan ...

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2004 Post-Hurricane Frances USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 Post-Hurricane ...

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2004 Post-Hurricane Charley West Florida EAARL Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 Post-Hurricane ...

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2004 Maine NOAA Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 NOAA Maine lidar ...

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2004 USACE Post-Ivan Florida Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 U.S. Army Corps of ...

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2004 Pre-Hurricane Ivan Eastern Gulf Coast United States Army Corps of Engineers (USACE) Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 Pre-Ivan Eastern ...

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2003 Pre- and Post-Hurricane Isabel USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2003 Pre- and Post ...

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2003 NOAA Oahu Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2003 NOAA Oahu lidar ...

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2002 NOAA/NASA/USGS Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2002 Post-Hurricane Lili ...

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2002 USGS Virgina and Maryland Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2001 Gulf Coast USGS ...

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2002 Post-Tropical Storm Fay University of Texas Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2002 University of Texas ...

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2001 Gulf Coast USGS/NASA ATM Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2001 Gulf Coast USGS ...

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2001 USACE Oahu Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2001 U.S. Army Corps of ...

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Fall 2000 USGS Mid-Atlantic Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2000 Atlantic Coast U.S. ...

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2000 USACE Oahu Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2000 U.S. Army Corps of ...

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Multichannel seismic-reflection data acquired off the coast of southern California - Part A 1997, 1998, 1999, and 2000

Multichannel seismic-reflection (MCS) data were collected in the California Continental Borderland as part of southern California Earthquake Hazards Task. Five data acquisition cruises conducted over a six-year span collected MCS data from offshore Santa Barbara, California south to the Exclusive Economic Zone boundary with Mexico. The primary mission was to map late Quaternary deformation as well as identify and characterize fault zones that have potential to impact high population areas of southern ...

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Input Data Boundary Outlines for DEMs of the North-Central California Coast (DEM_source_data.shp)

A GIS polygon shapefile outlining the boundaries of the native input datasets used to construct a seamless, 2-meter resolution digital elevation model (DEM) was constructed for the open-coast region of the San Francisco Bay Area (outside of the Golden Gate Bridge), extending from Half Moon Bay to Bodega Head along the North-central California coastline. The goal was to integrate the most recent high-resolution bathymetric and topographic datasets available (for example, Light Detection and Ranging (lidar) ...

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Arc ASCII and GeoTiff DEMs of the North-Central California Coast (DEM_#_ASCII and DEM_#_GeoTIFF)

A seamless, 2 meter resolution digital elevation model (DEM) was constructed for the open-coast region of the San Francisco Bay Area (outside of the Golden Gate Bridge), extending from Half Moon Bay to Bodega Head along the north-central California coastline. The goal was to integrate the most recent high-resolution bathymetric and topographic datasets available (for example, Light Detection and Ranging (lidar) topography, multibeam and single-beam sonar bathymetry) into a seamless surface model extending ...

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Hydro-flattened Elevation Area Outlines for DEMs of the North-Central California Coast (Hydro_flattened_water.shp)

A GIS polygon shapefile outlining the extent of small lakes or ponds within the terrain that were assigned a hydo-flattened elevation during lidar post-processing. DEM elevations within these small areas reflect water surface elevations, not bathymetric elevations.

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Coverage Polygons for DEMs of the North-Central California Coast (DEM_coverage_areas.shp)

A GIS polygon shapefile outlining the extent of the 14 individual DEM sections that comprise the seamless, 2-meter resolution DEM for the open-coast region of the San Francisco Bay Area (outside of the Golden Gate Bridge), extending from Half Moon Bay to Bodega Head along the north-central California coastline. The goal was to integrate the most recent high-resolution bathymetric and topographic datasets available (for example, Light Detection and Ranging (lidar) topography, multibeam and single-beam sonar ...

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Model parameter input files to study three-dimensional flow over coral reef spur-and-groove morphology

This data set consists of physics-based Delft3D-FLOW and SWAN hydrodynamic models input files used to study the wave-induced 3D flow over spur-and-groove (SAG) formations. SAG are a common and impressive characteristic of coral reefs. They are composed of a series of submerged shore-normal coral ridges (spurs) separated by shore-normal patches of sediment (grooves) on the fore reef of coral reef environments. Although their existence and geometrical properties are well documented, the literature concerning ...

