The land area of Decatur, AL was 54 in 2016.
Land Area
Water Area
Land area is a measurement providing the size, in square miles, of the land portions of geographic entities for which the Census Bureau tabulates and disseminates data. Area is calculated from the specific boundary recorded for each entity in the Census Bureau's geographic database. Land area is based on current information in the TIGER® data base, calculated for use with Census 2010.
Water Area figures include inland, coastal, Great Lakes, and territorial sea water. Inland water consists of any lake, reservoir, pond, or similar body of water that is recorded in the Census Bureau's geographic database. It also includes any river, creek, canal, stream, or similar feature that is recorded in that database as a two- dimensional feature (rather than as a single line). The portions of the oceans and related large embayments (such as Chesapeake Bay and Puget Sound), the Gulf of Mexico, and the Caribbean Sea that belong to the United States and its territories are classified as coastal and territorial waters; the Great Lakes are treated as a separate water entity. Rivers and bays that empty into these bodies of water are treated as inland water from the point beyond which they are narrower than 1 nautical mile across. Identification of land and inland, coastal, territorial, and Great Lakes waters is for data presentation purposes only and does not necessarily reflect their legal definitions.
Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API -
Geographic and Area Datasets Involving Decatur, AL
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OLAS/SCL WASH Household Survey Interpolated Dataset
mydata.iadb.org | Last Updated 2024-09-26T15:04:31.000ZThis dataset is an interpolated version of the OLAS/SCL Household Survey Data Set, and includes data from Latin America and Caribbean countries from 2003-2023. The interpolation can be used for understanding trends in water and sanitation access in the region.
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Land Use_data
opendata.utah.gov | Last Updated 2024-04-10T19:40:16.000ZThis dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the Northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the Southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe’s Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe’s Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS.
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Final Disadvantaged Communities (DAC) 2023
data.ny.gov | Last Updated 2024-07-01T16:29:21.000ZThe Climate Leadership and Community Protection Act (CLCPA) directs the Climate Justice Working Group (CJWG) to establish criteria for defining disadvantaged communities. This dataset identifies areas throughout the State that meet the final disadvantaged community definition as voted on by the Climate Justice Working Group. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, accelerate economic growth, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.
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SLR Passive Flooding - 3.2 Ft. Scenario
highways.hidot.hawaii.gov | Last Updated 2023-03-24T02:38:08.000ZPassive flooding was modeled by the University of Hawaii Coastal Geology Group using a modified "bathtub" approach following methods described in Cooper et al. 2013. The passive flooding model provides an initial assessment of low-lying areas susceptible to flooding by sea level rise. Passive flooding includes areas that are hydrologically connected to the ocean (marine flooding) and low-lying areas that are not hydrologically connected to the ocean (groundwater). Data used in modeling passive flooding include global sea level rise projections, digital elevation models (DEM), and the mean higher high water (MHHW) datum from local tide gauges. DEMs used in this study are freely available from NOAA and the U.S. Army Corps of Engineers (USACE). DEMs are derived from aerial light detection and ranging (LiDAR) data. The horizontal and vertical positional accuracies of the DEMs conform to flood hazard mapping standards of the Federal Emergency Management Agency (FEMA 2012). The IPCC AR5 RCP8.5 sea level rise scenario was used in modeling exposure to passive flooding from sea level rise at 0.5, 1.1, 2.0, and 3.2 feet. This particular layer depicts passive flooding using the 3.2-ft (0.9767-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2100, questions remain around the exact timing of sea level rise and recent observations and projections suggest a sooner arrival. Passive flooding was modeled using the DEMs in geographic information systems software to identify areas below a certain sea level height (flooded by sea level rise) when raising water