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In order to predict the impacts of climate change induced sea-level rise on Pacific Northwest coastal habitats, the Sea Level Affecting Marshes Model (SLAMM) was utilized to simulate future coastal habitat configurations under various sea-level rise scenarios. The model was run for 2025, 2050, 2075, and 2100. Historical or "initial condition" habitat classifications are also available for some sites. The sea-level rise scenarios include: 1. A1B greenhouse gas emission mean : 0.39 meter rise by 2100 2. A1B greenhouse gas emission maximum : 0.69 meter rise by 2100 3. 1 meter rise by 2100 4. 1.5 meter rise by 2100 5. 2 meter rise by 2100 Due to differing site conditions, local sea-level rise varies slightly from...
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In order to predict the impacts of climate change induced sea-level rise on Pacific Northwest coastal habitats, the Sea Level Affecting Marshes Model (SLAMM) was utilized to simulate future coastal habitat configurations under various sea-level rise scenarios. The model was run for 2025, 2050, 2075, and 2100. Historical or "initial condition" habitat classifications are also available for some sites. The sea-level rise scenarios include: 1. A1B greenhouse gas emission mean : 0.39 meter rise by 2100 2. A1B greenhouse gas emission maximum : 0.69 meter rise by 2100 3. 1 meter rise by 2100 4. 1.5 meter rise by 2100 5. 2 meter rise by 2100 Due to differing site conditions, local sea-level rise varies slightly from...
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In order to predict the impacts of climate change induced sea-level rise on Pacific Northwest coastal habitats, the Sea Level Affecting Marshes Model (SLAMM) was utilized to simulate future coastal habitat configurations under various sea-level rise scenarios. The model was run for 2025, 2050, 2075, and 2100. Historical or "initial condition" habitat classifications are also available for some sites. The sea-level rise scenarios include: 1. A1B greenhouse gas emission mean : 0.39 meter rise by 2100 2. A1B greenhouse gas emission maximum : 0.69 meter rise by 2100 3. 1 meter rise by 2100 4. 1.5 meter rise by 2100 5. 2 meter rise by 2100 Due to differing site conditions, local sea-level rise varies slightly from...
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In order to predict the impacts of climate change induced sea-level rise on Pacific Northwest coastal habitats, the Sea Level Affecting Marshes Model (SLAMM) was utilized to simulate future coastal habitat configurations under various sea-level rise scenarios. The model was run for 2025, 2050, 2075, and 2100. Historical or "initial condition" habitat classifications are also available for some sites. The sea-level rise scenarios include: 1. A1B greenhouse gas emission mean : 0.39 meter rise by 2100 2. A1B greenhouse gas emission maximum : 0.69 meter rise by 2100 3. 1 meter rise by 2100 4. 1.5 meter rise by 2100 5. 2 meter rise by 2100 Due to differing site conditions, local sea-level rise varies slightly from...
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In order to predict the impacts of climate change induced sea-level rise on Pacific Northwest coastal habitats, the Sea Level Affecting Marshes Model (SLAMM) was utilized to simulate future coastal habitat configurations under various sea-level rise scenarios. The model was run for 2025, 2050, 2075, and 2100. Historical or "initial condition" habitat classifications are also available for some sites. The sea-level rise scenarios include: 1. A1B greenhouse gas emission mean : 0.39 meter rise by 2100 2. A1B greenhouse gas emission maximum : 0.69 meter rise by 2100 3. 1 meter rise by 2100 4. 1.5 meter rise by 2100 5. 2 meter rise by 2100 Due to differing site conditions, local sea-level rise varies slightly from...
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OBIS-USA brings together marine biological occurrence data – recorded observations of identifiable marine species at a known time and place, collected primarily from U.S. Waters or with U.S. funding. Coordinated by the Science Analytics and Synthesis (SAS) Program of the United States Geological Survey (USGS), OBIS-USA, strives to meet national data integration and dissemination needs for marine data about organisms and ecosystems. OBIS-USA is part of an international data sharing network (Ocean Biogeographic Information System, OBIS) coordinated by the Intergovernmental Oceanographic Commission, of UNESCO (United Nations Educational, Science and Cultural Organization) International Oceanographic Data and Information...
