The water area of Sea Breeze, NC was 0 in 2018.
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 Sea Breeze, NC
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DWR Dam Safety Jurisdictional Dam
data.colorado.gov | Last Updated 2024-10-24T06:04:40.000ZA Jurisdictional Dam is a dam creating a reservoir with a capacity of more than 100 acre-feet, or creates a reservoir with a surface area in excess of 20 acres at the high-water line, or exceeds 10 feet in height measured vertically from the elevation of the lowest point of the natural surface of the ground where that point occurs along the longitudinal centerline of the dam up to the crest of the emergency spillway of the dam. For reservoirs created by excavation, or where the invert of the outlet conduit is placed below the surface of the natural ground at its lowest point beneath the dam, the jurisdictional height shall be measured from the invert of the outlet at the longitudinal centerline of the embankment or from the bottom of the excavation at the longitudinal centerline of the dam, whichever is greatest. Jurisdictional height is defined in Rule 4.2.19. The State Engineer shall have final authority over determination of the jurisdictional height of the dam.
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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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Weather Data (Hourly) - Environment Canada
data.edmonton.ca | Last Updated 2024-10-24T15:15:48.000ZEnvironment Canada Data License Agreement: http://climate.weather.gc.ca/prods_servs/attachment1_e.html Environment Canada Terms and Conditions: http://www.ec.gc.ca/default.asp?lang=En&xml=5830C36B-1773-4E3E-AF8C-B21F54633E0A Environment Canada Quality of Historic Weather Data Statement: http://climate.weather.gc.ca/climate_data/data_quality_e.html Please direct any inquiries directly to Environment Canada: http://climate.weather.gc.ca/contactus/contact_us_e.html
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RSBS SMO: Part 2 of 2, New York State Residential Statewide Baseline Study: Single and Multifamily Occupant Telephone or Web Survey
data.ny.gov | Last Updated 2019-11-15T21:50:04.000ZHow does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. This is part 2 (contains: Clothes Washing and Drying; Water Heating; Home Lighting; Pool and Spa; Small Household Appliances; and Miscellaneous Equipment) of 2; part 1 (https://data.ny.gov/d/3m6x-h3qa) contains: Behavior and Demographics; Building Shell; Kitchen Appliances; and Heating and Cooling. The New York State Energy Research and Development Authority (NYSERDA), in collaboration with the New York State Department of Public Service (DPS), conducted a statewide residential baseline study (study) from 2011 to 2014 of the single-family and multifamily residential housing segments, including new construction, and a broad range of energy uses and efficiency measures. This dataset includes 2,982 single-family and 379 multifamily occupant survey completes for a total of 3,361 responses. The survey involved 2,285 Web, 1,041 telephone, and 35 mini-inspection surveys. The survey collected information on the following building characteristics: building shell, kitchen appliances, heating and cooling equipment, water heating equipment, clothes washing and drying equipment, lighting, pool and spa equipment, small household appliances, miscellaneous energy consuming equipment, as well as behaviors and characteristics of respondents.