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Physics-based numerical model simulations of wave propagation over and around theoretical atoll and island morphologies for sea-level rise scenarios

Schematic atoll models with varying theoretical morphologies were used to evaluate the relative control of individual morphological parameters on alongshore transport gradients. Here we present physics-based numerical SWAN model results of incident wave transformations for a range of atoll and island morphologies and sea-level rise scenarios. Model results are presented in NetCDF format, accompanied by a README text file that lists the parameters used in each model run. These data accompany the following ...

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Coral reef profiles for wave-runup prediction

This data release includes representative cluster profiles (RCPs) from a large (>24,000) selection of coral reef topobathymetric cross-shore profiles (Scott and others, 2020). We used statistics, machine learning, and numerical modelling to develop the set of RCPs, which can be used to accurately represent the shoreline hydrodynamics of a large variety of coral reef-lined coasts around the globe. In two stages, the data were reduced by clustering cross-shore profiles based on morphology and hydrodynamic ...

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Model parameter input files to compare the influence of channels in fringing coral reefs on alongshore variations in wave-driven runup along the shoreline

An extensive set of physics-based XBeach Non-hydrostatic hydrodynamic model simulations (with input files here included) were used to evaluate the influence of shore-normal reef channels on flooding along fringing reef-lined coasts, specifically during extreme wave conditions when the risk for coastal flooding and the resulting impact to coastal communities is greatest. These input files accompany the modeling conducted for the following publication: Storlazzi, C.D., Rey, A.E., and van Dongeren, A.R., 2022, ...

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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2018-01-29

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. Point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ...

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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-12-21

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. Point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ...

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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-12-07

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using a UAS-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. Point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Ricoh GR camera in DNG format and processed using structure-from-motion photogrammetry with ...

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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-10-12

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using a UAS-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Ricoh GR camera in DNG format and processed using structure-from-motion photogrammetry ...

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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-06-26

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ...

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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-06-13

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ...

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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-05-27

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ...

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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-05-19

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point Cloud Coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ...

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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-03-08

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ...

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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 1967-10-18

Presented here is a point cloud produced by the U.S. Geological Survey (USGS) from historical U.S. Air Force vertical aerial imagery, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was downloaded from USGS Eros Data Center and processed using structure-from-motion ...

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Quaternary faults offshore of California

A comprehensive map of Quaternary faults has been generated for offshore of California. The Quaternary fault map includes mapped geometries and attribute information for offshore fault systems located in California State and Federal waters. The polyline shapefile has been compiled from previously published mapping where relatively dense, high-resolution marine geophysical data exist. The data are also available in kml format and are accompanied by a pdf containing citations for the compiled source data. In ...

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HyCReWW database: A hybrid coral reef wave and water level metamodel

We developed the HyCReWW metamodel to predict wave run-up under a wide range of coral reef morphometric and offshore forcing characteristics. Due to the complexity and high dimensionality of the problem, we assumed an idealized one-dimensional reef profile, characterized by seven primary parameters. XBeach Non-Hydrostatic was chosen to create the synthetic dataset and Radial Basis Functions implemented in Matlab were chosen for interpolation. Results demonstrate the applicability of the metamodel to obtain ...

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Dynamically downscaled future wave projections from SWAN model results for the main Hawaiian Islands

Projected wave climate trends from WAVEWATCH3 model output were used as input for nearshore wave models (for example, SWAN) for the main Hawaiian Islands to derive data and statistical measures (mean and top 5 percent values) of wave height, wave period, and wave direction for the recent past (1996-2005) and future projections (2026-2045 and 2085-2100). Three-hourly global climate model (GCM) wind speed and wind direction output from four different GCMs provided by the Coupled Model Inter-Comparison Project ...

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Water level and velocity measurements from the 2012 University of Western Australia Fringing Reef Experiment (UWAFRE)

This data release contains water level and velocity measurements from wave runup experiments performed in a laboratory flume setting. Wave-driven water level variability (and runup at the shoreline) is a significant cause of coastal flooding induced by storms. Wave runup is challenging to predict, particularly along tropical coral reef-fringed coastlines due to the steep bathymetric profiles and large bottom roughness generated by reef organisms. The 2012 University of Western Australia Fringing Reef ...

