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This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios...
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This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average...
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This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios...
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This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average...
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This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions)...
In the next 100 years, accelerated sea-level rise (SLR) and urbanization will greatly modify coastal landscapes across the globe. More than one-half of coastal wetlands in the contiguous United States are located along the Gulf of Mexico coast. In addition to supporting fish and wildlife habitat, these highly productive wetlands support many ecosystem goods and services including storm protection, recreation, clean water, and carbon sequestration. Historically, tidal saline wetlands (TSWs) have adapted to sea-level fluctuations through lateral and vertical movement on the landscape. As sea levels rise in the future, some TSWs will adapt and migrate landward in undeveloped low-lying areas where migration corridors...
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This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average...
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This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions)...
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This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average...
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This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios...
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This dataset is part of an extensive analysis of sea-level rise impacts on coastal habitats along the Chesapeake Bay, Delaware Bay, and the ocean beaches of southern New Jersey, Delaware, Maryland, and Virginia. The National Wildlife Federation commissioned Jonathan S. Clough of Warren Pinnacle Consulting, Inc., to apply the Sea Level Affecting Marshes Model (SLAMM, Version 5.0) to the Chesapeake Bay region. The SLAMM model is widely regarded as the premier research tool for simulating the dominant processes involved in wetland conversions and shoreline modifications during long-term sea-level rise. Our analysis looked at a range of sea-level rise scenarios from the 2001 Intergovernmental Panel on Climate Change...
Ecosystems of the southeastern United States face a large number of threats to their ecological integrity, including loss of habitat, climate change, exotic species invasion, and many more. NatureServe staff, in conjunction with the South Atlantic Landscape Conservation Cooperative (SALCC), have compiled this report in order to better understand how these threats impact ecosystems and the level of impact to these ecosystems, thereby addressing a key information need identified by the SALCC. We have organized the threats according to the first and second level threats classification of the Conservation Measures Partnership (Salafsky et al. 2008). The Conservation Measures Partnership maintains a standard nomenclature...
Seascapes symbolize both the physical dimensions of ocean and coastal areas, as well as the meanings humans ascribe to their observations, interactions, and relationships to the sea. In Hawaiʻi, seascapes are particularly important given that the ocean contributes considerably to the well-being of coastal communities which are vulnerable to climate change and other human pressures. Monitoring and understanding how these areas are changing requires fully knowing the relationship between physical and cultural dynamics. Mechanical sensors in the marine environment monitor wave regimes, streamflow, rainfall, and other parameters that scientists and managers use to predict the effects and implications of climate change,...
Conservation of oceanic island species presents many ecological and logistical challenges. The Northwestern Hawaiian Islands (NWHI) include 300,000 km2 of ocean waters and 10 groups of sub-tropical islands and atolls of high conservation value. Designated as Papahanaumokuakea Marine National Monument, the islands provide habitat for four endangered species of terrestrial birds. Despite their protected status, many of these species are faced with the ongoing threat of extinction due to stochastic catastrophes such as disease, invasive mammal introductions, tsunamis, and hurricanes. A longer term threat to NWHI species is sea level rise associated with global climate change. On a 50-year time scale, many low-lying...
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In the next 100 years, accelerated sea-level rise (SLR) and urbanization will greatly modify coastal landscapes across the globe. More than one-half of coastal wetlands in the contiguous United States are located along the Gulf of Mexico coast. In addition to supporting fish and wildlife habitat, these highly productive wetlands support many ecosystem goods and services including storm protection, recreation, clean water, and carbon sequestration. Historically, tidal saline wetlands (TSWs) have adapted to sea-level fluctuations through lateral and vertical movement on the landscape. As sea levels rise in the future, some TSWs will adapt and migrate landward in undeveloped low-lying areas where migration corridors...
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In the next 100 years, accelerated sea-level rise (SLR) and urbanization will greatly modify coastal landscapes across the globe. More than one-half of coastal wetlands in the contiguous United States are located along the Gulf of Mexico coast. In addition to supporting fish and wildlife habitat, these highly productive wetlands support many ecosystem goods and services including storm protection, recreation, clean water, and carbon sequestration. Historically, tidal saline wetlands (TSWs) have adapted to sea-level fluctuations through lateral and vertical movement on the landscape. As sea levels rise in the future, some TSWs will adapt and migrate landward in undeveloped low-lying areas where migration corridors...
Designed by scientists to simplify consistent data collection and management, the iPlover smartphone application gives trained resource managers an easy-to-use platform where they can collect and share data about coastal habitat utilization across a diverse community of field technicians, scientists, and managers. With the click of a button, users can contribute biological and geomorphological data to regional models designed to forecast the habitat outlook for piping plover, and other species that depend upon sandy beach habitat.iPlover app is available for iPhones and Androids on the USGS Mobile Application Directory. The app is free, but users must ask for and receive an approved login to use it. Training is...
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In 2010, 39 percent of the U.S.population lived near the coast. This population is expected to increase by 8 percent from 2010 to 2020. Coastal regions are also home to species and habitats that provide critical services to humans, such as wetlands that buffer coasts from storms. Therefore, sea-level rise and the associated changes in coastlines challenge both human communities and ecosystems. Understanding which coastal lands will be vulnerable to sea-level rise is critical for policy makers, land-use planners, and coastal residents. Focusing on the coastal region from Virginia to Maine, researchers examined a range of different possible sea-level rise scenarios, combined with information on features of the coastal...
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This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated...
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This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios...


map background search result map search result map Evaluating Sea-level Rise Impacts in the Northeastern U.S. Chesapeake Bay region sea-level rise modelling - Habitat classification, 2050 (1 meter scenario) Data_Series_969_Tidal_Saline_Wetland_Migration_2040 Data_Series_969_Tidal_Saline_Wetland_Migration_2060 CoSMoS v3.1 flood hazard projections: 1-year storm in San Luis Obispo County CoSMoS v3.1 flood hazard projections: average conditions in San Barbara County CoSMoS v3.1 wave-hazard projections: 1-year storm in San Luis Obispo County CoSMoS v3.1 flood depth and duration projections: 1-year storm in San Mateo County CoSMoS v3.1 flood depth and duration projections: 20-year storm in San Mateo County CoSMoS v3.1 flood hazard projections: 100-year storm in San Mateo County CoSMoS v3.1 flood depth and duration projections: 1-year storm in San Francisco County CoSMoS v3.1 water level projections: 20-year storm in San Francisco County CoSMoS v3.1 ocean-currents hazards: 100-year storm in San Francisco County CoSMoS v3.1 flood depth and duration projections: average conditions in San Francisco County CoSMoS v3.1 flood hazard projections: 20-year storm in Santa Cruz County CoSMoS v3.1 flood depth and duration projections: 20-year storm in San Mateo County CoSMoS v3.1 flood depth and duration projections: 1-year storm in San Mateo County CoSMoS v3.1 flood hazard projections: 100-year storm in San Mateo County CoSMoS v3.1 flood hazard projections: average conditions in San Barbara County CoSMoS v3.1 flood hazard projections: 20-year storm in Santa Cruz County CoSMoS v3.1 flood hazard projections: 1-year storm in San Luis Obispo County CoSMoS v3.1 wave-hazard projections: 1-year storm in San Luis Obispo County Chesapeake Bay region sea-level rise modelling - Habitat classification, 2050 (1 meter scenario) Evaluating Sea-level Rise Impacts in the Northeastern U.S. Data_Series_969_Tidal_Saline_Wetland_Migration_2040 Data_Series_969_Tidal_Saline_Wetland_Migration_2060