levels above current Mean Higher High Water (MHHW) tidal datum. In other words, water levels are shown as they would appear during MHHW, or the average higher high water height of each tidal day. The area flooded was derived by subtracting a tidal surface model from the DEM. Assumptions and Limitations: In many areas around the State, representing sea level rise from passive marine flooding will likely produce an underestimate of the area inundated or permanently submerged because the model does not account for waves and coastal erosion, important processes along Hawaii's highly dynamic coasts. For this reason, coastal erosion and annual high wave flooding (provided separately) are also modeled to provide a more comprehensive picture of the extent of hazard exposure. The passive flooding model does not explicitly include flooding through storm drain systems and other underground infrastructure, which would contribute to flooding in many low-lying areas identified in the model. The DEMs used in the modeling depict a smoothed topographic surface and do not identify basements, parking garages, and other development below ground that would be affected by marine and groundwater flooding with sea level rise. The passive flooding model is intended to provide an initial screening tool for sea level rise vulnerability. More detailed hydrologic and engineering modeling may be necessary to fully assess passive marine flooding hazards at the scale of individual properties. Data compiled by the Pacific Islands Ocean Observing System (PacIOOS) for the Hawaii Sea Level Rise Viewer hosted at https://pacioos.org/shoreline/slr-hawaii/. For further information, please see the Hawaii Sea Level Rise Vulnerability and Adaptation Report: https://climateadaptation.hawaii.gov/wp-content/uploads/2017/12/SLR-Report_Dec2017.pdf
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SLR Passive Flooding - 2.0 Ft. Scenario
highways.hidot.hawaii.gov | Last Updated 2023-03-24T19:24:49.000ZPassive flooding was modeled by the University of Hawaii Coastal Geology Group using a modified "bathtub" approach following methods described in Cooper et al. 2013. The passive flooding model provides an initial assessment of low-lying areas susceptible to flooding by sea level rise. Passive flooding includes areas that are hydrologically connected to the ocean (marine flooding) and low-lying areas that are not hydrologically connected to the ocean (groundwater). Data used in modeling passive flooding include global sea level rise projections, digital elevation models (DEM), and the mean higher high water (MHHW) datum from local tide gauges. DEMs used in this study are freely available from NOAA and the U.S. Army Corps of Engineers (USACE). DEMs are derived from aerial light detection and ranging (LiDAR) data. The horizontal and vertical positional accuracies of the DEMs conform to flood hazard mapping standards of the Federal Emergency Management Agency (FEMA 2012). The IPCC AR5 RCP8.5 sea level rise scenario was used in modeling exposure to passive flooding from sea level rise at 0.5, 1.1, 2.0, and 3.2 feet. This particular layer depicts passive flooding using the 2.0-ft (0.5991-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2075, questions remain around the exact timing of sea level rise and recent observations and projections suggest a sooner arrival. Passive flooding was modeled using the DEMs in geographic information systems software to identify areas below a certain sea level height (flooded by sea level rise) when raising water levels above current Mean Higher High Water (MHHW) tidal datum. In other words, water levels are shown as they would appear during MHHW, or the average higher high water height of each tidal day. The area flooded was derived by subtracting a tidal surface model from the DEM. Assumptions and Limitations: In many areas around the State, representing sea level rise from passive marine flooding will likely produce an underestimate of the area inundated or permanently submerged because the model does not account for waves and coastal erosion, important processes along Hawaii's highly dynamic coasts. For this reason, coastal erosion and annual high wave flooding (provided separately) are also modeled to provide a more comprehensive picture of the extent of hazard exposure. The passive flooding model does not explicitly include flooding through storm drain systems and other underground infrastructure, which would contribute to flooding in many low-lying areas identified in the model. The DEMs used in the modeling depict a smoothed topographic surface and do not identify basements, parking garages, and other development below ground that would be affected by marine and groundwater flooding with sea level rise. The passive flooding model is intended to provide an initial screening tool for sea level rise vulnerability. More detailed hydrologic and engineering modeling may be necessary to fully assess passive marine flooding hazards at the scale of individual properties. Data compiled by the Pacific Islands Ocean Observing System (PacIOOS) for the Hawaii Sea Level Rise Viewer hosted at https://pacioos.org/shoreline/slr-hawaii/. For further information, please see the Hawaii Sea Level Rise Vulnerability and Adaptation Report: https://climateadaptation.hawaii.gov/wp-content/uploads/2017/12/SLR-Report_Dec2017.pdf