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Photographs of vibracores collected during a Monterey Bay Aquarium Research Institute cruise in November 2019 offshore of south-central California (USGS FAN 2019-667-FA)

This dataset includes photographs of 49 vibracores that were collected by the Monterey Bay Aquarium Research Institute (MBARI) in November 2019 aboard the R/V Western Flyer using the remotely operated vehicle (ROV) Doc Ricketts. The collection of these cores was funded entirely by MBARI, and the cores have been donated to the U.S. Geological Survey (USGS). The cores were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM) and are located in the same study area as the ...

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Location data for vibracores collected during a Monterey Bay Aquarium Research Institute cruise in November 2019 offshore of south-central California (USGS FAN 2019-667-FA)

This dataset includes the location information for 49 vibracores that were collected by the Monterey Bay Aquarium Research Institute (MBARI) in November 2019 aboard the R/V Western Flyer using the remotely operated vehicle (ROV) Doc Ricketts. The collection of these cores was funded entirely by MBARI, and the cores have been donated to the U.S. Geological Survey (USGS). The cores were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM) and are located in the same study ...

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Photographs of vibracores collected during a Monterey Bay Aquarium Research Institute cruise in February 2019 offshore of south-central California (USGS FAN 2019-603-FA)

This dataset includes photographs of 49 vibracores that were collected by the Monterey Bay Aquarium Research Institute (MBARI) in February 2019 aboard the R/V Western Flyer using the remotely operated vehicle (ROV) Doc Ricketts. The collection of these cores was funded entirely by MBARI, and the cores have been donated to the U.S. Geological Survey (USGS). The cores were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM) and are located in the same study area as the ...

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Location and depth data for vibracores collected during a Monterey Bay Aquarium Research Institute cruise in February 2019 offshore of south-central California (USGS FAN 2019-603-FA)

This dataset includes the location and depth information for 49 vibracores that were collected by the Monterey Bay Aquarium Research Institute (MBARI) in February 2019 aboard the R/V Western Flyer using the remotely operated vehicle (ROV) Doc Ricketts. The collection of these cores was funded entirely by MBARI, and the cores have been donated to the U.S. Geological Survey (USGS). The cores were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM) and are located in the ...

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Porewater chloride and sulfate concentrations from piston and gravity cores collected in September 2019 offshore of south-central California (USGS FAN 2019-642-FA)

This dataset includes concentrations chloride and sulfate in porewater from piston and gravity cores collected in September 2019 offshore of south-central California aboard the R/V Bold Horizon. This dataset is one of several collected as part of the Bureau of Ocean Energy Management (BOEM)-funded California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy ...

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Multi-Sensor Core Logger (MSCL) data of piston and gravity cores collected in September 2019 offshore of south-central California (USGS FAN 2019-642-FA)

This dataset includes multi-sensor core logger (MSCL) data for 39 piston and gravity cores that were collected as part of a groundtruthing survey in September 2019 aboard the R/V Bold Horizon. This dataset is one of several collected as part of the Bureau of Ocean Energy Management (BOEM)-funded California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure ...

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Photographs of piston and gravity cores collected in September 2019 offshore of south-central California (USGS FAN 2019-642-FA)

This dataset includes photographs of 39 piston and gravity cores that were collected as part of a groundtruthing survey in September 2019 aboard the R/V Bold Horizon. This dataset is one of several collected as part of the Bureau of Ocean Energy Management (BOEM)-funded California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure (namely floating wind ...

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Location and depth data for piston and gravity cores collected in September 2019 offshore of south-central California (USGS FAN 2019-642-FA)

This dataset includes the location and depth information for 39 piston and gravity cores that were collected as part of a groundtruthing survey in September 2019 aboard the R/V Bold Horizon. This dataset is one of several collected as part of the Bureau of Ocean Energy Management (BOEM)-funded California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure ...

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Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation

Coast Train is a library of images of coastal environments, annotations, and corresponding thematic label masks (or ‘label images’) collated for the purposes of training and evaluating machine learning (ML), deep learning, and other models for image segmentation. It includes image sets from both geospatial satellite, aerial, and UAV imagery and orthomosaics, as well as non-geospatial oblique and nadir imagery. Images include a diverse range of coastal environments from the U.S. Pacific, Gulf of Mexico, ...

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2005 EAARL Fire Island Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005 Fire Island USGS ...

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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Wreck Island, VA, 2014

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 ...

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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Smith Island, VA, 2014

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 ...

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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Smith Island, VA, 2014

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 ...

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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Ship Shoal Island, VA, 2014

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 ...

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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Ship Shoal Island, VA, 2014

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 ...