Tags: Arctic Ocean, Atlantic Ocean, Bay of Fundy, Beaufort Sea, Bering Sea, All tags...
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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...
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In order to predict the impacts of climate change induced sea-level rise on Pacific Northwest coastal habitats, the Sea Level Affecting Marshes Model (SLAMM) was utilized to simulate future coastal habitat configurations under various sea-level rise scenarios. The model was run for 2025, 2050, 2075, and 2100. Historical or "initial condition" habitat classifications are also available for some sites. The sea-level rise scenarios include: 1. A1B greenhouse gas emission mean : 0.39 meter rise by 2100 2. A1B greenhouse gas emission maximum : 0.69 meter rise by 2100 3. 1 meter rise by 2100 4. 1.5 meter rise by 2100 5. 2 meter rise by 2100 Due to differing site conditions, local sea-level rise varies slightly from...
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In order to predict the impacts of climate change induced sea-level rise on Pacific Northwest coastal habitats, the Sea Level Affecting Marshes Model (SLAMM) was utilized to simulate future coastal habitat configurations under various sea-level rise scenarios. The model was run for 2025, 2050, 2075, and 2100. Historical or "initial condition" habitat classifications are also available for some sites. The sea-level rise scenarios include: 1. A1B greenhouse gas emission mean : 0.39 meter rise by 2100 2. A1B greenhouse gas emission maximum : 0.69 meter rise by 2100 3. 1 meter rise by 2100 4. 1.5 meter rise by 2100 5. 2 meter rise by 2100 Due to differing site conditions, local sea-level rise varies slightly from...
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In order to predict the impacts of climate change induced sea-level rise on Pacific Northwest coastal habitats, the Sea Level Affecting Marshes Model (SLAMM) was utilized to simulate future coastal habitat configurations under various sea-level rise scenarios. The model was run for 2025, 2050, 2075, and 2100. Historical or "initial condition" habitat classifications are also available for some sites. The sea-level rise scenarios include: 1. A1B greenhouse gas emission mean : 0.39 meter rise by 2100 2. A1B greenhouse gas emission maximum : 0.69 meter rise by 2100 3. 1 meter rise by 2100 4. 1.5 meter rise by 2100 5. 2 meter rise by 2100 Due to differing site conditions, local sea-level rise varies slightly from...
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In order to predict the impacts of climate change induced sea-level rise on Pacific Northwest coastal habitats, the Sea Level Affecting Marshes Model (SLAMM) was utilized to simulate future coastal habitat configurations under various sea-level rise scenarios. The model was run for 2025, 2050, 2075, and 2100. Historical or "initial condition" habitat classifications are also available for some sites. The sea-level rise scenarios include: 1. A1B greenhouse gas emission mean : 0.39 meter rise by 2100 2. A1B greenhouse gas emission maximum : 0.69 meter rise by 2100 3. 1 meter rise by 2100 4. 1.5 meter rise by 2100 5. 2 meter rise by 2100 Due to differing site conditions, local sea-level rise varies slightly from...
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In order to predict the impacts of climate change induced sea-level rise on Pacific Northwest coastal habitats, the Sea Level Affecting Marshes Model (SLAMM) was utilized to simulate future coastal habitat configurations under various sea-level rise scenarios. The model was run for 2025, 2050, 2075, and 2100. Historical or "initial condition" habitat classifications are also available for some sites. The sea-level rise scenarios include: 1. A1B greenhouse gas emission mean : 0.39 meter rise by 2100 2. A1B greenhouse gas emission maximum : 0.69 meter rise by 2100 3. 1 meter rise by 2100 4. 1.5 meter rise by 2100 5. 2 meter rise by 2100 Due to differing site conditions, local sea-level rise varies slightly from...
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In order to predict the impacts of climate change induced sea-level rise on Pacific Northwest coastal habitats, the Sea Level Affecting Marshes Model (SLAMM) was utilized to simulate future coastal habitat configurations under various sea-level rise scenarios. The model was run for 2025, 2050, 2075, and 2100. Historical or "initial condition" habitat classifications are also available for some sites. The sea-level rise scenarios include: 1. A1B greenhouse gas emission mean : 0.39 meter rise by 2100 2. A1B greenhouse gas emission maximum : 0.69 meter rise by 2100 3. 1 meter rise by 2100 4. 1.5 meter rise by 2100 5. 2 meter rise by 2100 Due to differing site conditions, local sea-level rise varies slightly from...