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SLR Coastal Erosion - 1.1 Ft. Scenario
highways.hidot.hawaii.gov | Last Updated 2023-03-24T01:38:18.000ZUPDATED - Nov. 2020. Studies of historical shoreline change using aerial photographs and survey maps show that 70% of beaches on Kauai, Oahu, and Maui are eroding (receding landward) (Fletcher et al. 2012). Beaches exist in a delicate balance between existing water levels, wave energy, and sand supply. Coastal erosion modeling was conducted for sandy shorelines of Kauai, Oahu, and Maui by the University of Hawaii Coastal Geology Group. The methods are described in Anderson et al. (2015) and combine historical shoreline change data with a model of beach profile response to sea level rise from Davidson-Arnott (2005) in order to estimate probabilities of future exposure to erosion at transects (shore-perpendicular measurement locations) spaced approximately 20 meters apart along the shoreline. The model accounts for localized alongshore variability in shoreline change by incorporating trends from the historical erosion mapping studies. Historical data used to model coastal erosion consisted of: (1) historical shoreline positions and erosion rates measured from high-resolution (0.5 meters) ortho-rectified aerial photographs and NOAA topographic charts dating back to the early 1900s (Fletcher et al. 2012, Romine et al. 2013), and (2) beach profile field survey data (Gibbs et al. 2001, Fletcher et al. 2012). The vegetation line was identified in the most recent aerial photography dating from 2006 to 2008. The output of the modeling is the estimated exposure zone to future erosion hazards. Based on the model and IPCC AR5 RCP8.5 sea level rise scenario, there is an 80% probability that land impacted by erosion would be confined within the exposure zone at that particular time. The exposure zones extend landward from the current-day shoreline (vegetation line) up to the 80% cumulative probability contour from sea level rise at 0.5, 1.1, 2.0, and 3.2 feet, which incorporates the uncertainty (upper and lower bounds) of the IPCC RCP8.5 sea level rise projection. This particular layer depicts coastal erosion using the 1.1-ft (0.3224-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2050, questions remain around the exact timing of sea level rise and recent observations and projections suggest a sooner arrival. Assumptions and Limitations: Historical shoreline change data and beach profiles needed to model coastal erosion are available only for sandy shores of Kauai, Oahu, and Maui. Exposure was not modeled for less-erodible rocky coasts and bluffs, though the latter can be prone to sudden failure in some areas. In addition, modeling did not account for: (1) existing seawalls or other coastal armoring in the backshore; (2) increasing wave energy across the fringing reef with sea level rise; (3) possible changes in reef accretion and nearshore sediment processes with sea level rise; and (4) possible changes to sediment supply from future shoreline development and engineering, such as construction or removal of coastal armoring or other coastal engineering. Data compiled by the Pacific Islands Ocean Observing System (PacIOOS) for the Hawaii Sea Level Rise Viewer hosted at https://pacioos.org/shoreline/slr-hawaii/. Users of these data should cite the following publication: Anderson, T.R., Fletcher, C.H., Barbee, M.M., Frazer, L.N., and B.M. Romine (2015). Doubling of Coastal Erosion Under Rising Sea Level by Mid-Century in Hawaii, Natural Hazards, doi:10.1007/s11069-015-1698-6. For further information, please see the Hawaii Sea Level Rise Vulnerability and Adaptation Report: https://climateadaptation.hawaii.gov/wp-content/uploads/2017/12/SLR-Report_Dec2017.pdf
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SLR Passive Flooding - 0.5 Ft. Scenario