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Weather Data (Daily) - Environment Canada
data.edmonton.ca | Last Updated 2024-10-24T16:27:56.000ZEnvironment Canada Data Licence Agreement: http://climate.weather.gc.ca/prods_servs/attachment1_e.html Environment Canada Terms and Conditions: http://www.ec.gc.ca/default.asp?lang=En&xml=5830C36B-1773-4E3E-AF8C-B21F54633E0A Environment Canada Quality of Historic Weather Data Statement: http://climate.weather.gc.ca/climate_data/data_quality_e.html Please direct any inquiries directly to Environment Canada: http://climate.weather.gc.ca/contactus/contact_us_e.html
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SLR Annual High Wave Flooding - 2.0 Ft. Scenario
highways.hidot.hawaii.gov | Last Updated 2023-03-24T01:40:19.000ZHawaii is exposed to large waves annually on all open coasts due to its location in the Central North Pacific Ocean. The distance over which waves run-up and wash across the shoreline will increase with sea level rise. As water levels increase, less wave energy will be dissipated through breaking on nearshore reefs and waves will arrive at a higher elevation at the shoreline. Computer model simulations of future annual high wave flooding were conducted by the University of Hawaii Coastal Geology Group using the XBeach (for eXtreme Beach behavior) numerical model developed by a consortium of research institutions. The model propagates the maximum annually recurring wave, calculated from offshore wave buoy data, over the reef and to the shore along one-dimensional (1D) cross-shore profiles extracted from a 1-meter DEM. Profiles are spaced 20 meters apart along the coast. This approach was used to model the transformation of the wave as it breaks across the reef and includes shallow water wave processes such as wave set-up and overtopping. The IPCC AR5 RCP8.5 sea level rise scenario was used in modeling exposure to annual high wave flooding from sea level rise at 0.5, 1.1, 2.0, and 3.2 feet. This particular layer depicts annual high wave 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. Historical data used to model annual high wave flooding include hourly measurements of significant wave height, peak wave period, and peak wave direction, and was acquired from offshore wave buoy data from PacIOOS. Maximum surface elevation and depth of the annual high wave flooding is calculated from the mean of the five highest modeled water elevations at each model location along each profile. Output from the simulations is interpolated between transects and compiled in a 5-meter map grid. Depth grid cells with values less than 10 centimeters are not included in the impact assessment. This was done to remove very thin layers of water excursions that (1) are beyond the accuracy of the model and (2) might not constitute a significant impact to land and resources when only occurring once annually. Any low-lying flooded areas that are not connected to the ocean are also removed. Annual high wave flood modeling covered wave-exposed coasts with low-lying development on Maui, Oahu, and Kauai. Annual high wave flooding was not available for the islands of Hawaii, Molokai, and Lanai, nor for harbors or other back-reef areas throughout all the islands. Additional studies would be needed to add the annual high wave flooding for those areas. The maximum annually recurring wave parameters (significant wave height, period, direction) were statistically determined using historical wave climate records and do not include potential changes in future wave climate, the effects of storm surge, or less-frequent high wave events (e.g., a 1-in-10 year wave event). In some locations, the extent of flooding modeled was limited by the extent of the 1-meter DEM. Changes in shoreline location due to coastal erosion are not included in this modeling. As shorelines retreat, annual high wave flooding will reach farther inland along retreating shorelines. Waves are propagated along a "bare earth" DEM which is void of shoreline structures, buildings, and vegetation, and waves are assumed to flow over an impermeable surface. The DEM represents a land surface at one particular time, and may not be representative of the beach shape during the season of most severe wave impact, particularly for highly variable north and west-exposed beaches. Undesirable artifacts of 1D modeling include over-predicted flooding along some transects with deep, shore-perpendicular indentations in the sea bottom such as nearshore reef channels. The 1D modeling does not account for the pres
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SLR Annual High Wave Flooding - 3.2 Ft. Scenario