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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Rhode Island National Wildlife Refuge, RI, 2014

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 ...

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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Rhode Island National Wildlife Refuge, RI, 2014

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 ...

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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Parker River, MA, 2014

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 ...

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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Parker River, MA, 2014

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 ...

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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Parramore Island, VA, 2014

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 ...

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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

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Myrtle Island, VA, 2014

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 ...

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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Myrtle Island, VA, 2014

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 ...

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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Monomoy Island, MA, 2013-2014

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 ...

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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Monomoy Island, MA, 2013-2014

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Metompkin Island, VA, 2014

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 ...

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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Metompkin Island, VA, 2014

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Fisherman Island, VA, 2014

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 ...

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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Fisherman Island, VA, 2014

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Cobb Island, VA, 2014

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 ...

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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cobb Island, VA, 2014

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Coast Guard Beach, MA, 2013-2014

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 ...

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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Coast Guard Beach, MA, 2013-2014

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Cape Lookout, NC, 2014

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 ...

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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cape Lookout, NC, 2014

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Cape Hatteras, NC, 2014

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 ...

Info
points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cape Hatteras, NC, 2014

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Assawoman Island, VA, 2014

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 ...

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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Assawoman Island, VA, 2014

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Assateague Island, MD & VA, 2014

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 ...

Info
points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Assateague Island, MD & VA, 2014

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2013–2014

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 ...

Info
points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Rockaway Peninsula, NY, 2013–2014

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2012

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 ...

Info
points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Rockaway Peninsula, NY, 2012

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2010–2011

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 ...

Info
points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Rockaway Peninsula, NY, 2010–2011

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 ...

Info
points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Fire Island, NY, 2014–2015

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 ...

Info
points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Fire Island, NY, 2012

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 ...

Info
points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Fire Island, NY, 2010–2011

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Fire Island, NY, 2014–2015

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Fire Island, NY, 2012

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Fire Island, NY, 2010–2011

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Edwin B. Forsythe NWR, NJ, 2013–2014

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Edwin B. Forsythe NWR, NJ, 2012

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Edwin B. Forsythe NWR, NJ, 2010

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 ...

Info
points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Edwin B. Forsythe NWR, NJ, 2013–2014

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 ...

Info
points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Edwin B. Forsythe NWR, NJ, 2012

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 ...

Info
points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Edwin B. Forsythe NWR, NJ, 2010

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2013–2014

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 ...

Info
points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cedar Island, VA, 2013–2014

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013

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 ...

Info
points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cedar Island, VA, 2012–2013

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 ...

Info
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2010–2011

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 ...

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Point shapefile of probability of shoreline change along the U.S. Atlantic Coast (ProbSLC_AtlanticData.shp)

During the 21st century, sea-level rise will have a wide range of effects on coastal environments, human development and infrastructure in coastal areas. Consequently there is a need to develop modeling or other analytical approaches that can be used to evaluate potential impacts to inform coastal management. This shapefile provides the data that were used to develop and evaluate the performance of a Bayesian network (BN) that was developed to predict long-term shoreline change associated with sea-level ...

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Hurricane Zeta Overwash Extents

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Louisiana coast and attributed to coastal processes during [Atlantic Basin] Hurricane Zeta, which made landfall in the U.S. on October 28, 2020.

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XYZ point data - Post Hurricane Sandy Beach Profile Survey Fire Island Inlet to Moriches Inlet 2013

The U.S. Army Corps of Engineers(USACE) contracted a beach survey of Fire Island, New York from September 17–October 6, 2013, for the purpose of planning a beach reconstruction project following Hurricane Sandy. This dataset contains elevation data of subaerial morphology and nearshore bathymetry collected using real time kinematic global positioning system (RTK-GPS) and hydrography techniques. The data were provided to the U.S. Geological Survey(USGS) to contribute to an existing monitoring dataset of ...

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Wetland-Change Data Derived from Landsat Imagery, Assateague Island to Metompkin Island, Maryland and Virginia, 1984 to 2015: Wetland Persistence Analysis

This U.S. Geological Survey (USGS) data release includes geospatial datasets that were created for the analysis of Virginia and Maryland Atlantic coastal wetland changes over time. Wetland change was determined by assessing two metrics: wetland persistence and land-cover switching. Because seasonal water levels, beach width, and vegetation differences can affect change analyses, only images acquired during the spring (March, April, and May) were included in the wetland-change metrics (N=10). To assess ...