highways.hidot.hawaii.gov | Last Updated 2023-03-24T02:12:39.000ZPassive flooding was modeled by the University of Hawaii Coastal Geology Group using a modified "bathtub" approach following methods described in Cooper et al. 2013. The passive flooding model provides an initial assessment of low-lying areas susceptible to flooding by sea level rise. Passive flooding includes areas that are hydrologically connected to the ocean (marine flooding) and low-lying areas that are not hydrologically connected to the ocean (groundwater). Data used in modeling passive flooding include global sea level rise projections, digital elevation models (DEM), and the mean higher high water (MHHW) datum from local tide gauges. DEMs used in this study are freely available from NOAA and the U.S. Army Corps of Engineers (USACE). DEMs are derived from aerial light detection and ranging (LiDAR) data. The horizontal and vertical positional accuracies of the DEMs conform to flood hazard mapping standards of the Federal Emergency Management Agency (FEMA 2012). The IPCC AR5 RCP8.5 sea level rise scenario was used in modeling exposure to passive flooding from sea level rise at 0.5, 1.1, 2.0, and 3.2 feet. This particular layer depicts passive flooding using the 0.5-ft (0.1660-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2030, questions remain around the exact timing of sea level rise and recent observations and projections suggest a sooner arrival. Passive flooding was modeled using the DEMs in geographic information systems software to identify areas below a certain sea level height (flooded by sea level rise) when raising water levels above current Mean Higher High Water (MHHW) tidal datum. In other words, water levels are shown as they would appear during MHHW, or the average higher high water height of each tidal day. The area flooded was derived by subtracting a tidal surface model from the DEM. Assumptions and Limitations: In many areas around the State, representing sea level rise from passive marine flooding will likely produce an underestimate of the area inundated or permanently submerged because the model does not account for waves and coastal erosion, important processes along Hawaii's highly dynamic coasts. For this reason, coastal erosion and annual high wave flooding (provided separately) are also modeled to provide a more comprehensive picture of the extent of hazard exposure. The passive flooding model does not explicitly include flooding through storm drain systems and other underground infrastructure, which would contribute to flooding in many low-lying areas identified in the model. The DEMs used in the modeling depict a smoothed topographic surface and do not identify basements, parking garages, and other development below ground that would be affected by marine and groundwater flooding with sea level rise. The passive flooding model is intended to provide an initial screening tool for sea level rise vulnerability. More detailed hydrologic and engineering modeling may be necessary to fully assess passive marine flooding hazards at the scale of individual properties. Data compiled by the Pacific Islands Ocean Observing System (PacIOOS) for the Hawaii Sea Level Rise Viewer hosted at https://pacioos.org/shoreline/slr-hawaii/. For further information, please see the Hawaii Sea Level Rise Vulnerability and Adaptation Report: https://climateadaptation.hawaii.gov/wp-content/uploads/2017/12/SLR-Report_Dec2017.pdf
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SLR Passive Flooding - 1.1 Ft. Scenario
highways.hidot.hawaii.gov | Last Updated 2023-03-24T02:14:20.000ZPassive flooding was modeled by the University of Hawaii Coastal Geology Group using a modified "bathtub" approach following methods described in Cooper et al. 2013. The passive flooding model provides an initial assessment of low-lying areas susceptible to flooding by sea level rise. Passive flooding includes areas that are hydrologically connected to the ocean (marine flooding) and low-lying areas that are not hydrologically connected to the ocean (groundwater). Data used in modeling passive flooding include global sea level rise projections, digital elevation models (DEM), and the mean higher high water (MHHW) datum from local tide gauges. DEMs used in this study are freely available from NOAA and the U.S. Army Corps of Engineers (USACE). DEMs are derived from aerial light detection and ranging (LiDAR) data. The horizontal and vertical positional accuracies of the DEMs conform to flood hazard mapping standards of the Federal Emergency Management Agency (FEMA 2012). The IPCC AR5 RCP8.5 sea level rise scenario was used in modeling exposure to passive flooding from sea level rise at 0.5, 1.1, 2.0, and 3.2 feet. This particular layer depicts passive flooding using the 1.1-ft (0.3224-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2050, questions remain around the exact timing of sea level rise and recent observations and projections suggest a sooner arrival. Passive flooding was modeled