highways.hidot.hawaii.gov | Last Updated 2023-03-24T01:41:54.000ZHawaii is exposed to large waves annually on all open coasts due to its location in the Central North Pacific Ocean. The distance over which waves run-up and wash across the shoreline will increase with sea level rise. As water levels increase, less wave energy will be dissipated through breaking on nearshore reefs and waves will arrive at a higher elevation at the shoreline. Computer model simulations of future annual high wave flooding were conducted by the University of Hawaii Coastal Geology Group using the XBeach (for eXtreme Beach behavior) numerical model developed by a consortium of research institutions. The model propagates the maximum annually recurring wave, calculated from offshore wave buoy data, over the reef and to the shore along one-dimensional (1D) cross-shore profiles extracted from a 1-meter DEM. Profiles are spaced 20 meters apart along the coast. This approach was used to model the transformation of the wave as it breaks across the reef and includes shallow water wave processes such as wave set-up and overtopping. The IPCC AR5 RCP8.5 sea level rise scenario was used in modeling exposure to annual high wave flooding from sea level rise at 0.5, 1.1, 2.0, and 3.2 feet. This particular layer depicts annual high wave 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. Historical data used to model annual high wave flooding include hourly measurements of significant wave height, peak wave period, and peak wave direction, and was acquired from offshore wave buoy data from PacIOOS. Maximum surface elevation and depth of the annual high wave flooding is calculated from the mean of the five highest modeled water elevations at each model location along each profile. Output from the simulations is interpolated between transects and compiled in a 5-meter map grid. Depth grid cells with values less than 10 centimeters are not included in the impact assessment. This was done to remove very thin layers of water excursions that (1) are beyond the accuracy of the model and (2) might not constitute a significant impact to land and resources when only occurring once annually. Any low-lying flooded areas that are not connected to the ocean are also removed. Annual high wave flood modeling covered wave-exposed coasts with low-lying development on Maui, Oahu, and Kauai. Annual high wave flooding was not available for the islands of Hawaii, Molokai, and Lanai, nor for harbors or other back-reef areas throughout all the islands. Additional studies would be needed to add the annual high wave flooding for those areas. The maximum annually recurring wave parameters (significant wave height, period, direction) were statistically determined using historical wave climate records and do not include potential changes in future wave climate, the effects of storm surge, or less-frequent high wave events (e.g., a 1-in-10 year wave event). In some locations, the extent of flooding modeled was limited by the extent of the 1-meter DEM. Changes in shoreline location due to coastal erosion are not included in this modeling. As shorelines retreat, annual high wave flooding will reach farther inland along retreating shorelines. Waves are propagated along a "bare earth" DEM which is void of shoreline structures, buildings, and vegetation, and waves are assumed to flow over an impermeable surface. The DEM represents a land surface at one particular time, and may not be representative of the beach shape during the season of most severe wave impact, particularly for highly variable north and west-exposed beaches. Undesirable artifacts of 1D modeling include over-predicted flooding along some transects with deep, shore-perpendicular indentations in the sea bottom such as nearshore reef channels. The 1D modeling does not account for the pres
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SLR Annual High Wave Flooding - 0.5 Ft. Scenario
highways.hidot.hawaii.gov | Last Updated 2023-03-24T01:40:03.000ZHawaii is exposed to large waves annually on all open coasts due to its location in the Central North Pacific Ocean. The distance over which waves run-up and wash across the shoreline will increase with sea level rise. As water levels increase, less wave energy will be dissipated through breaking on nearshore reefs and waves will arrive at a higher elevation at the shoreline. Computer model simulations of future annual high wave flooding were conducted by the University of Hawaii Coastal Geology Group using the XBeach (for eXtreme Beach behavior) numerical model developed by a consortium of research institutions. The model propagates the maximum annually recurring wave, calculated from offshore wave buoy data, over the reef and to the shore along one-dimensional (1D) cross-shore profiles extracted from a 1-meter DEM. Profiles are spaced 20 meters apart along the coast. This approach was used to model the transformation of the wave as it breaks across the reef and includes shallow water wave processes such as wave set-up and overtopping. The IPCC AR5 RCP8.5 sea level rise scenario was used in modeling exposure to annual high wave flooding from sea level rise at 0.5, 1.1, 2.0, and 3.2 feet. This particular layer depicts annual high wave 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. Historical data used to model annual high wave flooding include hourly measurements of significant wave height, peak wave period, and peak wave direction, and was acquired from offshore wave buoy data from PacIOOS. Maximum surface elevation and depth of the annual high wave flooding is calculated from the mean of the five highest modeled water elevations at each model location along each profile. Output from the simulations is interpolated between transects and compiled in a 5-meter map grid. Depth grid cells with values less than 10 centimeters are not included in the impact assessment. This was done to remove very thin layers of water excursions that (1) are beyond the accuracy of the model and (2) might not constitute a significant impact to land and resources when only occurring once annually. Any low-lying flooded areas that are not connected to the ocean are also removed. Annual high wave flood modeling covered wave-exposed coasts with