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Hurricane Sally Overwash Extents

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Florida and Alabama coast and attributed to coastal processes during [Atlantic Basin] Hurricane Sally, which made landfall in the U.S. on September 16, 2020.

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Hurricane Michael Overwash Extents (version 2.0, 20210916)

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the Florida coast and attributed to coastal processes during [Atlantic Basin] Hurricane Michael, which made landfall in the U.S. on October 10, 2018.

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Hurricane Matthew Overwash Extents (version 2.0, 20210916)

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Florida, Georgia, North Carolina,and South Carolina coasts and attributed to coastal processes during [Atlantic Basin] Hurricane Matthew, which made landfall in the U.S. on October 8, 2018.

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Massachusetts raw (non-interpolated) Beach Slope Point Data

The National Assessment of Coastal Change Hazards project derives beach morphology features from lidar elevation data for the purpose of understanding and predicting storm impacts to our nation's coastlines. This dataset defines beach slopes along the United States Northeast Atlantic Ocean for Massachusetts for data collected at various times between 2000 and 2013

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Massachusetts Mean (interpolated) Beach Slope Point Data

The National Assessment of Coastal Change Hazards project derives beach morphology features from lidar elevation data for the purpose of understanding and predicting storm impacts to our nation's coastlines. This dataset defines mean beach slopes for Massachusetts for data collected at various times between 2000 and 2013.

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Hurricane Laura Overwash Extents

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Louisiana coast and attributed to coastal processes during [Atlantic Basin] Hurricane Laura, which made landfall in the U.S. on August 27, 2020.

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Land-Cover Data Derived from Landsat Satellite Imagery, Assateague Island to Metompkin Island, Maryland and Virginia, 1985 and 2015

This U.S. Geological Survey (USGS) data release includes geospatial datasets that were created to analyze wetland changes along the Virginia and Maryland Atlantic coasts between 1984 and 2015. Wetland change was determined by assessing two metrics: wetland persistence and land-cover switching. Because seasonal water levels, beach width, and vegetation differences can affect change analyses, only images acquired during the spring (March, April, and May) were included in the wetland-change metrics (N=10). ...

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Wetland-Change Data Derived from Landsat Imagery, Assateague Island to Metompkin Island, Maryland and Virginia, 1984 to 2015: Land-cover Change Analysis

This U.S. Geological Survey (USGS) data release includes geospatial datasets that were created for the analysis of Virginia and Maryland Atlantic coastal wetland changes over time. Wetland change was determined by assessing two metrics: wetland persistence and land-cover switching. Because seasonal water levels, beach width, and vegetation differences can affect change analyses, only images acquired during the spring (March, April, and May) were included in the wetland-change metrics (N=10). Land-cover ...

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Hurricane Irma Overwash Extents (version 2.0, 20210916)

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Florida coast and attributed to coastal processes during [Atlantic Basin] Hurricane Irma, which made landfall in the U.S. on September 9, 2017.

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Hurricane Matthew Overwash Extents

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the southeast coast of the United States from Florida to North Carolina and attributed to coastal processes during [Atlantic Basin] Hurricane Matthew, which made landfall in the U.S. on October 8, 2016.

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Hurricane Florence Overwash Extents

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the southeast coast of the United States from North Carolina to Virginia and attributed to coastal processes during [Atlantic Basin] Hurricane Florence, which made landfall in the U.S. on September 14, 2018.

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Hurricane Delta Overwash Extents

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Louisiana coast and attributed to coastal processes during [Atlantic Basin] Hurricane Delta, which made landfall in the U.S. on October 9, 2020.

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Laboratory Observations of Artificial Sand and Oil Agglomerates Video and Velocity Data: Sea Floor Interaction Experiment Interpretive Video

Weathered oil in the surf-zone after an oil spill may mix with suspended sediments to form sand and oil agglomerates (SOA). Sand and oil agglomerates may form in mats on the scale of tens of meters (m), and may break apart into pieces between 1 and 10 centimeters (cm) in diameter. These more mobile pieces are susceptible to alongshore and cross-shore transport, and lead to beach re-oiling on the time scale of months to years following a spill. The U.S. Geological Survey (USGS) conducted experiments March 10 ...