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In order to predict the impacts of climate change induced sea-level rise on Pacific Northwest coastal habitats, the Sea Level Affecting Marshes Model (SLAMM) was utilized to simulate future coastal habitat configurations under various sea-level rise scenarios. The model was run for 2025, 2050, 2075, and 2100. Historical or "initial condition" habitat classifications are also available for some sites. The sea-level rise scenarios include: 1. A1B greenhouse gas emission mean : 0.39 meter rise by 2100 2. A1B greenhouse gas emission maximum : 0.69 meter rise by 2100 3. 1 meter rise by 2100 4. 1.5 meter rise by 2100 5. 2 meter rise by 2100 Due to differing site conditions, local sea-level rise varies slightly from...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...


map background search result map search result map Chesapeake Bay region sea-level rise modelling - Habitat classification, 1996 Pacific Northwest sea-level rise modelling - Habitat classification for site four (2075, A1B mean scenario) Pacific Northwest sea-level rise modelling - Habitat classification for site four (2050, A1B maximum scenario) Pacific Northwest sea-level rise modelling - Habitat classification for site four (2050, 1 meter rise scenario) Pacific Northwest sea-level rise modelling - Habitat classification for site four (2025, A1B maximum scenario) Pacific Northwest sea-level rise modelling - Habitat classification for site four (2025, 1 meter rise scenario) Pacific Northwest sea-level rise modelling - Habitat classification for site one (1980) Pacific Northwest sea-level rise modelling - Habitat classification for site two (1977) Pacific Northwest sea-level rise modelling - Habitat classification for site two (2100, 1 meter rise scenario, dikes removed) Pacific Northwest sea-level rise modelling - Habitat classification for site two (2075, 2 meter rise scenario, dikes removed) Pacific Northwest sea-level rise modelling - Habitat classification for site two (2050, A1B maximum scenario, dikes removed) Pacific Northwest sea-level rise modelling - Habitat classification for site one (2050, 1 meter rise scenario) Pacific Northwest sea-level rise modelling - Habitat classification for site one (2025, 1.5 meter rise scenario) SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 Development: Development delineation: Edwin B. Forsythe NWR, NJ, 2010 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2012 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Edwin B. Forsythe NWR, NJ, 2013–2014 ElevMHW: Elevation adjusted to local mean high water: Fire Island, NY, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2012 Development: Development delineation: Edwin B. Forsythe NWR, NJ, 2010 Pacific Northwest sea-level rise modelling - Habitat classification for site four (2075, A1B mean scenario) Pacific Northwest sea-level rise modelling - Habitat classification for site four (2050, A1B maximum scenario) Pacific Northwest sea-level rise modelling - Habitat classification for site four (2050, 1 meter rise scenario) Pacific Northwest sea-level rise modelling - Habitat classification for site four (2025, A1B maximum scenario) Pacific Northwest sea-level rise modelling - Habitat classification for site four (2025, 1 meter rise scenario) Pacific Northwest sea-level rise modelling - Habitat classification for site one (1980) Pacific Northwest sea-level rise modelling - Habitat classification for site one (2050, 1 meter rise scenario) Pacific Northwest sea-level rise modelling - Habitat classification for site one (2025, 1.5 meter rise scenario) DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2012 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Edwin B. Forsythe NWR, NJ, 2013–2014 ElevMHW: Elevation adjusted to local mean high water: Fire Island, NY, 2014 Pacific Northwest sea-level rise modelling - Habitat classification for site two (1977) Pacific Northwest sea-level rise modelling - Habitat classification for site two (2100, 1 meter rise scenario, dikes removed) Pacific Northwest sea-level rise modelling - Habitat classification for site two (2075, 2 meter rise scenario, dikes removed) Pacific Northwest sea-level rise modelling - Habitat classification for site two (2050, A1B maximum scenario, dikes removed) Chesapeake Bay region sea-level rise modelling - Habitat classification, 1996