low-lying development on Maui, Oahu, and Kauai. Annual high wave flooding was not available for the islands of Hawaii, Molokai, and Lanai, nor for harbors or other back-reef areas throughout all the islands. Additional studies would be needed to add the annual high wave flooding for those areas. The maximum annually recurring wave parameters (significant wave height, period, direction) were statistically determined using historical wave climate records and do not include potential changes in future wave climate, the effects of storm surge, or less-frequent high wave events (e.g., a 1-in-10 year wave event). In some locations, the extent of flooding modeled was limited by the extent of the 1-meter DEM. Changes in shoreline location due to coastal erosion are not included in this modeling. As shorelines retreat, annual high wave flooding will reach farther inland along retreating shorelines. Waves are propagated along a "bare earth" DEM which is void of shoreline structures, buildings, and vegetation, and waves are assumed to flow over an impermeable surface. The DEM represents a land surface at one particular time, and may not be representative of the beach shape during the season of most severe wave impact, particularly for highly variable north and west-exposed beaches. Undesirable artifacts of 1D modeling include over-predicted flooding along some transects with deep, shore-perpendicular indentations in the sea bottom such as nearshore reef channels. The 1D modeling does not account for the pres
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SLR Annual High Wave Flooding - 1.1 Ft. Scenario
highways.hidot.hawaii.gov | Last Updated 2023-03-24T01:40:36.000ZHawaii is exposed to large waves annually on all open coasts due to its location in the Central North Pacific Ocean. The distance over which waves run-up and wash across the shoreline will increase with sea level rise. As water levels increase, less wave energy will be dissipated through breaking on nearshore reefs and waves will arrive at a higher elevation at the shoreline. Computer model simulations of future annual high wave flooding were conducted by the University of Hawaii Coastal Geology Group using the XBeach (for eXtreme Beach behavior) numerical model developed by a consortium of research institutions. The model propagates the maximum annually recurring wave, calculated from offshore wave buoy data, over the reef and to the shore along one-dimensional (1D) cross-shore profiles extracted from a 1-meter DEM. Profiles are spaced 20 meters apart along the coast. This approach was used to model the transformation of the wave as it breaks across the reef and includes shallow water wave processes such as wave set-up and overtopping. The IPCC AR5 RCP8.5 sea level rise scenario was used in modeling exposure to annual high wave flooding from sea level rise at 0.5, 1.1, 2.0, and 3.2 feet. This particular layer depicts annual high wave 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. Historical data used to model annual high wave flooding include hourly measurements of significant wave height, peak wave period, and peak wave direction, and was acquired from offshore wave buoy data from PacIOOS. Maximum surface elevation and depth of the annual high wave flooding is calculated from the mean of the five highest modeled water elevations at each model location along each profile. Output from the simulations is interpolated between transects and compiled in a 5-meter map grid. Depth grid cells with values less than 10 centimeters are not included in the impact assessment. This was done to remove very thin layers of water excursions that (1) are beyond the accuracy of the model and (2) might not constitute a significant impact to land and resources when only occurring once annually. Any low-lying flooded areas that are not connected to the ocean are also removed. Annual high wave flood modeling covered wave-exposed coasts with low-lying development on Maui, Oahu, and Kauai. Annual high wave flooding was not available for the islands of Hawaii, Molokai, and Lanai, nor for harbors or other back-reef areas throughout all the islands. Additional studies would be needed to add the annual high wave flooding for those areas. The maximum annually recurring wave parameters (significant wave height, period, direction) were statistically determined using historical wave climate records and do not include potential changes in future wave climate, the effects of storm surge, or less-frequent high wave events (e.g., a 1-in-10 year wave event). In some locations, the extent of flooding modeled was limited by the extent of the 1-meter DEM. Changes in shoreline location due to coastal erosion are not included in this modeling. As shorelines retreat, annual high wave flooding will reach farther inland along retreating shorelines. Waves are propagated along a "bare earth" DEM which is void of shoreline structures, buildings, and vegetation, and waves are assumed to flow over an impermeable surface. The DEM represents a land surface at one particular time, and may not be representative of the beach shape during the season of most severe wave impact, particularly for highly variable north and west-exposed beaches. Undesirable artifacts of 1D modeling include over-predicted flooding along some transects with deep, shore-perpendicular indentations in the sea bottom such as nearshore reef channels. The 1D modeling does not account for the pres