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Laboratory Observations of Artificial Sand and Oil Agglomerates Video and Velocity Data: Sea Floor Interaction Experiment Flow Velocity

Weathered oil in the surf-zone after an oil spill may mix with suspended sediments to form sand and oil agglomerates (SOA). Sand and oil agglomerates may form in mats on the scale of tens of meters (m), and may break apart into pieces between 1 and 10 centimeters (cm) in diameter. These more mobile pieces are susceptible to alongshore and cross-shore transport, and lead to beach re-oiling on the time scale of months to years following a spill. The U.S. Geological Survey (USGS) conducted experiments March 10 ...

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Laboratory Observations of Artificial Sand and Oil Agglomerates Video and Velocity Data: Sea Floor Interaction Experiment Interpretive Video

Weathered oil in the surf-zone after an oil spill may mix with suspended sediments to form sand and oil agglomerates (SOA). Sand and oil agglomerates may form in mats on the scale of tens of meters (m), and may break apart into pieces between 1 and 10 centimeters (cm) in diameter. These more mobile pieces are susceptible to alongshore and cross-shore transport, and lead to beach re-oiling on the time scale of months to years following a spill. The U.S. Geological Survey (USGS) conducted experiments March 10 ...

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Laboratory Observations of Artificial Sand and Oil Agglomerates: Video and Velocity Data: Sea Floor Interaction Experiment Video (GoPro)

Weathered oil in the surf-zone after an oil spill may mix with suspended sediments to form sand and oil agglomerates (SOA). Sand and oil agglomerates may form in mats on the scale of tens of meters (m), and may break apart into pieces between 1 and 10 centimeters (cm) in diameter. These more mobile pieces are susceptible to alongshore and cross-shore transport, and lead to beach re-oiling on the time scale of months to years following a spill. The U.S. Geological Survey (USGS) conducted experiments March 10 ...

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Laboratory Observations of Artificial Sand and Oil Agglomerates: Video and Velocity Data: Sea Floor Interaction Experiment Preview Video (GoPro)

Weathered oil in the surf-zone after an oil spill may mix with suspended sediments to form sand and oil agglomerates (SOA). Sand and oil agglomerates may form in mats on the scale of tens of meters (m), and may break apart into pieces between 1 and 10 centimeters (cm) in diameter. These more mobile pieces are susceptible to alongshore and cross-shore transport, and lead to beach re-oiling on the time scale of months to years following a spill. The U.S. Geological Survey (USGS) conducted experiments March 10 ...

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Laboratory Observations of Artificial Sand and Oil Agglomerates Video and Velocity Data: False-Floor Experiment Interpretive Video

Weathered oil in the surf-zone after an oil spill may mix with suspended sediments to form sand and oil agglomerates (SOA). Sand and oil agglomerates may form in mats on the scale of tens of meters (m), and may break apart into pieces between 1 and 10 centimeters (cm) in diameter. These more mobile pieces are susceptible to alongshore and cross-shore transport, and lead to beach re-oiling on the time scale of months to years following a spill. The U.S. Geological Survey (USGS) conducted experiments March 10 ...

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Laboratory Observations of Artificial Sand and Oil Agglomerates Video and Velocity Data: False-Floor Experiment Flow Velocity and Shear Stress

Weathered oil in the surf-zone after an oil spill may mix with suspended sediments to form sand and oil agglomerates (SOA). Sand and oil agglomerates may form in mats on the scale of tens of meters (m), and may break apart into pieces between 1 and 10 centimeters (cm) in diameter. These more mobile pieces are susceptible to alongshore and cross-shore transport, and lead to beach re-oiling on the time scale of months to years following a spill. The U.S. Geological Survey (USGS) conducted experiments March 10 ...

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1999 Fall Texas USGS/NASA/NOAA ATM Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1999 Fall Gulf Coast ...

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1999 Atlantic Coast NASA/NOAA/USGS ATM Lidar-Derived Dune Crest, Toe and Shoreline, post-Hurricane Floyd

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1999 Atlantic Coast ...

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1998 MA, RI, CT, NY, NJ, DE, MD, PA, and VA USGS/NASA ATM2 Lidar-derived dune crest, toe and shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2001 Gulf Coast USGS ...

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1998 Fall Gulf Coast Lidar-Derived Dune Crest, Toe and Shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1998 Fall Gulf Coast ...

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1998 Southeast ATM Lidar-derived dune crest, toe and shoreline

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1998 Southeast USGS/NASA ...

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