using the DEMs in geographic information systems software to identify areas below a certain sea level height (flooded by sea level rise) when raising water levels above current Mean Higher High Water (MHHW) tidal datum. In other words, water levels are shown as they would appear during MHHW, or the average higher high water height of each tidal day. The area flooded was derived by subtracting a tidal surface model from the DEM. Assumptions and Limitations: In many areas around the State, representing sea level rise from passive marine flooding will likely produce an underestimate of the area inundated or permanently submerged because the model does not account for waves and coastal erosion, important processes along Hawaii's highly dynamic coasts. For this reason, coastal erosion and annual high wave flooding (provided separately) are also modeled to provide a more comprehensive picture of the extent of hazard exposure. The passive flooding model does not explicitly include flooding through storm drain systems and other underground infrastructure, which would contribute to flooding in many low-lying areas identified in the model. The DEMs used in the modeling depict a smoothed topographic surface and do not identify basements, parking garages, and other development below ground that would be affected by marine and groundwater flooding with sea level rise. The passive flooding model is intended to provide an initial screening tool for sea level rise vulnerability. More detailed hydrologic and engineering modeling may be necessary to fully assess passive marine flooding hazards at the scale of individual properties. Data compiled by the Pacific Islands Ocean Observing System (PacIOOS) for the Hawaii Sea Level Rise Viewer hosted at https://pacioos.org/shoreline/slr-hawaii/. For further information, please see the Hawaii Sea Level Rise Vulnerability and Adaptation Report: https://climateadaptation.hawaii.gov/wp-content/uploads/2017/12/SLR-Report_Dec2017.pdf
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SLR Coastal Erosion - 0.5 Ft. Scenario
highways.hidot.hawaii.gov | Last Updated 2023-03-24T01:38:37.000ZUPDATED - Nov. 2020. Studies of historical shoreline change using aerial photographs and survey maps show that 70% of beaches on Kauai, Oahu, and Maui are eroding (receding landward) (Fletcher et al. 2012). Beaches exist in a delicate balance between existing water levels, wave energy, and sand supply. Coastal erosion modeling was conducted for sandy shorelines of Kauai, Oahu, and Maui by the University of Hawaii Coastal Geology Group. The methods are described in Anderson et al. (2015) and combine historical shoreline change data with a model of beach profile response to sea level rise from Davidson-Arnott (2005) in order to estimate probabilities of future exposure to erosion at transects (shore-perpendicular measurement locations) spaced approximately 20 meters apart along the shoreline. The model accounts for localized alongshore variability in shoreline change by incorporating trends from the historical erosion mapping studies. Historical data used to model coastal erosion consisted of: (1) historical shoreline positions and erosion rates measured from high-resolution (0.5 meters) ortho-rectified aerial photographs and NOAA topographic charts dating back to the early 1900s (Fletcher et al. 2012, Romine et al. 2013), and (2) beach profile field survey data (Gibbs et al. 2001, Fletcher et al. 2012). The vegetation line was identified in the most recent aerial photography dating from 2006 to 2008. The output of the modeling is the estimated exposure zone to future erosion hazards. Based on the model and IPCC AR5 RCP8.5 sea level rise scenario, there is an 80% probability that land impacted by erosion would be confined within the exposure zone at that particular time. The exposure zones extend landward from the current-day shoreline (vegetation line) up to the 80% cumulative probability contour from sea level rise at 0.5, 1.1, 2.0, and 3.2 feet, which incorporates the uncertainty (upper and lower bounds) of the IPCC RCP8.5 sea level rise projection. This particular layer depicts coastal erosion using the 0.5-ft (0.1660-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2030, questions remain around the exact timing of sea level rise and recent observations and projections suggest a sooner arrival. Assumptions and Limitations: Historical shoreline change data and beach profiles needed to model coastal erosion are available only for sandy shores of Kauai, Oahu, and Maui. Exposure was not modeled for less-erodible rocky coasts and bluffs, though the latter can be prone to sudden failure in some areas. In addition, modeling did not account for: (1) existing seawalls or other coastal armoring in the backshore; (2) increasing wave energy across the fringing reef with sea level rise; (3) possible changes in reef accretion and nearshore sediment processes with sea level rise; and (4) possible changes to sediment supply from future shoreline development and engineering, such as construction or removal of coastal armoring or other coastal engineering. Data compiled by the Pacific Islands Ocean Observing System (PacIOOS) for the Hawaii Sea Level Rise Viewer hosted at https://pacioos.org/shoreline/slr-hawaii/. Users of these data should cite the following publication: Anderson, T.R., Fletcher, C.H., Barbee, M.M., Frazer, L.N., and B.M. Romine (2015). Doubling of Coastal Erosion Under Rising Sea Level by Mid-Century in Hawaii, Natural Hazards, doi:10.1007/s11069-015-1698-6. For further information, please see the Hawaii Sea Level Rise Vulnerability and Adaptation Report: https://climateadaptation.hawaii.gov/wp-content/uploads/2017/12/SLR-Report_Dec2017.pdf
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SLR Coastal Erosion - 3.2 Ft. Scenario
highways.hidot.hawaii.gov | Last Updated 2023-03-24T01:37:41.000ZUPDATED - Nov. 2020. Studies of historical shoreline change using aerial photographs and survey maps show that 70% of beaches on Kauai, Oahu, and Maui are eroding (receding landward) (Fletcher et al. 2012). Beaches exist in a delicate balance between existing water levels, wave energy, and sand supply. Coastal erosion modeling was conducted for sandy shorelines of Kauai, Oahu, and Maui by the University of Hawaii Coastal Geology Group. The methods are described in Anderson et al. (2015) and combine historical shoreline change data with a model of beach profile response to sea level rise from Davidson-Arnott (2005) in order to estimate probabilities of future exposure to erosion at transects (shore-perpendicular measurement locations) spaced approximately 20 meters apart along the shoreline. The model accounts for localized alongshore variability in shoreline change by incorporating trends from the historical erosion mapping studies. Historical data used to model coastal erosion consisted of: (1) historical shoreline positions and erosion rates measured from high-resolution (0.5 meters) ortho-rectified aerial photographs and NOAA topographic charts dating back to the early 1900s (Fletcher et al. 2012, Romine et al. 2013), and (2) beach profile field survey data (Gibbs et al. 2001, Fletcher et al. 2012). The vegetation line was identified in the most recent aerial photography dating from 2006 to 2008. The output of the modeling is the estimated exposure zone to future erosion hazards. Based on the model and IPCC AR5 RCP8.5 sea level rise scenario, there is an 80% probability that land impacted by erosion would be confined within the exposure zone at that particular time. The exposure zones extend landward from the current-day shoreline (vegetation line) up to the 80% cumulative probability contour from sea level rise at 0.5, 1.1, 2.0, and 3.2 feet, which incorporates the uncertainty (upper and lower bounds) of the IPCC RCP8.5 sea level rise projection. This particular layer depicts coastal erosion using the 3.2-ft (0.9767-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2100, questions remain around the exact timing of sea level rise and recent observations and projections suggest a sooner arrival. Assumptions and Limitations: Historical shoreline change data and beach profiles needed to model coastal erosion are available only for sandy shores of Kauai, Oahu, and Maui. Exposure was not modeled for less-erodible rocky coasts and bluffs, though the latter can be prone to sudden failure in some areas. In addition, modeling did not account for: (1) existing seawalls or other coastal armoring in the backshore; (2) increasing wave energy across the fringing reef with sea level rise; (3) possible changes in reef accretion and nearshore sediment processes with sea level rise; and (4) possible changes to sediment supply from future shoreline development and engineering, such as construction or removal of coastal armoring or other coastal engineering. Data compiled by the Pacific Islands Ocean Observing System (PacIOOS) for the Hawaii Sea Level Rise Viewer hosted at https://pacioos.org/shoreline/slr-hawaii/. Users of these data should cite the following publication: Anderson, T.R., Fletcher, C.H., Barbee, M.M., Frazer, L.N., and B.M. Romine (2015). Doubling of Coastal Erosion Under Rising Sea Level by Mid-Century in Hawaii, Natural Hazards, doi:10.1007/s11069-015-1698-6. For further information, please see the Hawaii Sea Level Rise Vulnerability and Adaptation Report: https://climateadaptation.hawaii.gov/wp-content/uploads/2017/12/SLR-Report_Dec2017.pdf